SUMMARY: Existing SAM visualization tools like 'samtools tview' (Li et al., 2009) are limited to a small region of the genome, and tools like Tablet (Milne et al., 2010) are limited to a relatively small number of reads and may fail outright on large datasets. We need to visualize complex ChIP-Seq and RNA-Seq features such as polarity as well as coverage across whole 3 Gbp genomes such as Human. We have addressed these problems in a lightweight visualization system called SAMSCOPE accelerated by OpenGL. The extensive pre-processing and fast OpenGL interface of SAMSCOPE provides instantaneous and intuitive browsing of complex data at all levels of detail across multiple experiments. AVAILABILITY AND IMPLEMENTATION: The SAMSCOPE software, implemented in C++ for Linux, with source code, binary packages and documentation are freely available from http://samscope.dna.bio.keio.ac.jp.

UNLABELLED: Since tens of millions of chemical compounds have been accumulated in public chemical databases, fast comprehensive computational methods to predict interactions between chemical compounds and proteins are needed for virtual screening of lead compounds. Previously, we proposed a novel method for predicting protein-chemical interactions using two-layer Support Vector Machine classifiers that require only readily available biochemical data, i.e. amino acid sequences of proteins and structure formulas of chemical compounds. In this article, the method has been implemented as the COPICAT web service, with an easy-to-use front-end interface. Users can simply submit a protein-chemical interaction prediction job using a pre-trained classifier, or can even train their own classification model by uploading training data. COPICAT's fast and accurate computational prediction has enhanced lead compound discovery against a database of tens of millions of chemical compounds, implying that the search space for drug discovery is extended by >1000 times compared with currently well-used high-throughput screening methodologies. AVAILABILITY: The COPICAT server is available at http://copicat.dna.bio.keio.ac.jp. All functions, including the prediction function are freely available via anonymous login without registration. Registered users, however, can use the system more intensively.

MOTIVATION: The accurate detection of orthologous segments (also referred to as syntenic segments) plays a key role in comparative genomics, as it is useful for inferring genome rearrangement scenarios and computing whole-genome alignments. Although a number of algorithms for detecting orthologous segments have been proposed, none of them contain a framework for optimizing their parameter values. METHODS: In the present study, we propose an algorithm, named OSfinder (Orthologous Segment finder), which uses a novel scoring scheme based on stochastic models. OSfinder takes as input the positions of short homologous regions (also referred to as anchors) and explicitly discriminates orthologous anchors from non-orthologous anchors by using Markov chain models which represent respective geometric distributions of lengths of orthologous and non-orthologous anchors. Such stochastic modeling makes it possible to optimize parameter values by maximizing the likelihood of the input dataset, and to automate the setting of the optimal parameter values. RESULTS: We validated the accuracies of orthology-mapping algorithms on the basis of their consistency with the orthology annotation of genes. Our evaluation tests using mammalian and bacterial genomes demonstrated that OSfinder shows higher accuracy than previous algorithms. AVAILABILITY: The OSfinder software was implemented as a C++ program. The software is freely available at http://osfinder.dna.bio.keio.ac.jp under the GNU General Public License. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics 25(7), 853–860. The publishers regret that there was an error in the corresponding author displayed. The corresponding author of this article is Yasubumi Sakakibara rather than Tsuyoshi Hachiya. The author group should have read: Tsuyoshi Hachiya1, Yasunori Osana2, Kris Popendorf1 and Yasubumi Sakakibara1,*