MOTIVATION: Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them. RESULTS: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual cell types and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families. AVAILABILITY AND IMPLEMENTATION: https://github.com/bio-ontology-research-group/DeepSVP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Several antimalarial drugs exist, but differences between life cycle stages among malaria species pose challenges for developing more effective therapies.To understand the diversity among stages and species, we reconstructed genome-scale metabolic models (GeMMs) of metabolism for five life cycle stages and five species of Plasmodium spanning the blood, transmission, and mosquito stages.The stage-specific models of Plasmodium falciparum uncovered stage-dependent changes in central carbon metabolism and predicted potential targets that could affect several life cycle stages.The species-specific models further highlight differences between experimental animal models and the humaninfecting species.Comparisons between human-and rodent-infecting species revealed differences in thiamine (vitamin B1), choline, and pantothenate (vitamin B5) metabolism.Thus, we show that genome-scale analysis of multiple stages and species of Plasmodium can prioritize potential drug targets that could be both anti-malarials and transmission blocking agents, in addition to guiding translation from non-human experimental disease models. Author summaryMalaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite.Plasmodial infections are difficult to treat for a myriad of reasons, but the ability of the organism to remain latent in hosts and the complex life cycles greatly contributed to the difficulty in treat malaria.Genome-scale metabolic models (GeMMs) enable hierarchical integration of disparate data types into a framework

BACKGROUND: Immunoglobulin (IG or antibody) and the T-cell receptor (TR) are pivotal proteins in the immune system of higher organisms. In cancer immunotherapy, the immune responses mediated by tumor-epitope-binding IG or TR play important roles in anticancer effects. Although there are public databases specific for immunological genes, their contents have not been associated with clinical studies. Therefore, we developed an integrated database of IG/TR data reported in cancer studies (the Cancer-related Immunological Gene Database [CIG-DB]). DESCRIPTION: This database is designed as a platform to explore public human and murine IG/TR genes sequenced in cancer studies. A total of 38,308 annotation entries for IG/TR proteins were collected from GenBank/DDBJ/EMBL and the Protein Data Bank, and 2,740 non-redundant corresponding MEDLINE references were appended. Next, we filtered the MEDLINE texts by MeSH terms, titles, and abstracts containing keywords related to cancer. After we performed a manual check, we classified the protein entries into two groups: 611 on cancer therapy (Group I) and 1,470 on hematological tumors (Group II). Thus, a total of 2,081 cancer-related IG and TR entries were tabularized. To effectively classify future entries, we developed a computational method based on text mining and canonical discriminant analysis by parsing MeSH/title/abstract words. We performed a leave-one-out cross validation for the method, which showed high accuracy rates: 94.6% for IG references and 94.7% for TR references. We also collected 920 epitope sequences bound with IG/TR. The CIG-DB is equipped with search engines for amino acid sequences and MEDLINE references, sequence analysis tools, and a 3D viewer. This database is accessible without charge or registration at http://www.scchr-cigdb.jp/, and the search results are freely downloadable. CONCLUSIONS: The CIG-DB serves as a bridge between immunological gene data and cancer studies, presenting annotation on IG, TR, and their epitopes. This database contains IG and TR data classified into two cancer-related groups and is able to automatically classify accumulating entries into these groups. The entries in Group I are particularly crucial for cancer immunotherapy, providing supportive information for genetic engineering of novel antibody medicines, tumor-specific TR, and peptide vaccines.

SUMMARY: G-compass is designed for efficient comparative genome analysis between human and other vertebrate genomes. The current version of G-compass allows us to browse two corresponding genomic regions between human and another species in parallel. One-to-one evolutionarily conserved regions (i.e. orthologous regions) between species are highlighted along the genomes. Information such as locations of duplicated regions, copy number variations and mammalian ultra-conserved elements is also provided. These features of G-compass enable us to easily determine patterns of genomic rearrangements and changes in gene orders through evolutionary time. Since G-compass is a satellite database of H-InvDB, which is a comprehensive annotation resource for human genes and transcripts, users can easily refer to manually curated functional annotations and other abundant biological information for each human transcript. G-compass is expected to be a valuable tool for comparing human and model organisms and promoting the exchange of functional information. AVAILABILITY: G-compass is freely available at http://www.h-invitational.jp/g-compass/. CONTACT: [email protected]

BACKGROUND: Parsimony methods are widely used in molecular evolution to estimate the most plausible phylogeny for a set of characters. Sankoff parsimony determines the minimum number of changes required in a given phylogeny when a cost is associated to transitions between character states. Although optimizations exist to reduce the computations in the number of taxa, the original algorithm takes time O(n(2)) in the number of states, making it impractical for large values of n. RESULTS: In this study we introduce an optimization of Sankoff parsimony for the reconstruction of ancestral states when ultrametric or additive cost matrices are used. We analyzed its performance for randomly generated matrices, Jukes-Cantor and Kimura's two-parameter models of DNA evolution, and in the reconstruction of elongation factor-1alpha and ancestral metabolic states of a group of eukaryotes, showing that in all cases the execution time is significantly less than with the original implementation. CONCLUSION: The algorithms here presented provide a fast computation of Sankoff parsimony for a given phylogeny. Problems where the number of states is large, such as reconstruction of ancestral metabolism, are particularly adequate for this optimization. Since we are reducing the computations required to calculate the parsimony cost of a single tree, our method can be combined with optimizations in the number of taxa that aim at finding the most parsimonious tree.

UNLABELLED: ADAPTSITE is a program package for detecting natural selection at single amino acid sites, using a multiple alignment of protein-coding sequences for a given phylogenetic tree. The program infers ancestral codons at all interior nodes, and computes the total numbers of synonymous (c(S)) and nonsynonymous (c(N)) substitutions as well as the average numbers of synonymous (s(S)) and nonsynonymous (s(N)) sites for each codon site. The probabilities of occurrence of synonymous and nonsynonymous substitutions are approximated by s(S) / (s(S) + s(N)) and s(N) / (s(S) + s(N)), respectively. The null hypothesis of selective neutrality is tested for each codon site, assuming a binomial distribution for the probability of obtaining c(S) and c(N). AVAILABILITY: ADAPTSITE is available free of charge at the World-Wide Web sites http://mep.bio.psu.edu/adaptivevol.html and http://www.cib.nig.ac.jp/dda/yossuzuk/welcome.html. The package includes the source code written in C, binary files for UNIX operating systems, manual, and example files.

Our comparisons of complete genome sequences revealed that the genome structures have been extensively shuffled among eubacteria, particularly when the orders of orthologous genes were examined. Moreover, archaebacterial and eukaryotic genome structures were found to be unstable, too, as were the cases of eubacteria. We then turned our attention to operon structures, which were expected to be well conserved during evolution because of their regulatory importance. Surprisingly enough, however, we found that even within operons, gene orders have not been conserved, with exception to only a few cases such as ribosomal operons. When we reconstructed the ancestral genome structure of eubacteria and archaebacteria, and examined the relative instability of the genome structures among eubacteria, we found that there were differences in the degree of the genome instability among the examined species. The genome instability appears to be correlated with the number of insertion sequences. Interestingly enough, the intensity of the intrastrand bias of nucleotide composition (G-C skew) was found to be affected by the genome instability, implying that accumulation of strand-specific mutations depends heavily upon the stability of a genome. These findings imply that the gene orders have not been essential for survival of microbes in long-term evolution, and that the evolutionary instability of the genome structures is an intrinsic nature common to eubacteria, archaebacteria and eukaryotes. For eukaryotic genomes, we found that a lot of gene fusion events might have happened in the early evolution of eukaryotes so as to compensate for the loss of bacterial operon structures. The evolutionary instability of the genome structures can be one of the most important factors in understanding the evolutionary processes of the genome evolution.

MOTIVATION: The DNA Data Bank of Japan (DDBJ) has developed a new DNA database system with a new schema design to accommodate rapid change and growth of requirements on the system. RESULTS: The new schema and systems were created using an object-oriented design approach. The design was accomplished in accordance with ANSI/SPARC three-level schema architecture. First, the conceptual schema was designed using a functional model named AIS (associative information structure) and was visualized in extended diagram format. The model is a natural extension of an ER (entity relationship) model and describes real-world objects in binary associations between entities with the concept of order. Second, the schema was mapped on a relational database as a physical schema. All details are concentrated in this schema and the layer lying above enjoys physical independence. Finally, as another layer, external modeling was introduced for the database applications interface. It provides set-at-a-time basis operations and was implemented as a C++ object-oriented library. On this common framework of a new schema, a new annotator's workbench named Yamato II and a World Wide Web (WWW) submission system named Sakura have been successfully developed to improve drastically daily transactions in the DDBJ. AVAILABILITY: Sakura is available at the following address: http://sakura.ddbj.nig.ac.jp. CONTACT: [email protected]