HitPredict is a consolidated resource of experimentally identified, physical protein–protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein–protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein–protein interactions from several species for the study of gene groups.Database URL: http://hintdb.hgc.jp/htp

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Transformation and clonal proliferation of T-cells infected with human T-cell leukemia virus type-I (HTLV-1) cause adult T-cell leukemia. We took advantage of next-generation sequencing technology to develop and internally validate a new methodology for isolating integration sites and estimating the number of cells in each HTLV-1-infected clone (clone size). Initial analysis was performed with DNA samples from infected individuals. We then used appropriate controls with known integration sites and clonality status to confirm the accuracy of our system, which indeed had the least errors among the currently available techniques. Results suggest potential clinical and biological applications of the new method.

Post-translational modification refers to the biological mechanism involved in the enzymatic modification of proteins after being translated in the ribosome. This mechanism comprises a wide range of structural modifications, which bring dramatic variations to the biological function of proteins. One of the recently discovered modifications is succinylation. Although succinylation can be detected through mass spectrometry, its current experimental detection turns out to be a timely process unable to meet the exponential growth of sequenced proteins. Therefore, the implementation of fast and accurate computational methods has emerged as a feasible solution. This paper proposes a novel classification approach, which effectively incorporates the secondary structure and evolutionary information of proteins through profile bigrams for succinylation prediction. The proposed predictor, abbreviated as SSEvol-Suc, made use of the above features for training an AdaBoost classifier and consequently predicting succinylated lysine residues. When SSEvol-Suc was compared with four benchmark predictors, it outperformed them in metrics such as sensitivity (0.909), accuracy (0.875) and Matthews correlation coefficient (0.75).

BackgroundPost-translational modification is considered an important biological mechanism with critical impact on the diversification of the proteome. Although a long list of such modifications has been studied, succinylation of lysine residues has recently attracted the interest of the scientific community. The experimental detection of succinylation sites is an expensive process, which consumes a lot of time and resources. Therefore, computational predictors of this covalent modification have emerged as a last resort to tackling lysine succinylation.ResultsIn this paper, we propose a novel computational predictor called ‘Success’, which efficiently uses the structural and evolutionary information of amino acids for predicting succinylation sites. To do this, each lysine was described as a vector that combined the above information of surrounding amino acids. We then designed a support vector machine with a radial basis function kernel for discriminating between succinylated and non-succinylated residues. We finally compared the Success predictor with three state-of-the-art predictors in the literature. As a result, our proposed predictor showed a significant improvement over the compared predictors in statistical metrics, such as sensitivity (0.866), accuracy (0.838) and Matthews correlation coefficient (0.677) on a benchmark dataset.ConclusionsThe proposed predictor effectively uses the structural and evolutionary information of the amino acids surrounding a lysine. The bigram feature extraction approach, while retaining the same number of features, facilitates a better description of lysines. A support vector machine with a radial basis function kernel was used to discriminate between modified and unmodified lysines. The aforementioned aspects make the Success predictor outperform three state-of-the-art predictors in succinylation detection.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4336-8) contains supplementary material, which is available to authorized users.

BackgroundBiological data comprises various topologies or a mixture of forms, which makes its analysis extremely complicated. With this data increasing in a daily basis, the design and development of efficient and accurate statistical methods has become absolutely necessary. Specific analyses, such as those related to genome-wide association studies and multi-omics information, are often aimed at clustering sub-conditions of cancers and other diseases. Hierarchical clustering methods, which can be categorized into agglomerative and divisive, have been widely used in such situations. However, unlike agglomerative methods divisive clustering approaches have consistently proved to be computationally expensive.ResultsThe proposed clustering algorithm (DRAGON) was verified on mutation and microarray data, and was gauged against standard clustering methods in the literature. Its validation included synthetic and significant biological data. When validated on mixed-lineage leukemia data, DRAGON achieved the highest clustering accuracy with data of four different dimensions. Consequently, DRAGON outperformed previous methods with 3-,4- and 5-dimensional acute leukemia data. When tested on mutation data, DRAGON achieved the best performance with 2-dimensional information.ConclusionsThis work proposes a computationally efficient divisive hierarchical clustering method, which can compete equally with agglomerative approaches. The proposed method turned out to correctly cluster data with distinct topologies. A MATLAB implementation can be extraced from http://www.riken.jp/en/research/labs/ims/med_sci_math/ or http://www.alok-ai-lab.com Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1965-5) contains supplementary material, which is available to authorized users.

Adult T-cell leukemia/lymphoma (ATLL) is a peripheral T-cell neoplasm caused by the transformation of HTLV-1-infected T cells. ATLL, especially its aggressive form, is known for its poor prognosis, even with intensive chemotherapy. ATLL cells are considered to be monoclonal; however, multiclonal proliferation or emergence of a new clone over time has been reported based on Southern blot analysis, although direct molecular evidence remains elusive. Furthermore, it is thought that clonal change may be a cause of early drug resistance in ATLL. To directly analyze potential clonal changes in ATLL during its clinical course, we used inverse PCR to detect integration sites in combination with a newly developed method using next-generation sequencing, and compared ATLL cell clonality at different time points. The results of inverse PCR indicated that the major clone was altered in three of 19 patients. Together with results from five patients, using this new method, we found direct evidence of clonal change occurring during the clinical course or in response to chemotherapy in ATLL. These results also highlight the importance of clonality analysis for understanding the mechanisms of ATLL development and drug resistance.