Background: Optical mapping is a technique for capturing fluorescent signal patterns of long DNA molecules (in the range of 0.1–1 Mbp). Recently, it has been complementing the widely used short-read sequencing technology by assisting with scaffolding and detecting large and complex structural variations (SVs). Here, we introduce a fast, robust and accurate tool called OMBlast for aligning optical maps, the set of signal locations on the molecules generated from optical mapping. Our method is based on the seed-and-extend approach from sequence alignment, with modifications specific to optical mapping. Results: Experiments with both synthetic and our real data demonstrate that OMBlast has higher accuracy and faster mapping speed than existing alignment methods. Our tool also shows significant improvement when aligning data with SVs. Availability and Implementation: OMBlast is implemented for Java 1.7 and is released under a GPL license. OMBlast can be downloaded from https://github.com/aldenleung/OMBlast and run directly on machines equipped with a Java virtual machine. Contact: [email protected] and [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online.

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

MOTIVATION: The growth of next-generation sequencing means that more effective and efficient archiving methods are needed to store the generated data for public dissemination and in anticipation of more mature analytical methods later. This article examines methods for compressing the quality score component of the data to partly address this problem. RESULTS: We compare several compression policies for quality scores, in terms of both compression effectiveness and overall efficiency. The policies employ lossy and lossless transformations with one of several coding schemes. Experiments show that both lossy and lossless transformations are useful, and that simple coding methods, which consume less computing resources, are highly competitive, especially when random access to reads is needed. AVAILABILITY AND IMPLEMENTATION: Our C++ implementation, released under the Lesser General Public License, is available for download at http://www.cb.k.u-tokyo.ac.jp/asailab/members/rwan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract To bound memory consumption, most compression systems provide a facility that controls the amount of data that may be processed at once—usually as a block size, but sometimes as a direct megabyte limit. In this work we consider the Re‐Pair mechanism of Larsson and Moffat (2000), which processes large messages as disjoint blocks to limit memory consumption. We show that the blocks emitted by Re‐Pair can be postprocessed to yield further savings, and describe techniques that allow files of 500 MB or more to be compressed in a holistic manner using less than that much main memory. The block merging process we describe has the additional advantage of allowing new text to be appended to the end of the compressed file.

No abstract available.

We describe a software system for managing text files of up to several hundred megabytes that combines a number of useful facilities: effective text compression; phrase browsing; and fast interactive searching. Mechanisms for compressing text have been studied for many years, and a wide range of effective methods has been developed. But compression of text makes it unwieldy in other ways – it must be decompressed before being viewed, and it is harder to directly search. One way of addressing these concerns is to build an index for the source document, and search via a query interface [Witten et al., 1999]. Then the passages sought by the user can be identified using Boolean or ranked queries, and only those selected passages need be fetched and decompressed. Another alternative is to use a compression mechanism that is amenable to compressed pattern matching, and undertake the equivalent of an exhaustive linear search in the compressed text to locate passages of interest [de Moura et al., 2000]. Again, only small fragments of the source document might be eventually presented to the user. In this presentation we consider a third approach, and describe a software compression and searching system – dubbed RE-STORE – that supports browsing within the compressed text based on phrases extracted from the text, and fast identification and decompression of the passages in the text containing those phrases. In our system, the user selects one or more terms of interest from a static list that is somewhat akin to a vocabulary derived from the document, and is then free