GEO series
Benchmarking long-read RNA-sequencing technologies with LongBench: a cross-platform reference dataset profiling cancer cell lines with bulk and single-cell approaches
GSE303762
Homo sapiens (human)
Single-cell RNA-seq
38 samples
Submitted 2025/09/01
Platform GPL26167Platform GPL34382Platform GPL30173
At a glance
Single-cell RNA-seq · human · 38 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 38 samples: 38 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.
Summary (written by the submitters)
Long-read RNA sequencing technologies offer unparalleled in- sights into transcriptomes by enabling full-length sequencing of RNA molecules, uncovering novel isoforms and alternative splicing events. While long-read sequencing platforms, such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), have historically been associated with higher error rates, recent advancements in both platforms have significantly en- hanced read accuracy, broadening their applicability for tran- scriptomic studies.
Read the full summary ↓
With the rapid evolution of sequencing protocols and bioin- formatics tools, the trade-offs between sequencing throughput, read length, accuracy, and cost present significant challenges in selecting the optimal approach. Systematic benchmarking studies that compare these options are crucial to inform fu- ture research directions. However, many existing benchmark- ing datasets with matched data across multiple platforms have limitations, including: 1) a lack of realistic biological replicates, which may restrict the generalisability of differential analysis results to real-world scenarios, and 2) the use of earlier sequenc- ing kits, which may not reflect the latest advancements in se- quencing technology, limiting their relevance for future studies that typically use newer sequencing protocols. Here we present LongBench, a comprehensive benchmarking dataset designed to fill these critical gaps. Derived from eight lung cancer cell lines with synthetic RNA spike-ins, LongBench includes bulk, single-cell, and single-nucleus RNA-seq data from three state-of-the-art long-read sequencing platforms — ONT PCR-cDNA, ONT direct RNA, PacBio Kinnex — alongside Il- lumina short-read data for robust cross-platform comparisons. The LongBench dataset is a valuable resource for benchmarking and improving sequencing protocols and bioinformatics tools. With the LongBench dataset we present a systematic evaluation of transcript capture, quantification, and differential expression analyses, examining the strengths and limitations of each se- quencing platform in various biological contexts, enabling re- searchers to make more informed decisions on platform and method selection.
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 GSE303762_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1171488 and SRA study SRP537843. Searching any of these in the dataset finder brings you back here.
Study design
38 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 38 sample titles: 38 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
- GSM9135472 Oxford Nanopore direct RNA-Seq of lung cancer cell line H1975 (LUAD)
- GSM9135473 Oxford Nanopore direct RNA-Seq of lung cancer cell line HCC827 (LUAD)
- GSM9135474 Oxford Nanopore direct RNA-Seq of lung cancer cell line H2228 (LUAD)
- GSM9135475 Oxford Nanopore direct RNA-Seq of lung cancer cell line H146 (SCLC-A)
- GSM9135476 Oxford Nanopore direct RNA-Seq of lung cancer cell line H69 (SCLC-A)
- GSM9135477 Oxford Nanopore direct RNA-Seq of lung cancer cell line SHP77 (SCLC-A)
- GSM9135478 Oxford Nanopore direct RNA-Seq of lung cancer cell line H526 (SCLC-P)
- GSM9135479 Oxford Nanopore direct RNA-Seq of lung cancer cell line H211 (SCLC-P)
- GSM9135480 Oxford Nanopore PCR-Barcoding RNA-Seq of lung cancer cell line H1975 (LUAD)
- GSM9135481 Oxford Nanopore PCR-Barcoding RNA-Seq of lung cancer cell line HCC827 (LUAD)
- GSM9135482 Oxford Nanopore PCR-Barcoding RNA-Seq of lung cancer cell line H2228 (LUAD)
- GSM9135483 Oxford Nanopore PCR-Barcoding RNA-Seq of lung cancer cell line H146 (SCLC-A)
- GSM9135484 Oxford Nanopore PCR-BarcodingRNA-Seq of lung cancer cell line H69 (SCLC-A)
- GSM9135485 Oxford Nanopore PCR-Barcoding RNA-Seq of lung cancer cell line SHP77 (SCLC-A)
- GSM9135486 Oxford Nanopore PCR-Barcoding RNA-Seq of lung cancer cell line H526 (SCLC-P)
- GSM9135487 Oxford Nanopore PCR-Barcoding RNA-Seq of lung cancer cell line H211 (SCLC-P)
- GSM9135488 Illumina Bulk RNA-Seq of lung cancer cell line H1975 (LUAD)
- GSM9135489 Illumina Bulk RNA-Seq of lung cancer cell line HCC827 (LUAD)
- GSM9135490 Illumina Bulk RNA-Seq of lung cancer cell line H2228 (LUAD)
- GSM9135491 Illumina Bulk RNA-Seq of lung cancer cell line H146 (SCLC-A)
- GSM9135492 Illumina Bulk RNA-Seq of lung cancer cell line H69 (SCLC-A)
- GSM9135493 Illumina Bulk RNA-Seq of lung cancer cell line SHP77 (SCLC-A)
- GSM9135494 Illumina Bulk RNA-Seq of lung cancer cell line H526 (SCLC-P)
- GSM9135495 Illumina Bulk RNA-Seq of lung cancer cell line H211 (SCLC-P)
- GSM9135496 PacBio Kinnex Bulk RNA-Seq of lung cancer cell line H1975 (LUAD)
- GSM9135497 PacBio Kinnex Bulk RNA-Seq of lung cancer cell line HCC827 (LUAD)
- GSM9135498 PacBio Kinnex Bulk RNA-Seq of lung cancer cell line H2228 (LUAD)
- GSM9135499 PacBio Kinnex Bulk RNA-Seq of lung cancer cell line H146 (SCLC-A)
- GSM9135500 PacBio Kinnex Bulk RNA-Seq of lung cancer cell line H69 (SCLC-A)
- GSM9135501 PacBio Kinnex Bulk RNA-Seq of lung cancer cell line SHP77 (SCLC-A)
- GSM9135502 PacBio Kinnex Bulk RNA-Seq of lung cancer cell line H526 (SCLC-P)
- GSM9135503 PacBio Kinnex Bulk RNA-Seq of lung cancer cell line H211 (SCLC-P)
- GSM9135504 Oxford Nanopore scRNA-Seq of lung cancer cell line mixture
- GSM9135505 Oxford Nanopore snRNA-Seq of lung cancer cell line mixture
- GSM9135506 Illumina scRNA-Seq of lung cancer cell line mixture
- GSM9135507 Illumina snRNA-Seq of lung cancer cell line mixture
- GSM9135508 PacBio scRNA-Seq of lung cancer cell line mixture
- GSM9135509 PacBio snRNA-Seq of lung cancer cell line mixture
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Share this dataset
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