Next-generation sequencing (NGS) is increasingly being adopted as the backbone of biomedical research. With the commercialization of various affordable desktop sequencers, NGS will be reached by increasing numbers of cellular and molecular biologists, necessitating community consensus on bioinformatics protocols to tackle the exponential increase in quantity of sequence data. The current resources for NGS informatics are extremely fragmented. Finding a centralized synthesis is difficult. A multitude of tools exist for NGS data analysis; however, none of these satisfies all possible uses and needs. This gap in functionality could be filled by integrating different methods in customized pipelines, an approach helped by the open-source nature of many NGS programmes. Drawing from community spirit and with the use of the Wikipedia framework, we have initiated a collaborative NGS resource: The NGS WikiBook. We have collected a sufficient amount of text to incentivize a broader community to contribute to it. Users can search, browse, edit and create new content, so as to facilitate self-learning and feedback to the community. The overall structure and style for this dynamic material is designed for the bench biologists and non-bioinformaticians. The flexibility of online material allows the readers to ignore details in a first read, yet have immediate access to the information they need. Each chapter comes with practical exercises so readers may familiarize themselves with each step. The NGS WikiBook aims to create a collective laboratory book and protocol that explains the key concepts and describes best practices in this fast-evolving field.

SUMMARY: There is an immediate need for tools to both analyse and visualize in real-time single-nucleotide polymorphisms, insertions and deletions, and other structural variants from new sequence file formats. We have developed VarB software that can be used to visualize variant call format files in real time, as well as identify regions under balancing selection and informative markers to differentiate user-defined groups (e.g. populations). We demonstrate its utility using sequence data from 50 Plasmodium falciparum isolates comprising two different continents and confirm known signals from genomic regions that contain important antigenic and anti-malarial drug-resistance genes. AVAILABILITY AND IMPLEMENTATION: The C++-based software VarB and user manual are available from www.pathogenseq.org/varb. CONTACT: [email protected]

Wikipedia is the world's most successful online encyclopedia, now containing over 3.3 million English language articles. It is probably the largest collection of knowledge ever assembled, and is certainly the most widely accessible. Wikipedia can be edited by anyone with Internet access that chooses to, but does it provide reliable information? A 2005 study by Nature found that a selection of Wikipedia articles on scientific subjects were comparable to a professionally edited encyclopedia [1], suggesting a community of volunteers can generate and sustain surprisingly accurate content. For better or worse, people are guided to Wikipedia when searching the Web for biomedical information [2]. So there is an increasing need for the scientific community to engage with Wikipedia to ensure that the information it contains is accurate and current. For scientists, contributing to Wikipedia is an excellent way of fulfilling public engagement responsibilities and sharing expertise. For example, some Wikipedian scientists have successfully integrated biological data with Wikipedia to promote community annotation [3], [4]. This, in turn, encourages wider access to the linked data via Wikipedia. Others have used the wiki model to develop their own specialist, collaborative databases [5]–[8]. Taking your first steps into Wikipedia can be daunting, but here we provide some tips that should make the editing process go smoothly.

UNLABELLED: Array-based comparative genomic hybridization (CGH) technology is used to discover and validate genomic structural variation, including copy number variants, insertions, deletions and other structural variants (SVs). The visualization and summarization of the array CGH data outputs, potentially across many samples, is an important process in the identification and analysis of SVs. We have developed a software tool for SV analysis using data from array CGH technologies, which is also amenable to short-read sequence data. AVAILABILITY AND IMPLEMENTATION: SnoopCGH is written in java and is available from http://snoopcgh.sourceforge.net/

Abstract Motivation: High throughput sequencing technologies generate large amounts of short reads. Mapping these to a reference sequence consumes large amounts of processing time and memory, and read mapping errors can lead to noisy or incorrect alignments. SNP-o-matic is a fast, memory-efficient and stringent read mapping tool offering a variety of analytical output functions, with an emphasis on genotyping. Availability: http://snpomatic.sourceforge.net Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.