Evidence map›Paper›PMID 37783886›Full record

ArticleNature methods2023

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures.

Xueyi Dong, Mei R M Du, Quentin Gouil, Luyi Tian, Jafar S Jabbari, Rory Bowden, Pedro L Baldoni, Yunshun Chen, Gordon K Smyth, Shanika L Amarasinghe and 2 more

Abstract read
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In one paragraph

Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

75 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Systematic review and meta-analysis of bulk RNAseq studies in human Alzheimer's disease brain tissue.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Pooled it
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15 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Xueyi DongThe Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia. dong.x@wehi.edu.au.ORCID 0000-0003-1136-3117
Mei R M Du *The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.ORCID 0000-0001-6915-6765
Quentin Gouil *The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.ORCID 0000-0002-5142-7886
Luyi TianThe Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
Jafar S JabbariThe Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.ORCID 0000-0002-6302-4857
Rory BowdenThe Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.ORCID 0000-0001-8596-0366
Pedro L BaldoniThe Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.ORCID 0000-0002-9510-8326
Yunshun ChenThe Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.ORCID 0000-0003-4911-5653
Gordon K SmythThe Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.ORCID 0000-0001-9221-2892
Shanika L AmarasingheThe Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
Charity W LawThe Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
Matthew E RitchieThe Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia. mritchie@wehi.edu.au.ORCID 0000-0002-7383-0609

Funding

Department of Health | National Health and Medical Research Council (NHMRC) 2007996Department of Health | National Health and Medical Research Council (NHMRC) 2017257
6 · The paper itself

Abstract

The lack of benchmark data sets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (sequins). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that StringTie2 and bambu outperformed other tools from the six isoform detection tools tested, DESeq2, edgeR and limma-voom were best among the five differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the five tools compared, which suggests further methods development is needed for this application.

Indexed as

Gene Expression ProfilingHigh-Throughput Nucleotide SequencingBenchmarkingHumansProtein IsoformsRNAProtein IsoformsRNA

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.