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: Insertional mutagenesis from virus infection is an important pathogenic risk for the development of cancer. Despite the advent of high-throughput sequencing, discovery of viral integration sites and expressed viral fusion events are still limited. Here, we present ViralFusionSeq (VFS), which combines soft-clipping information, read-pair analysis and targeted de novo assembly to discover and annotate viral-human fusions. VFS was used in an RNA-Seq experiment, simulated DNA-Seq experiment and re-analysis of published DNA-Seq datasets. Our experiments demonstrated that VFS is both sensitive and highly accurate. AVAILABILITY: VFS is distributed under GPL version 3 at http://hkbic.cuhk.edu.hk/software/viralfusionseq

Abstract Summary: The affordability of high-throughput sequencing has created an unprecedented surge in the use of genomic data in basic, translational and clinical research. The rapid evolution of sequencing technology, coupled with its broad adoption across biology and medicine, necessitates fast, collaborative interdisciplinary discussion. SEQanswers provides a real-time knowledge-sharing resource to address this need, covering experimental and computational aspects of sequencing and sequence analysis. Developers of popular analysis tools are among the >4000 active members, and ~40 peer-reviewed publications have referenced SEQanswers. Availability: The SEQanswers community is freely accessible at http://SEQanswers.com/ Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: RNA sequencing (RNA-seq) measures gene expression levels and permits splicing analysis. Many existing aligners are capable of mapping millions of sequencing reads onto a reference genome. For reads that can be mapped to multiple positions along the reference genome (multireads), these aligners may either randomly assign them to a location, or discard them altogether. Either way could bias downstream analyses. Meanwhile, challenges remain in the alignment of reads spanning across splice junctions. Existing splicing-aware aligners that rely on the read-count method in identifying junction sites are inevitably affected by sequencing depths. RESULTS: The distance between aligned positions of paired-end (PE) reads or two parts of a spliced read is dependent on the experiment protocol and gene structures. We here proposed a new method that employs an empirical geometric-tail (GT) distribution of intron lengths to make a rational choice in multireads selection and splice-sites detection, according to the aligned distances from PE and sliced reads. CONCLUSIONS: GT models that combine sequence similarity from alignment, and together with the probability of length distribution, could accurately determine the location of both multireads and spliced reads.

RNA sequencing (RNA-Seq) measures gene expression levels and permits splicing analysis. There are many aligners that provide ultra-fast mapping of millions of sequencing reads onto a reference genome. However, random assignment or removal of reads matching multiple positions along the reference genome could bias downstream analyses. Meanwhile, challenges remain in the alignment of reads spanning across splice junctions. Existing splicing-aware aligners that rely on the read-count method in identifying junction sites are inevitably affected by sequencing depths. We here proposed a new method that employs an empirical geometric-tail (GT) distribution of intron lengths to make a rational choice in multireads selection and junction sites detection.