  Ultra high-throughput sequencing of transcriptomes (RNA-Seq) has enabled the
accurate estimation of gene expression at individual isoform level. However,
systematic biases introduced during the sequencing and mapping processes as
well as incompleteness of the transcript annotation databases may cause the
estimates of isoform abundances to be unreliable, and in some cases, highly
inaccurate. This paper introduces a penalized likelihood approach to detect and
correct for such biases in a robust manner. Our model extends those previously
proposed by introducing bias parameters for reads. An L1 penalty is used for
the selection of non-zero bias parameters. We introduce an efficient algorithm
for model fitting and analyze the statistical properties of the proposed model.
Our experimental studies on both simulated and real datasets suggest that the
model has the potential to improve isoform-specific gene expression estimates
and identify incompletely annotated gene models.
