Statistical inferences for isoform expression in RNA-Seq

By Hui Jiang, Wing Hung Wong. Bioinform., 2009

484 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3118d6b3909b4b397b2a05972c9a6daa20780672

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In Cancer Research

In Molecular Biology

Citations in the corpus

Computation for ChIP-seq and RNA-seq studies (2009, methodology): “Given a set of annotations, all tools can tally the reads that fall on known gene models, and several tools like RSA”
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome (2011, background): “The reads produced can be used for a number of transcriptome analyses, including transcript quantification [2-7], differential expression testing [8,9], reference-based gene annotation [6,10], and de novo transcript assembly [11,12].”
MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery (2010, background): “Splice junctions constitute the building blocks for these algorithms (19,32–35).”
RNA sequencing: advances, challenges and opportunities (2010, unknown): “By counting the number of reads mapping to each exon and spanning each splice junction, these approaches also allowed the splice efficiency of each junction to be determined and the levels of distinct isoforms to be quantifie”
DEGseq: an R package for identifying differentially expressed genes from RNA-seq data (2010, background): “RNA sequencing could be modeled as a random sampling process, in which each read is sampled independently and uniformly from every possible nucleotides in the sample (Jiang et al., 2009).”
Identification of novel transcripts in annotated genomes using RNA-Seq (2011, background): “In previous work, it has been shown that incomplete annotations can bias gene expression estimates (Jiang and Wong, 2009; Trapnell et al., 2010).”
Differential expression analysis for sequence count data (2010, methodology): “Tests for differential abundance that are based on a Poisson model, such as proposed by Jiang and Wong (2009) or Wang et al.”
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks (2012, methodology): “Furthermore, because the number of reads produced from an RNA transcript is a function of that transcript's abundance, read density can be used to measure transcrip”
Improving RNA-Seq expression estimates by correcting for fragment bias (2011, background): “This observation has been the basis of assumptions underlying a number of RNA-Seq analysis approaches that, in computer science terms, invert the ‘reduction’ of transcriptome estimation to DNA sequencing [2-6].”
From RNA-seq reads to differential expression results (2010, unknown): “In the past year, several methods have emerged that use RNA-seq data for abundance estimation [8,9], detection of alternative splicing [10-12], RNA editing [13] and novel transcripts [11,14].”
Repetitive DNA and next-generation sequencing: computational challenges and solutions (2011, methodology): “A similar idea was developed into a more sophisticated statistical model by Jiang and Won”
Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels (2012, background): “In the absence of a known genome, it is necessary to assemble these sequences de novo, taking into account possible alternative isoforms and the dynamic range of expression values.”
RNA-Seq gene expression estimation with read mapping uncertainty (2009, methodology): “Like the statistical method of Jiang and Wong (2009), our model can be used to estimate individual isoform expression levels.”
Computational methods for transcriptome annotation and quantification using RNA-seq (2011, methodology): “…‘isoform-expression methods’ such as Cufflinks 29 and mixture of isoforms (MISO) 30 , handle uncertainty by constructing a ‘likelihood function’ that models the sequencing process and identifies isoform abundance estimates that best explain the reads obtained in the experiment 29,30,53,66 ( Fig.”