ATTED-II ( http://atted.jp ) is a coexpression database for plant species with parallel views of multiple coexpression data sets and network analysis tools. The user can efficiently find functional gene relationships and design experiments to identify gene functions by reverse genetics and general molecular biology techniques. Here, we report updates to ATTED-II (version 8.0), including new and updated coexpression data and analysis tools. ATTED-II now includes eight microarray- and six RNA sequencing-based coexpression data sets for seven dicot species (Arabidopsis, field mustard, soybean, barrel medick, poplar, tomato and grape) and two monocot species (rice and maize). Stand-alone coexpression analyses tend to have low reliability. Therefore, examining evolutionarily conserved coexpression is a more effective approach from the viewpoints of reliability and evolutionary importance. In contrast, the reliability of species-specific coexpression data remains poor. Our assessment scores for individual coexpression data sets indicated that the quality of the new coexpression data sets in ATTED-II is higher than for any previous coexpression data set. In addition, five species (Arabidopsis, soybean, tomato, rice and maize) in ATTED-II are now supported by both microarray- and RNA sequencing-based coexpression data, which has increased the reliability. Consequently, ATTED-II can now provide lineage-specific coexpression information. As an example of the use of ATTED-II to explore lineage-specific coexpression, we demonstrate monocot- and dicot-specific coexpression of cell wall genes. With the expanded coexpression data for multilevel evaluation, ATTED-II provides new opportunities to investigate lineage-specific evolution in plants.

The COXPRESdb (http://coxpresdb.jp) provides gene coexpression relationships for animal species. Here, we report the updates of the database, mainly focusing on the following two points. For the first point, we added RNAseq-based gene coexpression data for three species (human, mouse and fly), and largely increased the number of microarray experiments to nine species. The increase of the number of expression data with multiple platforms could enhance the reliability of coexpression data. For the second point, we refined the data assessment procedures, for each coexpressed gene list and for the total performance of a platform. The assessment of coexpressed gene list now uses more reasonable P-values derived from platform-specific null distribution. These developments greatly reduced pseudo-predictions for directly associated genes, thus expanding the reliability of coexpression data to design new experiments and to discuss experimental results.

ATTED-II (http://atted.jp) is a database of coexpressed genes that was originally developed to identify functionally related genes in Arabidopsis and rice. Herein, we describe an updated version of ATTED-II, which expands this resource to include additional agriculturally important plants. To improve the quality of the coexpression data for Arabidopsis and rice, we included more gene expression data from microarray and RNA sequencing studies. The RNA sequencing-based coexpression data now cover 94% of the Arabidopsis protein-encoding genes, representing a substantial increase from previously available microarray-based coexpression data (76% coverage). We also generated coexpression data for four dicots (soybean, poplar, grape and alfalfa) and one monocot (maize). As both the quantity and quality of expression data for the non-model species are generally poorer than for the model species, we verified coexpression data associated with these new species using multiple methods. First, the overall performance of the coexpression data was evaluated using gene ontology annotations and the coincidence of a genomic feature. Secondly, the reliability of each guide gene was determined by comparing coexpressed gene lists between platforms. With the expanded and newly evaluated coexpression data, ATTED-II represents an important resource for identifying functionally related genes in agriculturally important plants.

Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens’ pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases.

Coexpressed gene databases are valuable resources for identifying new gene functions or functional modules in metabolic pathways and signaling pathways. Although coexpressed gene databases are a fundamental platform in the field of plant biology, their use in animal studies is relatively limited. The COXPRESdb (http://coxpresdb.jp) provides coexpression relationships for multiple animal species, as comparisons of coexpressed gene lists can enhance the reliability of gene coexpression determinations. Here, we report the updates of the database, mainly focusing on the following two points. First, we updated our coexpression data by including recent microarray data for the previous seven species (human, mouse, rat, chicken, fly, zebrafish and nematode) and adding four new species (monkey, dog, budding yeast and fission yeast), along with a new human microarray platform. A reliability scoring function was also implemented, based on coexpression conservation to filter out coexpression with low reliability. Second, the network drawing function was updated, to implement automatic cluster analyses with enrichment analyses in Gene Ontology and in cis elements, along with interactive network analyses with Cytoscape Web. With these updates, COXPRESdb will become a more powerful tool for analyses of functional and regulatory networks of genes in a variety of animal species.

The complete human genome sequence is used as a reference for next-generation sequencing analyses. However, some ethnic ancestries are under-represented in the reference genome (e.g., GRCh37) due to its bias toward European and African ancestries. Here, we perform de novo assembly of three Japanese male genomes using > 100× Pacific Biosciences long reads and Bionano Genomics optical maps per sample. We integrate the genomes using the major allele for consensus and anchor the scaffolds using genetic and radiation hybrid maps to reconstruct each chromosome. The resulting genome sequence, JG1, is contiguous, accurate, and carries the Japanese major allele at most loci. We adopt JG1 as the reference for confirmatory exome re-analyses of seven rare-disease Japanese families and find that re-analysis using JG1 reduces total candidate variant calls versus GRCh37 while retaining disease-causing variants. These results suggest that integrating multiple genomes from a single population can aid genome analyses of that population.