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原文連結
論文資訊
- 類型：已發表論文
- 日期：2011
摘要
With the availability of 基因組-wide transcription data and massive comparative sequencing, the discrimination of coding from noncoding RNAs and the assessment of coding potential in 演化arily conserved regions arose as a core analysis task. Here we present RNAcode, a program to detect coding regions in multiple sequence alignments that is optimized for emerging applications not covered by current 蛋白質 gene-finding software. Our algorithm combines 資訊 from nucleotide substitution and gap patterns in a unified framework and also deals with real-life issues such as alignment and sequencing errors. It uses an explicit 統計 model with no 機器學習 component and can therefore be applied "out of the box,'' without any training, to data from all domains of life. We describe the RNAcode method and apply it in c
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