Exploiting conserved structure for faster annotation of non-coding RNAs without loss of accuracy
Exploiting conserved structure for faster annotation of non-coding RNAs without loss of accuracy
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DOI：
10.1093/bioinformatics/bth925
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发表时间：
2004-08-04
期刊：
影响因子：
5.8
通讯作者：
Ruzzo, Walter L.
中科院分区：
文献类型：
--
作者：
Weinberg, Zasha;Ruzzo, Walter L.
动机：非编码RNA(NcRNAs)--不编码蛋白质的功能性RNA分子--被分成数百个同源物家族。为了在大型基因组数据库中发现ncRNA基因家族的新成员，协方差模型(CMS)是一个有用的统计工具，因为它们同时使用了序列和RNA的二级结构信息。不幸的是，CM搜索速度很慢。之前，我们引入了“严格的过滤器”，这显然不会牺牲CMS的准确性，尽管通常扫描速度要快得多。在CM的基础上，使用隐马尔可夫模型(HMM)建立了一个严格的过滤器，并过滤基因组数据库，消除了可证明不能被注释为同源序列的序列。CM仅在剩余部分上运行。一些具有重要生物学意义的ncRNA家族不能用这种技术有效地扫描，这主要是因为相对于一级序列而言，保守的二级结构在识别这些家族时具有重要意义。结果：通过用有限的二级结构信息扩充Profile HMM，我们获得了严格的过滤器，可以加速CM从Rfam数据库和tRNAscan-SE中的tRNA模型中搜索几乎所有已知的ncRNA家族。这些过滤器在几周而不是几年内扫描一个8G数据库，并发现启发式技术遗漏的同源物，以加快CM搜索。
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.