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Bioinformatics strategy for unveiling hidden genome signatures and biodiversity

揭示隐藏基因组特征和生物多样性的生物信息学策略

基本信息

批准号：
16570190
负责人：
IKEMURA Toshimichi
金额：
$ 2.3万
依托单位：
The Graduate University for Advanced Studies (Sokendai), Hayama Center for Advanced Research
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2005
项目状态：
已结题

项目摘要

Novel tools are needed for comprehensive comparisons of interspecies characteristics of massive amounts of genomic sequences currently available. An unsupervised neural network algorithm, Self-Organizing Map (SOM), is an effective tool for clustering and visualizing high-dimensional complex data on a single map. We modified the conventional SOM, on the basis of batch-learning SOM, for genome informatics making the learning process and resulting map independent of the order of data input. We generated the SOMs for tri-and tetranucleotide frequencies in 10-and 100-kb sequence fragments from 38 eukaryotes for which almost complete genome sequences are available. SOM recognized species-specific characteristics (key combinations of oligonucleotide frequencies) in the genomic sequences, permitting species-specific classification of the sequences without any information regarding the species. We also generated the SOM for tetranucleotide frequencies in 1-kb sequence fragments from the human g … More enome and found sequences for four functional categories (5' and 3' UTRs, CDSs and introns) were classified primarily according to the categories. Because the classification and visualization power is very high, SOM is an efficient and powerful tool for extracting a wide range of genome information.SOM that was constructed with oligonucleotide frequencies in 10-kb sequences from human genome sequences identified oligonucleotides with frequencies characteristically biased from random occurrence level, and 10-kb sequences rich in these biased oligonucleotides were self-organized on the map. Because these oligonucleotides often corresponded to functional signal sequences (e.g. binding sites for transcription factors) or their constituent elements, we categorized occurrence patterns and frequencies of such pentanucleotides in the human genome that are thought to regulate transcription. SOM analysis is dependent only on oligonucleotide frequencies and thus applicable even for the sequenced genomes with little additional experimental data. In order to know TSS, experimental data were required, but to know start sites of protein-coding sequences, such data were not required in most cases. When known signal sequences of various species with enough experimental data are characterized systematically, we can develop an in silico method of signal sequence prediction for a wide range of species. Recently, we have developed a novel bioinformatics tool for phylogenetic classification of genomic sequence fragments derived from uncultured microorganism mixtures in environmental and clinical samples Less

需要新的工具来全面比较目前可用的大量基因组序列的种间特征。自组织映射（SOM）是一种无监督的神经网络算法，它是一种有效的工具，用于在单个映射上对高维复杂数据进行聚类和可视化。我们修改了传统的SOM，批量学习SOM的基础上，基因组信息学的学习过程和产生的地图独立于数据输入的顺序。我们产生的SOM的三，四核苷酸频率在10-和100-kb的序列片段，从38个真核生物，几乎完整的基因组序列。SOM识别基因组序列中的物种特异性特征（寡核苷酸频率的关键组合），允许对序列进行物种特异性分类，而无需任何关于物种的信息。我们还生成了人类基因1-kb序列片段中四核苷酸频率的SOM ...更多信息主要根据类别将基因组和已发现的四种功能类别（5'和3' UTR、CDS和内含子）的序列进行分类。SOM具有很高的分类能力和可视化能力，是一种有效的基因组信息提取工具，利用人类基因组10 kb序列中的寡核苷酸频率构建SOM，识别出频率偏离随机水平的寡核苷酸，并将富含这些寡核苷酸的10 kb序列自组织到图谱上。由于这些寡核苷酸通常对应于功能信号序列（例如转录因子的结合位点）或其组成元件，因此我们对人类基因组中被认为调节转录的五核苷酸的发生模式和频率进行了分类。SOM分析仅依赖于寡核苷酸频率，因此即使对于具有很少额外实验数据的测序基因组也适用。为了了解TSS，需要实验数据，但要了解蛋白质编码序列的起始位点，在大多数情况下不需要这些数据。当已知的信号序列的各种物种与足够的实验数据的特点是系统的，我们可以开发一个在硅片上的信号序列预测方法，为广泛的物种。最近，我们开发了一种新的生物信息学工具，用于环境和临床样品中未培养微生物混合物的基因组序列片段的系统发育分类。