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Estimation of alternative splicing isoform frequencies from RNA-Seq data.

Nicolae M, Mangul S, Măndoiu II, Zelikovsky A - Algorithms Mol Biol (2011)

Bottom Line: Massively parallel whole transcriptome sequencing, commonly referred as RNA-Seq, is quickly becoming the technology of choice for gene expression profiling.However, due to the short read length delivered by current sequencing technologies, estimation of expression levels for alternative splicing gene isoforms remains challenging.Simulation experiments confirm previous findings that, for a fixed sequencing cost, using reads longer than 25-36 bases does not necessarily lead to better accuracy for estimating expression levels of annotated isoforms and genes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science & Engineering, University of Connecticut,371 Fairfield Rd,, Unit 2155, Storrs, CT 06269-2155, USA. man09004@engr.uconn.edu.

ABSTRACT

Background: Massively parallel whole transcriptome sequencing, commonly referred as RNA-Seq, is quickly becoming the technology of choice for gene expression profiling. However, due to the short read length delivered by current sequencing technologies, estimation of expression levels for alternative splicing gene isoforms remains challenging.

Results: In this paper we present a novel expectation-maximization algorithm for inference of isoform- and gene-specific expression levels from RNA-Seq data. Our algorithm, referred to as IsoEM, is based on disambiguating information provided by the distribution of insert sizes generated during sequencing library preparation, and takes advantage of base quality scores, strand and read pairing information when available. The open source Java implementation of IsoEM is freely available at http://dna.engr.uconn.edu/software/IsoEM/.

Conclusions: Empirical experiments on both synthetic and real RNA-Seq datasets show that IsoEM has scalable running time and outperforms existing methods of isoform and gene expression level estimation. Simulation experiments confirm previous findings that, for a fixed sequencing cost, using reads longer than 25-36 bases does not necessarily lead to better accuracy for estimating expression levels of annotated isoforms and genes.

No MeSH data available.


The expectation-maximization algorithm used by IsoEM.
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Figure 2: The expectation-maximization algorithm used by IsoEM.

Mentions: Since some reads match multiple isoforms, their isoform of origin cannot be established unambiguously. The IsoEM algorithm (see Figure 2) overcomes this difficulty by simultaneously estimating the frequencies and imputing the missing read origin within an iterative framework. After initializing frequencies f (j) at random, the algorithm repeatedly performs the next two steps until convergence:


Estimation of alternative splicing isoform frequencies from RNA-Seq data.

Nicolae M, Mangul S, Măndoiu II, Zelikovsky A - Algorithms Mol Biol (2011)

The expectation-maximization algorithm used by IsoEM.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3107792&req=5

Figure 2: The expectation-maximization algorithm used by IsoEM.
Mentions: Since some reads match multiple isoforms, their isoform of origin cannot be established unambiguously. The IsoEM algorithm (see Figure 2) overcomes this difficulty by simultaneously estimating the frequencies and imputing the missing read origin within an iterative framework. After initializing frequencies f (j) at random, the algorithm repeatedly performs the next two steps until convergence:

Bottom Line: Massively parallel whole transcriptome sequencing, commonly referred as RNA-Seq, is quickly becoming the technology of choice for gene expression profiling.However, due to the short read length delivered by current sequencing technologies, estimation of expression levels for alternative splicing gene isoforms remains challenging.Simulation experiments confirm previous findings that, for a fixed sequencing cost, using reads longer than 25-36 bases does not necessarily lead to better accuracy for estimating expression levels of annotated isoforms and genes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science & Engineering, University of Connecticut,371 Fairfield Rd,, Unit 2155, Storrs, CT 06269-2155, USA. man09004@engr.uconn.edu.

ABSTRACT

Background: Massively parallel whole transcriptome sequencing, commonly referred as RNA-Seq, is quickly becoming the technology of choice for gene expression profiling. However, due to the short read length delivered by current sequencing technologies, estimation of expression levels for alternative splicing gene isoforms remains challenging.

Results: In this paper we present a novel expectation-maximization algorithm for inference of isoform- and gene-specific expression levels from RNA-Seq data. Our algorithm, referred to as IsoEM, is based on disambiguating information provided by the distribution of insert sizes generated during sequencing library preparation, and takes advantage of base quality scores, strand and read pairing information when available. The open source Java implementation of IsoEM is freely available at http://dna.engr.uconn.edu/software/IsoEM/.

Conclusions: Empirical experiments on both synthetic and real RNA-Seq datasets show that IsoEM has scalable running time and outperforms existing methods of isoform and gene expression level estimation. Simulation experiments confirm previous findings that, for a fixed sequencing cost, using reads longer than 25-36 bases does not necessarily lead to better accuracy for estimating expression levels of annotated isoforms and genes.

No MeSH data available.