RNA sequencing (RNA-seq) provides an unbiased approach to identify changes in gene expression caused by genetic mutations or interventions. We recently used RNA-seq to identify differentially expressed genes in a panel of nine long-lived mutants in C. elegans. These mutants represented multiple different pathways of lifespan extension including decreased insulin-IGF1 signaling (daf-2), mild impairment of mitochondrial function (clk-1, isp-1, nuo-6), dietary restriction (eat-2), decreased chemosensation (osm-5), decreased translation (ife-2), increased mitochondrial superoxide (sod-2) and germline ablation (glp-1). In order make this data accessible to the research community, we have developed an online tool that allows users to examine the expression of a gene of interest in individual long-lived mutants or across all nine long-lived mutants. With this tool, users can enter one or multiple genes and select one or multiple mutants. The tool can generate a graph showing the expression compared to wild-type or other strains, a table indicating fold change and significance, and expression levels in each biological replicate of each mutant. Overall, this tool will enable users to gain insight into how their genes of interest are modulated in long-lived genetic mutants and the extent to which expression of that gene is correlated with long life.
","acknowledgements":"We thank Meeta Mistry for processing and analysis of the RNA-seq data that made this online tool possible. The coding for the online tool was entirely written by John Hutchinson.
","authors":[{"affiliations":["Diamond Age, Somerville, Massachusetts, USA","Harvard Chan Bioinformatics Core, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA"],"departments":["",""],"credit":["conceptualization","methodology","visualization","writing_reviewEditing"],"email":"john@diamondage.com","firstName":"John N.","lastName":"Hutchinson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada"],"departments":["","",""],"credit":["writing_originalDraft","writing_reviewEditing"],"email":"jiaxi.guan@mail.mcgill.ca","firstName":"Jiaxi","lastName":"Guan","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada"],"departments":["","",""],"credit":["writing_originalDraft","writing_reviewEditing"],"email":"grant.booth@mail.mcgill.ca","firstName":"Grant F.","lastName":"Booth","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, Quebec, Canada"],"departments":["","","",""],"credit":["conceptualization","supervision","visualization","writing_originalDraft","writing_reviewEditing","project"],"email":"jeremy.vanraamsdonk@mcgill.ca","firstName":"Jeremy M","lastName":"Van Raamsdonk","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":null,"WBId":"","orcid":"0000-0001-8376-9605"}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":null},"extendedData":[{"description":"Figure S1 and S2
","doi":null,"resourceType":"Image","name":"Supplemental Figures.pdf","url":"https://portal.micropublication.org/uploads/26ea4a496a55d5b2340525e3a004faad.pdf"}],"funding":"This work was supported by the Canadian Institutes of Health Research (CIHR; http://www.cihr-irsc.gc.ca/; JVR) and the Natural Sciences and Engineering Research Council of Canada (NSERC; https://www.nserc-crsng.gc.ca/index_eng.asp; JVR). JVR received a Senior Research Scholar career award from the Fonds de Recherche du Quebec Santé (FRQS) and Parkinson Quebec. Work done by Meeta Mistry and John Hutchinson at the Harvard Chan Bioinformatics Core was funded in part by Havard Medical School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
","image":{"url":"https://portal.micropublication.org/uploads/773758c8772a4309719698b40105f1f5.jpg"},"imageCaption":"This figure is showing the output for the expression of the gene cdr-2 in clk-1, isp-1 and nuo-6 mutants. (A) The “TSM Plots” tab will display a graph comparing the expression levels of the selected gene(s) in the selected strain(s) to the expression in wild-type worms. All individual values are shown as well as the average. (B) The “Statistics” tab will show whether there are significant differences in the expression of the selected gene(s) from wild-type worms including the fold change (log2) and adjusted p-value (padj). It also displays the mean and standard deviation for each strain. (C) The “TPMs” tab will display the transcripts per million for each individual sample for the gene(s) and strain(s) selected.
","imageTitle":"Sample Outputs from Gene Lookup Shiny App.
","methods":"All samples were processed using an RNA-seq pipeline implemented in the bcbio-nextgen project (https://bcbio-nextgen.readthedocs.org/en/latest/). Raw reads were examined for quality issues using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to ensure library generation and sequencing are suitable for further analysis. Adapter sequences, other contaminant sequences such as polyA tails and low quality sequences with PHRED quality scores less than five were trimmed from reads using atropos (https://github.com/jdidion/atropos; 10.5281/zenodo.596588). Trimmed reads were aligned to the C. elegans genome, augmented with transcript information from Ensembl using STAR (Dobin et al., 2013). Alignments were checked for evenness of coverage, rRNA content, genomic context of alignments (for example, alignments in known transcripts and introns), complexity and other quality checks using a combination of FastQC, Qualimap (Garcia-Alcalde et al., 2012), MultiQC (https://github.com/ewels/MultiQC) and custom tools. Counts of reads aligning to known genes were generated by featureCounts (Liao et al., 2014) and used for additional quality checks. In parallel, Transcripts Per Million (TPM) measurements per isoform were generated by quasi-alignment using Salmon (Patro et al., 2017). Differential expression at the gene level was called with DESeq2 (Love et al., 2014) using shrinkage. In brief, the largest fold changes not due to low counts were used inform a prior distribution and shrink fold changes that lack statistical information. Counts per gene were estimated from the Salmon quasi-alignments by tximport (Soneson et al., 2015) as quantitating at the isoform level has been shown to produce more accurate results at the gene level.
","reagents":"The application can be accessed here: https://vanraamsdonk.shinyapps.io/mutant_comparison_viewer/
","patternDescription":"Aging can be described as the progressive loss of function at both a molecular and cellular level driven by the accumulation of molecular damage and altered molecular signaling leading to an overall decline in an organism’s ability to repair and maintain itself (Li et al., 2024). Aging is the greatest risk factor for the development of a multitude of chronic diseases including age-onset neurodegenerative disorders. As a result, the geroscience hypothesis proposes that by gaining insight into the aging process, this knowledge can be used to mitigate multiple chronic diseases simultaneously thereby allowing individuals to live healthier, longer lives. One of the main goals of geroscience is to advance our understanding of the biology of aging to identify novel molecular targets to modulate the aging process.
One approach that has been used to elucidate the molecular mechanism contributing to aging is RNA sequencing (RNA-seq). RNA-seq provides a genome-wide, unbiased method to characterize global gene expression patterns associated with aging and longevity (Bairakdar et al., 2023; Mosley et al., 2025). Rather than focusing on individual candidate genes, RNA-seq enables the identification of coordinated transcriptional changes, pathway-level alterations, and gene expression programs that reflect broader biological states (Marguerat and Bahler, 2010; Wang et al., 2009). In this way, different long-lived mutants or lifespan-extending interventions can be defined by characteristic transcriptomic signatures or expression profiles (Tyshkovskiy et al., 2023). Such comparative transcriptomic analysis provides a data-driven framework to better understand the molecular basis of aging and to generate new mechanistic hypotheses.
To gain insight into shared and unique molecular mechanisms contributing to lifespan extension, we used RNA-seq to examine gene expression in a panel of nine long-lived mutants that represent multiple biological pathways known to regulate lifespan in C. elegans. Reduced insulin/IGF-1 signaling was examined using the daf-2 mutant, one of the earliest and most well-characterized longevity models (Kenyon et al., 1993). Dietary restriction was modeled using eat-2 mutants, which exhibit extended lifespan due to reduced food intake (Lakowski and Hekimi, 1998). Decreased protein translation was represented by ife-2 mutants (Syntichaki et al., 2007), while altered chemosensory signaling was examined using osm-5 mutants (Apfeld and Kenyon, 1999). Germline ablation–associated longevity was modeled using glp-1 mutants (Hsin and Kenyon, 1999). To assess mild impairment of mitochondrial function, we included clk-1, isp-1, and nuo-6 mutants (Feng et al., 2001; Lakowski and Hekimi, 1996; Yang and Hekimi, 2010). Finally, we used sod-2 mutants, which have increased levels of mitochondrial superoxide (Van Raamsdonk and Hekimi, 2009). Together, these models represent multiple independent genetic perturbations that lead to lifespan extension, enabling comparative analysis of different longevity strategies.
In comparing differentially expressed genes across the panel of nine long-lived mutants, we identified a number of genes that are upregulated or downregulated in at least six long-lived mutants but relatively few in seven or more (Rudich et al., 2025). The commonly upregulated genes were enriched for genes involved in immunity, defense and metabolism, while downregulated genes were enriched for genes involved in translation and gene expression. Interestingly, we found that the nine long-lived mutants clustered into three different longevity groups according to their gene expression. Group 1 longevity mutants contained daf-2, glp-1, clk-1, isp-1, nuo-6 and sod-2; group 2 contained eat-2 and osm-5; and group 3 contained ife-2. Group 1 and group 2 longevity mutants exhibited modulation of specific genetic pathways in opposite directions (Rudich et al., 2025). For example, target genes of the DAF-16-mediated stress response pathway and mitochondrial unfolded protein response are significantly upregulated in group 1 longevity mutants but downregulated or unchanged in group 2 longevity mutants. This suggests that longevity can be achieved through multiple distinct molecular strategies.
To make our gene expression data accessible to the research community and facilitate the identification of genes associated with longevity, we created an interactive web-based Shiny application that enables exploration of RNA-seq gene expression across multiple long-lived C. elegans mutants relative to wild-type controls. The application can be found here: https://vanraamsdonk.shinyapps.io/mutant_comparison_viewer/. Users can select one or more mutants for comparison, including clk-1, daf-2, eat-2, glp-1, ife-2, isp-1, nuo-6, osm-5, and sod-2. The application accepts one or multiple genes as input and supports common gene names (e.g., aap-1), WormBase gene IDs (e.g., WBGene00000001), and sequence names (e.g., Y110A7A.10). This interface allows rapid cross-model comparison of gene expression across distinct longevity pathways without requiring programming expertise. All datasets shown in the app were processed using a standardized RNA-seq pipeline (bcbio-nextgen: https://zenodo.org/records/5781867 ). Differential expression was computed with DESeq2 (with shrinkage) (Love et al., 2014), and transcripts per million (TPM) values were derived from Salmon-based quantification.
To use the application, users first select one or more mutant strains under the “Select Comparisons” panel (For screenshots on how to use the application see Figure S1). Genes of interest are then entered into the input field using gene names, WormBase gene IDs, or sequence names. After submitting the query, the application automatically generates expression plots and corresponding statistical summaries for the selected strains relative to wild-type controls.
The application displays gene expression results in three main formats (Figure 1). The “TSM Plots” tab shows expression levels for each selected mutant compared to wild-type controls, including individual sample values and the average for each strain. This allows users to see the direction of change and the variation within each group. The “Statistics” tab provides quantitative results, including log2 fold change and adjusted p-value (padj), which indicate the magnitude and statistical significance of the difference. The “TPMs” tab lists the TPM values for each individual sample, allowing users to view the underlying expression levels. Together, these outputs help users evaluate whether a gene is consistently increased or decreased across different longevity mutants.
In addition to visualization and summary statistics, the application provides access to the full underlying datasets (Figure S2). Under the “Datasets” tab, users can view sample metadata, differential expression statistics generated by DESeq2, and TPM values for all genes. The DESeq2 table includes log2 fold change, p-values, adjusted p-values, and expression means for each strain compared to wild type. These tables can be downloaded, allowing users to perform further analysis outside of the web interface. Access to the full datasets makes it possible to further explore similarities and differences in gene regulation across distinct longevity pathways.
In conclusion, this tool provides a simple way to compare gene expression across multiple long-lived mutants within a single framework. By examining the same gene across different longevity pathways, researchers can identify shared changes as well as pathway-specific patterns of regulation. This approach may help guide further experiments aimed at understanding how distinct genetic interventions influence aging at the molecular level.
","references":[{"reference":"Apfeld J, Kenyon C. 1999. Regulation of lifespan by sensory perception in Caenorhabditis elegans. Nature 402: 804-809.
","pubmedId":"","doi":"10.1038/45544"},{"reference":"Bairakdar MD, Tewari A, Truttmann MC. 2023. A meta-analysis of RNA-Seq studies to identify novel genes that regulate aging. Experimental Gerontology 173: 112107.
","pubmedId":"","doi":"10.1016/j.exger.2023.112107"},{"reference":"Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al., Gingeras. 2012. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29: 15-21.
","pubmedId":"","doi":"10.1093/bioinformatics/bts635"},{"reference":"Feng J, Bussière Fdr, Hekimi S. 2001. Mitochondrial Electron Transport Is a Key Determinant of Life Span in Caenorhabditis elegans. Developmental Cell 1: 633-644.
","pubmedId":"","doi":" 10.1016/s1534-5807(01)00071-5"},{"reference":"García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, et al., Conesa. 2012. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics 28: 2678-2679.
","pubmedId":"","doi":"10.1093/bioinformatics/bts503"},{"reference":"Hsin H, Kenyon C. 1999. Signals from the reproductive system regulate the lifespan of C. elegans. Nature 399: 362-366.
","pubmedId":"","doi":"10.1038/20694"},{"reference":"Kenyon C, Chang J, Gensch E, Rudner A, Tabtiang R. 1993. A C. elegans mutant that lives twice as long as wild type. Nature 366: 461-464.
","pubmedId":"","doi":"10.1038/366461a0"},{"reference":"Lakowski B, Hekimi S. 1996. Determination of Life-Span in\n \n Caenorhabditis elegans\n \n by Four Clock Genes. Science 272: 1010-1013.
","pubmedId":"","doi":"10.1126/science.272.5264.1010"},{"reference":"Lakowski B, Hekimi S. 1998. The genetics of caloric restriction in\n Caenorhabditis elegans. Proceedings of the National Academy of Sciences 95: 13091-13096.
","pubmedId":"","doi":" 10.1073/pnas.95.22.13091"},{"reference":"Li Y, Tian X, Luo J, Bao T, Wang S, Wu X. 2024. Molecular mechanisms of aging and anti-aging strategies. Cell Communication and Signaling 22: 10.1186/s12964-024-01663-1.
","pubmedId":"","doi":"10.1186/s12964-024-01663-1"},{"reference":"Liao Y, Smyth GK, Shi W. 2013. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30: 923-930.
","pubmedId":"","doi":"10.1093/bioinformatics/btt656"},{"reference":"Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15: 10.1186/s13059-014-0550-8.
","pubmedId":"","doi":"10.1186/s13059-014-0550-8"},{"reference":"Marguerat S, Bähler Jr. 2009. RNA-seq: from technology to biology. Cellular and Molecular Life Sciences 67: 569-579.
","pubmedId":"","doi":"10.1007/s00018-009-0180-6"},{"reference":"Mosley MC, Kinser HE, Martin OMF, Stroustrup N, Schedl T, Kornfeld K, Pincus Z. 2024. Similarities and differences in the gene expression signatures of physiological age versus future lifespan. Aging Cell 24: 10.1111/acel.14428.
","pubmedId":"","doi":"10.1111/acel.14428"},{"reference":"Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. 2017. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods 14: 417-419.
","pubmedId":"","doi":"10.1038/nmeth.4197"},{"reference":"Rudich ZD, Tamez González AA, Guan J, Booth G, Soo SK, Anglas U, et al., Van Raamsdonk. 2025. Multiple Molecular Pathways to Longevity: Opposing Gene Expression Programs Define Distinct Aging Strategies. : 10.64898/2025.12.29.696944.
","pubmedId":"","doi":"10.64898/2025.12.29.696944"},{"reference":"Soneson C, Love MI, Robinson MD. 2016. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4: 1521.
","pubmedId":"","doi":"10.12688/f1000research.7563.2"},{"reference":"Syntichaki P, Troulinaki K, Tavernarakis N. 2007. eIF4E function in somatic cells modulates ageing in Caenorhabditis elegans. Nature 445: 922-926.
","pubmedId":"","doi":"10.1038/nature05603"},{"reference":"Tyshkovskiy A, Ma S, Shindyapina AV, Tikhonov S, Lee SG, Bozaykut P, et al., Gladyshev. 2023. Distinct longevity mechanisms across and within species and their association with aging. Cell 186: 2929-2949.e20.
","pubmedId":"","doi":"10.1016/j.cell.2023.05.002"},{"reference":"Van Raamsdonk JM, Hekimi S. 2009. Deletion of the Mitochondrial Superoxide Dismutase sod-2 Extends Lifespan in Caenorhabditis elegans. PLoS Genetics 5: e1000361.
","pubmedId":"","doi":"10.1371/journal.pgen.1000361"},{"reference":"Wang Z, Gerstein M, Snyder M. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10: 57-63.
","pubmedId":"","doi":"10.1038/nrg2484"},{"reference":"Yang W, Hekimi S. 2010. Two modes of mitochondrial dysfunction lead independently to lifespan extension in Caenorhabditis elegans. Aging Cell 9: 433-447.
","pubmedId":"","doi":"10.1111/j.1474-9726.2010.00571.x"}],"title":"Interactive online tool to identify genes associated with longevity in a panel of long-lived genetic mutants in C. elegans
","reviews":[{"reviewer":{"displayName":"Nicole Templeman"},"openAcknowledgement":false,"status":{"submitted":true}}],"curatorReviews":[]},{"id":"25576468-8eb3-478e-8529-12ac94bd0577","decision":"accept","abstract":"We recently used RNA sequencing to identify differentially expressed genes in a panel of nine long-lived mutants in C. elegans representing multiple different pathways of lifespan extension. In order make this data accessible to the research community, we have developed an online tool that allows users to examine the expression of a gene of interest in individual long-lived mutants or across all nine long-lived mutants. This tool will enable users to gain insight into how their genes of interest are modulated in long-lived genetic mutants and the extent to which expression of that gene is correlated with long life.
","acknowledgements":"We thank Meeta Mistry for processing and analysis of the RNA-seq data that made this online tool possible. The coding for the online tool was entirely written by John Hutchinson.
","authors":[{"affiliations":["Diamond Age, Somerville, Massachusetts, USA","Harvard Chan Bioinformatics Core, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA"],"departments":["",""],"credit":["conceptualization","methodology","visualization","writing_reviewEditing"],"email":"john@diamondage.com","firstName":"John N.","lastName":"Hutchinson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada"],"departments":["","",""],"credit":["writing_originalDraft","writing_reviewEditing"],"email":"jiaxi.guan@mail.mcgill.ca","firstName":"Jiaxi","lastName":"Guan","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada"],"departments":["","",""],"credit":["writing_originalDraft","writing_reviewEditing"],"email":"grant.booth@mail.mcgill.ca","firstName":"Grant F.","lastName":"Booth","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, Quebec, Canada"],"departments":["","","",""],"credit":["conceptualization","supervision","visualization","writing_originalDraft","writing_reviewEditing","project"],"email":"jeremy.vanraamsdonk@mcgill.ca","firstName":"Jeremy M","lastName":"Van Raamsdonk","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":null,"WBId":"","orcid":"0000-0001-8376-9605"}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":null},"extendedData":[{"description":"Figure S1 and S2
","doi":"10.22002/kadh3-bqp75","resourceType":"Image","name":"Supplemental Figures.pdf","url":"https://portal.micropublication.org/uploads/26ea4a496a55d5b2340525e3a004faad.pdf"},{"description":"Software release github zipfile.
","doi":"10.22002/8vmke-tn046","resourceType":"Software","name":"van_raamsdonk_mutantviewer_rshinyapp-1.0.0.zip","url":"https://portal.micropublication.org/uploads/6eef78d6119f0bbde74563a2eb9db2fe.zip"}],"funding":"This work was supported by the Canadian Institutes of Health Research (CIHR; http://www.cihr-irsc.gc.ca/; JVR) and the Natural Sciences and Engineering Research Council of Canada (NSERC; https://www.nserc-crsng.gc.ca/index_eng.asp; JVR). JVR received a Senior Research Scholar career award from the Fonds de Recherche du Quebec Santé (FRQS) and Parkinson Quebec. Work done by Meeta Mistry and John Hutchinson at the Harvard Chan Bioinformatics Core was funded in part by Havard Medical School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
","image":{"url":"https://portal.micropublication.org/uploads/b25e95046f4a4206d8885e333745152b.jpg"},"imageCaption":"This figure is showing the output for the expression of the gene cdr-2 in clk-1, isp-1 and nuo-6 mutants. (A) The “TSM Plots” tab will display a graph comparing the expression levels of the selected gene(s) in the selected strain(s) to the expression in wild-type worms. All individual values are shown as well as the average. (B) The “Statistics” tab will show whether there are significant differences in the expression of the selected gene(s) from wild-type worms including the fold change (log2) and adjusted p-value (padj). It also displays the mean and standard deviation for each strain. (C) The “TPMs” tab will display the transcripts per million for each individual sample for the gene(s) and strain(s) selected.
","imageTitle":"Sample Outputs from Gene Lookup Shiny App.
","methods":"Strains were maintained on NGM plates with OP50 bacteria at 20°C. Worms were collected for RNA sequencing at the pre-fertile young adult stage. A minimum of six biological replicates per strain were used. Each biological replicate consisted of a separate plate of worms generated by a limited lay on a different day than the other replicates. RNA was isolated using TRIZOL as described previously (Machiela et al., 2016). Sequencing libraries were generated with the Kapa Biosystems stranded mRNA-Seq kit for the Illumina platform. Libraries were sequenced using 1×75 bp sequencing on the Illumina NextSeq 500 platform to a depth of ∼30M reads per sample (Dues et al., 2017). All samples were processed using an RNA-seq pipeline implemented in the bcbio-nextgen project (https://bcbio-nextgen.readthedocs.org/en/latest/). Raw reads were examined for quality issues using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to ensure library generation and sequencing are suitable for further analysis. Adapter sequences, other contaminant sequences such as polyA tails and low quality sequences with PHRED quality scores less than five were trimmed from reads using atropos (https://github.com/jdidion/atropos; 10.5281/zenodo.596588). Trimmed reads were aligned to the C. elegans genome, augmented with transcript information from Ensembl using STAR (Dobin et al., 2013). Alignments were checked for evenness of coverage, rRNA content, genomic context of alignments (for example, alignments in known transcripts and introns), complexity and other quality checks using a combination of FastQC, Qualimap (Garcia-Alcalde et al., 2012), MultiQC (https://github.com/ewels/MultiQC) and custom tools. Counts of reads aligning to known genes were generated by featureCounts (Liao et al., 2014) and used for additional quality checks. In parallel, Transcripts Per Million (TPM) measurements per isoform were generated by quasi-alignment using Salmon (Patro et al., 2017). Differential expression at the gene level was called with DESeq2 (Love et al., 2014) using shrinkage. In brief, the largest fold changes not due to low counts were used inform a prior distribution and shrink fold changes that lack statistical information. Counts per gene were estimated from the Salmon quasi-alignments by tximport (Soneson et al., 2015) as quantitating at the isoform level has been shown to produce more accurate results at the gene level.
","reagents":"The following worm strains were used:
N2 (wild-type)
ife-2 (ok306)
clk-1(qm30)
sod-2(ok1030)
eat-2(ad1116)
osm-5(p813)
nuo-6(qm200)
isp-1(qm150)
daf-2(e1370)
glp-1(e2141)
The application can be accessed here: https://vanraamsdonk.shinyapps.io/mutant_comparison_viewer/
","patternDescription":"Aging can be described as the progressive loss of function at both a molecular and cellular level driven by the accumulation of molecular damage and altered molecular signaling leading to an overall decline in an organism’s ability to repair and maintain itself (Li et al., 2024). Aging is the greatest risk factor for the development of a multitude of chronic diseases including age-onset neurodegenerative disorders. As a result, the geroscience hypothesis proposes that by gaining insight into the aging process, this knowledge can be used to mitigate multiple chronic diseases simultaneously thereby allowing individuals to live healthier, longer lives. One of the main goals of geroscience is to advance our understanding of the biology of aging to identify novel molecular targets to modulate the aging process.
One approach that has been used to elucidate the molecular mechanism contributing to aging is RNA sequencing (RNA-seq). RNA-seq provides a genome-wide, unbiased method to characterize global gene expression patterns associated with aging and longevity (Bairakdar et al., 2023; Mosley et al., 2025). Rather than focusing on individual candidate genes, RNA-seq enables the identification of coordinated transcriptional changes, pathway-level alterations, and gene expression programs that reflect broader biological states (Marguerat and Bahler, 2010; Wang et al., 2009). In this way, different long-lived mutants or lifespan-extending interventions can be defined by characteristic transcriptomic signatures or expression profiles (Tyshkovskiy et al., 2023). Such comparative transcriptomic analysis provides a data-driven framework to better understand the molecular basis of aging and to generate new mechanistic hypotheses.
To gain insight into shared and unique molecular mechanisms contributing to lifespan extension, we used RNA-seq to examine gene expression in a panel of nine long-lived mutants that represent multiple biological pathways known to regulate lifespan in C. elegans. Reduced insulin/IGF-1 signaling was examined using the daf-2(e1370) mutant, one of the earliest and most well-characterized longevity models (Kenyon et al., 1993). Dietary restriction was modeled using eat-2(ad1116) mutants, which exhibit extended lifespan due to reduced food intake (Lakowski and Hekimi, 1998). Decreased protein translation was represented by ife-2(ok306) mutants (Syntichaki et al., 2007), while altered chemosensory signaling was examined using osm-5(p813) mutants (Apfeld and Kenyon, 1999). Germline ablation–associated longevity was modeled using glp-1(e2141) mutants (Hsin and Kenyon, 1999). To assess mild impairment of mitochondrial function, we included clk-1(qm30), isp-1(qm150), and nuo-6(qm200) mutants (Feng et al., 2001; Lakowski and Hekimi, 1996; Yang and Hekimi, 2010). Finally, we used sod-2(ok1030) mutants, which have increased levels of mitochondrial superoxide (Van Raamsdonk and Hekimi, 2009). Together, these models represent multiple independent genetic perturbations that lead to lifespan extension, enabling comparative analysis of different longevity strategies.
In comparing differentially expressed genes across the panel of nine long-lived mutants, we identified a number of genes that are upregulated or downregulated in at least six long-lived mutants but relatively few in seven or more (Rudich et al., 2025). The commonly upregulated genes were enriched for genes involved in immunity, defense and metabolism, while downregulated genes were enriched for genes involved in translation and gene expression. Interestingly, we found that the nine long-lived mutants clustered into three different longevity groups according to their gene expression. Group 1 longevity mutants contained daf-2, glp-1, clk-1, isp-1, nuo-6 and sod-2; group 2 contained eat-2 and osm-5; and group 3 contained ife-2. Group 1 and group 2 longevity mutants exhibited modulation of specific genetic pathways in opposite directions (Rudich et al., 2025). For example, target genes of the DAF-16-mediated stress response pathway and mitochondrial unfolded protein response are significantly upregulated in group 1 longevity mutants but downregulated or unchanged in group 2 longevity mutants. This suggests that longevity can be achieved through multiple distinct molecular strategies.
To make our gene expression data accessible to the research community and facilitate the identification of genes associated with longevity, we created an interactive web-based Shiny application that enables exploration of RNA-seq gene expression across multiple long-lived C. elegans mutants relative to wild-type controls. The application can be found here: https://vanraamsdonk.shinyapps.io/mutant_comparison_viewer/. Users can select one or more mutants for comparison, including clk-1, daf-2, eat-2, glp-1, ife-2, isp-1, nuo-6, osm-5, and sod-2. The application accepts one or multiple genes as input and supports common gene names (e.g., aap-1), WormBase gene IDs (e.g., WBGene00000001), and sequence names (e.g., Y110A7A.10). This interface allows rapid cross-model comparison of gene expression across distinct longevity pathways without requiring programming expertise. All datasets shown in the app were processed using a standardized RNA-seq pipeline (bcbio-nextgen: https://zenodo.org/records/5781867 ). Differential expression was computed with DESeq2 (with shrinkage) (Love et al., 2014), and transcripts per million (TPM) values were derived from Salmon-based quantification.
To use the application, users first select one or more mutant strains under the “Select Comparisons” panel (For screenshots on how to use the application see Figure S1). Genes of interest are then entered into the input field using gene names, WormBase gene IDs, or sequence names. After submitting the query, the application automatically generates expression plots and corresponding statistical summaries for the selected strains relative to wild-type controls.
The application displays gene expression results in three main formats (Figure 1). The “TSM Plots” tab shows expression levels for each selected mutant compared to wild-type controls, including individual sample values and the average for each strain. This allows users to see the direction of change and the variation within each group. The “Statistics” tab provides quantitative results, including log2 fold change and adjusted p-value (padj), which indicate the magnitude and statistical significance of the difference. The “TPMs” tab lists the TPM values for each individual sample, allowing users to view the underlying expression levels. Together, these outputs help users evaluate whether a gene is consistently increased or decreased across different longevity mutants.
In addition to visualization and summary statistics, the application provides access to the full underlying datasets (Figure S2). Under the “Datasets” tab, users can view sample metadata, differential expression statistics generated by DESeq2, and TPM values for all genes. The DESeq2 table includes log2 fold change, p-values, adjusted p-values, and expression means for each strain compared to wild type. These tables can be downloaded, allowing users to perform further analysis outside of the web interface. Access to the full datasets makes it possible to further explore similarities and differences in gene regulation across distinct longevity pathways.
In conclusion, this tool provides a simple way to compare gene expression across multiple long-lived mutants within a single framework. By examining the same gene across different longevity pathways, researchers can identify shared changes as well as pathway-specific patterns of regulation. This approach may help guide further experiments aimed at understanding how distinct genetic interventions influence aging at the molecular level.
","references":[{"reference":"Apfeld J, Kenyon C. 1999. Regulation of lifespan by sensory perception in Caenorhabditis elegans. Nature 402: 804-809.
","pubmedId":"","doi":"10.1038/45544"},{"reference":"Bairakdar MD, Tewari A, Truttmann MC. 2023. A meta-analysis of RNA-Seq studies to identify novel genes that regulate aging. Experimental Gerontology 173: 112107.
","pubmedId":"","doi":"10.1016/j.exger.2023.112107"},{"reference":"Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al., Gingeras. 2012. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29: 15-21.
","pubmedId":"","doi":"10.1093/bioinformatics/bts635"},{"reference":"Dues DJ, Schaar CE, Johnson BK, Bowman MJ, Winn ME, Senchuk MM, Van Raamsdonk JM. 2017. Uncoupling of oxidative stress resistance and lifespan in long-lived isp-1 mitochondrial mutants in Caenorhabditis elegans. Free Radical Biology and Medicine 108: 362-373.
","pubmedId":"","doi":"10.1016/j.freeradbiomed.2017.04.004"},{"reference":"Feng J, Bussière Fdr, Hekimi S. 2001. Mitochondrial Electron Transport Is a Key Determinant of Life Span in Caenorhabditis elegans. Developmental Cell 1: 633-644.
","pubmedId":"","doi":" 10.1016/s1534-5807(01)00071-5"},{"reference":"García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, et al., Conesa. 2012. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics 28: 2678-2679.
","pubmedId":"","doi":"10.1093/bioinformatics/bts503"},{"reference":"Hsin H, Kenyon C. 1999. Signals from the reproductive system regulate the lifespan of C. elegans. Nature 399: 362-366.
","pubmedId":"","doi":"10.1038/20694"},{"reference":"Kenyon C, Chang J, Gensch E, Rudner A, Tabtiang R. 1993. A C. elegans mutant that lives twice as long as wild type. Nature 366: 461-464.
","pubmedId":"","doi":"10.1038/366461a0"},{"reference":"Lakowski B, Hekimi S. 1996. Determination of Life-Span in\n \n Caenorhabditis elegans\n \n by Four Clock Genes. Science 272: 1010-1013.
","pubmedId":"","doi":"10.1126/science.272.5264.1010"},{"reference":"Lakowski B, Hekimi S. 1998. The genetics of caloric restriction in\n Caenorhabditis elegans. Proceedings of the National Academy of Sciences 95: 13091-13096.
","pubmedId":"","doi":" 10.1073/pnas.95.22.13091"},{"reference":"Li Y, Tian X, Luo J, Bao T, Wang S, Wu X. 2024. Molecular mechanisms of aging and anti-aging strategies. Cell Communication and Signaling 22: 10.1186/s12964-024-01663-1.
","pubmedId":"","doi":"10.1186/s12964-024-01663-1"},{"reference":"Liao Y, Smyth GK, Shi W. 2013. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30: 923-930.
","pubmedId":"","doi":"10.1093/bioinformatics/btt656"},{"reference":"Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15: 10.1186/s13059-014-0550-8.
","pubmedId":"","doi":"10.1186/s13059-014-0550-8"},{"reference":"Machiela E, Dues DJ, Senchuk MM, Van Raamsdonk JM. 2016. Oxidative stress is increased in C. elegans models of Huntington’s disease but does not contribute to polyglutamine toxicity phenotypes. Neurobiology of Disease 96: 1-11.
","pubmedId":"","doi":"10.1016/j.nbd.2016.08.008"},{"reference":"Marguerat S, Bähler Jr. 2009. RNA-seq: from technology to biology. Cellular and Molecular Life Sciences 67: 569-579.
","pubmedId":"","doi":"10.1007/s00018-009-0180-6"},{"reference":"Mosley MC, Kinser HE, Martin OMF, Stroustrup N, Schedl T, Kornfeld K, Pincus Z. 2024. Similarities and differences in the gene expression signatures of physiological age versus future lifespan. Aging Cell 24: 10.1111/acel.14428.
","pubmedId":"","doi":"10.1111/acel.14428"},{"reference":"Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. 2017. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods 14: 417-419.
","pubmedId":"","doi":"10.1038/nmeth.4197"},{"reference":"Rudich ZD, Tamez González AA, Guan J, Booth G, Soo SK, Anglas U, et al., Van Raamsdonk. 2025. Multiple Molecular Pathways to Longevity: Opposing Gene Expression Programs Define Distinct Aging Strategies. : 10.64898/2025.12.29.696944.
","pubmedId":"","doi":"10.64898/2025.12.29.696944"},{"reference":"Soneson C, Love MI, Robinson MD. 2016. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4: 1521.
","pubmedId":"","doi":"10.12688/f1000research.7563.2"},{"reference":"Syntichaki P, Troulinaki K, Tavernarakis N. 2007. eIF4E function in somatic cells modulates ageing in Caenorhabditis elegans. Nature 445: 922-926.
","pubmedId":"","doi":"10.1038/nature05603"},{"reference":"Tyshkovskiy A, Ma S, Shindyapina AV, Tikhonov S, Lee SG, Bozaykut P, et al., Gladyshev. 2023. Distinct longevity mechanisms across and within species and their association with aging. Cell 186: 2929-2949.e20.
","pubmedId":"","doi":"10.1016/j.cell.2023.05.002"},{"reference":"Van Raamsdonk JM, Hekimi S. 2009. Deletion of the Mitochondrial Superoxide Dismutase sod-2 Extends Lifespan in Caenorhabditis elegans. PLoS Genetics 5: e1000361.
","pubmedId":"","doi":"10.1371/journal.pgen.1000361"},{"reference":"Wang Z, Gerstein M, Snyder M. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10: 57-63.
","pubmedId":"","doi":"10.1038/nrg2484"},{"reference":"Yang W, Hekimi S. 2010. Two modes of mitochondrial dysfunction lead independently to lifespan extension in Caenorhabditis elegans. Aging Cell 9: 433-447.
","pubmedId":"","doi":"10.1111/j.1474-9726.2010.00571.x"}],"title":"Interactive online tool to identify genes associated with longevity in a panel of long-lived genetic mutants in C. elegans
","reviews":[],"curatorReviews":[]},{"id":"2ab3891f-b8fd-4d94-b540-d6c4f14608c0","decision":"accept","abstract":"We recently used RNA sequencing to identify differentially expressed genes in a panel of nine long-lived mutants in C. elegans representing multiple different pathways of lifespan extension. In order make this data accessible to the research community, we have developed an online tool that allows users to examine the expression of a gene of interest in individual long-lived mutants or across all nine long-lived mutants. This tool will enable users to gain insight into how their genes of interest are modulated in long-lived genetic mutants and the extent to which expression of that gene is correlated with long life.
","acknowledgements":"We thank Meeta Mistry for processing and analysis of the RNA-seq data that made this online tool possible. The coding for the online tool was entirely written by John Hutchinson.
","authors":[{"affiliations":["Diamond Age Data Science, Somerville, Massachusetts, USA","Harvard Chan Bioinformatics Core, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA"],"departments":["",""],"credit":["conceptualization","methodology","visualization","writing_reviewEditing"],"email":"john@diamondage.com","firstName":"John N.","lastName":"Hutchinson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada"],"departments":["","",""],"credit":["writing_originalDraft","writing_reviewEditing"],"email":"jiaxi.guan@mail.mcgill.ca","firstName":"Jiaxi","lastName":"Guan","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada"],"departments":["","",""],"credit":["writing_originalDraft","writing_reviewEditing"],"email":"grant.booth@mail.mcgill.ca","firstName":"Grant F.","lastName":"Booth","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, Quebec, Canada"],"departments":["","","",""],"credit":["conceptualization","supervision","visualization","writing_originalDraft","writing_reviewEditing","project"],"email":"jeremy.vanraamsdonk@mcgill.ca","firstName":"Jeremy M","lastName":"Van Raamsdonk","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":null,"WBId":"","orcid":"0000-0001-8376-9605"}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":null},"extendedData":[{"description":"Software release github zipfile.
","doi":"10.22002/8vmke-tn046","resourceType":"Software","name":"van_raamsdonk_mutantviewer_rshinyapp-1.0.0.zip","url":"https://portal.micropublication.org/uploads/6eef78d6119f0bbde74563a2eb9db2fe.zip"},{"description":"Extended Figure 1, 2
","doi":"10.22002/fq62m-ypz91","resourceType":"Image","name":"Supplemental Figures.pdf","url":"https://portal.micropublication.org/uploads/156b33a60dd18e6ef5953b00f06b2c09.pdf"}],"funding":"This work was supported by the Canadian Institutes of Health Research (CIHR; http://www.cihr-irsc.gc.ca/; JVR) and the Natural Sciences and Engineering Research Council of Canada (NSERC; https://www.nserc-crsng.gc.ca/index_eng.asp; JVR). JVR received a Senior Research Scholar career award from the Fonds de Recherche du Quebec Santé (FRQS) and Parkinson Quebec. Work done by Meeta Mistry and John Hutchinson at the Harvard Chan Bioinformatics Core was funded in part by Havard Medical School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
","image":{"url":"https://portal.micropublication.org/uploads/8fdf5ff0fc92511f816014100058cbe1.jpg"},"imageCaption":"This figure is showing the output for the expression of the gene cdr-2 in clk-1, isp-1 and nuo-6 mutants. (A) The “TSM Plots” tab will display a graph comparing the expression levels of the selected gene(s) in the selected strain(s) to the expression in wild-type worms. All individual values are shown as well as the average. (B) The “Statistics” tab will show whether there are significant differences in the expression of the selected gene(s) from wild-type worms including the fold change (log2) and adjusted p-value (padj). It also displays the mean and standard deviation for each strain. (C) The “TPMs” tab will display the transcripts per million for each individual sample for the gene(s) and strain(s) selected.
","imageTitle":"Sample Outputs from Gene Lookup Shiny App.
","methods":"Strains were maintained on NGM plates with OP50 bacteria at 20°C. Worms were collected for RNA sequencing at the pre-fertile young adult stage. A minimum of six biological replicates per strain were used. Each biological replicate consisted of a separate plate of worms generated by a limited lay on a different day than the other replicates. RNA was isolated using TRIZOL as described previously (Machiela et al., 2016). Sequencing libraries were generated with the Kapa Biosystems stranded mRNA-Seq kit for the Illumina platform. Libraries were sequenced using 1×75 bp sequencing on the Illumina NextSeq 500 platform to a depth of ∼30M reads per sample (Dues et al., 2017). All samples were processed using an RNA-seq pipeline implemented in the bcbio-nextgen project (https://bcbio-nextgen.readthedocs.org/en/latest/). Raw reads were examined for quality issues using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to ensure library generation and sequencing are suitable for further analysis. Adapter sequences, other contaminant sequences such as polyA tails and low quality sequences with PHRED quality scores less than five were trimmed from reads using atropos (https://github.com/jdidion/atropos; 10.5281/zenodo.596588). Trimmed reads were aligned to the C. elegans genome, augmented with transcript information from Ensembl using STAR (Dobin et al., 2013). Alignments were checked for evenness of coverage, rRNA content, genomic context of alignments (for example, alignments in known transcripts and introns), complexity and other quality checks using a combination of FastQC, Qualimap (Garcia-Alcalde et al., 2012), MultiQC (https://github.com/ewels/MultiQC) and custom tools. Counts of reads aligning to known genes were generated by featureCounts (Liao et al., 2014) and used for additional quality checks. In parallel, Transcripts Per Million (TPM) measurements per isoform were generated by quasi-alignment using Salmon (Patro et al., 2017). Differential expression at the gene level was called with DESeq2 (Love et al., 2014) using shrinkage. In brief, the largest fold changes not due to low counts were used inform a prior distribution and shrink fold changes that lack statistical information. Counts per gene were estimated from the Salmon quasi-alignments by tximport (Soneson et al., 2015) as quantitating at the isoform level has been shown to produce more accurate results at the gene level.
","reagents":"The following worm strains were used:
Original Strain Name | Genotype | Source |
N2 | wild-type | CGC |
KX15 | ife-2 (ok306) | CGC |
MQ130 | clk-1(qm30) | Hekimi lab |
MQ1451 | sod-2(ok1030) | Hekimi lab |
MQ631 | eat-2(ad1116) | CGC |
PR813 | osm-5(p813) | CGC |
MQ1333 | nuo-6(qm200) | Hekimi lab |
MQ887 | isp-1(qm150) | Hekimi lab |
CB1370 | daf-2(e1370) | CGC |
CB4037 | glp-1(e2141) | CGC |
The application can be accessed here: https://vanraamsdonk.shinyapps.io/mutant_comparison_viewer/
","patternDescription":"Aging can be described as the progressive loss of function at both a molecular and cellular level driven by the accumulation of molecular damage and altered molecular signaling leading to an overall decline in an organism’s ability to repair and maintain itself (Li et al., 2024). Aging is the greatest risk factor for the development of a multitude of chronic diseases including age-onset neurodegenerative disorders. As a result, the geroscience hypothesis proposes that by gaining insight into the aging process, this knowledge can be used to mitigate multiple chronic diseases simultaneously thereby allowing individuals to live healthier, longer lives. One of the main goals of geroscience is to advance our understanding of the biology of aging to identify novel molecular targets to modulate the aging process.
One approach that has been used to elucidate the molecular mechanism contributing to aging is RNA sequencing (RNA-seq). RNA-seq provides a genome-wide, unbiased method to characterize global gene expression patterns associated with aging and longevity (Bairakdar et al., 2023; Mosley et al., 2025). Rather than focusing on individual candidate genes, RNA-seq enables the identification of coordinated transcriptional changes, pathway-level alterations, and gene expression programs that reflect broader biological states (Marguerat and Bahler, 2010; Wang et al., 2009). In this way, different long-lived mutants or lifespan-extending interventions can be defined by characteristic transcriptomic signatures or expression profiles (Tyshkovskiy et al., 2023). Such comparative transcriptomic analysis provides a data-driven framework to better understand the molecular basis of aging and to generate new mechanistic hypotheses.
To gain insight into shared and unique molecular mechanisms contributing to lifespan extension, we used RNA-seq to examine gene expression in a panel of nine long-lived mutants that represent multiple biological pathways known to regulate lifespan in C. elegans. Reduced insulin/IGF-1 signaling was examined using the daf-2(e1370) mutant, one of the earliest and most well-characterized longevity models (Kenyon et al., 1993). Dietary restriction was modeled using eat-2(ad1116) mutants, which exhibit extended lifespan due to reduced food intake (Lakowski and Hekimi, 1998). Decreased protein translation was represented by ife-2(ok306) mutants (Syntichaki et al., 2007), while altered chemosensory signaling was examined using osm-5(p813) mutants (Apfeld and Kenyon, 1999). Germline ablation–associated longevity was modeled using glp-1(e2141) mutants (Hsin and Kenyon, 1999). To assess mild impairment of mitochondrial function, we included clk-1(qm30), isp-1(qm150), and nuo-6(qm200) mutants (Feng et al., 2001; Lakowski and Hekimi, 1996; Yang and Hekimi, 2010). Finally, we used sod-2(ok1030) mutants, which have increased levels of mitochondrial superoxide (Van Raamsdonk and Hekimi, 2009). Together, these models represent multiple independent genetic perturbations that lead to lifespan extension, enabling comparative analysis of different longevity strategies.
In comparing differentially expressed genes across the panel of nine long-lived mutants, we identified a number of genes that are upregulated or downregulated in at least six long-lived mutants but relatively few in seven or more (Rudich et al., 2025). The commonly upregulated genes were enriched for genes involved in immunity, defense and metabolism, while downregulated genes were enriched for genes involved in translation and gene expression. Interestingly, we found that the nine long-lived mutants clustered into three different longevity groups according to their gene expression. Group 1 longevity mutants contained daf-2, glp-1, clk-1, isp-1, nuo-6 and sod-2; group 2 contained eat-2 and osm-5; and group 3 contained ife-2. Group 1 and group 2 longevity mutants exhibited modulation of specific genetic pathways in opposite directions (Rudich et al., 2025). For example, target genes of the DAF-16-mediated stress response pathway and mitochondrial unfolded protein response are significantly upregulated in group 1 longevity mutants but downregulated or unchanged in group 2 longevity mutants. This suggests that longevity can be achieved through multiple distinct molecular strategies.
To make our gene expression data accessible to the research community and facilitate the identification of genes associated with longevity, we created an interactive web-based Shiny application that enables exploration of RNA-seq gene expression across multiple long-lived C. elegans mutants relative to wild-type controls. The application can be found here: https://vanraamsdonk.shinyapps.io/mutant_comparison_viewer/. Users can select one or more mutants for comparison, including clk-1, daf-2, eat-2, glp-1, ife-2, isp-1, nuo-6, osm-5, and sod-2. The application accepts one or multiple genes as input and supports common gene names (e.g., aap-1), WormBase gene IDs (e.g., WBGene00000001), and sequence names (e.g., Y110A7A.10). This interface allows rapid cross-model comparison of gene expression across distinct longevity pathways without requiring programming expertise. All datasets shown in the app were processed using a standardized RNA-seq pipeline (bcbio-nextgen: https://zenodo.org/records/5781867 ). Differential expression was computed with DESeq2 (with shrinkage) (Love et al., 2014), and transcripts per million (TPM) values were derived from Salmon-based quantification.
To use the application, users first select one or more mutant strains under the “Select Comparisons” panel (For screenshots on how to use the application see Extended Figure 1). Genes of interest are then entered into the input field using gene names, WormBase gene IDs, or sequence names. After submitting the query, the application automatically generates expression plots and corresponding statistical summaries for the selected strains relative to wild-type controls.
The application displays gene expression results in three main formats (Figure 1). The “TSM Plots” tab shows expression levels for each selected mutant compared to wild-type controls, including individual sample values and the average for each strain. This allows users to see the direction of change and the variation within each group. The “Statistics” tab provides quantitative results, including log2 fold change and adjusted p-value (padj), which indicate the magnitude and statistical significance of the difference. The “TPMs” tab lists the TPM values for each individual sample, allowing users to view the underlying expression levels. Together, these outputs help users evaluate whether a gene is consistently increased or decreased across different longevity mutants.
In addition to visualization and summary statistics, the application provides access to the full underlying datasets (Extended Figure 2). Under the “Datasets” tab, users can view sample metadata, differential expression statistics generated by DESeq2, and TPM values for all genes. The DESeq2 table includes log2 fold change, p-values, adjusted p-values, and expression means for each strain compared to wild type. These tables can be downloaded, allowing users to perform further analysis outside of the web interface. Access to the full datasets makes it possible to further explore similarities and differences in gene regulation across distinct longevity pathways.
In conclusion, this tool provides a simple way to compare gene expression across multiple long-lived mutants within a single framework. By examining the same gene across different longevity pathways, researchers can identify shared changes as well as pathway-specific patterns of regulation. This approach may help guide further experiments aimed at understanding how distinct genetic interventions influence aging at the molecular level.
","references":[{"reference":"Apfeld J, Kenyon C. 1999. Regulation of lifespan by sensory perception in Caenorhabditis elegans. Nature 402: 804-809.
","pubmedId":"","doi":"10.1038/45544"},{"reference":"Bairakdar MD, Tewari A, Truttmann MC. 2023. A meta-analysis of RNA-Seq studies to identify novel genes that regulate aging. Experimental Gerontology 173: 112107.
","pubmedId":"","doi":"10.1016/j.exger.2023.112107"},{"reference":"Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al., Gingeras. 2012. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29: 15-21.
","pubmedId":"","doi":"10.1093/bioinformatics/bts635"},{"reference":"Dues DJ, Schaar CE, Johnson BK, Bowman MJ, Winn ME, Senchuk MM, Van Raamsdonk JM. 2017. Uncoupling of oxidative stress resistance and lifespan in long-lived isp-1 mitochondrial mutants in Caenorhabditis elegans. Free Radical Biology and Medicine 108: 362-373.
","pubmedId":"","doi":"10.1016/j.freeradbiomed.2017.04.004"},{"reference":"Feng J, Bussière Fdr, Hekimi S. 2001. Mitochondrial Electron Transport Is a Key Determinant of Life Span in Caenorhabditis elegans. Developmental Cell 1: 633-644.
","pubmedId":"","doi":" 10.1016/s1534-5807(01)00071-5"},{"reference":"García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, et al., Conesa. 2012. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics 28: 2678-2679.
","pubmedId":"","doi":"10.1093/bioinformatics/bts503"},{"reference":"Hsin H, Kenyon C. 1999. Signals from the reproductive system regulate the lifespan of C. elegans. Nature 399: 362-366.
","pubmedId":"","doi":"10.1038/20694"},{"reference":"Kenyon C, Chang J, Gensch E, Rudner A, Tabtiang R. 1993. A C. elegans mutant that lives twice as long as wild type. Nature 366: 461-464.
","pubmedId":"","doi":"10.1038/366461a0"},{"reference":"Lakowski B, Hekimi S. 1996. Determination of Life-Span in Caenorhabditis elegans by Four Clock Genes. Science 272: 1010-1013.
","pubmedId":"","doi":"10.1126/science.272.5264.1010"},{"reference":"Lakowski B, Hekimi S. 1998. The genetics of caloric restriction in\n Caenorhabditis elegans. Proceedings of the National Academy of Sciences 95: 13091-13096.
","pubmedId":"","doi":" 10.1073/pnas.95.22.13091"},{"reference":"Li Y, Tian X, Luo J, Bao T, Wang S, Wu X. 2024. Molecular mechanisms of aging and anti-aging strategies. Cell Communication and Signaling 22: 10.1186/s12964-024-01663-1.
","pubmedId":"","doi":"10.1186/s12964-024-01663-1"},{"reference":"Liao Y, Smyth GK, Shi W. 2013. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30: 923-930.
","pubmedId":"","doi":"10.1093/bioinformatics/btt656"},{"reference":"Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15: 10.1186/s13059-014-0550-8.
","pubmedId":"","doi":"10.1186/s13059-014-0550-8"},{"reference":"Machiela E, Dues DJ, Senchuk MM, Van Raamsdonk JM. 2016. Oxidative stress is increased in C. elegans models of Huntington’s disease but does not contribute to polyglutamine toxicity phenotypes. Neurobiology of Disease 96: 1-11.
","pubmedId":"","doi":"10.1016/j.nbd.2016.08.008"},{"reference":"Marguerat S, Bähler Jr. 2009. RNA-seq: from technology to biology. Cellular and Molecular Life Sciences 67: 569-579.
","pubmedId":"","doi":"10.1007/s00018-009-0180-6"},{"reference":"Mosley MC, Kinser HE, Martin OMF, Stroustrup N, Schedl T, Kornfeld K, Pincus Z. 2024. Similarities and differences in the gene expression signatures of physiological age versus future lifespan. Aging Cell 24: 10.1111/acel.14428.
","pubmedId":"","doi":"10.1111/acel.14428"},{"reference":"Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. 2017. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods 14: 417-419.
","pubmedId":"","doi":"10.1038/nmeth.4197"},{"reference":"Rudich ZD, Tamez González AA, Guan J, Booth G, Soo SK, Anglas U, et al., Van Raamsdonk. 2025. Multiple Molecular Pathways to Longevity: Opposing Gene Expression Programs Define Distinct Aging Strategies. : 10.64898/2025.12.29.696944.
","pubmedId":"","doi":"10.64898/2025.12.29.696944"},{"reference":"Soneson C, Love MI, Robinson MD. 2016. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4: 1521.
","pubmedId":"","doi":"10.12688/f1000research.7563.2"},{"reference":"Syntichaki P, Troulinaki K, Tavernarakis N. 2007. eIF4E function in somatic cells modulates ageing in Caenorhabditis elegans. Nature 445: 922-926.
","pubmedId":"","doi":"10.1038/nature05603"},{"reference":"Tyshkovskiy A, Ma S, Shindyapina AV, Tikhonov S, Lee SG, Bozaykut P, et al., Gladyshev. 2023. Distinct longevity mechanisms across and within species and their association with aging. Cell 186: 2929-2949.e20.
","pubmedId":"","doi":"10.1016/j.cell.2023.05.002"},{"reference":"Van Raamsdonk JM, Hekimi S. 2009. Deletion of the Mitochondrial Superoxide Dismutase sod-2 Extends Lifespan in Caenorhabditis elegans. PLoS Genetics 5: e1000361.
","pubmedId":"","doi":"10.1371/journal.pgen.1000361"},{"reference":"Wang Z, Gerstein M, Snyder M. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10: 57-63.
","pubmedId":"","doi":"10.1038/nrg2484"},{"reference":"Yang W, Hekimi S. 2010. Two modes of mitochondrial dysfunction lead independently to lifespan extension in Caenorhabditis elegans. Aging Cell 9: 433-447.
","pubmedId":"","doi":"10.1111/j.1474-9726.2010.00571.x"}],"title":"Interactive online tool to identify genes associated with longevity in a panel of long-lived genetic mutants in C. elegans
","reviews":[],"curatorReviews":[]},{"id":"d9d6e53e-8cf0-4d53-9b3b-3fd67d0c8126","decision":"publish","abstract":"We recently used RNA sequencing to identify differentially expressed genes in a panel of nine long-lived mutants in C. elegans representing multiple different pathways of lifespan extension. In order make this data accessible to the research community, we have developed an online tool that allows users to examine the expression of a gene of interest in individual long-lived mutants or across all nine long-lived mutants. This tool will enable users to gain insight into how their genes of interest are modulated in long-lived genetic mutants and the extent to which expression of that gene is correlated with long life.
","acknowledgements":"We thank Meeta Mistry for processing and analysis of the RNA-seq data that made this online tool possible. The coding for the online tool was entirely written by John Hutchinson.
","authors":[{"affiliations":["Diamond Age Data Science, Somerville, Massachusetts, USA","Harvard Chan Bioinformatics Core, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA"],"departments":["",""],"credit":["conceptualization","methodology","visualization","writing_reviewEditing"],"email":"john@diamondage.com","firstName":"John N.","lastName":"Hutchinson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada"],"departments":["","",""],"credit":["writing_originalDraft","writing_reviewEditing"],"email":"jiaxi.guan@mail.mcgill.ca","firstName":"Jiaxi","lastName":"Guan","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada"],"departments":["","",""],"credit":["writing_originalDraft","writing_reviewEditing"],"email":"grant.booth@mail.mcgill.ca","firstName":"Grant F.","lastName":"Booth","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":true,"WBId":null,"orcid":null},{"affiliations":["Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada","Metabolic Disorders and Complications Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada","Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, Quebec, Canada"],"departments":["","","",""],"credit":["conceptualization","supervision","visualization","writing_originalDraft","writing_reviewEditing","project"],"email":"jeremy.vanraamsdonk@mcgill.ca","firstName":"Jeremy M","lastName":"Van Raamsdonk","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":null,"WBId":"","orcid":"0000-0001-8376-9605"}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":null},"extendedData":[{"description":"Software release github zipfile.
","doi":"10.22002/8vmke-tn046","resourceType":"Software","name":"van_raamsdonk_mutantviewer_rshinyapp-1.0.0.zip","url":"https://portal.micropublication.org/uploads/6eef78d6119f0bbde74563a2eb9db2fe.zip"},{"description":"Extended Figure 1, 2
","doi":"10.22002/fq62m-ypz91","resourceType":"Image","name":"Supplemental Figures.pdf","url":"https://portal.micropublication.org/uploads/156b33a60dd18e6ef5953b00f06b2c09.pdf"}],"funding":"This work was supported by the Canadian Institutes of Health Research (CIHR; http://www.cihr-irsc.gc.ca/; JVR) and the Natural Sciences and Engineering Research Council of Canada (NSERC; https://www.nserc-crsng.gc.ca/index_eng.asp; JVR). JVR received a Senior Research Scholar career award from the Fonds de Recherche du Quebec Santé (FRQS) and Parkinson Quebec. Work done by Meeta Mistry and John Hutchinson at the Harvard Chan Bioinformatics Core was funded in part by Havard Medical School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
","image":{"url":"https://portal.micropublication.org/uploads/e0e97f7e4d78705a534b9a37fad4bdf1.jpg"},"imageCaption":"This figure is showing the output for the expression of the gene cdr-2 in clk-1, isp-1 and nuo-6 mutants. (A) The “TSM Plots” tab will display a graph comparing the expression levels of the selected gene(s) in the selected strain(s) to the expression in wild-type worms. All individual values are shown as well as the average. (B) The “Statistics” tab will show whether there are significant differences in the expression of the selected gene(s) from wild-type worms including the fold change (log2) and adjusted p-value (padj). It also displays the mean and standard deviation for each strain. (C) The “TPMs” tab will display the transcripts per million for each individual sample for the gene(s) and strain(s) selected.
","imageTitle":"Sample Outputs from Gene Lookup Shiny App.
","methods":"Strains were maintained on NGM plates with OP50 bacteria at 20°C. Worms were collected for RNA sequencing at the pre-fertile young adult stage. A minimum of six biological replicates per strain were used. Each biological replicate consisted of a separate plate of worms generated by a limited lay on a different day than the other replicates. RNA was isolated using TRIZOL as described previously (Machiela et al., 2016). Sequencing libraries were generated with the Kapa Biosystems stranded mRNA-Seq kit for the Illumina platform. Libraries were sequenced using 1×75 bp sequencing on the Illumina NextSeq 500 platform to a depth of ∼30M reads per sample (Dues et al., 2017). All samples were processed using an RNA-seq pipeline implemented in the bcbio-nextgen project (https://bcbio-nextgen.readthedocs.org/en/latest/). Raw reads were examined for quality issues using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to ensure library generation and sequencing are suitable for further analysis. Adapter sequences, other contaminant sequences such as polyA tails and low quality sequences with PHRED quality scores less than five were trimmed from reads using atropos (https://github.com/jdidion/atropos; 10.5281/zenodo.596588). Trimmed reads were aligned to the C. elegans genome, augmented with transcript information from Ensembl using STAR (Dobin et al., 2013). Alignments were checked for evenness of coverage, rRNA content, genomic context of alignments (for example, alignments in known transcripts and introns), complexity and other quality checks using a combination of FastQC, Qualimap (Garcia-Alcalde et al., 2012), MultiQC (https://github.com/ewels/MultiQC) and custom tools. Counts of reads aligning to known genes were generated by featureCounts (Liao et al., 2014) and used for additional quality checks. In parallel, Transcripts Per Million (TPM) measurements per isoform were generated by quasi-alignment using Salmon (Patro et al., 2017). Differential expression at the gene level was called with DESeq2 (Love et al., 2014) using shrinkage. In brief, the largest fold changes not due to low counts were used inform a prior distribution and shrink fold changes that lack statistical information. Counts per gene were estimated from the Salmon quasi-alignments by tximport (Soneson et al., 2015) as quantitating at the isoform level has been shown to produce more accurate results at the gene level.
","reagents":"The following worm strains were used:
Original Strain Name | Genotype | Source |
N2 | wild-type | CGC |
KX15 | ife-2 (ok306) | CGC |
MQ130 | clk-1(qm30) | Hekimi lab |
MQ1451 | sod-2(ok1030) | Hekimi lab |
MQ631 | eat-2(ad1116) | CGC |
PR813 | osm-5(p813) | CGC |
MQ1333 | nuo-6(qm200) | Hekimi lab |
MQ887 | isp-1(qm150) | Hekimi lab |
CB1370 | daf-2(e1370) | CGC |
CB4037 | glp-1(e2141) | CGC |
The application can be accessed here: https://vanraamsdonk.shinyapps.io/mutant_comparison_viewer/
","patternDescription":"Aging can be described as the progressive loss of function at both a molecular and cellular level driven by the accumulation of molecular damage and altered molecular signaling leading to an overall decline in an organism’s ability to repair and maintain itself (Li et al., 2024). Aging is the greatest risk factor for the development of a multitude of chronic diseases including age-onset neurodegenerative disorders. As a result, the geroscience hypothesis proposes that by gaining insight into the aging process, this knowledge can be used to mitigate multiple chronic diseases simultaneously thereby allowing individuals to live healthier, longer lives. One of the main goals of geroscience is to advance our understanding of the biology of aging to identify novel molecular targets to modulate the aging process.
One approach that has been used to elucidate the molecular mechanism contributing to aging is RNA sequencing (RNA-seq). RNA-seq provides a genome-wide, unbiased method to characterize global gene expression patterns associated with aging and longevity (Bairakdar et al., 2023; Mosley et al., 2025). Rather than focusing on individual candidate genes, RNA-seq enables the identification of coordinated transcriptional changes, pathway-level alterations, and gene expression programs that reflect broader biological states (Marguerat and Bahler, 2010; Wang et al., 2009). In this way, different long-lived mutants or lifespan-extending interventions can be defined by characteristic transcriptomic signatures or expression profiles (Tyshkovskiy et al., 2023). Such comparative transcriptomic analysis provides a data-driven framework to better understand the molecular basis of aging and to generate new mechanistic hypotheses.
To gain insight into shared and unique molecular mechanisms contributing to lifespan extension, we used RNA-seq to examine gene expression in a panel of nine long-lived mutants that represent multiple biological pathways known to regulate lifespan in C. elegans. Reduced insulin/IGF-1 signaling was examined using the daf-2(e1370) mutant, one of the earliest and most well-characterized longevity models (Kenyon et al., 1993). Dietary restriction was modeled using eat-2(ad1116) mutants, which exhibit extended lifespan due to reduced food intake (Lakowski and Hekimi, 1998). Decreased protein translation was represented by ife-2(ok306) mutants (Syntichaki et al., 2007), while altered chemosensory signaling was examined using osm-5(p813) mutants (Apfeld and Kenyon, 1999). Germline ablation–associated longevity was modeled using glp-1(e2141) mutants (Hsin and Kenyon, 1999). To assess mild impairment of mitochondrial function, we included clk-1(qm30), isp-1(qm150), and nuo-6(qm200) mutants (Feng et al., 2001; Lakowski and Hekimi, 1996; Yang and Hekimi, 2010). Finally, we used sod-2(ok1030) mutants, which have increased levels of mitochondrial superoxide (Van Raamsdonk and Hekimi, 2009). Together, these models represent multiple independent genetic perturbations that lead to lifespan extension, enabling comparative analysis of different longevity strategies.
In comparing differentially expressed genes across the panel of nine long-lived mutants, we identified a number of genes that are upregulated or downregulated in at least six long-lived mutants but relatively few in seven or more (Rudich et al., 2025). The commonly upregulated genes were enriched for genes involved in immunity, defense and metabolism, while downregulated genes were enriched for genes involved in translation and gene expression. Interestingly, we found that the nine long-lived mutants clustered into three different longevity groups according to their gene expression. Group 1 longevity mutants contained daf-2, glp-1, clk-1, isp-1, nuo-6 and sod-2; group 2 contained eat-2 and osm-5; and group 3 contained ife-2. Group 1 and group 2 longevity mutants exhibited modulation of specific genetic pathways in opposite directions (Rudich et al., 2025). For example, target genes of the DAF-16-mediated stress response pathway and mitochondrial unfolded protein response are significantly upregulated in group 1 longevity mutants but downregulated or unchanged in group 2 longevity mutants. This suggests that longevity can be achieved through multiple distinct molecular strategies.
To make our gene expression data accessible to the research community and facilitate the identification of genes associated with longevity, we created an interactive web-based Shiny application that enables exploration of RNA-seq gene expression across multiple long-lived C. elegans mutants relative to wild-type controls. The application can be found here: https://vanraamsdonk.shinyapps.io/mutant_comparison_viewer/. Users can select one or more mutants for comparison, including clk-1, daf-2, eat-2, glp-1, ife-2, isp-1, nuo-6, osm-5, and sod-2. The application accepts one or multiple genes as input and supports common gene names (e.g., aap-1), WormBase gene IDs (e.g., WBGene00000001), and sequence names (e.g., Y110A7A.10). This interface allows rapid cross-model comparison of gene expression across distinct longevity pathways without requiring programming expertise. All datasets shown in the app were processed using a standardized RNA-seq pipeline (bcbio-nextgen: https://zenodo.org/records/5781867 ). Differential expression was computed with DESeq2 (with shrinkage) (Love et al., 2014), and transcripts per million (TPM) values were derived from Salmon-based quantification.
To use the application, users first select one or more mutant strains under the “Select Comparisons” panel (For screenshots on how to use the application see Extended Figure 1). Genes of interest are then entered into the input field using gene names, WormBase gene IDs, or sequence names. After submitting the query, the application automatically generates expression plots and corresponding statistical summaries for the selected strains relative to wild-type controls.
The application displays gene expression results in three main formats (Figure 1). The “TSM Plots” tab shows expression levels for each selected mutant compared to wild-type controls, including individual sample values and the average for each strain. This allows users to see the direction of change and the variation within each group. The “Statistics” tab provides quantitative results, including log2 fold change and adjusted p-value (padj), which indicate the magnitude and statistical significance of the difference. The “TPMs” tab lists the TPM values for each individual sample, allowing users to view the underlying expression levels. Together, these outputs help users evaluate whether a gene is consistently increased or decreased across different longevity mutants.
In addition to visualization and summary statistics, the application provides access to the full underlying datasets (Extended Figure 2). Under the “Datasets” tab, users can view sample metadata, differential expression statistics generated by DESeq2, and TPM values for all genes. The DESeq2 table includes log2 fold change, p-values, adjusted p-values, and expression means for each strain compared to wild type. These tables can be downloaded, allowing users to perform further analysis outside of the web interface. Access to the full datasets makes it possible to further explore similarities and differences in gene regulation across distinct longevity pathways.
In conclusion, this tool provides a simple way to compare gene expression across multiple long-lived mutants within a single framework. By examining the same gene across different longevity pathways, researchers can identify shared changes as well as pathway-specific patterns of regulation. This approach may help guide further experiments aimed at understanding how distinct genetic interventions influence aging at the molecular level.
","references":[{"reference":"Apfeld J, Kenyon C. 1999. Regulation of lifespan by sensory perception in Caenorhabditis elegans. Nature 402: 804-809.
","pubmedId":"","doi":"10.1038/45544"},{"reference":"Bairakdar MD, Tewari A, Truttmann MC. 2023. A meta-analysis of RNA-Seq studies to identify novel genes that regulate aging. Experimental Gerontology 173: 112107.
","pubmedId":"","doi":"10.1016/j.exger.2023.112107"},{"reference":"Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al., Gingeras. 2012. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29: 15-21.
","pubmedId":"","doi":"10.1093/bioinformatics/bts635"},{"reference":"Dues DJ, Schaar CE, Johnson BK, Bowman MJ, Winn ME, Senchuk MM, Van Raamsdonk JM. 2017. Uncoupling of oxidative stress resistance and lifespan in long-lived isp-1 mitochondrial mutants in Caenorhabditis elegans. Free Radical Biology and Medicine 108: 362-373.
","pubmedId":"","doi":"10.1016/j.freeradbiomed.2017.04.004"},{"reference":"Feng J, Bussière Fdr, Hekimi S. 2001. Mitochondrial Electron Transport Is a Key Determinant of Life Span in Caenorhabditis elegans. Developmental Cell 1: 633-644.
","pubmedId":"","doi":" 10.1016/s1534-5807(01)00071-5"},{"reference":"García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, et al., Conesa. 2012. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics 28: 2678-2679.
","pubmedId":"","doi":"10.1093/bioinformatics/bts503"},{"reference":"Hsin H, Kenyon C. 1999. Signals from the reproductive system regulate the lifespan of C. elegans. Nature 399: 362-366.
","pubmedId":"","doi":"10.1038/20694"},{"reference":"Kenyon C, Chang J, Gensch E, Rudner A, Tabtiang R. 1993. A C. elegans mutant that lives twice as long as wild type. Nature 366: 461-464.
","pubmedId":"","doi":"10.1038/366461a0"},{"reference":"Lakowski B, Hekimi S. 1996. Determination of Life-Span in Caenorhabditis elegans by Four Clock Genes. Science 272: 1010-1013.
","pubmedId":"","doi":"10.1126/science.272.5264.1010"},{"reference":"Lakowski B, Hekimi S. 1998. The genetics of caloric restriction in\n Caenorhabditis elegans. Proceedings of the National Academy of Sciences 95: 13091-13096.
","pubmedId":"","doi":" 10.1073/pnas.95.22.13091"},{"reference":"Li Y, Tian X, Luo J, Bao T, Wang S, Wu X. 2024. Molecular mechanisms of aging and anti-aging strategies. Cell Communication and Signaling 22: 10.1186/s12964-024-01663-1.
","pubmedId":"","doi":"10.1186/s12964-024-01663-1"},{"reference":"Liao Y, Smyth GK, Shi W. 2013. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30: 923-930.
","pubmedId":"","doi":"10.1093/bioinformatics/btt656"},{"reference":"Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15: 10.1186/s13059-014-0550-8.
","pubmedId":"","doi":"10.1186/s13059-014-0550-8"},{"reference":"Machiela E, Dues DJ, Senchuk MM, Van Raamsdonk JM. 2016. Oxidative stress is increased in C. elegans models of Huntington’s disease but does not contribute to polyglutamine toxicity phenotypes. Neurobiology of Disease 96: 1-11.
","pubmedId":"","doi":"10.1016/j.nbd.2016.08.008"},{"reference":"Marguerat S, Bähler Jr. 2009. RNA-seq: from technology to biology. Cellular and Molecular Life Sciences 67: 569-579.
","pubmedId":"","doi":"10.1007/s00018-009-0180-6"},{"reference":"Mosley MC, Kinser HE, Martin OMF, Stroustrup N, Schedl T, Kornfeld K, Pincus Z. 2024. Similarities and differences in the gene expression signatures of physiological age versus future lifespan. Aging Cell 24: 10.1111/acel.14428.
","pubmedId":"","doi":"10.1111/acel.14428"},{"reference":"Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. 2017. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods 14: 417-419.
","pubmedId":"","doi":"10.1038/nmeth.4197"},{"reference":"Rudich ZD, Tamez González AA, Guan J, Booth G, Soo SK, Anglas U, et al., Van Raamsdonk. 2025. Multiple Molecular Pathways to Longevity: Opposing Gene Expression Programs Define Distinct Aging Strategies. : 10.64898/2025.12.29.696944.
","pubmedId":"","doi":"10.64898/2025.12.29.696944"},{"reference":"Soneson C, Love MI, Robinson MD. 2016. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4: 1521.
","pubmedId":"","doi":"10.12688/f1000research.7563.2"},{"reference":"Syntichaki P, Troulinaki K, Tavernarakis N. 2007. eIF4E function in somatic cells modulates ageing in Caenorhabditis elegans. Nature 445: 922-926.
","pubmedId":"","doi":"10.1038/nature05603"},{"reference":"Tyshkovskiy A, Ma S, Shindyapina AV, Tikhonov S, Lee SG, Bozaykut P, et al., Gladyshev. 2023. Distinct longevity mechanisms across and within species and their association with aging. Cell 186: 2929-2949.e20.
","pubmedId":"","doi":"10.1016/j.cell.2023.05.002"},{"reference":"Van Raamsdonk JM, Hekimi S. 2009. Deletion of the Mitochondrial Superoxide Dismutase sod-2 Extends Lifespan in Caenorhabditis elegans. PLoS Genetics 5: e1000361.
","pubmedId":"","doi":"10.1371/journal.pgen.1000361"},{"reference":"Wang Z, Gerstein M, Snyder M. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10: 57-63.
","pubmedId":"","doi":"10.1038/nrg2484"},{"reference":"Yang W, Hekimi S. 2010. Two modes of mitochondrial dysfunction lead independently to lifespan extension in Caenorhabditis elegans. Aging Cell 9: 433-447.
","pubmedId":"","doi":"10.1111/j.1474-9726.2010.00571.x"}],"title":"Interactive online tool to identify genes associated with longevity in a panel of long-lived genetic mutants in C. elegans
","reviews":[],"curatorReviews":[]}]}},"species":{"species":[{"value":"acer saccharum","label":"Acer saccharum","imageSrc":"","imageAlt":"","mod":"TreeGenes","modLink":"https://treegenesdb.org","linkVariable":""},{"value":"achillea millefolium","label":"Achillea millefolium","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"acinetobacter baylyi","label":"Acinetobacter baylyi","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"actinobacteria bacterium","label":"Actinobacteria bacterium","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"adelges tsugae","label":"Adelges tsugae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"adenocaulon chilense","label":"Adenocaulon chilense","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"aedes japonicus","label":"Aedes japonicus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"aegorhinus vitulus","label":"Aegorhinus vitulus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"alaimidae","label":"Alaimidae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"allobates femoralis","label":"Allobates femoralis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"alnus glutinosa","label":"Alnus glutinosa","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"alosa aestivalis","label":"Alosa aestivalis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"alosa pseudoharengus","label":"Alosa pseudoharengus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"alternaria alternata","label":"Alternaria alternata","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"amynthas agrestis","label":"Amynthas Agrestis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ancylostoma caninum","label":"Ancylostoma caninum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ancylostoma ceylanicum","label":"Ancylostoma ceylanicum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"anemone multifida","label":"Anemone multifida","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"anguilla rostrata","label":"Anguilla rostrata","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"anisakis simplex","label":"Anisakis simplex","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"anomala albopilosa","label":"Anomala albopilosa","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"anthomyiidae sp","label":"Anthomyiidae sp","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"anthomyiidae sp","label":"Anthomyiidae sp","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"arabidopsis","label":"Arabidopsis","imageSrc":"arabidopsis.png","imageAlt":"Arabidopsis graphic by Zoe Zorn CC BY 4.0","mod":"TAIR","modLink":"https://arabidopsis.org","linkVariable":""},{"value":"architeuthis dux","label":"Architeuthis dux","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"arion vulgaris","label":"Arion vulgaris","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"armeria","label":"Armeria","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"artemia","label":"Artemia","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"arthrobacter sp.","label":"Arthrobacter sp.","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ascaridia","label":"Ascaridia","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ascaridia galli","label":"Ascaridia galli","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"asparagopsis taxiformis","label":"Asparagopsis taxiformis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"astatotilapia burtoni","label":"Astatotilapia burtoni","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"avena sativa","label":"Avena sativa","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"aves","label":"Aves","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bacillus","label":"Bacillus (firmicutes)","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bacillus cereus","label":"Bacillus cereus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bacillus mycoides","label":"Bacillus mycoides","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bacillus subtilis","label":"Bacillus subtilis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bacillus thuringiensis","label":"Bacillus thuringiensis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bacillus toyonensis","label":"Bacillus toyonensis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bacillus wiedmannii","label":"Bacillus wiedmannii","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bacteria","label":"Bacteria","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bacteriophage","label":"Bacteriophage","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bactrocera","label":"Bactrocera sp.","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"batrachospermum gelatinosum","label":"Batrachospermum gelatinosum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"betula lenta","label":"Betula lenta","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"betula nigra","label":"Betula nigra","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bombus dahlbohmii","label":"Bombus dahlbohmii","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bombus terrestris","label":"Bombus terrestris","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bombyx mori","label":"Bombyx mori","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bos taurus","label":"Bos Taurus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"brachygobius doriae","label":"Brachygobius doriae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"brassica oleracea","label":"Brassica oleracea","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"brassica rapa","label":"Brassica rapa","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"brugia malayi","label":"Brugia malayi","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"burkholderia thailandensis","label":"Burkholderia thailandensis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"buttiauxella","label":"Buttiauxella","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"caenorhabditis brenneri","label":"Caenorhabditis brenneri","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"caenorhabditis briggsae","label":"Caenorhabditis briggsae","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"c. elegans","label":"Caenorhabditis elegans","imageSrc":"c-elegans.jpg","imageAlt":"C. elegans graphic by Zoe Zorn CC BY 4.0","mod":"WormBase","modLink":"https://wormbase.org","linkVariable":""},{"value":"caenorhabditis inopinata","label":"Caenorhabditis inopinata","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"caenorhabditis japonica","label":"Caenorhabditis japonica","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"caenorhabditis nigoni","label":"Caenorhabditis nigoni","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"caenorhabditis remanei","label":"Caenorhabditis remanei","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"caenorhabditis tropicalis","label":"Caenorhabditis tropicalis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"calidifontibacillus","label":"Calidifontibacillus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"calidifontibacillus erzuremensis","label":"Calidifontibacillus erzuremensis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"calliphora sp","label":"Calliphora sp","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"caltha sagittata","label":"Caltha sagittata","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"cambarus latimanus","label":"Cambarus latimanus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"candida albicans","label":"Candida albicans","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"canis familiaris","label":"Canis familiaris","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"cannabis sativa","label":"Cannabis sativa","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"caretta caretta","label":"Caretta caretta","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"cassiopea xamachana","label":"Cassiopea xamachana","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"caulobacter vibrioides","label":"Caulobacter vibrioides","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"cephalopods","label":"Cephalopoda","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"cerastium arvense","label":"Cerastium arvense","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ceriodaphnia","label":"Ceriodaphnia","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ceroglossus suturalis","label":"Ceroglossus suturalis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"chaetoceros","label":"Chaetoceros","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"chamaecrista fasciculata","label":"Chamaecrista fasciculata","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"chilicola chalcidiformis","label":"Chilicola chalcidiformis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"chitinimonas","label":"Chitinimonas","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"chlamydomonas reinhardtii","label":"Chlamydomonas reinhardtii","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"chromobacterium","label":"Chromobacterium","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"chrysemys picta","label":"Chrysemys picta","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"chrysoperla rufilabris","label":"Chrysoperla rufilabris","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"citrus","label":"Citrus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"clavibacter sp.","label":"Clavibacter sp.","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"colinus virginianus","label":"Colinus virginianus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"crassostrea virginica","label":"Crassostrea virginica","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"crithidia fasciculata","label":"Crithidia fasciculata","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"cutibacterium acnes","label":"Cutibacterium acnes","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"cyanobacteria","label":"Cyanobacteria","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"daphnia","label":"Daphnia","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"daphnia pulex","label":"Daphnia pulex","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"diabrotica virgifera","label":"Diabrotica virgifera","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"diabrotica virgifera virgifera virus 1","label":"Diabrotica virgifera virgifera virus 1","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"d. discoideum","label":"Dictyostelium discoideum","imageSrc":"dicty.png","imageAlt":"D. discoideum","mod":"dictyBase","modLink":"http://dictybase.org","linkVariable":""},{"value":"diptera","label":"Diptera","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"dotocryptus bellicosus","label":"Dotocryptus bellicosus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"drechmeria coniospora","label":"Drechmeria coniospora","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"drosophila","label":"Drosophila","imageSrc":"drosophila.png","imageAlt":"Drosophila graphic by Zoe Zorn CC BY 4.0","mod":"FlyBase","modLink":"https://flybase.org/doi/","linkVariable":"doi"},{"value":"dryopteris campyloptera","label":"Dryopteris campyloptera","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"dryopteris expansa","label":"Dryopteris expansa","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"dryopteris intermedia","label":"Dryopteris intermedia","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"dugesia dorotocephala","label":"Dugesia dorotocephala","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"elasmobranchii","label":"Elasmobranchii","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"embryophyta","label":"Embryophyta","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"enoploteuthis chunii","label":"Enoploteuthis chunii","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"enterobacter aerogenes","label":"Enterobacter aerogenes","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"enterococcus raffinosus","label":"Enterococcus raffinosus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"epichloë coenophiala","label":"Epichloë coenophiala","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"equus caballus","label":"Equus caballus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"erigeron sp","label":"Erigeron sp","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"eristalis","label":"Eristalis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"eruca vesicaria","label":"Eruca vesicaria","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"erwinia carotovora","label":"Erwinia carotovora","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"erythronium americanum","label":"Erythronium americanum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"escherichia coli","label":"Escherichia coli","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"eukaryota","label":"Eukaryotes","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"felis catus","label":"Felis catus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"francisella novicida","label":"Francisella novicida","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"francisella tularensis","label":"Francisella tularensis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"fraxinus americana","label":"Fraxinus americana","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"fucus distichus","label":"Fucus distichus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"fungi","label":"Fungi","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"gasteropelecus sp.","label":"Gasteropelecus sp.","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"geranium sp","label":"Geranium sp","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"girardia","label":"Girardia","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"glaucomys volans","label":"Glaucomys volans","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"glycine max","label":"Glycine max","imageSrc":"","imageAlt":"","mod":"Soybase","modLink":"https://soybase.org","linkVariable":""},{"value":"glyptemys insculpta","label":"Glyptemys insculpta","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"gossypium hirsutum","label":"Gossypium hirsutum","imageSrc":"","imageAlt":"","mod":"CottonGen","modLink":"https://www.cottongen.org/","linkVariable":""},{"value":"gromphadorhina portentosa","label":"Gromphadorhina portentosa","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"gryllodes sigillatus","label":"Gryllodes sigillatus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"haliotis rufescens","label":"Haliotis rufescens","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"hepacivirus hominis","label":"Hepatitis C Virus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"herpes simplex virus type 1","label":"Herpes simplex virus type 1","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"human","label":"Human","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"human coronavirus oc43","label":"Human coronavirus OC43","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"hydra vulgaris","label":"Hydra vulgaris","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"hydropsyche sp","label":"Hydropsyche sp","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"hymenoptera","label":"Hymenoptera","imageSrc":"","imageAlt":"","mod":"Hymenoptera Genome Database","modLink":"https://hymenoptera.elsiklab.missouri.edu/","linkVariable":""},{"value":"hypochaeris radicata","label":"Hypochaeris radicata","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"hypodynerus vespiformis","label":"Hypodynerus vespiformis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"iflaviridae","label":"Iflaviridae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"iflavuris","label":"Iflavirus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ipomoea hederacea","label":"Ipomoea hederacea","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ischnomera","label":"Ischnomera","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ischnomera ruficollis","label":"Ischnomera ruficollis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"julidochromis marlieri","label":"Julidochromis marlieri","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"juniperus virginiana","label":"Juniperus virginiana","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"kluyveromyces marxianus","label":"Kluyveromyces marxianus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"l. casei","label":"L. casei","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"lacticaseibacillus casei","label":"Lacticaseibacillus casei","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"larentiinae sp","label":"Larentiinae sp","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"laurus nobilis","label":"Laurus nobilis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"lepidoptera","label":"Lepidoptera","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"leucanthemum vulgare","label":"Leucanthemum vulgare","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"linepithema humile","label":"Linepithema humile","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"liometopum occidentale","label":"Liometopum occidentale","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"lolium arundinaceum","label":"Lolium arundinaceum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"lumbriculus variegatus","label":"Lumbriculus variegatus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"lumbricus terrestris","label":"Lumbricus terrestris","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"lupinus polyphyllus","label":"Lupinus polyphyllus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"lycorma delicatula","label":"Lycorma delicatula","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"lynx rufus","label":"Lynx rufus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"magnaporthe oryzae","label":"Magnaporthe oryzae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"mammalia","label":"Mammalia","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"manihot esculenta","label":"Manihot esculenta","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"medicago lupulina","label":"Medicago lupulina","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"meloidogyne","label":"Meloidogyne","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"mimus polyglottos","label":"Mimus polyglottos","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"bryophyta","label":"Mosses","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"mouse","label":"Mouse","imageSrc":"","imageAlt":"","mod":"MGI","modLink":"https://informatics.jax.org","linkVariable":""},{"value":"m. minutoides","label":"Mus minutoides","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"mycobacterium smegmatis","label":"Mycobacterium smegmatis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"nakaseomyces glabratus","label":"Nakaseomyces glabratus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"nauphoeta cinerea","label":"Nauphoeta cinerea","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"neurospora","label":"Neurospora","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"n. benthamiana","label":"Nicotiana benthamiana","imageSrc":"","imageAlt":"","mod":"Solgenomics Network","modLink":"https://solgenomics.net/organism/Nicotiana_benthamiana/genome","linkVariable":""},{"value":"nicotiana tabacum","label":"Nicotiana tabacum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"noctuidae","label":"Noctuidae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"noctuidae sp","label":"Noctuidae sp","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"nothobranchius furzeri","label":"Nothobranchius furzeri","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"onchocerca volvulus","label":"Onchocerca volvulus","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"orconectes virilis","label":"Orconectes virilis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ormia ochracea","label":"Ormia ochracea","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"o. sativa","label":"Oryza sativa","imageSrc":"","imageAlt":"","mod":"Gramene","modLink":"https://www.gramene.org/","linkVariable":""},{"value":"other","label":"Other","imageSrc":"","imageAlt":"","mod":null,"modLink":null,"linkVariable":null},{"value":"oxalis enneaphylla","label":"Oxalis enneaphylla","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"paenarthrobacter nicotinovorans","label":"Paenarthrobacter nicotinovorans","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"paenarthrobacter nicotinovorans","label":"Paenarthrobacter nicotinovorans","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pantoea","label":"Pantoea","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pantoea agglomerans","label":"Pantoea agglomerans","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"papaver sp","label":"Papaver sp","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"paramecium bursaria","label":"Paramecium bursaria","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"partitiviridae","label":"Partitiviridae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pelodiscus sinensis","label":"Pelodiscus sinensis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"perezia recurvata","label":"Perezia recurvata","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"petromyzon marinus","label":"Petromyzon marinus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"photinus pyralis","label":"Photinus pyralis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"photinus pyralis associated partiti-like virus","label":"Photinus pyralis associated partiti-like virus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"photinus pyralis iflavirus 1","label":"Photinus pyralis iflavirus 1","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"physcomitrium patens","label":"Physcomitrium patens","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pinus strobus","label":"Pinus strobus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pinus taeda","label":"Pinus taeda","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"platycheirus","label":"Platycheirus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"plectus sambesii","label":"Plectus sambesii","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pogonomyrmex occidentalis","label":"Pogonomyrmex occidentalis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"poncirus trifoliata","label":"Poncirus trifoliata","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"populus deltoides","label":"Populus deltoides","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"potato virus y","label":"Potato virus Y","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"primula magellanica","label":"Primula magellanica","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pristionchus pacificus","label":"Pristionchus pacificus","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"prunus persica","label":"Prunus persica","imageSrc":"","imageAlt":"","mod":"Genome Database for Rosaceae","modLink":"https://www.rosaceae.org/","linkVariable":""},{"value":"psalmopoeus iriminia","label":"Psalmopoeus iriminia","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pseudanabaena sp.","label":"Pseudanabaena sp.","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pseudomonas","label":"Pseudomonas","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pseudomonas aeruginosa","label":"Pseudomonas aeruginosa","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pseudomonas glycinae","label":"Pseudomonas glycinae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pseudomonas putida","label":"Pseudomonas putida","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pseudomonas syringae","label":"Pseudomonas syringae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"pterophyllum scalare","label":"Pterophyllum scalare","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"python regius","label":"Python regius","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"quercus macrocarpa","label":"Quercus macrocarpa","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ralstonia solanacearum","label":"Ralstonia solanacearum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ranitomeya imitator","label":"Ranitomeya imitator","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ranunculus peduncularis","label":"Ranunculus peduncularis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"rat","label":"Rat","imageSrc":"","imageAlt":"","mod":"RGD","modLink":"https://rgd.mcw.edu","linkVariable":""},{"value":"rheinheimera","label":"Rheinheimera","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ribes rubrum","label":"Ribes rubrum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"sars-cov-2","label":"SARS-CoV-2","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"s. cerevisiae","label":"Saccharomyces cerevisiae","imageSrc":"yeast.png","imageAlt":"Yeast graphic by Zoe Zorn CC BY 4.0","mod":"SGD","modLink":"https://yeastgenome.org","linkVariable":""},{"value":"saccharomyces paradoxus","label":"Saccharomyces paradoxus ","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"s. uvarum","label":"Saccharomyces uvarum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"schistosoma","label":"Schistosoma","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"schizosaccharomyces japonicus","label":"Schizosaccharomyces japonicus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"s. pombe","label":"Schizosaccharomyces pombe","imageSrc":"pombe.png","imageAlt":"Pombe graphic by Zoe Zorn © Caltech","mod":"PomBase","modLink":"https://www.pombase.org/reference/PMID:","linkVariable":"pmId"},{"value":"schmidtea mediterranea","label":"Schmidtea mediterranea","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"senecio sp","label":"Senecio sp","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"simocephalus","label":"Simocephalus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"siraitia grosvenorii","label":"Siraitia grosvenorii","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"solanum lycopersicum","label":"Solanum lycopersicum","imageSrc":"","imageAlt":"","mod":"Solgenomics Network","modLink":"https://solgenomics.net/organism/1/view/","linkVariable":""},{"value":"sorghum","label":"Sorghum","imageSrc":"","imageAlt":"","mod":"SorghumBase","modLink":"https://www.sorghumbase.org","linkVariable":""},{"value":"spiroplasma eriocheiris","label":"Spiroplasma eriocheiris","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"staphylococcus aureus","label":"Staphylococcus aureus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"staphylococcus epidermidis","label":"Staphylococcus epidermidis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"steinernema carpocapsae","label":"Steinernema carpocapsae","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"https://wormbase.org","linkVariable":""},{"value":"steinernema hermaphroditum","label":"Steinernema hermaphroditum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"stenotrophomonas geniculata","label":"Stenotrophomonas geniculata","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"streptococcus gordonii ","label":"Streptococcus gordonii ","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"streptococcus mutans","label":"Streptococcus mutans","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":" streptococcus pneumoniae","label":"Streptococcus pneumoniae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"s. purpuratus","label":"Strongylocentrotus purpuratus","imageSrc":"","imageAlt":"","mod":"Echinobase","modLink":"https://www.echinobase.org","linkVariable":""},{"value":"strongyloides ratti","label":"Strongyloides ratti","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"sulfolobus","label":"Sulfolobus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"symphoricarpos albus","label":"Symphoricarpos albus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"syncirsodes","label":"Syncirsodes","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"synechococcus elongatus","label":"Synechococcus elongatus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"syrphidae","label":"Syrphidae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"tarantobelus jeffdanielsi","label":"Tarantobelus jeffdanielsi","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"taraxacum officinale","label":"Taraxacum officinale","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"tatochila theodice","label":"Tatochila theodice","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"tetrahymena","label":"Tetrahymena","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"tetramorium immigrans","label":"Tetramorium immigrans","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"tomato brown rugose fruit virus","label":"ToBRFV","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"trachemys scripta","label":"Trachemys scripta","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"tribolium castaneum","label":"Tribolium castaneum","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"trichoptera","label":"Trichoptera","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"trichuris muris","label":"Trichuris muris","imageSrc":"","imageAlt":"","mod":"WormBase","modLink":"www.wormbase.org","linkVariable":""},{"value":"trifolium repens","label":"Trifolium repens","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"trypoxylus dichotomus","label":"Trypoxylus dichotomus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"tsuga canadensis","label":"Tsuga canadensis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"ulva expansa","label":"Ulva expansa","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"universal","label":"Universal","imageSrc":"","imageAlt":"","mod":null,"modLink":null,"linkVariable":null},{"value":"vargula hilgendorfii","label":"Vargula hilgendorfii","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"vespula vulgaris","label":"Vespula vulgaris","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"virus","label":"Virus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"watasenia scintillans","label":"Watasenia scintillans","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"wolbachia pipientis","label":"Wolbachia pipientis","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"xenopus","label":"Xenopus","imageSrc":"xenopus.png","imageAlt":"Xenopus graphic by Zoe Zorn CC BY 4.0","mod":"XenBase","modLink":"https://xenbase.org","linkVariable":""},{"value":"xenorhabdus griffiniae","label":"Xenorhabdus griffiniae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"yramea cytheris","label":"Yramea cytheris","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"zaprionus indianus","label":"Zaprionus indianus","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"zea mays","label":"Zea mays","imageSrc":"","imageAlt":"","mod":"MaizeGDB","modLink":"https://www.maizegdb.org","linkVariable":""},{"value":"zebrafish","label":"Zebrafish","imageSrc":"zebrafish.png","imageAlt":"Zebrafish graphic by Zoe Zorn CC BY 4.0","mod":"ZFIN","modLink":"https://zfin.org","linkVariable":""}]}},"pageContext":{"id":"a3ae2ab2-7c02-4a7d-a764-bb7a7020dbbf","citedBy":[],"parsedCsv":{"csvHeader":[],"csvData":[]}}}, "staticQueryHashes": ["2114697108"]}