MGMR: leveraging RNA-Seq population data to optimize expression estimation
<p>Abstract</p> <p>Background</p> <p>RNA-Seq is a technique that uses Next Generation Sequencing to identify transcripts and estimate transcription levels. When applying this technique for quantification, one must contend with reads that align to multiple positions in t...
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| Format: | Article |
| Language: | English |
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BMC
2012-04-01
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| Series: | BMC Bioinformatics |
| _version_ | 1819040800463388672 |
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| author | Rozov Roye Halperin Eran Shamir Ron |
| author_facet | Rozov Roye Halperin Eran Shamir Ron |
| author_sort | Rozov Roye |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>RNA-Seq is a technique that uses Next Generation Sequencing to identify transcripts and estimate transcription levels. When applying this technique for quantification, one must contend with reads that align to multiple positions in the genome (multireads). Previous efforts to resolve multireads have shown that RNA-Seq expression estimation can be improved using probabilistic allocation of reads to genes. These methods use a probabilistic generative model for data generation and resolve ambiguity using likelihood-based approaches. In many instances, RNA-seq experiments are performed in the context of a population. The generative models of current methods do not take into account such population information, and it is an open question whether this information can improve quantification of the individual samples</p> <p>Results</p> <p>In order to explore the contribution of population level information in RNA-seq quantification, we apply a hierarchical probabilistic generative model, which assumes that expression levels of different individuals are sampled from a Dirichlet distribution with parameters specific to the population, and reads are sampled from the distribution of expression levels. We introduce an optimization procedure for the estimation of the model parameters, and use HapMap data and simulated data to demonstrate that the model yields a significant improvement in the accuracy of expression levels of paralogous genes.</p> <p>Conclusions</p> <p>We provide a proof of principal of the benefit of drawing on population commonalities to estimate expression. The results of our experiments demonstrate this approach can be beneficial, primarily for estimation at the gene level.</p> |
| first_indexed | 2024-12-21T09:14:51Z |
| format | Article |
| id | doaj.art-c9702c0ddf9949df90dec430c9220edc |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T09:14:51Z |
| publishDate | 2012-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c9702c0ddf9949df90dec430c9220edc2022-12-21T19:09:09ZengBMCBMC Bioinformatics1471-21052012-04-0113Suppl 6S210.1186/1471-2105-13-S6-S2MGMR: leveraging RNA-Seq population data to optimize expression estimationRozov RoyeHalperin EranShamir Ron<p>Abstract</p> <p>Background</p> <p>RNA-Seq is a technique that uses Next Generation Sequencing to identify transcripts and estimate transcription levels. When applying this technique for quantification, one must contend with reads that align to multiple positions in the genome (multireads). Previous efforts to resolve multireads have shown that RNA-Seq expression estimation can be improved using probabilistic allocation of reads to genes. These methods use a probabilistic generative model for data generation and resolve ambiguity using likelihood-based approaches. In many instances, RNA-seq experiments are performed in the context of a population. The generative models of current methods do not take into account such population information, and it is an open question whether this information can improve quantification of the individual samples</p> <p>Results</p> <p>In order to explore the contribution of population level information in RNA-seq quantification, we apply a hierarchical probabilistic generative model, which assumes that expression levels of different individuals are sampled from a Dirichlet distribution with parameters specific to the population, and reads are sampled from the distribution of expression levels. We introduce an optimization procedure for the estimation of the model parameters, and use HapMap data and simulated data to demonstrate that the model yields a significant improvement in the accuracy of expression levels of paralogous genes.</p> <p>Conclusions</p> <p>We provide a proof of principal of the benefit of drawing on population commonalities to estimate expression. The results of our experiments demonstrate this approach can be beneficial, primarily for estimation at the gene level.</p> |
| spellingShingle | Rozov Roye Halperin Eran Shamir Ron MGMR: leveraging RNA-Seq population data to optimize expression estimation BMC Bioinformatics |
| title | MGMR: leveraging RNA-Seq population data to optimize expression estimation |
| title_full | MGMR: leveraging RNA-Seq population data to optimize expression estimation |
| title_fullStr | MGMR: leveraging RNA-Seq population data to optimize expression estimation |
| title_full_unstemmed | MGMR: leveraging RNA-Seq population data to optimize expression estimation |
| title_short | MGMR: leveraging RNA-Seq population data to optimize expression estimation |
| title_sort | mgmr leveraging rna seq population data to optimize expression estimation |
| work_keys_str_mv | AT rozovroye mgmrleveragingrnaseqpopulationdatatooptimizeexpressionestimation AT halperineran mgmrleveragingrnaseqpopulationdatatooptimizeexpressionestimation AT shamirron mgmrleveragingrnaseqpopulationdatatooptimizeexpressionestimation |