GEO series

Deep mutational scanning of CHD2 for variant interpretation in neurodevelopmental disorders [RNA-seq]

GSE316724 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/02/01 Platform GPL34284

At a glance

RNA-seq (gene expression) · human · 16 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: 16 samples: 9 × HAP1_path vs 5 × HAP1_benign vs 2 × HAP1_wtGroups 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.

Summary (written by the submitters)

De novo pathogenic variants in CHD2 are one of the most common causes of the neurodevelopmental disorders (NDDs) that include refractory epilepsy, intellectual disability and autism spectrum disorders. In addition, somatic CHD2 variants, acquired later in life, are highly recurrent in certain types of leukemias. For both types of conditions, loss-of-CHD2 function is the likely pathogenic mechanism, and most variants are truncations.

Read the full summary ↓

However, there is also evidence that missense variants can disrupt or abrogate CHD2 function, leading to loss-of-function, though they are harder to interpret, and many are classified as variants of uncertain significance (VUS). VUS are one of the biggest challenges for human geneticists, and represent the biggest class of variants returned on clinical genetic test reports, in part because we have a poor understanding of how missense variants impact protein function. Our research addresses these challenges by developing a multiplex assay of variant effect (MAVE) to discriminate between pathogenic and benign missense variants. We leveraged the fact that CHD2 is a chromatin remodeler and thus affects gene expression, protein abundance and the DNA methylation landscape to attempt to develop high-throughput assays. We aimed to establish a CHD2 transcriptomic signature using mRNA sequencing from multiple pathogenic and benign variant HAP1 cell lines. While CHD2 is a well-known chromatin remodeler and is expressed at appreciable levels in these cells, we were unable to establish a CHD2-related transcriptomic signature in HAP1s, possibly due to the cell line choice as most manifestations in patients are neurological.

This dataset

Open GSE316724 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 GSE316724_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 PRJNA1403838 and SRA study SRP663453. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

9 × HAP1_path vs 5 × HAP1_benign vs 2 × HAP1_wt

Supports a between-group comparison across 16 samples.

How we read this: 3 replicated groups read from 16 sample titles; they account for 16 of them. 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

GSM9459013 HAP1_benign_1 HAP1_benign
GSM9459014 HAP1_benign_2 HAP1_benign
GSM9459015 HAP1_benign_3 HAP1_benign
GSM9459016 HAP1_benign_4 HAP1_benign
GSM9459017 HAP1_benign_5 HAP1_benign
GSM9459018 HAP1_path_1 HAP1_path
GSM9459019 HAP1_path_2 HAP1_path
GSM9459020 HAP1_path_3 HAP1_path
GSM9459021 HAP1_path_4 HAP1_path
GSM9459022 HAP1_path_5 HAP1_path
GSM9459023 HAP1_path_6 HAP1_path
GSM9459024 HAP1_path_7 HAP1_path
GSM9459025 HAP1_path_8 HAP1_path
GSM9459026 HAP1_path_9 HAP1_path
GSM9459027 HAP1_wt_1 HAP1_wt
GSM9459028 HAP1_wt_3 HAP1_wt

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