QuickIsoSeq for Isoform Quantification in Large-Scale RNA Sequencing.
Isoform quantification
QuickIsoSeq
RNA-seq
RNA-seq pipeline
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2021
2021
Historique:
entrez:
9
4
2021
pubmed:
10
4
2021
medline:
23
6
2021
Statut:
ppublish
Résumé
RNA-sequencing (RNA-seq) is a powerful technology for transcriptome profiling. While most RNA-seq projects focus on gene-level quantification and analysis, there is growing evidence that most mammalian genes are alternatively spliced to generate different isoforms that can be subsequently translated to protein molecules with diverse or even opposing biological functions. Quantifying the expression levels of these isoforms is key to understanding the genes biological functions in healthy tissues and the progression of diseases. Among open source tools developed for isoform quantification, Salmon, Kallisto, and RSEM are recommended based upon previous systematic evaluation of these tools using both experimental and simulated RNA-seq datasets. However, isoform quantification in practical RNA-seq data analysis needs to deal with many QC issues, such as the abundance of rRNAs in mRNA-seq, the efficiency of globin RNA depletion in whole blood samples, and potential sample swapping. To overcome these practical challenges, QuickIsoSeq was developed for large-scale RNA-seq isoform quantification along with QC. In this chapter, we describe the pipeline and detailed the steps required to deploy and use it to analyze RNA-seq datasets in practice. The QuickIsoSeq package can be downloaded from https://github.com/shanrongzhao/QuickIsoSeq.
Identifiants
pubmed: 33835441
doi: 10.1007/978-1-0716-1307-8_8
doi:
Substances chimiques
Protein Isoforms
0
RNA, Messenger
0
RNA
63231-63-0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
135-145Commentaires et corrections
Type : ErratumIn
Références
Mortazavi A et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628
doi: 10.1038/nmeth.1226
Stark R, Grzelak M, Hadfield J (2019) RNA sequencing: the teenage years. Nat Rev Genet 20(11):631–656
doi: 10.1038/s41576-019-0150-2
Wang ET et al (2008) Alternative isoform regulation in human tissue transcriptomes. Nature 456:470–476
doi: 10.1038/nature07509
Harrow J et al (2012) GENCODE: the reference human genome annotation for the ENCODE project. Genome Res 22:1760–1774
doi: 10.1101/gr.135350.111
Aoubala M et al (2011) p53 directly transactivates Delta133p53alpha, regulating cell fate outcome in response to DNA damage. Cell Death Differ 18:248–258
doi: 10.1038/cdd.2010.91
Kim S, An SS (2016) Role of p53 isoforms and aggregations in cancer. Medicine (Baltimore) 95:e3993
doi: 10.1097/MD.0000000000003993
Mondal AM et al (2013) p53 isoforms regulate aging- and tumor-associated replicative senescence in T lymphocytes. J Clin Invest 123:5247–5257
doi: 10.1172/JCI70355
He W et al (2018) QuickRNASeq: guide for pipeline implementation and for interactive results visualization. Methods Mol Biol 1751:57–70
doi: 10.1007/978-1-4939-7710-9_4
Zhao S et al (2016) QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization. BMC Genomics 17:39
doi: 10.1186/s12864-015-2356-9
Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930
doi: 10.1093/bioinformatics/btt656
Zhang C et al (2018) Computational identification and validation of alternative splicing in ZSF1 rat RNA-seq data, a preclinical model for type 2 diabetic nephropathy. Sci Rep 8(1):7624
doi: 10.1038/s41598-018-26035-x
Zhao S, Xi L, Zhang B (2015) Union exon based approach for RNA-Seq gene quantification: to be or not to be? PLoS One 10(11):e0141910
doi: 10.1371/journal.pone.0141910
Zhang C et al (2017) Evaluation and comparison of computational tools for RNA-seq isoform quantification. BMC Genomics 18(1):583
doi: 10.1186/s12864-017-4002-1
Zhang C et al (2016) Bioinformatics tools for RNA-seq gene and Isoform quantification. Next Gen Sequence Appl 3:3
Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323
doi: 10.1186/1471-2105-12-323
Roberts A, Pachter L (2013) Streaming fragment assignment for real-time analysis of sequencing experiments. Nat Methods 10:71–73
doi: 10.1038/nmeth.2251
Nariai N et al (2014) TIGAR2: sensitive and accurate estimation of transcript isoform expression with longer RNA-Seq reads. BMC Genomics 15:S5
doi: 10.1186/1471-2164-15-S10-S5
Trapnell C et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515
doi: 10.1038/nbt.1621
Patro R, Mount SM, Kingsford C (2014) Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotechnol 32:462–464
doi: 10.1038/nbt.2862
Patro R et al (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14:417–419
doi: 10.1038/nmeth.4197
Bray NL et al (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34:525–527
doi: 10.1038/nbt.3519
Carithers LJ, Moore HM (2015) The Genotype-Tissue Expression (GTEx) Project. Biopreserv Biobank 13(5):307–308
doi: 10.1089/bio.2015.29031.hmm