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The genetic landscape of a human cell reveals conserved topology and principles of genetic networks

GSE296341 Homo sapiens (human) RNA-seq (gene expression) 61 samples Submitted 2026/05/07 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 61 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
61 samples: 61 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Global genetic interaction network of a human cell maps conserved principles and informs functional interpretation of gene co-essentiality profilesCell · 2026
Summary (written by the submitters)
We generated a genome-scale, genetic interaction network from the analysis of more than 5 million double mutants in the haploid human cell line, HAP1. The network comprises ~90,000 negative and positive genetic interactions, including thousands of extreme synthetic lethal and genetic suppression interactions. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments.
Read the full summary ↓
Systematic comparative analyses showed that the general principles and topology of genetic networks are conserved from yeast to humans. A genetic interaction network mapped in a single genetic background complements the DepMap cancer cell co-essentiality network, recapitulating many of the same functional connections and also capturing unique functional information to reveal roles of previously uncharacterized genes and molecular determinants of specific cancer cell line genetic dependencies.
Published in
Global genetic interaction network of a human cell maps conserved principles and informs functional interpretation of gene co-essentiality profiles
Billmann M, Costanzo M, Zhang X et al. · Cell 2026 · PMID 42049019 · doi:10.1016/j.cell.2026.03.044
This dataset
Open GSE296341 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE296341_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1258952 and SRA study SRP584191. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42049019) ↗
Study design
61 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 61 sample titles: 61 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 21 more — browse all 61 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.