Search

Motivation: Bacteriophages/phages are the viruses that infect and replicate within bacteria and archaea, and rich in human body. To investigate the relationship between phages and microbial communities, the identification of phages from metagenome sequences is the first step. Currently, there are two main methods for identifying phages: database-based (alignment-based) methods and alignment-free methods. Database-based methods typically use a large number of sequences as references; alignment-free methods usually learn the features of the sequences with machine learning and deep learning models., Results: We propose INHERIT which uses a deep representation learning model to integrate both database-based and alignment-free methods, combining the strengths of both. Pre-training is used as an alternative way of acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compare INHERIT with four existing methods on a third-party benchmark dataset. Our experiments show that INHERIT achieves a better performance with the F1-score of 0.9932. In addition, we find that pre-training two species separately helps the non-alignment deep learning model make more accurate predictions., Availability and Implementation: The codes of INHERIT are now available in: https://github.com/Celestial-Bai/INHERIT., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Motivation: The full spectrum of abnormalities in cancer-associated protein complexes remains largely unknown. Comparing the co-expression structure of each protein complex between tumor and healthy cells may provide insights regarding cancer-specific protein dysfunction. However, the technical limitations of mass spectrometry-based proteomics, including contamination with biological protein variants, causes noise that leads to non-negligible over- (or under-) estimating co-expression., Results: We propose a robust algorithm for identifying protein complex aberrations in cancer based on differential protein co-expression testing. Our method based on a copula is sufficient for improving identification accuracy with noisy data compared to conventional linear correlation-based approaches. As an application, we use large-scale proteomic data from renal cancer to show that important protein complexes, regulatory signaling pathways and drug targets can be identified. The proposed approach surpasses traditional linear correlations to provide insights into higher-order differential co-expression structures., Availability and Implementation: https://github.com/ymatts/RoDiCE., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Summary: It is known that some mutant peptides, such as those resulting from missense mutations and frameshift insertions, can bind to the major histocompatibility complex and be presented to antitumor T cells on the surface of a tumor cell. These peptides are termed neoantigen, and it is important to understand this process for cancer immunotherapy. Here, we introduce an R package termed Neoantimon that can predict a list of potential neoantigens from a variety of mutations, which include not only somatic point mutations but insertions, deletions and structural variants. Beyond the existing applications, Neoantimon is capable of attaching and reflecting several additional information, e.g. wild-type binding capability, allele specific RNA expression levels, single nucleotide polymorphism information and combinations of mutations to filter out infeasible peptides as neoantigen., Availability and Implementation: The R package is available at http://github/hase62/Neoantimon., (© The Author(s) 2020. Published by Oxford University Press.)

Motivation: Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled., Results: We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets., Availability and Implementation: https://github.com/takumorizo/OHVarfinDer., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

Motivation: DNA methylation is an important epigenetic modification related to a variety of diseases including cancers. We focus on the methylation data from Illumina's Infinium HumanMethylation450 BeadChip. One of the key issues of methylation analysis is to detect the differential methylation sites between case and control groups. Previous approaches describe data with simple summary statistics or kernel function, and then use statistical tests to determine the difference. However, a summary statistics-based approach cannot capture complicated underlying structure, and a kernel function-based approach lacks interpretability of results., Results: We propose a novel method D(3)M, for detection of differential distribution of methylation, based on distribution-valued data. Our method can detect the differences in high-order moments, such as shapes of underlying distributions in methylation profiles, based on the Wasserstein metric. We test the significance of the difference between case and control groups and provide an interpretable summary of the results. The simulation results show that the proposed method achieves promising accuracy and shows favorable results compared with previous methods. Glioblastoma multiforme and lower grade glioma data from The Cancer Genome Atlas show that our method supports recent biological advances and suggests new insights., Availability and Implementation: R implemented code is freely available from https://github.com/ymatts/D3M/ CONTACT: ymatsui@med.nagoya-u.ac.jp or shimamura@med.nagoya-u.ac.jp, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Summary: Somatic internal tandem duplications (ITDs) are known to play important roles in cancer pathogenesis. Although recent advances in high-throughput sequencing technologies have enabled genome-wide detection of various types of genomic mutations, including single nucleotide variants, indels and structural variations, only a few studies have focused on ITDs. We have developed an analytical tool called 'Genomon ITDetector' for genome-wide detection of somatic ITDs. After evaluating the sensitivity and precision of the proposed approach using synthetic data, we have demonstrated that it can successfully detect not only common ITDs involving FLT3, but also a number of ITDs affecting other putative driver genes in acute myeloid leukemia exome sequencing data. Availability and implementaion: Genomon ITDetector is freely available at https://github.com/ken0-1n/Genomon-ITDetector., (© Crown copyright 2014.)

Motivation: Identifying somatic changes from tumor and matched normal sequences has become a standard approach in cancer research. More specifically, this requires accurate detection of somatic point mutations with low allele frequencies in impure and heterogeneous cancer samples. Although haplotype phasing information derived by using heterozygous germ line variants near candidate mutations would improve accuracy, no somatic mutation caller that uses such information is currently available., Results: We propose a Bayesian hierarchical method, termed HapMuC, in which power is increased by using available information on heterozygous germ line variants located near candidate mutations. We first constructed two generative models (the mutation model and the error model). In the generative models, we prepared candidate haplotypes, considering a heterozygous germ line variant if available, and the observed reads were realigned to the haplotypes. We then inferred the haplotype frequencies and computed the marginal likelihoods using a variational Bayesian algorithm. Finally, we derived a Bayes factor for evaluating the possibility of the existence of somatic mutations. We also demonstrated that our algorithm has superior specificity and sensitivity compared with existing methods, as determined based on a simulation, the TCGA Mutation Calling Benchmark 4 datasets and data from the COLO-829 cell line., Availability and Implementation: The HapMuC source code is available from http://github.com/usuyama/hapmuc., (© The Author 2014. Published by Oxford University Press.)

Unlabelled: XiP (eXtensible integrative Pipeline) is a flexible, editable and modular environment with a user-friendly interface that does not require previous advanced programming skills to run, construct and edit workflows. XiP allows the construction of workflows by linking components written in both R and Java, the analysis of high-throughput data in grid engine systems and also the development of customized pipelines that can be encapsulated in a package and distributed. XiP already comes with several ready-to-use pipeline flows for the most common genomic and transcriptomic analysis and ∼300 computational components., Availability: XiP is open source, freely available under the Lesser General Public License (LGPL) and can be downloaded from http://xip.hgc.jp.

Unlabelled: Protein-protein interactions (PPIs) are mediated through specific regions on proteins. Some proteins have two or more protein interacting regions (IRs) and some IRs are competitively used for interactions with different proteins. IRView currently contains data for 3417 IRs in human and mouse proteins. The data were obtained from different sources and combined with annotated region data from InterPro. Information on non-synonymous single nucleotide polymorphism sites and variable regions owing to alternative mRNA splicing is also included. The IRView web interface displays all IR data, including user-uploaded data, on reference sequences so that the positional relationship between IRs can be easily understood. IRView should be useful for analyzing underlying relationships between the proteins behind the PPI networks., Availability: IRView is publicly available on the web at http://ir.hgc.jp/

Motivation: In cancer genomes, chromosomal regions harboring cancer genes are often subjected to genomic aberrations like copy number alteration and loss of heterozygosity. Given this, finding recurrent genomic aberrations is considered an apt approach for screening cancer genes. Although several permutation-based tests have been proposed for this purpose, none of them are designed to find recurrent aberrations from the genomic dataset without paired normal sample controls. Their application to unpaired genomic data may lead to false discoveries, because they retrieve pseudo-aberrations that exist in normal genomes as polymorphisms., Results: We develop a new parametric method named parametric aberration recurrence test (PART) to test for the recurrence of genomic aberrations. The introduction of Poisson-binomial statistics allow us to compute small P-values more efficiently and precisely than the previously proposed permutation-based approach. Moreover, we extended PART to cover unpaired data (PART-up) so that there is a statistical basis for analyzing unpaired genomic data. PART-up uses information from unpaired normal sample controls to remove pseudo-aberrations in unpaired genomic data. Using PART-up, we successfully predict recurrent genomic aberrations in cancer cell line samples whose paired normal sample controls are unavailable. This article thus proposes a powerful statistical framework for the identification of driver aberrations, which would be applicable to ever-increasing amounts of cancer genomic data seen in the era of next generation sequencing., Availability: Our implementations of PART and PART-up are available from http://www.hgc.jp/~niiyan/PART/manual.html.

Summary: Manual curation and validation of large-scale biological pathways are required to obtain high-quality pathway databases. In a typical curation process, model validation and model update based on appropriate feedback are repeated and requires considerable cooperation of scientists. We have developed a CSO (Cell System Ontology) validator to reduce the repetition and time during the curation process. This tool assists in quickly obtaining agreement among curators and domain experts and in providing a consistent and accurate pathway database., Availability: The tool is available on http://csovalidator.csml.org., Contact: masao@hgc.jp.

Motivation: Due to recent advances in high-throughput technologies, data on various types of genomic annotation have accumulated. These data will be crucially helpful for elucidating the combinatorial logic of transcription. Although several approaches have been proposed for inferring cooperativity among multiple factors, most approaches are haunted by the issues of normalization and threshold values., Results: In this article, we propose a rank-based non-parametric statistical test for measuring the effects between two gene sets. This method is free from the issues of normalization and threshold value determination for gene expression values. Furthermore, we have proposed an efficient Markov chain Monte Carlo method for calculating an approximate significance value of synergy. We have applied this approach for detecting synergistic combinations of transcription factor binding motifs and histone modifications., Availability: C implementation of the method is available from http://www.hgc.jp/~yshira/software/rankSynergy.zip., Contact: yshira@hgc.jp, Supplementary Information: Supplementary data are available at Bioinformatics online.

Summary: The Macrophage Pathway Knowledgebase (MACPAK) is a computational system that allows biomedical researchers to query and study the dynamic behaviors of macrophage molecular pathways. It integrates the knowledge of 230 reviews that were carefully checked by specialists for their accuracy and then converted to 230 dynamic mathematical pathway models. MACPAK comprises a total of 24 009 entities and 12 774 processes and is described in the Cell System Markup Language (CSML), an XML format that runs on the Cell Illustrator platform and can be visualized with a customized Cytoscape for further analysis., Availability: MACPAK can be accessed via an interactive web site at http://macpak.csml.org. The CSML pathway models are available under the Creative Commons license.

Unlabelled: SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles., Availability: SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site., Contact: tamada@ims.u-tokyo.ac.jp.

Unlabelled: Model checking is playing an increasingly important role in systems biology as larger and more complex biological pathways are being modeled. In this article we report the release of an efficient model checker MIRACH 1.0, which supports any model written in popular formats such as CSML and SBML. MIRACH is integrated with a Petri-net-based simulation engine, enabling efficient online (on-the-fly) checking. In our experiment, by using Levchenko et al. model, we reveal that timesaving gains by using MIRACH easily surpass 400% compared with its offline-based counterpart., Availability and Implementation: MIRACH 1.0 was developed using Java and thus executable on any platform installed with JDK 6.0 (not JRE 6.0) or later. MIRACH 1.0, along with its source codes, documentation and examples are available at http://sourceforge.net/projects/mirach/ under the LGPLv3 license.

Motivation: A number of unsupervised gene set screening methods have recently been developed for search of putative functional gene sets based on their expression profiles. Most of the methods statistically evaluate whether the expression profiles of each gene set are fit to assumed models: e.g. co-expression across all samples or a subgroup of samples. However, it is possible that they fail to capture informative gene sets whose expression profiles are not fit to the assumed models., Results: To overcome this limitation, we propose a model-free unsupervised gene set screening method, Matrix Information Enrichment Analysis (MIEA). Without assuming any specific models, MIEA screens gene sets based on information richness of their expression profiles. We extensively compared the performance of MIEA to those of other unsupervised gene set screening methods, using various types of simulated and real data. The benchmark tests demonstrated that MIEA can detect singular expression profiles that the other methods fail to find, and performs broadly well for various types of input data. Taken together, this study introduces MIEA as a broadly applicable gene set screening tool for mining regulatory programs from transcriptome data.

Unlabelled: We propose a likelihood ratio test (LRT) with Bartlett correction in order to identify Granger causality between sets of time series gene expression data. The performance of the proposed test is compared to a previously published bootstrap-based approach. LRT is shown to be significantly faster and statistically powerful even within non-Normal distributions. An R package named gGranger containing an implementation for both Granger causality identification tests is also provided., Availability: http://dnagarden.ims.u-tokyo.ac.jp/afujita/en/doku.php?id=ggranger.

Summary: Data assimilation (DA) is a computational approach that estimates unknown parameters in a pathway model using time-course information. Particle filtering, the underlying method used, is a well-established statistical method that approximates the joint posterior distributions of parameters by using sequentially generated Monte Carlo samples. In this article, we report the release of Java-based software (DA 1.0) with an intuitive and user-friendly interface to allow users to carry out parameters estimation using DA., Availability and Implementation: DA 1.0 was developed using Java and thus would be executable on any platform installed with JDK 6.0 (not JRE 6.0) or later. DA 1.0 is freely available for academic users and can be launched or downloaded from http://da.csml.org.

Motivation: Elucidating the differences between cellular responses to various biological conditions or external stimuli is an important challenge in systems biology. Many approaches have been developed to reverse engineer a cellular system, called gene network, from time series microarray data in order to understand a transcriptomic response under a condition of interest. Comparative topological analysis has also been applied based on the gene networks inferred independently from each of the multiple time series datasets under varying conditions to find critical differences between these networks. However, these comparisons often lead to misleading results, because each network contains considerable noise due to the limited length of the time series., Results: We propose an integrated approach for inferring multiple gene networks from time series expression data under varying conditions. To the best of our knowledge, our approach is the first reverse-engineering method that is intended for transcriptomic network comparison between varying conditions. Furthermore, we propose a state-of-the-art parameter estimation method, relevance-weighted recursive elastic net, for providing higher precision and recall than existing reverse-engineering methods. We analyze experimental data of MCF-7 human breast cancer cells stimulated by epidermal growth factor or heregulin with several doses and provide novel biological hypotheses through network comparison., Availability: The software NETCOMP is available at http://bonsai.ims.u-tokyo.ac.jp/ approximately shima/NETCOMP/.

Motivation: Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential first step to find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision., Results: The aim of our research is to present a new statistical technology towards data-driven construction of in silico biological pathways. The method starts with a knowledge-based modeling with hybrid functional Petri net. It then proceeds to the Bayesian learning of model parameters for which experimental data are available. This process exploits quantitative measurements of evolving biochemical reactions, e.g. gene expression data. Another important issue that we consider is statistical evaluation and comparison of the constructed hypothetical pathways. For this purpose, we have developed a new Bayesian information-theoretic measure that assesses the predictability and the biological robustness of in silico pathways., Availability: The FORTRAN source codes are available at the URL http://daweb.ism.ac.jpyoshidar/GDA/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Properly drawn biological networks are of great help in the comprehension of their characteristics. The quality of the layouts for retrieved biological networks is critical for pathway databases. However, since it is unrealistic to manually draw biological networks for every retrieval, automatic drawing algorithms are essential. Grid layout algorithms handle various biological properties such as aligning vertices having the same attributes and complicated positional constraints according to their subcellular localizations; thus, they succeed in providing biologically comprehensible layouts. However, existing grid layout algorithms are not suitable for real-time drawing, which is one of requisites for applications to pathway databases, due to their high-computational cost. In addition, they do not consider edge directions and their resulting layouts lack traceability for biochemical reactions and gene regulations, which are the most important features in biological networks., Results: We devise a new calculation method termed sweep calculation and reduce the time complexity of the current grid layout algorithms through its encoding and decoding processes. We conduct practical experiments by using 95 pathway models of various sizes from TRANSPATH and show that our new grid layout algorithm is much faster than existing grid layout algorithms. For the cost function, we introduce a new component that penalizes undesirable edge directions to avoid the lack of traceability in pathways due to the differences in direction between in-edges and out-edges of each vertex., Availability: Java implementations of our layout algorithms are available in Cell Illustrator., Contact: masao@ims.u-tokyo.ac.jp, Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway., Results: In this article, we present a novel approach based on the state space model to identify the transcriptional modules and module-based gene networks simultaneously. The state space model has the potential to infer large-scale gene networks, e.g. of order 10(3), from time-course gene expression profiles. Particularly, we succeeded in the identification of a cell cycle system by using the gene expression profiles of Saccharomyces cerevisiae in which the length of the time-course and number of genes were 24 and 4382, respectively. However, when analysing shorter time-course data, e.g. of length 10 or less, the parameter estimations of the state space model often fail due to overfitting. To extend the applicability of the state space model, we provide an approach to use the technical replicates of gene expression profiles, which are often measured in duplicate or triplicate. The use of technical replicates is important for achieving highly-efficient inferences of gene networks with short time-course data. The potential of the proposed method has been demonstrated through the time-course analysis of the gene expression profiles of human umbilical vein endothelial cells (HUVECs) undergoing growth factor deprivation-induced apoptosis., Availability: Supplementary Information and the software (TRANS-MNET) are available at http://daweb.ism.ac.jp/~yoshidar/software/ssm/.

Summary: One of the significant challenges in gene expression analysis is to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail owing to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis. The ArrayCluster is a freely available software to perform the mixed factors analysis. It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on.

Motivation: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data., Results: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.

Motivation: Estimating the network of regulative interactions between genes from gene expression measurements is a major challenge. Recently, we have shown that for gene networks of up to around 35 genes, optimal network models can be computed. However, even optimal gene network models will in general contain false edges, since the expression data will not unambiguously point to a single network., Results: In order to overcome this problem, we present a computational method to enumerate the most likely m networks and to extract a widely common subgraph (denoted as gene network motif) from these. We apply the method to bacterial gene expression data and extensively compare estimation results to knowledge. Our results reveal that gene network motifs are in significantly better agreement to biological knowledge than optimal network models. We also confirm this observation in a series of estimations using synthetic microarray data and compare estimations by our method with previous estimations for yeast. Furthermore, we use our method to estimate similarities and differences of the gene networks that regulate tryptophan metabolism in two related species and thereby demonstrate the analysis of gene network evolution., Availability: Commercial license negotiable with Gene Networks Inc. (cherkis@gene-networks.com), Contact: sascha-ott@gmx.net

Motivation: Sigma factors regulate the expression of genes in Bacillus subtilis at the transcriptional level. We assess the accuracy of a fold-change analysis, Bayesian networks, dynamic models and supervised learning based on coregulation in predicting gene regulation by sigma factors from gene expression data. To improve the prediction accuracy, we combine sequence information with expression data by adding their log-likelihood scores and by using a logistic regression model. We use the resulting score function to discover currently unknown gene regulations by sigma factors., Results: The coregulation-based supervised learning method gave the most accurate prediction of sigma factors from expression data. We found that the logistic regression model effectively combines expression data with sequence information. In a genome-wide search, highly significant logistic regression scores were found for several genes whose transcriptional regulation is currently unknown. We provide the corresponding RNA polymerase binding sites to enable a straightforward experimental verification of these predictions.

Summary: We have implemented k-means clustering, hierarchical clustering and self-organizing maps in a single multipurpose open-source library of C routines, callable from other C and C++ programs. Using this library, we have created an improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix. In addition, we generated a Python and a Perl interface to the C Clustering Library, thereby combining the flexibility of a scripting language with the speed of C., Availability: The C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License, while the Perl module Algorithm::Cluster was released under the Artistic License. The GUI code Cluster 3.0 for Windows, Macintosh and Linux/Unix, as well as the corresponding command-line program, were released under the same license as the original Cluster code. The complete source code is available at http://bonsai.ims.u-tokyo.ac.jp/mdehoon/software/cluster. Alternatively, Algorithm::Cluster can be downloaded from CPAN, while Pycluster is also available as part of the Biopython distribution.

We present a statistical method for estimating gene networks and detecting promoter elements simultaneously. When estimating a network from gene expression data alone, a common problem is that the number of microarrays is limited compared to the number of variables in the network model, making accurate estimation a difficult task. Our method overcomes this problem by integrating the microarray gene expression data and the DNA sequence information into a Bayesian network model. The basic idea of our method is that, if a parent gene is a transcription factor, its children may share a consensus motif in their promoter regions of the DNA sequences. Our method detects consensus motifs based on the structure of the estimated network, then re-estimates the network using the result of the motif detection. We continue this iteration until the network becomes stable. To show the effectiveness of our method, we conducted Monte Carlo simulations and applied our method to Saccharomyces cerevisiae data as a real application.

Motivation: Recently, the temporal response of genes to changes in their environment has been investigated using cDNA microarray technology by measuring the gene expression levels at a small number of time points. Conventional techniques for time series analysis are not suitable for such a short series of time-ordered data. The analysis of gene expression data has therefore usually been limited to a fold-change analysis, instead of a systematic statistical approach., Methods: We use the maximum likelihood method together with Akaike's Information Criterion to fit linear splines to a small set of time-ordered gene expression data in order to infer statistically meaningful information from the measurements. The significance of measured gene expression data is assessed using Student's t-test., Results: Previous gene expression measurements of the cyanobacterium Synechocystis sp. PCC6803 were reanalyzed using linear splines. The temporal response was identified of many genes that had been missed by a fold-change analysis. Based on our statistical analysis, we found that about four gene expression measurements or more are needed at each time point.

Motivation: The prediction of localization sites of various proteins is an important and challenging problem in the field of molecular biology. TargetP, by Emanuelsson et al. (J. Mol. Biol., 300, 1005-1016, 2000) is a neural network based system which is currently the best predictor in the literature for N-terminal sorting signals. One drawback of neural networks, however, is that it is generally difficult to understand and interpret how and why they make such predictions. In this paper, we aim to generate simple and interpretable rules as predictors, and still achieve a practical prediction accuracy. We adopt an approach which consists of an extensive search for simple rules and various attributes which is partially guided by human intuition., Results: We have succeeded in finding rules whose prediction accuracies come close to that of TargetP, while still retaining a very simple and interpretable form. We also discuss and interpret the discovered rules.

ProDDO represents a 'pre-screened' database that denotes disorder (or possible disorder) in proteins from the PDB.

Motivation: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network., Results: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.