Name
Abstract | ||
|---|---|---|
BACKGROUND: In eukaryotes, alternative splicing often generates multiple splice variants from a single gene. Here weexplore the use of RNA sequencing (RNA-Seq) datasets to address the isoform quantification problem. Given a set of known splice variants, the goal is to estimate the relative abundance of the individual variants. METHODS: Our method employs a linear models framework to estimate the ratios of known isoforms in a sample. A key feature of our method is that it takes into account the non-uniformity of RNA-Seq read positions along the targeted transcripts. RESULTS: Preliminary tests indicate that the model performs well on both simulated and real data. In two publicly available RNA-Seq datasets, we identified several alternatively-spliced genes with switch-like, on/off expression properties, as well as a number of other genes that varied more subtly in isoform expression. In many cases, genes exhibiting differential expression of alternatively spliced transcripts were not differentially expressed at the gene level. CONCLUSIONS: Given that changes in isoform expression level frequently involve a continuum of isoform ratios, rather than all-or-nothing expression, and that they are often independent of general gene expression changes, we anticipate that our research will contribute to revealing a so far uninvestigated layer of the transcriptome. We believe that, in the future, researchers will prioritize genes for functional analysis based not only on observed changes in gene expression levels, but also on changes in alternative splicing. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/BIBM.2009.70 | BMC Bioinformatics |
Keywords | Field | DocType |
rna-seq data,isoform quantification problem,individual variant,rna sequencing,key feature,linear models framework,known isoforms,preliminary test,multiple splice variant,towards reliable isoform quantification,splice variant,macromolecules,rna seq,alternative splicing,relative abundance,linear model,computational modeling,silicon,least squares approximation,genetics,biofuel,molecular biophysics,splicing,gene splicing,data mining | RNA,Gene isoform,Gene,RNA-Seq,Computer science,Linear model,splice,Alternative splicing,RNA splicing,Bioinformatics | Conference |
Volume | Issue | ISSN |
11 | S-3 | 1471-2105 |
Citations | PageRank | References |
8 | 2.12 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian E. Howard | 1 | 26 | 4.24 |
| Steffen Heber | 2 | 219 | 22.88 |