Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms

Patro, Rob; Mount, Stephen M.; Kingsford, Carl

doi:10.1038/nbt.2862

Cited by 602 publications

(584 citation statements)

References 23 publications

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“…Compared to microarrays, RNA expression profile has two majorly advantageous features to enable to identify complexity of isoform variability on a single gene to produce various splicing events on transcripts and another nature, allelic specific expression and allelic imbalance resulted from genetic differences in transcriptional rates (Beretta et al, 2014;Bernard et al, 2014;Deng et al, 2011;Hiller et al, 2009;Hiller and Wong, 2013;Howard and Heber, 2010;Hu et al, 2014;Jiang and Wong, 2009;Katz et al, 2010;Kaur et al, 2012;Kimes et al, 2014;Leon-Novelo et al, 2014;Lerch et al, 2012;Li and Jiang, 2012;Ma and Zhang, 2013;Mezlini et al, 2013;Mills et al, 2013;Nariai et al, 2013;Nariai et al, 2014;Ng et al, 2014;Nicolae et al, 2011;Niu et al, 2014;Pandey et al, 2013;Patro et al, 2014;Rehrauer et al, 2013;Safikhani et al, 2013;Shi and Jiang, 2013;Suo et al, 2014;Trapnell et al, 2010;Vardhanabhuti et al, 2013;Wang et al, 2010;Wu et al, 2011b;Yalamanchili et al, 2014;Zhang et al, 2014;Zheng and Chen, 2009). …”

Section: Resultsmentioning

confidence: 99%

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

Oh¹,

Kim

2016

Journal of the Korean Data and Information Science Society

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Thanks to recent advance of next generation sequencing techniques, RNA-seq enabled to have an unprecedented opportunity to identify transcript variants with isoform diversity and allelic imbalance (Anders et al., 2012) by different transcriptional rates. To date, it is well known that those features might be associated with the aberrant patterns of disease complexity such as tissue (Anders and Huber, 2010;Anders et al., 2012;Nariai et al., 2014) specific differential expression at isoform levels or tissue specific allelic imbalance in mal-functionality of disease processes, etc. Nevertheless, the knowledge of post-transcriptional modification and AI in transcriptomic and genomic areas has been little known in the traditional platforms due to the limitation of technology and insufficient resolution. We here stress the potential of isoform variability and allelic specific expression that are relevant to the abnormality of disease mechanisms in transcriptional genetic regulatory networks. In addition, we systematically review how robust Bayesian approaches in RNA-seq have been developed and utilized in this regard in the field.

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Section: Resultsmentioning

confidence: 99%

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

Oh¹,

Kim

2016

Journal of the Korean Data and Information Science Society

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“…In addition, the sample size in RNA-seq is much fewer than the number of variables (genes) (Anders et al, 2012;Beretta et al, 2014;Bernard et al, 2014;Bi and Davuluri, 2013;Bullard et al, 2010;Deng et al, 2011;Glaus et al, 2012;Han and Jiang, 2014;Hiller et al, 2009;Hiller and Wong, 2013;Howard and Heber, 2010;Hu et al, 2014;Jiang and Wong, 2009;Kaur et al, 2012;Kim et al, 2012;Kimes et al, 2014;Kumar et al, 2012;Lee et al, 2011;Leon-Novelo et al, 2014;Lerch et al, 2012;Li et al, 2014;Li et al, 2011;Li and Jiang, 2012;Ma and Zhang, 2013;Marioni et al, 2008;Mezlini et al, 2013;Mills et al, 2013;Mortazavi et al, 2008;Nariai et al, 2013;Nariai et al, 2014;Nicolae et al, 2011;Niu et al, 2014;Oh et al, 2013;Oshlack et al, 2010;Pandey et al, 2013;Patro et al, 2014;Pollier et al, 2013;Rehrauer et al, 2013;Roberts et al, 2011;Robinson and Oshlack, 2010;Ryan et al, 2012;Safikhani et al, 2013;<...>…”

Section: Resultsmentioning

confidence: 99%

“…And for an extreme case of gene, a gene has ∼ 1,000 isoform splicing events, suggesting that those isoforms with variability might be more closely related with disease and developmental processes as they generate different protein structure and functionalities, respectively (Beretta et al, 2014;Bernard et al, 2014;Deng et al, 2011;Hiller et al, 2009;Hiller and Wong, 2013;Howard and Heber, 2010;Hu et al, 2014;Jiang and Wong, 2009;Katz et al, 2010;Kaur et al, 2012;Kimes et al, 2014;Lerch et al, 2012;Li et al, 2011;Li and Jiang, 2012;Ma and Zhang, 2013;Mezlini et al, 2013;Mills et al, 2013;Nariai et al, 2013;Nariai et al, 2014;Nicolae et al, 2011;Niu et al, 2014;Patro et al, 2014;Rehrauer et al, 2013;Safikhani et al, 2013;Shi and Jiang, 2013;Suo et al, 2014;Trapnell et al, 2010;Vardhanabhuti et al, 2013;Wang et al, 2010b;Wang et al, 2010c;Wu et al, 2011;Yalamanchili et al, 2014;Zhang et al, 2014).…”

Section: Closing Remarksmentioning

confidence: 99%

How are Bayesian and Non-Parametric Methods Doing a Great Job in RNA-Seq Differential Expression Analysis? : A Review

2015

CSAM

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In a short history, RNA-seq data have established a revolutionary tool to directly decode various scenarios occurring on whole genome-wide expression profiles in regards with differential expression at gene, transcript, isoform, and exon specific quantification, genetic and genomic mutations, and etc. RNA-seq technique has been rapidly replacing arrays with seq-based platform experimental settings by revealing a couple of advantages such as identification of alternative splicing and allelic specific expression. The remarkable characteristics of highthroughput large-scale expression profile in RNA-seq are lied on expression levels of read counts, structure of correlated samples and genes, larger number of genes compared to sample size, different sampling rates, inevitable systematic RNA-seq biases, and etc. In this study, we will comprehensively review how robust Bayesian and non-parametric methods have a better performance than classical statistical approaches by explicitly incorporating such intrinsic RNA-seq specific features with flexible and more appropriate assumptions and distributions in practice.

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“…Whippet applies the concept of lightweight algorithms 10,11 to event-level splicing quantification by RNAseq, thus enabling a significant advancement in terms of both speed and resource usage for AS analysis relative to previously published methods. Whippet also simplifies AS analysis by eliminating the requirement for extensive computational resources and user knowledge.…”

Section: Discussionmentioning

confidence: 99%

Whippet: an efficient method for the detection and quantification of alternative splicing reveals extensive transcriptomic complexity

Sterne-Weiler

Weatheritt

Best

et al. 2017

Preprint

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Alternative splicing (AS) is a widespread process underlying the generation of transcriptomic and proteomic diversity in metazoans. Major challenges in comprehensively detecting and quantifying patterns of AS are that RNA-seq datasets are expanding near exponentially, while existing analysis tools are computationally inefficient and ineffective at handling complex splicing patterns. Here, we describe Whippet, a method that rapidly, and with minimal hardware requirements, models and quantifies splicing events of any complexity without significant loss of accuracy.Using an entropic measure of splicing complexity, Whippet reveals that approximately 33% of human protein coding genes contain complex AS events that result in substantial expression of multiple splice isoforms. These events frequently affect tandem arrays of folded protein domains. Remarkably, high-entropy AS events are more prevalent in tumour relative to matched normal tissues, and these differences correlate with increased expression of proto-oncogenic splicing factors. Whippet thus affords the rapid and accurate analysis of AS events of any complexity, and as such will facilitate biomedical research.

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Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms

Cited by 602 publications

References 23 publications

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

How are Bayesian and Non-Parametric Methods Doing a Great Job in RNA-Seq Differential Expression Analysis? : A Review

Whippet: an efficient method for the detection and quantification of alternative splicing reveals extensive transcriptomic complexity

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