We propose a parallel algorithm that solves the best k-mismatches alignment problem against a genomic reference using the "one sequence/multiple processes" paradigm and distributed memory. Our proposal is designed to take advantage of a computing cluster using MPI (Message Passing Interface) for communication. Our solution distributes the reference among different nodes and each sequence is processed concurrently by different nodes. When a (putative) best solution is found, the successful process propagates the information to other nodes, reducing search space and saving computation time. The distributed algorithm was developed in C++ and optimized for the PLX and FERMI supercomputers, but it is compatible with every OpenMPI-based cluster. It was included in the ERNE (Extended Randomized Numerical alignEr) package, whose aim is to provide an all-inclusive set of tools for short reads alignment and cleaning. ERNE is free software, distributed under the Open Source License (GPL V3) and can be downloaded at: http://erne.sourceforge.net. The algorithm described in this work is implemented in the ERNE-PMAP and ERNE-PBS5 programs, the former designed to align DNA and RNA sequences, while the latter is optimized for bisulphite-treated sequences.

MOTIVATION: Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. They have been used for hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. So far, the main platform for expression data has been DNA microarrays; however, the recent development of RNA-seq allows for higher accuracy and coverage of transcript populations. It is therefore important to assess the potential for biological investigation of coexpression networks derived from this novel technique in a condition-independent dataset. RESULTS: We collected 65 publicly available Illumina RNA-seq high quality Arabidopsis thaliana samples and generated Pearson correlation coexpression networks. These networks were then compared with those derived from analogous microarray data. We show how Variance-Stabilizing Transformed (VST) RNA-seq data samples are the most similar to microarray ones, with respect to inter-sample variation, correlation coefficient distribution and network topological architecture. Microarray networks show a slightly higher score in biology-derived quality assessments such as overlap with the known protein-protein interaction network and edge ontological agreement. Different coexpression network centralities are investigated; in particular, we show how betweenness centrality is generally a positive marker for essential genes in A.thaliana, regardless of the platform originating the data. In the end, we focus on a specific gene network case, showing that although microarray data seem more suited for gene network reverse engineering, RNA-seq offers the great advantage of extending coexpression analyses to the entire transcriptome.

SUMMARY: The advent of high-throughput sequencers (HTS) introduced the need of new tools in order to analyse the large amount of data that those machines are able to produce. The mandatory first step for a wide range of analyses is the alignment of the sequences against a reference genome. We present a major update to our rNA (randomized Numerical Aligner) tool. The main feature of rNA is the fact that it achieves an accuracy greater than the majority of other tools in a feasible amount of time. rNA executables and source codes are freely downloadable at http://iga-rna.sourceforge.net/. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The advent of Next Generation Sequencers (NGS) has driven the necessity to design new and more sophisticated tools in order to cope with the huge amount of data produced by these novel technologies. String alignment against a genome reference is the first and most important phase in every (re)-sequencing project. Recently, distributed tools able to align large amounts of sequences using clusters or clouds of computers, have been put forward. The aim of this work is to propose a new tool named mrNA (the MPI version of the original rNA program) able to align NGS data using a cluster of computers. mrNA was designed to tackle the main computational bottleneck of all classical parallel implementation of aligners: references longer than 4 Gbp. mrNA, together with rNA, are open source programs downloadable at http://iga-rna.sourceforge.net/.

Many software tools are currently available to solve the hard goal of assembling millions of fragments produced in sequencing projects. Such a variety includes packages for long and short reads, generated by classical and next-generation sequencing technologies. Often the result produced by different tools can diverge-sometime significantly-for many reasons: the underlying algorithm, the data structures employed, the heuristics implemented, default parameters, etc. On the ground of the above considerations, we were motivated in developing a methodology which may both guide in a comparison of different assembler's output and improve the overall quality of the genome assembly sequences,by merging the sequences produced by different assembly programs.