Gene count estimation with pytximport enables reproducible analysis of bulk RNA sequencing data in Python
Summary Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis too...
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| Published in: | Bioinformatics (Oxford, England) Vol. 40; no. 12 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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England
Oxford University Press
28.11.2024
Oxford Publishing Limited (England) |
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| ISSN: | 1367-4811, 1367-4803, 1367-4811 |
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| Abstract | Summary
Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python.
Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation, and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface.
With pytximport, we propose a bulk RNA-seq analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-seq dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations.
Availability and implementation
pytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io. |
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| AbstractList | Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python.Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation, and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface.With pytximport, we propose a bulk RNA-seq analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-seq dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations.SUMMARYTranscript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python.Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation, and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface.With pytximport, we propose a bulk RNA-seq analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-seq dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations.pytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io.AVAILABILITY AND IMPLEMENTATIONpytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io. Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python. Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation, and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface. With pytximport, we propose a bulk RNA-seq analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-seq dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations. Availability and implementation pytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io. Summary Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python. Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation, and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface. With pytximport, we propose a bulk RNA-seq analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-seq dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations. Availability and implementation pytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io. Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python.Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation, and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface.With pytximport, we propose a bulk RNA-seq analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-seq dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations. pytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io. |
| Author | Kuehl, Malte Wong, Milagros N Puelles, Victor G Wanner, Nicola Bonn, Stefan |
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| Cites_doi | 10.1093/bioadv/vbac016 10.1186/s13059-014-0550-8 10.1038/nbt.2450 10.1038/s41587-023-01733-8 10.12688/f1000research.7035.2 10.1186/1471-2164-10-22 10.1093/bioinformatics/btad547 10.12688/f1000research.15398.3 10.1186/s12859-021-04198-1 10.1101/2023.01.04.522742 10.1186/s13059-020-02151-8 10.1093/nar/gkad841 10.1093/nar/gkad1049 10.1038/nbt.2862 10.1038/nmeth.4197 10.5334/jors.148 10.1038/s41576-019-0150-2 10.1093/nar/gkad1025 10.1093/bioinformatics/bts480 10.12688/f1000research.7563.2 10.1186/s12864-017-4002-1 10.1186/s13059-018-1419-z 10.1101/2024.02.28.582591 10.1371/journal.pcbi.1003118 10.1101/2021.12.16.473007 10.1038/nmeth.4324 10.1038/nbt.3519 10.1186/1471-2105-12-323 10.1016/j.humimm.2021.02.012 10.1002/imt2.107 |
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| References | Love (2024121018345665400_btae700-B13) 2014; 15 Sarantopoulou (2024121018345665400_btae700-B21) 2021; 22 Badia-I-Mompel (2024121018345665400_btae700-B1) 2022; 2 Hoyer (2024121018345665400_btae700-B6) 2017; 5 Lawrence (2024121018345665400_btae700-B10) 2013; 9 Hu (2024121018345665400_btae700-B7) 2021; 82 Müller-Dott (2024121018345665400_btae700-B16) 2023; 51 Muzellec (2024121018345665400_btae700-B17) 2023; 39 Li (2024121018345665400_btae700-B11) 2011; 12 Patro (2024121018345665400_btae700-B19) 2014; 32 Stark (2024121018345665400_btae700-B25) 2019; 20 Virshup (2024121018345665400_btae700-B27) 2023; 41 Love (2024121018345665400_btae700-B12) 2016; 4 Zhang (2024121018345665400_btae700-B30) 2017; 18 Smedley (2024121018345665400_btae700-B22) 2009; 10 Srivastava (2024121018345665400_btae700-B24) 2020; 21 Soneson (2024121018345665400_btae700-B23) 2016; 4 Trapnell (2024121018345665400_btae700-B26) 2013; 31 Virshup (2024121018345665400_btae700-B28) 2021 Chen (2024121018345665400_btae700-B3) 2023; 2 Yi (2024121018345665400_btae700-B29) 2018; 19 Love (2024121018345665400_btae700-B14) 2018; 7 Milacic (2024121018345665400_btae700-B15) 2024; 52 Bray (2024121018345665400_btae700-B2) 2016; 34 Patro (2024121018345665400_btae700-B18) 2017; 14 Harrison (2024121018345665400_btae700-B4) 2024; 52 Jousheghani (2024121018345665400_btae700-B8) 2024 Pimentel (2024121018345665400_btae700-B20) 2017; 14 Köster (2024121018345665400_btae700-B9) 2012; 28 He (2024121018345665400_btae700-B5) 2023 |
| References_xml | – volume: 2 start-page: vbac016 year: 2022 ident: 2024121018345665400_btae700-B1 article-title: decoupleR: ensemble of computational methods to infer biological activities from omics data publication-title: Bioinform Adv doi: 10.1093/bioadv/vbac016 – volume: 15 start-page: 550 year: 2014 ident: 2024121018345665400_btae700-B13 article-title: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 publication-title: Genome Biol doi: 10.1186/s13059-014-0550-8 – volume: 31 start-page: 46 year: 2013 ident: 2024121018345665400_btae700-B26 article-title: Differential analysis of gene regulation at transcript resolution with RNA-seq publication-title: Nat Biotechnol doi: 10.1038/nbt.2450 – volume: 41 start-page: 604 year: 2023 ident: 2024121018345665400_btae700-B27 article-title: The scverse project provides a computational ecosystem for single-cell omics data analysis publication-title: Nat Biotechnol doi: 10.1038/s41587-023-01733-8 – volume: 4 year: 2016 ident: 2024121018345665400_btae700-B12 article-title: RNA-Seq workflow: gene-level exploratory analysis and differential expression publication-title: F1000Res doi: 10.12688/f1000research.7035.2 – volume: 10 start-page: 22 year: 2009 ident: 2024121018345665400_btae700-B22 article-title: BioMart—biological queries made easy publication-title: BMC Genomics doi: 10.1186/1471-2164-10-22 – volume: 39 start-page: btad547 year: 2023 ident: 2024121018345665400_btae700-B17 article-title: PyDESeq2: a python package for bulk RNA-seq differential expression analysis publication-title: Bioinformatics doi: 10.1093/bioinformatics/btad547 – volume: 7 start-page: 952 year: 2018 ident: 2024121018345665400_btae700-B14 article-title: Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification publication-title: F1000Res doi: 10.12688/f1000research.15398.3 – volume: 22 start-page: 266 year: 2021 ident: 2024121018345665400_btae700-B21 article-title: Comparative evaluation of full-length isoform quantification from RNA-Seq publication-title: BMC Bioinformatics doi: 10.1186/s12859-021-04198-1 – year: 2023 ident: 2024121018345665400_btae700-B5 doi: 10.1101/2023.01.04.522742 – volume: 21 start-page: 239 year: 2020 ident: 2024121018345665400_btae700-B24 article-title: Alignment and mapping methodology influence transcript abundance estimation publication-title: Genome Biol doi: 10.1186/s13059-020-02151-8 – volume: 51 start-page: 10934 year: 2023 ident: 2024121018345665400_btae700-B16 article-title: Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities publication-title: Nucleic Acids Res doi: 10.1093/nar/gkad841 – volume: 52 start-page: D891 year: 2024 ident: 2024121018345665400_btae700-B4 article-title: Ensembl 2024 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkad1049 – volume: 32 start-page: 462 year: 2014 ident: 2024121018345665400_btae700-B19 article-title: Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms publication-title: Nat Biotechnol doi: 10.1038/nbt.2862 – volume: 14 start-page: 417 year: 2017 ident: 2024121018345665400_btae700-B18 article-title: Salmon provides fast and bias-aware quantification of transcript expression publication-title: Nat Methods doi: 10.1038/nmeth.4197 – volume: 5 start-page: 10 year: 2017 ident: 2024121018345665400_btae700-B6 article-title: xarray: N-D labeled arrays and datasets in Python publication-title: JORS doi: 10.5334/jors.148 – volume: 20 start-page: 631 year: 2019 ident: 2024121018345665400_btae700-B25 article-title: RNA sequencing: the teenage years publication-title: Nat Rev Genet doi: 10.1038/s41576-019-0150-2 – volume: 52 start-page: D672 year: 2024 ident: 2024121018345665400_btae700-B15 article-title: The Reactome Pathway Knowledgebase 2024 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkad1025 – volume: 28 start-page: 2520 year: 2012 ident: 2024121018345665400_btae700-B9 article-title: Snakemake—a scalable bioinformatics workflow engine publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts480 – volume: 4 start-page: 1521 year: 2016 ident: 2024121018345665400_btae700-B23 article-title: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences publication-title: F1000Res doi: 10.12688/f1000research.7563.2 – volume: 18 start-page: 583 year: 2017 ident: 2024121018345665400_btae700-B30 article-title: Evaluation and comparison of computational tools for RNA-seq isoform quantification publication-title: BMC Genomics doi: 10.1186/s12864-017-4002-1 – volume: 19 start-page: 53 year: 2018 ident: 2024121018345665400_btae700-B29 article-title: Gene-level differential analysis at transcript-level resolution publication-title: Genome Biol doi: 10.1186/s13059-018-1419-z – year: 2024 ident: 2024121018345665400_btae700-B8 doi: 10.1101/2024.02.28.582591 – volume: 9 start-page: e1003118 year: 2013 ident: 2024121018345665400_btae700-B10 article-title: Software for computing and annotating genomic ranges publication-title: PLoS Comput Biol doi: 10.1371/journal.pcbi.1003118 – year: 2021 ident: 2024121018345665400_btae700-B28 doi: 10.1101/2021.12.16.473007 – volume: 14 start-page: 687 year: 2017 ident: 2024121018345665400_btae700-B20 article-title: Differential analysis of RNA-seq incorporating quantification uncertainty publication-title: Nat Methods doi: 10.1038/nmeth.4324 – volume: 34 start-page: 525 year: 2016 ident: 2024121018345665400_btae700-B2 article-title: Near-optimal probabilistic RNA-seq quantification publication-title: Nat Biotechnol doi: 10.1038/nbt.3519 – volume: 12 start-page: 323 year: 2011 ident: 2024121018345665400_btae700-B11 article-title: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-12-323 – volume: 82 start-page: 801 year: 2021 ident: 2024121018345665400_btae700-B7 article-title: Next-generation sequencing technologies: an overview publication-title: Hum Immunol doi: 10.1016/j.humimm.2021.02.012 – volume: 2 start-page: e107 year: 2023 ident: 2024121018345665400_btae700-B3 article-title: Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp publication-title: Imeta doi: 10.1002/imt2.107 |
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Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of... Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript... |
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| SubjectTerms | Applications Note Availability Bias Gene expression Gene Expression Profiling - methods Gene mapping Gene sequencing Line interfaces Packages Python Ribonucleic acid RNA Sequence Analysis, RNA - methods Software Source code Transcriptome Transcriptomes Transcriptomics Workflow |
| Title | Gene count estimation with pytximport enables reproducible analysis of bulk RNA sequencing data in Python |
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