Sven Warris

Fast selection of miRNA candidates based on large-scale pre-computed MFE sets of randomized sequences.

BackgroundSmall RNAs are important regulators of genome function, yet their prediction in genomes is still a major computational challenge. Statistical analyses of pre-miRNA sequences indicated that their 2D structure tends to have a minimal free energy (MFE) significantly lower than MFE values of equivalently randomized sequences with the same nucleotide composition, in contrast to other classes of non-coding RNA. The computation of many MFEs is, however, too intensive to allow for genome-wide screenings.ResultsUsing a local grid infrastructure, MFE distributions of random sequences were pre-calculated on a large scale. These distributions follow a normal distribution and can be used to determine the MFE distribution for any given sequence composition by interpolation. It allows on-the-fly calculation of the normal distribution for any candidate sequence composition.ConclusionThe speedup achieved makes genome-wide screening with this characteristic of a pre-miRNA sequence practical. Although this particular property alone will not be able to distinguish miRNAs from other sequences sufficiently discriminative, the MFE-based P-value should be added to the parameters of choice to be included in the selection of potential miRNA candidates for experimental verification.

Flexible, Fast and Accurate Sequence Alignment Profiling on GPGPU with PaSWAS

Motivation To obtain large-scale sequence alignments in a fast and flexible way is an important step in the analyses of next generation sequencing data. Applications based on the Smith-Waterman (SW) algorithm are often either not fast enough, limited to dedicated tasks or not sufficiently accurate due to statistical issues. Current SW implementations that run on graphics hardware do not report the alignment details necessary for further analysis. Results With the Parallel SW Alignment Software (PaSWAS) it is possible (a) to have easy access to the computational power of NVIDIA-based general purpose graphics processing units (GPGPUs) to perform high-speed sequence alignments, and (b) retrieve relevant information such as score, number of gaps and mismatches. The software reports multiple hits per alignment. The added value of the new SW implementation is demonstrated with two test cases: (1) tag recovery in next generation sequence data and (2) isotype assignment within an immunoglobulin 454 sequence data set. Both cases show the usability and versatility of the new parallel Smith-Waterman implementation.

Mining functional annotations across species

Motivation Numerous tools and databases exist to annotate and interpret the functions encoded in genomes (InterProScan, KEGG, GO etc.). However, analyzing and comparing functionality across a number of genomes, for example of related species, is not trivial. Results We present a novel approach, for which KEGG and Gene Ontology data are imported into a Neo4j graph database and InterProScan results from several species are added. Using the Neo4j plugin for Cytoscape, users can query this database and visualize functional annotations (sub)graphs, to compare and group functional annotation across species.

pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment

Background Our previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinformatics by making its use and extension more simple through more and better application of high-level languages commonly used in bioinformatics, such as Python. Results The novel application pyPaSWAS presents the parallel SW sequence alignment code fully packed in Python. It is a generic SW implementation running on several hardware platforms with multi-core systems and/or GPUs that provides accurate sequence alignments that also can be inspected for alignment details. Additionally, pyPaSWAS support the affine gap penalty. Python libraries are used for automated system configuration, I/O and logging. This way, the Python environment will stimulate further extension and use of pyPaSWAS. Conclusions pyPaSWAS presents an easy Python-based environment for accurate and retrievable parallel SW sequence alignments on GPUs and multi-core systems. The strategy of integrating Python with high-performance parallel compute languages to create a developer- and user-friendly environment should be considered for other computationally intensive bioinformatics algorithms.

Correcting palindromes in long reads after whole-genome amplification

Next-generation sequencing requires sufficient DNA to be available. If limited, whole-genome amplification is applied to generate additional amounts of DNA. Such amplification often results in many chimeric DNA fragments, in particular artificial palindromic sequences, which limit the usefulness of long reads from technologies such as PacBio and Oxford Nanopore. Here, we present Pacasus, a tool for correcting such errors in long reads. We demonstrate on two real-world datasets that it markedly improves subsequent read mapping and de novo assembly, yielding results similar to these that would be obtained with non-amplified DNA. With Pacasus long-read technologies become readily available for sequencing targets with very small amounts of DNA, such as single cells or even single chromosomes.