Bioinformatics strategy for unveiling hidden genome signatures and biodiversity

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。自组织地图（SOM）是一种无监督神经网络算法，是在单个地图上对高维复杂数据进行聚类和可视化的有效工具。我们在批量学习SOM的基础上改进了传统的基因组信息学SOM，使学习过程和生成的图谱与数据输入的顺序无关。我们在38种真核生物的10- kb和100-kb序列片段中生成了三核苷酸和四核苷酸频率的SOMs，这些真核生物几乎可以获得完整的基因组序列。SOM在基因组序列中识别物种特异性特征（寡核苷酸频率的关键组合），允许在没有任何物种信息的情况下对序列进行物种特异性分类。我们还生成了人类基因组中1 kb序列片段的四核苷酸频率的SOM。更多的基因组和发现的四个功能类别（5‘和3’ utr， CDSs和内含子）的序列主要根据这些类别进行分类。由于SOM具有很高的分类和可视化能力，因此它是一种高效而强大的工具，可用于提取广泛的基因组信息。利用人类基因组序列中10-kb序列的寡核苷酸频率构建SOM，识别出频率偏离随机发生水平的寡核苷酸，富含这些偏倚寡核苷酸的10-kb序列在图谱上是自组织的。由于这些寡核苷酸通常对应于功能性信号序列（例如转录因子的结合位点）或其组成元件，因此我们对这些被认为调节转录的五核苷酸在人类基因组中的出现模式和频率进行了分类。SOM分析仅依赖于寡核苷酸频率，因此甚至适用于几乎没有额外实验数据的测序基因组。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，大多数情况下不需要这些数据。当有足够的实验数据对已知的各种物种的信号序列进行系统表征后，我们就可以开发一种大范围物种信号序列预测的计算机方法。最近，我们开发了一种新的生物信息学工具，用于对环境和临床样品中未经培养的微生物混合物的基因组序列片段进行系统发育分类

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