Identification of novel transcripts in annotated genomes using RNA-Seq

By Adam Roberts, Harold Pimentel, Cole Trapnell, Lior S. Pachter. Bioinform., 2011

991 citations recorded by the source corpus.

SUMMARY We describe a new 'reference annotation based transcript assembly' problem for RNA-Seq data that involves assembling novel transcripts in the context of an existing annotation. This problem arises in the analysis of expression in model organisms, where it is desirable to leverage existing annotations for discovering novel transcripts. We present an algorithm for reference annotation-based transcript assembly and show how it can be used to rapidly investigate novel transcripts revealed by RNA-Seq in comparison with a reference annotation. AVAILABILITY The methods described in this article are implemented in the Cufflinks suite of software for RNA-Seq, freely available from http://bio.math.berkeley.edu/cufflinks. The software is released under the BOOST license. CONTACT cole@broadinstitute.org; lpachter@math.berkeley.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b12aed46c3aad959152d6e3dc4a2db9f7d480221

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Citations in the corpus

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome (2011, methodology): “0.5) [4], Cufflinks (v1.”
RNA Sequencing and Analysis (2015, positive): “computational programs infer transcript models from the accumulation of read alignments to the reference genome (Trapnell et al. 2010; Li et al. 2011; Roberts et al. 2011a; Mezlini et al. 2013) (Table 2).”
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks (2012, background): “These files contain familiar statistics such as fold change (in log 2 scale), P values (both raw and corrected for multiple testing) and gene-and transcript-related attributes such as common name and location in the genome.”
A survey of best practices for RNA-seq data analysis (2016, methodology): “Several methods, such as Cufflinks [23], iReckon [24], SLIDE [25] and StringTie [26], incorporate existing annotations by adding them to the possible list of isoforms.”
Identification of protein coding regions in RNA transcripts (2014, methodology): “Observed average lengths of assembled transcripts were shorter than that of reference transcripts, particularly the average lengths of the de novo assemblies made by Oases and Velvet (Sup-P plementary Figure S1A).”
CircNet: a database of circular RNAs derived from transcriptome sequencing data (2015, methodology): “Expression profiling was performed through integrating the tool Cufflinks (45,46) with STAR (47).”