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利用保守结构在不损失准确性的情况下更快地注释非编码RNA。

Exploiting conserved structure for faster annotation of non-coding RNAs without loss of accuracy.

作者信息

Weinberg Zasha, Ruzzo Walter L

机构信息

Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.

出版信息

Bioinformatics. 2004 Aug 4;20 Suppl 1:i334-41. doi: 10.1093/bioinformatics/bth925.

Abstract

MOTIVATION

Non-coding RNAs (ncRNAs)-functional RNA molecules not coding for proteins-are grouped into hundreds of families of homologs. To find new members of an ncRNA gene family in a large genome database, covariance models (CMs) are a useful statistical tool, as they use both sequence and RNA secondary structure information. Unfortunately, CM searches are slow. Previously, we introduced 'rigorous filters', which provably sacrifice none of CMs' accuracy, although often scanning much faster. A rigorous filter, using a profile hidden Markov model (HMM), is built based on the CM, and filters the genome database, eliminating sequences that provably could not be annotated as homologs. The CM is run only on the remainder. Some biologically important ncRNA families could not be scanned efficiently with this technique, largely due to the significance of conserved secondary structure relative to primary sequence in identifying these families. Current heuristic filters are also expected to perform poorly on such families.

RESULTS

By augmenting profile HMMs with limited secondary structure information, we obtain rigorous filters that accelerate CM searches for virtually all known ncRNA families from the Rfam Database and tRNA models in tRNAscan-SE. These filters scan an 8 gigabase database in weeks instead of years, and uncover homologs missed by heuristic techniques to speed CM searches.

AVAILABILITY

Software in development; contact the authors.

摘要

动机

非编码RNA(ncRNA)——即不编码蛋白质的功能性RNA分子——被分为数百个同源物家族。为了在大型基因组数据库中找到ncRNA基因家族的新成员,协方差模型(CM)是一种有用的统计工具,因为它们同时使用序列和RNA二级结构信息。不幸的是,CM搜索速度很慢。此前,我们引入了“严格筛选器”,它虽然扫描速度通常快得多,但经证明不会牺牲CM的任何准确性。一个基于CM构建的使用轮廓隐马尔可夫模型(HMM)的严格筛选器,对基因组数据库进行筛选,排除那些经证明不能被注释为同源物的序列。CM仅在剩余序列上运行。一些具有生物学重要性的ncRNA家族无法用这种技术高效扫描,这主要是因为在识别这些家族时,保守二级结构相对于一级序列具有重要意义。目前的启发式筛选器预计在这类家族上也表现不佳。

结果

通过用有限的二级结构信息增强轮廓HMM,我们获得了严格筛选器,可加速CM搜索Rfam数据库中几乎所有已知的ncRNA家族以及tRNAscan-SE中的tRNA模型。这些筛选器在数周而非数年的时间内就能扫描一个8GB的数据库,并发现启发式技术遗漏的同源物,从而加快CM搜索速度。

可用性

正在开发的软件;请联系作者。

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