Yuko Makita

DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information

DBTBS, first released in 1999, is a reference database on transcriptional regulation in Bacillus subtilis, summarizing the experimentally characterized transcription factors, their recognition sequences and the genes they regulate. Since the previous release, the original content was extended by the addition of the data contained in 569 new publications, the total of which now reaches 947. The number of B. subtilis promoters annotated in the database was more than doubled to 1475. In addition, 463 experimentally validated B. subtilis operons and their terminators have been included. Given the increase in the number of fully sequenced bacterial genomes, we decided to extend the usability of DBTBS in comparative regulatory genomics. We therefore created a new section on the conservation of the upstream regulatory sequences between homologous genes in 40 Gram-positive bacterial species, as well as on the presence of overrepresented hexameric motifs that may have regulatory functions. DBTBS can be accessed at: http://dbtbs.hgc.jp.

DBTBS: database of transcriptional regulation in Bacillus subtilis and its contribution to comparative genomics

DBTBS (http://dbtbs.hgc.jp) was originally released in 1999 as a reference database of published transcriptional regulation events in Bacillus subtilis, one of the best studied bacteria. It is essentially a compilation of transcription factors with their regulated genes as well as their recognition sequences, which were experimentally characterized and reported in the literature. Here we report its major update, which contains information on 114 transcription factors, including sigma factors, and 633 promoters of 525 genes. The number of references cited in the database has increased from 291 to 378. It also supports a function to find putative transcription factor binding sites within input sequences by using our collection of weight matrices and consensus patterns. Furthermore, though preliminarily, DBTBS now aims to contribute to comparative genomics by showing the presence or absence of potentially orthologous transcription factors and their corresponding cis-elements on the promoters of their potentially orthologously regulated genes in 50 eubacterial genomes.

Prediction of Transcriptional Terminators in Bacillus subtilis and Related Species

In prokaryotes, genes belonging to the same operon are transcribed in a single mRNA molecule. Transcription starts as the RNA polymerase binds to the promoter and continues until it reaches a transcriptional terminator. Some terminators rely on the presence of the Rho protein, whereas others function independently of Rho. Such Rho-independent terminators consist of an inverted repeat followed by a stretch of thymine residues, allowing us to predict their presence directly from the DNA sequence. Unlike in Escherichia coli, the Rho protein is dispensable in Bacillus subtilis, suggesting a limited role for Rho-dependent termination in this organism and possibly in other Firmicutes. We analyzed 463 experimentally known terminating sequences in B. subtilis and found a decision rule to distinguish Rho-independent transcriptional terminators from non-terminating sequences. The decision rule allowed us to find the boundaries of operons in B. subtilis with a sensitivity and specificity of about 94%. Using the same decision rule, we found an average sensitivity of 94% for 57 bacteria belonging to the Firmicutes phylum, and a considerably lower sensitivity for other bacteria. Our analysis shows that Rho-independent termination is dominant for Firmicutes in general, and that the properties of the transcriptional terminators are conserved. Terminator prediction can be used to reliably predict the operon structure in these organisms, even in the absence of experimentally known operons. Genome-wide predictions of Rho-independent terminators for the 57 Firmicutes are available in the Supporting Information section.

PosMed (Positional Medline): prioritizing genes with an artificial neural network comprising medical documents to accelerate positional cloning

PosMed (http://omicspace.riken.jp/) prioritizes candidate genes for positional cloning by employing our original database search engine GRASE, which uses an inferential process similar to an artificial neural network comprising documental neurons (or ‘documentrons’) that represent each document contained in databases such as MEDLINE and OMIM. Given a user-specified query, PosMed initially performs a full-text search of each documentron in the first-layer artificial neurons and then calculates the statistical significance of the connections between the hit documentrons and the second-layer artificial neurons representing each gene. When a chromosomal interval(s) is specified, PosMed explores the second-layer and third-layer artificial neurons representing genes within the chromosomal interval by evaluating the combined significance of the connections from the hit documentrons to the genes. PosMed is, therefore, a powerful tool that immediately ranks the candidate genes by connecting phenotypic keywords to the genes through connections representing not only gene–gene interactions but also other biological interactions (e.g. metabolite–gene, mutant mouse–gene, drug–gene, disease–gene and protein–protein interactions) and ortholog data. By utilizing orthologous connections, PosMed facilitates the ranking of human genes based on evidence found in other model species such as mouse. Currently, PosMed, an artificial superbrain that has learned a vast amount of biological knowledge ranging from genomes to phenomes (or ‘omic space’), supports the prioritization of positional candidate genes in humans, mouse, rat and Arabidopsis thaliana.

MOROKOSHI: Transcriptome Database in Sorghum bicolor

In transcriptome analysis, accurate annotation of each transcriptional unit and its expression profile is essential. A full-length cDNA (FL-cDNA) collection facilitates the refinement of transcriptional annotation, and accurate transcription start sites help to unravel transcriptional regulation. We constructed a normalized FL-cDNA library from eight growth stages of aerial tissues in Sorghum bicolor and isolated 37,607 clones. These clones were Sanger sequenced from the 5′ and/or 3′ ends and in total 38,981 high-quality expressed sequence tags (ESTs) were obtained. About one-third of the transcripts of known genes were captured as FL-cDNA clone resources. In addition to these, we also annotated 272 novel genes, 323 antisense transcripts and 1,672 candidate isoforms. These clones are available from the RIKEN Bioresource Center. After obtaining accurate annotation of transcriptional units, we performed expression profile analysis. We carried out spikelet-, seed- and stem-specific RNA sequencing (RNA-Seq) analysis and confirmed the expression of 70.6% of the newly identified genes. We also downloaded 23 sorghum RNA-Seq samples that are publicly available and these are shown on a genome browser together with our original FL-cDNA and RNA-Seq data. Using our original and publicly available data, we made an expression profile of each gene and identified the top 20 genes with the most similar expression. In addition, we visualized their relationships in gene co-expression networks. Users can access and compare various transcriptome data from S, bicolor at http://sorghum.riken.jp.

Melina II: a web tool for comparisons among several predictive algorithms to find potential motifs from promoter regions

We present the second version of Melina, a web-based tool for promoter analysis. Melina II shows potential DNA motifs in promoter regions with a combination of several available programs, Consensus, MEME, Gibbs sampler, MDscan and Weeder, as well as several parameter settings. It allows running a maximum of four programs simultaneously, and comparing their results with graphical representations. In addition, users can build a weight matrix from a predicted motif and apply it to upstream sequences of several typical genomes (human, mouse, S. cerevisiae, E. coli, B. subtilis or A. thaliana) or to public motif databases (JASPAR or DBTBS) in order to find similar motifs. Melina II is a client/server system developed by using Adobe (Macromedia) Flash and is accessible over the web at http://melina.hgc.jp.

The rubber tree genome shows expansion of gene family associated with rubber biosynthesis

Hevea brasiliensis Muell. Arg, a member of the family Euphorbiaceae, is the sole natural resource exploited for commercial production of high-quality natural rubber. The properties of natural rubber latex are almost irreplaceable by synthetic counterparts for many industrial applications. A paucity of knowledge on the molecular mechanisms of rubber biosynthesis in high yield traits still persists. Here we report the comprehensive genome-wide analysis of the widely planted H. brasiliensis clone, RRIM 600. The genome was assembled based on ~155-fold combined coverage with Illumina and PacBio sequence data and has a total length of 1.55 Gb with 72.5% comprising repetitive DNA sequences. A total of 84,440 high-confidence protein-coding genes were predicted. Comparative genomic analysis revealed strong synteny between H. brasiliensis and other Euphorbiaceae genomes. Our data suggest that H. brasiliensis’s capacity to produce high levels of latex can be attributed to the expansion of rubber biosynthesis-related genes in its genome and the high expression of these genes in latex. Using cap analysis gene expression data, we illustrate the tissue-specific transcription profiles of rubber biosynthesis-related genes, revealing alternative means of transcriptional regulation. Our study adds to the understanding of H. brasiliensis biology and provides valuable genomic resources for future agronomic-related improvement of the rubber tree.

The RIKEN integrated database of mammals

The RIKEN integrated database of mammals (http://scinets.org/db/mammal) is the official undertaking to integrate its mammalian databases produced from multiple large-scale programs that have been promoted by the institute. The database integrates not only RIKEN’s original databases, such as FANTOM, the ENU mutagenesis program, the RIKEN Cerebellar Development Transcriptome Database and the Bioresource Database, but also imported data from public databases, such as Ensembl, MGI and biomedical ontologies. Our integrated database has been implemented on the infrastructure of publication medium for databases, termed SciNetS/SciNeS, or the Scientists’ Networking System, where the data and metadata are structured as a semantic web and are downloadable in various standardized formats. The top-level ontology-based implementation of mammal-related data directly integrates the representative knowledge and individual data records in existing databases to ensure advanced cross-database searches and reduced unevenness of the data management operations. Through the development of this database, we propose a novel methodology for the development of standardized comprehensive management of heterogeneous data sets in multiple databases to improve the sustainability, accessibility, utility and publicity of the data of biomedical information.

PosMed-plus: an intelligent search engine that inferentially integrates cross-species information resources for molecular breeding of plants.

Molecular breeding of crops is an efficient way to upgrade plant functions useful to mankind. A key step is forward genetics or positional cloning to identify the genes that confer useful functions. In order to accelerate the whole research process, we have developed an integrated database system powered by an intelligent data-retrieval engine termed PosMed-plus (Positional Medline for plant upgrading science), allowing us to prioritize highly promising candidate genes in a given chromosomal interval(s) of Arabidopsis thaliana and rice, Oryza sativa. By inferentially integrating cross-species information resources including genomes, transcriptomes, proteomes, localizomes, phenomes and literature, the system compares a users query, such as phenotypic or functional keywords, with the literature associated with the relevant genes located within the interval. By utilizing orthologous and paralogous correspondences, PosMed-plus efficiently integrates cross-species information to facilitate the ranking of rice candidate genes based on evidence from other model species such as Arabidopsis. PosMed-plus is a plant science version of the PosMed system widely used by mammalian researchers, and provides both a powerful integrative search function and a rich integrative display of the integrated databases. PosMed-plus is the first cross-species integrated database that inferentially prioritizes candidate genes for forward genetics approaches in plant science, and will be expanded for wider use in plant upgrading in many species.

PosMed: ranking genes and bioresources based on Semantic Web Association Study

Positional MEDLINE (PosMed; http://biolod.org/PosMed) is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources connected directly or inferentially through a Semantic Web of biological databases such as MEDLINE, OMIM, pathways, co-expressions, molecular interactions and ontology terms. Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013.