关键词: RNA-Seq, 多样本, 偏差曲线, 稀疏, 基因和异构体表达水平

Abstract: With the rapid development of the next-generation high-throughput sequencing technology, RNA-Seq has become the standard and important technique for transcriptome analysis. For multi-sample RNA-Seq data, the existing expression estimation methods usually deal with each single RNA-Seq sample, and ignore the read distributions with high consistency between multiple samples. This paper proposes a novel method, MRSeq, to estimate expression using multi-sample RNA-Seq data. MRSeq introduces a bias curve estimation model to capture the common features of read distributions shared among multiple samples. The common features are embedded into the model by deviation weight to correct read distributions. Meanwhile, by adding a sparse constraint, the method considers the sparsity between gene and the corresponding isoform expression. Three real datasets are used to validate the proposed method on gene and isoform expression estimation. Compared with the popular methods, MRSeq obtains more accurate gene and isoform expression estimation, and more meaningful biological explanation.

Key words: RNA-Seq, multi-sample, bias curve, sparse-specific, gene and isoform expression