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...
Uloženo v:
| Vydáno v: | Bioinformatics (Oxford, England) Ročník 40; číslo 12 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Oxford University Press
28.11.2024
Oxford Publishing Limited (England) |
| Témata: | |
| ISSN: | 1367-4811, 1367-4803, 1367-4811 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| 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. |
|---|---|
| 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 |
| Author_xml | – sequence: 1 givenname: Malte orcidid: 0000-0003-4167-2498 surname: Kuehl fullname: Kuehl, Malte email: malte.kuehl@clin.au.dk – sequence: 2 givenname: Milagros N surname: Wong fullname: Wong, Milagros N – sequence: 3 givenname: Nicola surname: Wanner fullname: Wanner, Nicola – sequence: 4 givenname: Stefan orcidid: 0000-0003-4366-5662 surname: Bonn fullname: Bonn, Stefan – sequence: 5 givenname: Victor G orcidid: 0000-0002-7735-5462 surname: Puelles fullname: Puelles, Victor G |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39565903$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkV9PHCEUxUljU_9-BUPiS19WYVhgeDLGtGpiWmPaZwLMHRedhRGY1v32YnY12qc-wc393ZNz79lFWyEGQOiQkmNKFDuxPvrQx7Q0xbt8YosBScgntEOZkLN5S-nWu_822s35nhDCCRdf0DZTXHBF2A7yFxAAuziFgiEX_6IXA_7rywKPq_Lkl2NMtRWMHSDjBGOK3eR8rbAJZlhln3HssZ2GB3z74wxneJwgOB_ucGeKwT7gm1VZxLCPPvdmyHCweffQ7-_ffp1fzq5_Xlydn13P3Jw1ZUZ7Z4QA6KSUrZHcgO2lEVJwS2vB-dw1hCqhjOpYL7gjPbfKtFYQSxhv2R46XeuOk11C5yCUZAY9prpbWulovP7YCX6h7-IfTalolBK8KnzdKKRYl8lFL312MAwmQJyyZpRRTgiby4oe_YPexynVu1SqodVOS1VTqcP3lt68vMZQAbEGXIo5J-jfEEr0S976Y956k3cdpOvBOI3_O_MMHcG3gg |
| 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 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024. Published by Oxford University Press. 2024 The Author(s) 2024. Published by Oxford University Press. |
| Copyright_xml | – notice: The Author(s) 2024. Published by Oxford University Press. 2024 – notice: The Author(s) 2024. Published by Oxford University Press. |
| DBID | TOX AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TM 7TO 7U5 8BQ 8FD F28 FR3 H8D H8G H94 JG9 JQ2 K9. KR7 L7M L~C L~D P64 7X8 5PM |
| DOI | 10.1093/bioinformatics/btae700 |
| DatabaseName | Oxford Journals Open Access Collection CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library AIDS and Cancer Research Abstracts Materials Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Oncogenes and Growth Factors Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Nucleic Acids Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Materials Business File Aerospace Database Copper Technical Reference Library Engineered Materials Abstracts Biotechnology Research Abstracts AIDS and Cancer Research Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Materials Research Database MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: TOX name: Oxford Journals Open Access Collection url: https://academic.oup.com/journals/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1367-4811 |
| ExternalDocumentID | PMC11629965 39565903 10_1093_bioinformatics_btae700 10.1093/bioinformatics/btae700 |
| Genre | Research Support, Non-U.S. Gov't Journal Article Report |
| GrantInformation_xml | – fundername: Deutsche Forschungsgemeinschaft – fundername: NovoNordisk Foundation – fundername: BMBF – fundername: German Research Council – fundername: Collaborative Research Center grantid: 1192 [SFB 1192] – fundername: Deutsche Forschungsgemeinschaft grantid: FOR 5068 P9 – fundername: Young Investigator grantid: NNF21OC0066381 – fundername: ; – fundername: ; grantid: FOR 5068 P9 – fundername: ; grantid: 1192 [SFB 1192] – fundername: ; grantid: NNF21OC0066381 |
| GroupedDBID | --- -E4 -~X .-4 .2P .DC .GJ .I3 0R~ 1TH 23N 2WC 4.4 48X 53G 5GY 5WA 70D AAIJN AAIMJ AAJKP AAJQQ AAKPC AAMDB AAMVS AAOGV AAPQZ AAPXW AAUQX AAVAP AAVLN ABEFU ABEJV ABEUO ABGNP ABIXL ABNGD ABNKS ABPQP ABPTD ABQLI ABWST ABXVV ABZBJ ACGFS ACIWK ACPRK ACUFI ACUKT ACUXJ ACYTK ADBBV ADEYI ADEZT ADFTL ADGKP ADGZP ADHKW ADHZD ADMLS ADOCK ADPDF ADRDM ADRTK ADVEK ADYVW ADZTZ ADZXQ AECKG AEGPL AEJOX AEKKA AEKSI AELWJ AEMDU AENEX AENZO AEPUE AETBJ AEWNT AFFNX AFFZL AFGWE AFIYH AFOFC AFRAH AGINJ AGKEF AGQPQ AGQXC AGSYK AHMBA AHXPO AI. AIJHB AJEEA AJEUX AKHUL AKWXX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC AMNDL APIBT APWMN AQDSO ARIXL ASPBG ATTQO AVWKF AXUDD AYOIW AZFZN AZVOD BAWUL BAYMD BHONS BQDIO BQUQU BSWAC BTQHN C1A C45 CAG CDBKE COF CS3 CZ4 DAKXR DIK DILTD DU5 D~K EBD EBS EE~ EJD ELUNK EMOBN F5P F9B FEDTE FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GROUPED_DOAJ GX1 H13 H5~ HAR HVGLF HW0 HZ~ IOX J21 JXSIZ KAQDR KOP KQ8 KSI KSN M-Z MK~ ML0 N9A NGC NLBLG NMDNZ NOMLY NTWIH NU- NVLIB O0~ O9- OAWHX ODMLO OJQWA OK1 OVD OVEED O~Y P2P PAFKI PB- PEELM PQQKQ Q1. Q5Y R44 RD5 RIG RNI RNS ROL RPM RUSNO RW1 RXO RZF RZO SV3 TEORI TJP TLC TOX TR2 VH1 W8F WOQ X7H YAYTL YKOAZ YXANX ZGI ZKX ~91 ~KM AAYXX CITATION ROX CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TM 7TO 7U5 8BQ 8FD F28 FR3 H8D H8G H94 JG9 JQ2 K9. KR7 L7M L~C L~D P64 7X8 5PM |
| ID | FETCH-LOGICAL-c432t-1fca66eed7778a75aebf7a6765b15ae554c201969a9d3f65c0f5b9a8b60b03583 |
| IEDL.DBID | TOX |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001373044300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1367-4811 1367-4803 |
| IngestDate | Thu Aug 21 18:29:45 EDT 2025 Wed Oct 01 13:47:13 EDT 2025 Mon Oct 06 17:35:54 EDT 2025 Mon Jul 21 05:46:12 EDT 2025 Sat Nov 29 03:49:31 EST 2025 Mon Jun 30 08:34:43 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0 The Author(s) 2024. Published by Oxford University Press. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c432t-1fca66eed7778a75aebf7a6765b15ae554c201969a9d3f65c0f5b9a8b60b03583 |
| Notes | SourceType-Scholarly Journals-1 content type line 14 ObjectType-Report-1 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 Stefan Bonn and Victor G. Puelles jointly supervised the work. |
| ORCID | 0000-0002-7735-5462 0000-0003-4366-5662 0000-0003-4167-2498 |
| OpenAccessLink | https://dx.doi.org/10.1093/bioinformatics/btae700 |
| PMID | 39565903 |
| PQID | 3213588192 |
| PQPubID | 36124 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_11629965 proquest_miscellaneous_3131500347 proquest_journals_3213588192 pubmed_primary_39565903 crossref_primary_10_1093_bioinformatics_btae700 oup_primary_10_1093_bioinformatics_btae700 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-11-28 |
| PublicationDateYYYYMMDD | 2024-11-28 |
| PublicationDate_xml | – month: 11 year: 2024 text: 2024-11-28 day: 28 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: Oxford |
| PublicationTitle | Bioinformatics (Oxford, England) |
| PublicationTitleAlternate | Bioinformatics |
| PublicationYear | 2024 |
| Publisher | Oxford University Press Oxford Publishing Limited (England) |
| Publisher_xml | – name: Oxford University Press – name: Oxford Publishing Limited (England) |
| 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 |
| SSID | ssj0005056 |
| Score | 2.4731824 |
| Snippet | Summary
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... |
| SourceID | pubmedcentral proquest pubmed crossref oup |
| SourceType | Open Access Repository Aggregation Database Index Database Publisher |
| 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 |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39565903 https://www.proquest.com/docview/3213588192 https://www.proquest.com/docview/3131500347 https://pubmed.ncbi.nlm.nih.gov/PMC11629965 |
| Volume | 40 |
| WOSCitedRecordID | wos001373044300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4811 databaseCode: DOA dateStart: 20230101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4811 databaseCode: TOX dateStart: 19850101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwED_mUPDF74_qHBF8EsqapmnSxyGKT3PIhL2VpGuxKN3YOnH_vZe2m-tAUN8acqVp7sL9ktz9DuAmRh_FmUpsbugfvZgmtvaZtrlCrMyl9hyli2IToteTw2HQbwBd5sJsXuEHrKPTcUUiaoiLOzpXsXDMLp1yaSx78DT8DupAf77MA_7x1ZoLqqW1raHLzSDJNa_zsP-P8R7AXgUxSbe0iUNoxNkR7JRFJxfHkBqmaVLUiCCGYqPMXSTmQJZMFvlnariqsKtIqpoRw3ppSGFTbBFVUZiQcUL0_P2NPPe6pIrGRh9ITLwpSTPSXxhKghN4ebgf3D3aVcEFO_KYm9s0iVBF6DWFEFIJrmKdCOULn2uKDUQekVvw6ahgxBKfR07CdaCk9h3tMNTGKTSzcRafA0GcNvIQy3GFT0pEWrk6GgXS0dKXQnMLOksdhJOSVyMs78NZWJ_AsJpAC25RVb8Wbi01GlaLchYyl-IgDQOcBderblxO5o5EZfF4jjKUIUR2mCcsOCsNYPVJhntJHjjMAlkzjZWAoequ92Tpa0HZTamPft_nF3_5iUvYdRFCmcxHV7agmU_n8RVsRx95Opu2YUsMZbs4Q2gXi-ALnDYQQg |
| linkProvider | Oxford University Press |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Gene+count+estimation+with+pytximport+enables+reproducible+analysis+of+bulk+RNA+sequencing+data+in+Python&rft.jtitle=Bioinformatics+%28Oxford%2C+England%29&rft.au=Kuehl%2C+Malte&rft.au=Wong%2C+Milagros+N&rft.au=Wanner%2C+Nicola&rft.au=Bonn%2C+Stefan&rft.date=2024-11-28&rft.pub=Oxford+Publishing+Limited+%28England%29&rft.issn=1367-4803&rft.eissn=1367-4811&rft.volume=40&rft.issue=12&rft_id=info:doi/10.1093%2Fbioinformatics%2Fbtae700&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1367-4811&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1367-4811&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1367-4811&client=summon |