Evidence map›Paper›PMID 39322626›Full record

ArticleBriefings in bioinformatics2024

3t-seq: automatic gene expression analysis of single-copy genes, transposable elements, and tRNAs from RNA-seq data.

Francesco Tabaro, Matthieu Boulard

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Francesco TabaroEpigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Via Ercole Ramarini 32, Monterotondo 00015, Italy.ORCID 0000-0001-5135-8529
Matthieu BoulardEpigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Via Ercole Ramarini 32, Monterotondo 00015, Italy.ORCID 0000-0002-7559-5585

Funding

European Molecular Biology Laboratory
6 · The paper itself

Abstract

RNA sequencing is the gold-standard method to quantify transcriptomic changes between two conditions. The overwhelming majority of data analysis methods available are focused on polyadenylated RNA transcribed from single-copy genes and overlook transcripts from repeated sequences such as transposable elements (TEs). These self-autonomous genetic elements are increasingly studied, and specialized tools designed to handle multimapping sequencing reads are available. Transfer RNAs are transcribed by RNA polymerase III and are essential for protein translation. There is a need for integrated software that is able to analyze multiple types of RNA. Here, we present 3t-seq, a Snakemake pipeline for integrated differential expression analysis of transcripts from single-copy genes, TEs, and tRNA. 3t-seq produces an accessible report and easy-to-use results for downstream analysis starting from raw sequencing data and performing quality control, genome mapping, gene expression quantification, and statistical testing. It implements three methods to quantify TEs expression and one for tRNA genes. It provides an easy-to-configure method to manage software dependencies that lets the user focus on results. 3t-seq is released under MIT license and is available at https://github.com/boulardlab/3t-seq.

Indexed as

DNA Transposable ElementsRNA-SeqRNA, TransferSoftwareComputational BiologyGene Expression ProfilingHumansSequence Analysis, RNADNA Transposable ElementsRNA, Transferdifferential gene expression analysisRNA-seq data analysissingle-copy genesSnakemake pipelinetransposable elementstRNA

Identifiers

PMID39322626
PMCPMC11424182

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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.