Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
Enter gene symbol:

Select conditions below to toggle them from the plot:

GROUP CONDITION SAMPLES
Liver
GSM5833468 GSM5833469 GSM5833470 GSM5833474 GSM5833475 GSM5833476 GSM5833477 GSM5833482
GSM5833466 GSM5833467 GSM5833471 GSM5833472 GSM5833473 GSM5833478 GSM5833479 GSM5833480 GSM5833481
GSM5833485 GSM5833486 GSM5833488 GSM5833489 GSM5833490 GSM5833491 GSM5833492 GSM5833493 GSM5833494
GSM5833483 GSM5833484 GSM5833487 GSM5833495 GSM5833496 GSM5833497 GSM5833498 GSM5833499
Description

Submission Date: Jan 25, 2022

Summary: DNA variants that modulate lifespan provide insight into determinants of health, disease, and aging. Through analyses in the UM-HET3 mice of the Interventions Testing Program (ITP), we detected a sex-independent quantitative trait locus (QTL) on chromosome 12 and identified sex-specific QTLs, some of which detected only in older mice. Similar relations between life history and longevity were uncovered in mice and humans, underscoring the importance of early access to nutrients and early growth. We identified common, age- and sex-specific genetic effects on gene expression that we integrated with model organism and human data to create a hypothesis-building interactive resource of prioritized longevity and body weight genes. Finally, we validated Hipk1, Ddost, Hspg2, Fgd6, and Pdk1 as conserved longevity genes using C. elegans lifespan experiments.

GEO Accession ID: GSE194321

PMID: 36173858

Description

Submission Date: Jan 25, 2022

Summary: DNA variants that modulate lifespan provide insight into determinants of health, disease, and aging. Through analyses in the UM-HET3 mice of the Interventions Testing Program (ITP), we detected a sex-independent quantitative trait locus (QTL) on chromosome 12 and identified sex-specific QTLs, some of which detected only in older mice. Similar relations between life history and longevity were uncovered in mice and humans, underscoring the importance of early access to nutrients and early growth. We identified common, age- and sex-specific genetic effects on gene expression that we integrated with model organism and human data to create a hypothesis-building interactive resource of prioritized longevity and body weight genes. Finally, we validated Hipk1, Ddost, Hspg2, Fgd6, and Pdk1 as conserved longevity genes using C. elegans lifespan experiments.

GEO Accession ID: GSE194321

PMID: 36173858

Visualize Samples

Info Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.

Precomputed Differential Gene Expression

Info Differential expression signatures are automatically computed using the limma R package. More options for differential expression are available to compute below.

Signatures:

Select conditions:

Control Condition

Perturbation Condition

Only conditions with at least 1 replicate are available to select

Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
Select differential expression analysis method:
Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.