SLAM: Cross-species gene finding and alignment with a generalized pair hidden Markov model
SLAM: Cross-species gene finding and alignment with a generalized pair hidden Markov model
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DOI：
10.1101/gr.424203
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发表时间：
2003-03-01
期刊：
影响因子：
7
通讯作者：
Pachter, L
中科院分区：
文献类型：
--
作者：
Alexandersson, M;Cawley, S;Pachter, L
基于比较的基因识别是由相关生物体之间的保守区域比不同区域更有可能编码的原则驱动的。我们描述了一个基因结构和比对的概率框架，可以用来同时找到两个同线基因组区域的基因结构和比对。该方法的一个关键特征是通过找到两个同线序列之间的最佳比对来增强基因预测的能力，同时找到保留编码外显子之间的对应关系的具有生物学意义的比对。我们的概率框架是广义对隐马尔可夫模型，（1）广义隐马尔可夫模型，这是以前用于基因发现，和（2）对隐马尔可夫模型，其中有应用程序的序列比对的混合。我们已经建立了一个名为SLAM的基因发现和比对程序，该程序可以在两个相关但未注释的DNA序列中比对和识别基因的完整外显子/内含子结构。SLAM能够可靠地预测任何适当相关的生物体对的基因结构，最值得注意的是与以前的方法相比具有更少的假阳性预测（为智人/小家鼠和恶性疟原虫/间日疟原虫比较提供了示例）。通过区分保守的非编码序列（CNS）和保守的编码序列获得准确度。CNS注释是SLAM的一个新特征，可能对UTR、调控元件和其他非编码特征的注释有用。
Comparative-based gene recognition is driven by the principle that conserved regions between related organisms are more likely than divergent regions to be coding. We describe a probabilistic framework for gene structure and alignment that can be used to simultaneously find both the gene structure and alignment of two syntenic genomic regions. A key feature of the method is the ability to enhance gene predictions by finding the best alignment between two syntenic sequences, while at the same time finding biologically meaningful alignments that preserve the correspondence between coding exons. Our probabilistic framework is the generalized pair hidden Markov model, a hybrid of (1) generalized hidden Markov models, which have been used previously for gene finding, and (2) pair hidden Markov models, which have applications to sequence alignment. We have built a gene finding and alignment program called SLAM, which aligns and identifies complete exon/intron structures of genes in two related but unannotated sequences of DNA. SLAM is able to reliably predict gene structures for any suitably related pair of organisms, most notably with fewer false-positive predictions compared to previous methods (examples are provided for Homo sapien/Mus musculus and Plasmodium falciparum/Plasmodium vivax comparisons). Accuracy is obtained by distinguishing conserved noncoding sequence (CNS) from conserved coding sequence. CNS annotation is a novel feature of SLAM and may be useful for the annotation of UTRs, regulatory elements, and other noncoding features.