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.

中文翻译：

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一种稳健估计同种型表达的惩罚似然方法

转录组的超高通量测序 (RNA-Seq) 能够准确估计个体同种型水平的基因表达。然而，在测序和作图过程中引入的系统偏差以及转录本注释数据库的不完整可能会导致同种型丰度的估计不可靠，并且在某些情况下非常不准确。本文介绍了一种惩罚似然方法，以稳健的方式检测和纠正此类偏差。我们的模型通过引入读取偏差参数扩展了先前提出的模型。L1 惩罚用于选择非零偏差参数。我们引入了一种有效的模型拟合算法并分析了所提出模型的统计特性。