This paper presents a method for analyzing leukocyte migration in live animals by image processing. For detecting leukocyte movements, we adopt a tracking-by-matching approach that compares between consecutive time-frames in a time-lapse motion image observed with intravital microscopy. Since leukocytes migrate at a large variety of speeds, we developed a tool based on a matching algorithm, called DeepMatching, which uses a deep, multi-layer, convolutional architecture designed for matching images and can therefore efficiently determine dense correspondences in the presence of significant changes between images. DeepMatchihg was originally designed in computer vision research, and we modified the code to apply for cell tracking. Also we extended the matching method to handle not only 2D movements but also depth-directional ones in 3D motion images. By linking the leukocyte movements into their trajectories, we have detected leukocyte gathering points that are estimated as high concentration points of chemo-attractants. It is suggested that leukocytes may act as living micro-probes to the concentration of allergens in contact dermatitis by our method.

Single-cell RNA-seq analysis has been widely used for revealing heterogeneity of cells in various tissues and conditions. The goal of our research is to elucidate a variety of responses of individual leukocytes in inflammatory stimulation by analyzing their transcriptomic heterogeneity. In this research, we present an analysis of single-cell transcriptome data of mouse leukocytes in several inflammatory stimulations.

Recent epigenetics research has demonstrated that chromatin conformation plays an important role in various aspects of gene regulation. Chromosome Conformation Capture (3C) technology makes it possible to analyze the spatial organization of chromatin in a cell. Several algorithms for three-dimensional reconstruction of chromatin structure from 3C experimental data have been proposed. Compared to other algorithms, ShRec3D, one of the most advanced algorithms, can reconstruct a chromatin model in the shortest time for high-resolution whole-genome experimental data. However, ShRec3D employs a graph shortest path algorithm, which introduces errors in the resulting model. We propose an improved algorithm that optimizes shortest path distances using a genetic algorithm approach. The proposed algorithm and ShRec3D were compared using in silico 3C experimental data. Compared to ShRec3D, the proposed algorithm demonstrated significant improvement relative to the similarity between the algorithm's output and the original model with a reasonable increase to calculation time.

Fusion genes make for one of the mechanisms of tumorigenesis. The identification of fusion genes by RNA-Seq has attracted attention. Various methods for detecting fusion genes have been proposed, but their accuracy is not sufficient. One of the causes of this problem is the relatively short reading length in RNA-Seq data. Therefore, before mapping RNA-Seq data, we proposed a method, which is based on shifted short-read clustering (SSC), to identify shifted reads of the same origin and extend them as representative sequences. As a result, we assumed that the percentage of uniquely mapped reads would be increased, and the detection rates of the fusion genes could be improved. To verify these hypotheses, we applied the SSC method to RNA-Seq data from three cell lines (BT-474, MCF-7, and SKBR-3). When only one base was shifted, the average read lengths of BT-474, MCF-7, and SKBR-3 were extended from 201 to 223 bases (111%), 201 to 214 bases (106%), and 201 to 213 bases (106%), respectively. Furthermore, the effectiveness of the SSC method is demonstrated by comparing the performances of a fusion gene detection tool's results, STAR-Fusion, with and without the SSC method of the reads. The percentage of uniquely mapped reads of BT-474, MCF-7, and SKBR-3 were improved from 88% to 93%, 88% to 94%, and 92% to 95%, respectively. Finally, the fusion gene detection rates of BT-474, MCF-7, and SKBR-3 were increased from 48% to 57%, 49% to 53%, and 50% to 53% respectively. The SSC method is considered to be an effective method not only for improving the percentage of uniquely mapped reads but also for fusion gene detection.

BACKGROUND: Comprehensively understanding the dynamics of biological systems is among the biggest current challenges in biology and medicine. To acquire this understanding, researchers have measured the time-series expression profiles of cell lines of various organisms. Biological technologies have also drastically improved, providing a huge amount of information with support from bioinformatics and systems biology. However, the transitions between the activation and inactivation of gene regulations, at the temporal resolution of single time points, are difficult to extract from time-course gene expression profiles. RESULTS: Our proposed method reports the activation period of each gene regulation from gene expression profiles and a gene regulatory network. The correctness and effectiveness of the method were validated by analyzing the diauxic shift from glucose to lactose in Escherichia coli. The method completely detected the three periods of the shift; 1) consumption of glucose as nutrient source, 2) the period of seeking another nutrient source and 3) consumption of lactose as nutrient source. We then applied the method to mouse adipocyte differentiation data. Cell differentiation into adipocytes is known to involve two waves of the gene regulation cascade, and sub-waves are predicted. From the gene expression profiles of the cell differentiation process from ES to adipose cells (62 time points), our method acquired four periods; three periods covering the two known waves of the cascade, and a final period of gene regulations when the differentiation to adipocytes was completed. CONCLUSIONS: Our proposed method identifies the transitions of gene regulations from time-series gene expression profiles. Dynamic analyses are essential for deep understanding of biological systems and for identifying the causes of the onset of diseases such as diabetes and osteoporosis. The proposed method can greatly contribute to the progress of biology and medicine.

Gene expression profiling has been increasingly used in clinical practice. Integration of expression data across multiple experiments provides better insight into the heterogeneity of the biology being examined. A problem of the data integration, an experimental batch from platform or laboratory sources, remains a barrier to systematically analyzing data across different datasets. Several methods (such as, ComBat) have been proposed to remove batch effects. However, these methods often make assumptions about ideal distribution of the underlying data. Difficulties might be expected when comparing datasets that have fundamentally different (dataset-dependent) distributions. For example, clinical datasets are often collected from patient samples with various disease stages or conditions. Therefore, we have compared several mathematical transformations across many datasets, including the nonparametric Z scaling transformation method (NPZ) we have proposed for clinical use. We selected 2,813 patients with available information on estrogen receptor (ER) status or human epidermal growth factor receptor 2 (HER2) status from 24 Affymetrix HG-U133 (GPL96) or Affymetrix HG-U133 plus 2.0 (GPL570) datasets in the Gene Expression Omnibus database. The microarray expression data were processed with one of the four following methods: Raw (background correction and log transformation only), Microarray Suite 5.0 (MAS5), frozen robust multiarray analysis (fRMA), and radius minimax (RMX). The normalized data were sequentially transformed by using one of the following five methods: untransformed (without transformation), single-array-based transformations (RANK, Z, NPZ, or YuGene). Finally, we compared the ER and HER2 statuses assessed by immunohistochemical (IHC) staining with mRNA expression. We found that single-array-based transformation in addition to normalization improved the concordance rates of the IHC staining. We demonstrated the influence of transformation by using breast cancer samples and showed that adding single-array-based transformations to microarray expression data resulted in stronger correlations with IHC staining.

A better understanding of in vivo bio images is expected to contribute to the discovery of new drugs and mechanisms of disease. To improve the contributions of in vivo bioimaging, the extraction of a particular region is required in order to detect a particular cell's motion because manual image processing of a massive number of images is unrealistic. One of the issues for automatic image-segmentation is that conditions of image-taking are variable. Thus, some manual input and/or manual tuning of some parameters is required to adjust each image. To reduce manual operation for image processing of bone marrow cavity segmentation, we focused on the texture pattern of bone marrow cavity. In this paper, we propose a bone marrow cavity segmentation method using support vector machine and wavelet-based texture feature. The proposed method does not require manual inputs to obtain distribution of intensity before processing, because the texture patterns of bone marrow cavity regions are integrated into the system in advance. Moreover, it is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable because it does not require temporal variation or initial frame for the segmentation. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The bone marrow cavity segmentation, using graph-cuts with our texture pattern classification, performs well without manual inputs by a user.

Effective integration of heterogeneous data sources has been studied as the most pressing challenge in various fields; such as, high energy physics, astronomy, and life sciences. In this talk, we present a data integration system by using Globus Toolkit with OGSA-DAI. For associating related data among many databases, we have introduced metadata based on their domain ontologies. Using the system one can make a database access flow for describing a set of queries as a workflow, and can query across the databases without aware of their locations and schemas

MOTIVATION: Clustering of protein sequences is widely used for the functional characterization of proteins. However, it is still not easy to cluster distantly-related proteins, which have only regional similarity among their sequences. It is therefore necessary to develop an algorithm for clustering such distantly-related proteins. RESULTS: We have developed a time and space efficient clustering algorithm. It uses a graph representation where its vertices and edges denote proteins and their sequence similarities above a certain cutoff score, respectively. It repeatedly partitions the graph by removing edges that have small weights, which correspond to low sequence similarities. To find the appropriate partitions, we introduce a score combining the normalized cut and a locally minimal cut capacities. Our method is applied to the entire 40,703 human proteins in SWISS-PROT and TrEMBL. The resulting clusters shows a 76% recall (20,529 proteins) of the 26,917 classified by InterPro. It also finds relationships not found by other clustering methods. AVAILABILITY: The complete result of our algorithm for all the human proteins in SWISS-PROT and TrEMBL, and other supplementary information are available at http://motif.ics.es.osaka-u.ac.jp/Ncut-KL/

This paper presents a clustering method for the comparative analysis of genomes and metabolic pathways. Our method consists of several steps: (1) extract linear sequences of metabolic reactions (non-branching pathways) from a network of metabolic pathways, (2) perform pairwise alignments of every pair of non-branching pathways, and (3) explore a set of genes for each aligned non-branching pathways such that those genes encode enzymes of the pathway and the genes are closely located on a genome (such as operons or gene clusters). To measure the similarity between pathways, we formalized a scoring system by using the functional hierarchy of the EC numbers of enzymes. By applying our method to the metabolic pathways in Escherichia coli, we have obtained several pairs of pathways with similar reactions having enzymes encoded by neighbor genes on the E. coli genome. The resulting pathway pairs were mainly found in metabolisms between similar amino acids (tryptophan and histidine) and between similar sugars (fucose and rhamnose).

Abstract Motivation: Protein structure classification has been recognized as one of the most important research issues in protein structure analysis. A substantial number of methods for the classification have been proposed, and several databases have been constructed using these methods. Since some proteins with very similar sequences may exhibit structural diversities, we have proposed PDB-REPRDB: a database of representative protein chains from the Protein Data Bank (PDB), which strategy of selection is based not only on sequence similarity but also on structural similarity. Forty-eight representative sets whose similarity criteria were predetermined were made available over the World Wide Web (WWW). However, the sets were insufficient in number to satisfy users researching protein structures by various methods. Result: We have improved the system for PDB-REPRDB so that the user may obtain a quick selection of representative chains from PDB. The selection of representative chains can be dynamically configured according to the user’s requirement. The WWW interface provides a large degree of freedom in setting parameters, such as cut-off scores of sequence and structural similarity. This paper describes the method we use to classify chains and select the representatives in the system. We also describe the interface used to set the parameters. Availability: The system for PDB-REPRDB is available at the PAPIA WWW server (http://www.rwcp.or.jp/papia/). Contact: [email protected] * To whom correspondence should be addressed. 3 Present address: Information Processing Systems Department, NKK Corporation, 1-1-2 Marunouchi, Chiyoda-ku, Tokyo 100-8202, Japan

We developed a parallel processing system for analyzing phylogenetic relationships of microorganisms based on a maximum likelihood method. Methods for inferring relationships from molecular sequence data are especially valuable, given the enormous increases in DNA sequence data. The maximum likelihood method uses concrete models of the evolutionary process and are well-motivated statistically, but the computational cost has hindered the use of this method for inferring trees with more than about 20 organisms. We parallelized the maximum likelihood method by utilizing two types of parallelism, parallel evaluation of phylogenetic trees and parallel computation of likelihood values. By combining these two parallelisms, we obtained significant speedup on the Intel Touchstone DELTA.

We have developed a new tool, called fastDNAml, for constructing phylogenetic trees from DNA sequences. The program can be run on a wide variety of computers ranging from Unix workstations to massively parallel systems, and is available from the Ribosomal Database Project (RDP) by anonymous FTP. Our program uses a maximum likelihood approach and is based on version 3.3 of Felsenstein's dnaml program. Several enhancements, including algorithmic changes, significantly improve performance and reduce memory usage, making it feasible to construct even very large trees. Trees containing 40-100 taxa have been easily generated, and phylogenetic estimates are possible even when hundreds of sequences exist. We are currently using the tool to construct a phylogenetic tree based on 473 small subunit rRNA sequences from prokaryotes.