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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 E-Step of IsoEM algorithm based on read classes.
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Figure 4: The E-Step of IsoEM algorithm based on read classes.

Mentions: Once read classes are constructed, we only need a small modification of the E-step of IsoEM to use read classes instead of reads (Figure 4). Next we describe the union-find algorithm used for efficiently finding compatibility components and read classes in IsoEM. A read class is defined as 〈m,{(i, w) i = isoform, w = weight}〉, where m is called the multiplicity of the read class. Given a collection of reads, we want to:


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

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

The E-Step of IsoEM algorithm based on read classes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: The E-Step of IsoEM algorithm based on read classes.
Mentions: Once read classes are constructed, we only need a small modification of the E-step of IsoEM to use read classes instead of reads (Figure 4). Next we describe the union-find algorithm used for efficiently finding compatibility components and read classes in IsoEM. A read class is defined as 〈m,{(i, w) i = isoform, w = weight}〉, where m is called the multiplicity of the read class. Given a collection of reads, we want to:

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.