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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.


Comparison of Cufflinks (a) and IsoEM (b) estimates to qPCR expression levels reported in [31].
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Figure 8: Comparison of Cufflinks (a) and IsoEM (b) estimates to qPCR expression levels reported in [31].

Mentions: Since the available implementation of RSEM could not be run on transcript sets other than UCSC known genes, in Figures 7 and 8 we only compare Cufflinks and IsoEM estimates against qPCR values in [14], respectively [31]. Estimation accuracy of both Cufflinks and IsoEM is significantly lower than that observed in simulations. Likely explanations include poor quality of the transcript libraries used to perform the inference, sequencing library preparation biases not corrected for by the algorithms, and possible inaccuracies in qPCR estimates. Nevertheless, the relative performance of the two algorithms is consistent with simulation results, with IsoEM outperforming Cufflinks on both datasets.


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

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

Comparison of Cufflinks (a) and IsoEM (b) estimates to qPCR expression levels reported in [31].
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Comparison of Cufflinks (a) and IsoEM (b) estimates to qPCR expression levels reported in [31].
Mentions: Since the available implementation of RSEM could not be run on transcript sets other than UCSC known genes, in Figures 7 and 8 we only compare Cufflinks and IsoEM estimates against qPCR values in [14], respectively [31]. Estimation accuracy of both Cufflinks and IsoEM is significantly lower than that observed in simulations. Likely explanations include poor quality of the transcript libraries used to perform the inference, sequencing library preparation biases not corrected for by the algorithms, and possible inaccuracies in qPCR estimates. Nevertheless, the relative performance of the two algorithms is consistent with simulation results, with IsoEM outperforming Cufflinks on both datasets.

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.