Cancer affects millions of people worldwide. With the advent of novel DNA sequencing technologies,whole genome sequencing (WGS) is becoming an integral part of cancer diagnostics that can potentially enable tailored treatments of individual patients. To alleviate these problems,a user-friendly, portable and extendable SV calling workflow, sv-callers developed, that includes four state-of-the-art tools to detect SVs in cancer genomes using on-premises HPC systems. The workflow's parallel execution environment enables to scale from a single computer to high-performance compute clusters with minimal effort. The workflow supports SV analysis in either germline or somatic mode, and requires a list of (paired) WGS samples including a reference genome as input. Users may change the workflow parameters and/or software versions using the YAML configuration files. We performed SV analyses on single and paired (tumor/normal) WGS samples, and report on the results obtained using different academic HPC systems.

Motivation: Despite successful applications of data clustering and visualization techniques in molecular sequence identification, current technologies still do not scale to large biological datasets. Results: We address this problem by a new multi-threaded tool, fMLC, primarily developed to cluster DNA sequences, that is supplemented with an interactive web-based visualization component, DiVE. fMLC enabled to compare, cluster and visualize 350K ITS fungal sequences at the species level. It took less than two hours to compare and cluster the dataset, which is twelve times faster than the time reported previously. Availability and implementation: https://github.com/FastMLC/fMLC (doi: 10.5281/zenodo.926820). Contact: [email protected] or [email protected].

BACKGROUND: (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. RESULTS: The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. CONCLUSION: QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.

Bioinformatics (2010) 26(19) 2482–2483 The authors of the above article would like it to be known that the principle of Multi-netclust is incorrectly illustrated in Figure 1. The connected clusters shown in B and C do not correspond with the aggregation rules/formulae indicated below the illustrations. Swapping the clusters with one another restores this correspondence. The authors apologize for this error.

UNLABELLED: Multi-netclust is a simple tool that allows users to extract connected clusters of data represented by different networks given in the form of matrices. The tool uses user-defined threshold values to combine the matrices, and uses a straightforward, memory-efficient graph algorithm to find clusters that are connected in all or in either of the networks. The tool is written in C/C++ and is available either as a form-based or as a command-line-based program running on Linux platforms. The algorithm is fast, processing a network of > 10(6) nodes and 10(8) edges takes only a few minutes on an ordinary computer. AVAILABILITY: http://www.bioinformatics.nl/netclust/.