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| Content Provider | PubMed Central |
|---|---|
| Author | Ji, Yuan Xu, Yanxun Zhang, Qiong Tsui, Kam-wah Yuan, Yuan Norris, Clift Liang, Shoudan Liang, Han |
| Abstract | Next-generation sequencing (NGS) technology generates millions of short reads, which provide valuable information for various aspects of cellular activities and biological functions. A key step in NGS applications (e.g., RNA-Seq) is to map short reads to correct genomic locations within the source genome. While most reads are mapped to a unique location, a significant proportion of reads align to multiple genomic locations with equal or similar numbers of mismatches; these are called multireads. The ambiguity in mapping the multireads may lead to bias in downstream analyses. Currently, most practitioners discard the multireads in their analysis, resulting in a loss of valuable information, especially for the genes with similar sequences. To refine the read mapping, we develop a Bayesian model that computes the posterior probability of mapping a multiread to each competing location. The probabilities are used for downstream analyses, such as the quantification of gene expression. We show through simulation studies and RNA-Seq analysis of real life data that the Bayesian method yields better mapping than the current leading methods. We provide a C++ program for downloading that is being packaged into a user-friendly software. |
| Related Links | http://dx.doi.org/10.1111/j.1541-0420.2011.01605.x |
| Starting Page | 1215 |
| File Format | |
| ISSN | 15410420 |
| e-ISSN | 15410420 |
| Journal | Biometrics |
| Issue Number | 4 |
| Volume Number | 67 |
| Language | English |
| Publisher Date | 2011-12-01 |
| Access Restriction | Open |
| Subject Keyword | Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Statistics and Probability Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology Agricultural and Biological Sciences |
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