Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models.
Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models.
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DOI：
10.1186/1471-2105-7-142
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发表时间：
2006-03-16
影响因子：
3
通讯作者：
Merkl R
中科院分区：
文献类型：
--
作者：
Waack S;Keller O;Asper R;Brodag T;Damm C;Fricke WF;Surovcik K;Meinicke P;Merkl R
水平基因转移（HGT）被认为是一种强大的进化力量，以实质性的方式塑造微生物基因组的内容。 HGT 与基因发生、复制或突变的区别在于速度的差异，能够快速适应不断变化的环境需求。为了精确表征，需要能够以高可靠性识别传输事件的算法。通常，转移的 DNA 片段具有相当长的长度，包含多个基因，被称为基因组岛 (GI)，或更具体地说是致病性岛或共生岛。我们已经实施了 SIGI-HMM 程序，可以预测 GI 和每个外来基因的推定供体。它基于对所研究的基因组中每个单独基因的密码子使用 (CU) 的分析。将每个基因的 CU 与一组精心挑选的代表微生物供体或高表达基因的 CU 表进行比较。多项测试用于识别假定的外来基因、预测假定的供体并掩盖假定的高表达基因。因此，我们确定了在基因水平上工作的非齐次隐马尔可夫模型的状态和发射概率。对于转移概率，我们利用经典测试理论，旨在以一致的方式集成灵敏度控制器。 SIGI-HMM是用JAVA编写的并且是公开可用的。它接受根据 EMBL 格式创建的任何文件作为输入。它以基因组浏览器可读的通用 GFF 格式生成输出。基准测试表明 SIGI-HMM 的输出与已知的结果一致。它的预测与带注释的地理标志以及不同方法生成的预测一致。 SIGI-HMM 是一种用于识别微生物基因组中 GI 的灵敏工具。它允许以交互方式详细分析基因组，并生成或测试有关获得基因起源的假设。
Horizontal gene transfer (HGT) is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs) or more specifically pathogenicity or symbiotic islands. We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU) of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired genes.
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影响因子：
3.9
作者：
Lawrence, JG;Ochman, H
通讯作者：
Ochman, H
影响因子：
10.7
作者：
Daubin, V;Perrière, G
通讯作者：
Perrière, G
影响因子：
56.9
作者：
Brüggemann, H;Henne, A;Gottschalk, G
通讯作者：
Gottschalk, G
影响因子：
64.8
作者：
Heidelberg JF;Eisen JA;Nelson WC;Clayton RA;Gwinn ML;Dodson RJ;Haft DH;Hickey EK;Peterson JD;Umayam L;Gill SR;Nelson KE;Read TD;Tettelin H;Richardson D;Ermolaeva MD;Vamathevan J;Bass S;Qin H;Dragoi I;Sellers P;McDonald L;Utterback T;Fleishmann RD;Nierman WC;White O;Salzberg SL;Smith HO;Colwell RR;Mekalanos JJ;Venter JC;Fraser CM
通讯作者：
Fraser CM
影响因子：
5.6
作者：
Burge, C;Karlin, S
通讯作者：
Karlin, S