Inference of Isoforms from Short Sequence Reads

Feng, Jianxing; Li, Wei; Jiang, Tao

doi:10.1007/978-3-642-12683-3_10

Cited by 43 publications

(79 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Available abundance estimation methods include direct computation (9,10) and model-based approaches. Many model-based studies (1,(11)(12)(13)(14) have used maximum-likelihood approaches to estimate isoform abundance. There are also efforts on formulating the abundance estimation problem as a linear model (15), where the independent and dependent variables are isoform expression levels and categorized RNA-Seq read counts, respectively.…”

mentioning

confidence: 99%

Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation

Jiang

Brown

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

129

142

View full text Add to dashboard Cite

Since the inception of next-generation mRNA sequencing (RNASeq) technology, various attempts have been made to utilize RNA-Seq data in assembling full-length mRNA isoforms de novo and estimating abundance of isoforms. However, for genes with more than a few exons, the problem tends to be challenging and often involves identifiability issues in statistical modeling. We have developed a statistical method called "sparse linear modeling of RNA-Seq data for isoform discovery and abundance estimation" (SLIDE) that takes exon boundaries and RNA-Seq data as input to discern the set of mRNA isoforms that are most likely to present in an RNA-Seq sample. SLIDE is based on a linear model with a design matrix that models the sampling probability of RNA-Seq reads from different mRNA isoforms. To tackle the model unidentifiability issue, SLIDE uses a modified Lasso procedure for parameter estimation. Compared with deterministic isoform assembly algorithms (e.g., Cufflinks), SLIDE considers the stochastic aspects of RNA-Seq reads in exons from different isoforms and thus has increased power in detecting more novel isoforms. Another advantage of SLIDE is its flexibility of incorporating other transcriptomic data such as RACE, CAGE, and EST into its model to further increase isoform discovery accuracy. SLIDE can also work downstream of other RNA-Seq assembly algorithms to integrate newly discovered genes and exons. Besides isoform discovery, SLIDE sequentially uses the same linear model to estimate the abundance of discovered isoforms. Simulation and real data studies show that SLIDE performs as well as or better than major competitors in both isoform discovery and abundance estimation. The SLIDE software package is available at https://sites.google. com/site/jingyijli/SLIDE.zip.mRNA isoform discovery | single-end vs. paired-end sequencing | fragment length distribution | GC contents | penalized estimation T he recently developed next-generation mRNA sequencing (RNA-Seq) assay, with deep coverage and base level resolution, has provided a view of eukaryotic transcriptomes of unprecedented detail and clarity. Unlike microarrays, RNA-Seq data have novel splice junction information in addition to gene expression, thus facilitating whole-transcriptome assembly and mRNA isoform quantification. RNA-Seq data includes both single-end and paired-end reads, where a single-end read is a sequenced end of a cDNA fragment from an mRNA transcript, and a pairedend read is a mate pair corresponding to both ends of a cDNA fragment.In the mRNA isoform discovery field, one of the most widely used software packages is Cufflinks (1). It builds a set of genes and exons solely from RNA-Seq data first, and subsequently uses a deterministic approach to find a minimal set of isoforms that can explain all the cDNA fragments indicated by paired-end reads. Cufflinks mainly uses qualitative exon expression and junction information in its isoform discovery, lacking a quantitative consideration of RNA-Seq data. Although Cufflinks gives very useful results, we no...

show abstract

mentioning

confidence: 99%

Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation

Jiang

Brown

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

129

142

View full text Add to dashboard Cite

show abstract

“…Here, we assume that reads from a transcript are uniformly distributed among the transcript. As justified in the literature (Feng et al, 2011), the distribution of the FPKM values can be approximated by a Gaussian distribution based on the assumption. Hence, the Dirichlet infinite mixture model is appropriate to fit the 65 observed FPKM values from RNA-Seq data.…”

Section: Testing Differential Transcript Expressionmentioning

confidence: 99%

“…. ; x N g, can then be determined by using the abundance values X Ã that minimizes the following residual sum of squares as done in IsoInfer (Feng et al, 2011):…”

mentioning

confidence: 99%

SDEAP: a splice graph based differential transcript expression analysis tool for population data

Yang

Jiang

2016

Bioinformatics

Self Cite

View full text Add to dashboard Cite

Motivation: Differential transcript expression (DTE) analysis without predefined conditions is critical to biological studies. For example, it can be used to discover biomarkers to classify cancer samples into previously unknown subtypes such that better diagnosis and therapy methods can be developed for the subtypes. Although several DTE tools for population data, i.e. data without 20 known biological conditions, have been published, these tools either assume binary conditions in the input population or require the number of conditions as a part of the input. Fixing the number of conditions to binary is unrealistic and may distort the results of a DTE analysis. Estimating the correct number of conditions in a population could also be challenging for a routine user. Moreover, the existing tools only provide differential usages of exons, which may be insufficient to 25 interpret the patterns of alternative splicing across samples and restrains the applications of the tools from many biology studies.Results: We propose a novel DTE analysis algorithm, called SDEAP, that estimates the number of conditions directly from the input samples using a Dirichlet mixture model and discovers alternative splicing events using a new graph modular decomposition algorithm. By taking advantage of the above 30 technical improvement, SDEAP was able to outperform the other DTE analysis methods in our extensive experiments on simulated data and real data with qPCR validation. The prediction of SDEAP also allowed us to classify the samples of cancer subtypes and cell-cycle phases more accurately.

show abstract

“…Up until now, much software is available for gene expression analysis based on the RNA-Seq data (Table 3). Some is designed for quantifying the expression of known genes or isoforms and some others do not need the prior gene structure annotation information [7,10,[35][36][37][38][39]. Cufflinks [7] assembles the alignments into a parsimonious set of transcripts and then estimates the relative abundances of these transcripts based on how many reads are mapped onto them.…”

Section: Gene and Isoform Expression Quantificationmentioning

confidence: 99%

Overview of available methods for diverse RNA-Seq data analyses

Chen

Wang

Shi

2011

Sci. China Life Sci.

View full text Add to dashboard Cite

RNA-Seq technology is becoming widely used in various transcriptomics studies; however, analyzing and interpreting the RNA-Seq data face serious challenges. With the development of high-throughput sequencing technologies, the sequencing cost is dropping dramatically with the sequencing output increasing sharply. However, the sequencing reads are still short in length and contain various sequencing errors. Moreover, the intricate transcriptome is always more complicated than we expect. These challenges proffer the urgent need of efficient bioinformatics algorithms to effectively handle the large amount of transcriptome sequencing data and carry out diverse related studies. This review summarizes a number of frequently-used applications of transcriptome sequencing and their related analyzing strategies, including short read mapping, exon-exon splice junction detection, gene or isoform expression quantification, differential expression analysis and transcriptome reconstruction. To date, RNA-Seq has been applied to a number of species for various research, such as inferring alternative splicing [4,5], quantifying the expression of genes and transcripts [6,7], detecting gene fusions [8,9], revealing long noncoding RNAs (lncRNAs) [10], and identifying single nucleotide variants (SNVs) in expressed exons [11]. Although RNA-Seq has brought tremendous benefits to these studies, it also faces many challenges from both sequencing technologies themselves and bioinformatics analyses of the data. In detail, RNA-Seq has biases in library construction, and strand-specific libraries are still not easy to be produced but are important for determining the orientation of transcripts [1]. Furthermore, RNA-Seq generates a large amount of data, and the read length is generally short and sequencing errors exist in the reads. These aspects challenge the corresponding methods and algorithms to effectively process the large amount of RNA-Seq data.

show abstract

Inference of Isoforms from Short Sequence Reads

Cited by 43 publications

References 59 publications

Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation

Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation

SDEAP: a splice graph based differential transcript expression analysis tool for population data

Overview of available methods for diverse RNA-Seq data analyses

Contact Info

Product

Resources

About