Computational methods for transcriptome annotation and quantification using RNA-seq

By Manuel Garber, Manfred G. Grabherr, M. Guttman, Cole Trapnell. Nature Methods, 2011

1,073 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/da15cb940b5854ee826e74d78f3b1085286ac785

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In Molecular Biology

Citations in the corpus

Identification of protein coding regions in RNA transcripts (2014, background): “Prior to the advent of next-generation sequencing (NGS), transcriptome data were scarce and limited to full messenger RNA (mRNA) and expressed sequence tag (EST) libraries covering at best a few hundred genes of a given species (1).”
A beginner's guide to eukaryotic genome annotation (2012, methodology): “The use of RNA-seq data currently lies at the cutting edge of genome annotation, and the available toolset is evolving quickly 47 .”
De novo assembly of bacterial transcriptomes from RNA-seq data (2015, methodology): “…following RNA-seq experiments, the large resulting data sets are subjected to various stages of computational analysis, such as quality control, normalization, transcriptome assembly, quantification of transcript abundance, and testing for differential gene expression under various conditions [2].”
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks (2012, background): “For a further review of these topics, see Garber et al. 12 .”
StringTie enables improved reconstruction of a transcriptome from RNA-seq reads (2015, background): “Exon sharing among transcripts, ambiguous read mappings due to close paralogs and low levels of gene expression are all factors that make the quantification task difficult 6 .”
A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. (2014, neutral): “From a technological perspective, MeRIP-Seq can be considered a marriage of three relatively well-studied techniques: ChIP-Seq [6, 7], RNA-Seq [8, 9] and MeDIP-Seq [10, 11], yet it brings new computational challenges not addressed previously [12].”
A survey of best practices for RNA-seq data analysis (2016, background): “Furthermore, sequencing can involve single-end (SE) or paired-end (PE) reads, although the latter is preferable for de novo transcript discovery or isoform expression analysis [4, 5].”
Complexity of the Alternative Splicing Landscape in Plants[C][W][OPEN] (2013, background): “These studies suggest the functional significance of AS as a result of tandem acceptor in plants.”
Translating RNA sequencing into clinical diagnostics: opportunities and challenges (2016, background): “Recognizing that excellent bioinformatics reviews are available elsewhere 105 , here we attempt only to highlight the major overarching bioinformatics challenges.”
Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. (2011, background): “This is comparable with previously observed discrepancies in reconstruction of lowly expressed protein-coding genes (Garber et al. 2011) and is handled below.”
Computational solutions for omics data (2013, methodology): “To accomplish these tasks, numerous statistical methods, such as gene set enrichment analysis (GSEA) 71 , GenePattern 72 , joint clustering 73,74 and DEseq 75 , have been devised and widely applied to analyse differentially expressed genes and gene sets 56,57 .”