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The impact of PCR duplication on RNAseq data generated using NovaSeq 6000, NovaSeq X, AVITI and G4 sequencers

GSE261432 Homo sapiens (human) RNA-seq (gene expression) 144 samples Submitted 2024/03/13 Platform GPL34295Platform GPL34281Platform GPL18573Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 144 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
144 samples: 144 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
The impact of PCR duplication on RNAseq data generated using NovaSeq 6000, NovaSeq X, AVITI, and G4 sequencersGenome biology · 2025
Summary (written by the submitters)
Gene expression profiles generated with RNA sequencing can be biased by RNA amount and methods utilised for cDNA library generation. Polymerase chain reaction (PCR) amplification can generate high PCR duplicate proportions and introduce bias in transcript counts. In this study, we investigate the impact of input amount and PCR cycle number on the PCR duplication rate and on the RNA-seq data quality.
Read the full summary ↓
We used a range of inputs (1 ng - 1,000 ng) and assessed the PCR duplication rate using unique molecular identifiers (UMIs). For broader applicability, we sequenced the data on four different short-read sequencing platforms: Illumina NovaSeq 6000, Illumina NovaSeq X, Element Biosciences AVITI, and Singular Genomics G4. We highlight the limitations of using input amounts below 125 ng and the advantages for using UMIs for deduplication. We contrast the data obtained from different sequencers and discuss the benefits and drawbacks of using Illumina library conversion kits.
Published in
The impact of PCR duplication on RNAseq data generated using NovaSeq 6000, NovaSeq X, AVITI, and G4 sequencers
Zajac N, Vlachos IS, Sajibu S et al. · Genome biology 2025 · PMID 40437619 · doi:10.1186/s13059-025-03613-7
This dataset
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.

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 GSE261432_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1086933 and SRA study SRP494838. Searching any of these in the dataset finder brings you back here.

Study design
144 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 144 sample titles: 144 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

+ 104 more — browse all 144 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.