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The discovery of Allele-Specific Methylation (ASM) is an important research field in biology as it regulates genomic imprinting, which has been identified as the cause of some genetic diseases. Nevertheless, the high computational cost of the bioinformatic tools developed for this purpose prevents their application to large-scale datasets. Hence, much faster tools are required to further progress in this research field. In this work we present PARamrfinder, a parallel tool that applies a statistical model to identify ASM in data from high-throughput short-read bisulfite sequencing. It is based on the state-of-the-art sequential tool amrfinder, which is able to detect ASM at regional level from Bisulfite Sequencing (BS-Seq) experiments in the absence of Single Nucleotide Polymorphism information. PARamrfinder provides the same Allelically Methylated Regions as amrfinder but at significantly reduced runtime thanks to exploiting the compute capabilities of common multicore CPU clusters and MPI RMA operations to attain an efficient dynamic workload balance. As an example, our tool is up to 567 times faster for real data experiments on a cluster with 8 nodes, each one containing two 16-core processors. The source code of PARamrfinder, as well as a reference manual, is available at https://github.com/UDC-GAC/PARamrfinder. [ABSTRACT FROM AUTHOR]

Gene co-expression networks are gaining attention in the present days as useful representations of biologically interesting interactions among genes. The most computationally demanding step to generate these networks is the construction of the correlation similarity matrix, as all pairwise combinations must be analyzed and complexity increases quadratically with the number of genes. In this paper we present MPICorMat, a hybrid MPI/OpenMP parallel approach to construct similarity matrices based on Pearson’s correlation. It is based on a previous tool (RMTGeneNet) that has been used on several biological studies and proved accurate. Our tool obtains the same results as RMTGeneNet but significantly reduces runtime on multicore clusters. For instance, MPICorMat generates the correlation matrix of a dataset with 61,170 genes and 160 samples in less than one minute using 16 nodes with two Intel Xeon Sandy-Bridge processors each (256 total cores), while the original tool needed almost 4.5 hours. The tool is also compared to another available approach to construct correlation matrices on multicore clusters, showing better scalability and performance. MPICorMat is an open-source software and it is publicly available at https://sourceforge.net/projects/mpicormat/ . [ABSTRACT FROM AUTHOR]

Dry eye syndrome is a public health problem, and one of the most common conditions seen by eye care specialists. Among the clinical tests for its diagnosis, the evaluation of the interference patterns observed in the tear film lipid layer is often employed. In this sense, tear film maps illustrate the spatial distribution of the patterns over the whole tear film and provide useful information to practitioners. However, the creation of a single map usually takes tens of minutes. Medical experts currently demand applications with lower response time in order to provide a faster diagnosis for their patients. In this work, we explore different parallel approaches to accelerate the definition of the tear film map by exploiting the power of today's ubiquitous multicore systems. They can be executed on any multicore system without special software or hardware requirements. The experimental evaluation determines the best approach (on-demand with dynamic seed distribution) and proves that it can significantly decrease the runtime. For instance, the average runtime of our experiments with 50 real-world images on a system with AMD Opteron processors is reduced from more than 20 minutes to one minute and 12 seconds. [ABSTRACT FROM AUTHOR]

The growth of next-generation sequencing (NGS) datasets poses a challenge to the alignment of reads to reference genomes in terms of alignment quality and execution speed. Some available aligners have been shown to obtain high quality mappings at the expense of long execution times. Finding fast yet accurate software solutions is of high importance to research, since availability and size of NGS datasets continue to increase. In this work we present an efficient parallelization approach for NGS short-read alignment on multi-core clusters. Our approach takes advantage of a distributed shared memory programming model based on the new UPC++ language. Experimental results using the CUSHAW3 aligner show that our implementation based on dynamic scheduling obtains good scalability on multi-core clusters. Through our evaluation, we are able to complete the single-end and paired-end alignments of 246 million reads of length 150 base-pairs in 11.54 and 16.64 minutes, respectively, using 32 nodes with four AMD Opteron 6272 16-core CPUs per node. In contrast, the multi-threaded original tool needs 2.77 and 5.54 hours to perform the same alignments on the 64 cores of one node. The source code of our parallel implementation is publicly available at the CUSHAW3 homepage (). [ABSTRACT FROM AUTHOR]

Abstract: The popularity of Partitioned Global Address Space (PGAS) languages has increased during the last years thanks to their high programmability and performance through an effcient exploitation of data locality. This paper describes the implementation of effcient parallel dense triangular solvers in the PGAS language Unified Parallel C (UPC). The solvers are built on top of sequential BLAS functions and exploit the particularities of the PGAS paradigm. Furthermore, the numerical routines developed implement an automatic process that adapts the algorithms to the characteristics of the system where they are executed. The triangular solvers have been experimentally evaluated in two different multicore clusters and compared to message-passing based counterparts, demonstrating good scalability and effciency. [Copyright &y& Elsevier]

Current next generation sequencing technologies often generate duplicated or nearduplicated reads that (depending on the application scenario) do not provide any interesting biological information but can increase memory requirements and computational time of downstream analysis. In this work we present ParDRe, a de novo parallel tool to remove duplicated and nearduplicated reads through the clustering of Single-End or Paired-End sequences from fasta or fastq files. It uses a novel bitwise approach to compare the suffixes of DNA strings and employs hybrid MPI/multithreading to reduce runtime on multicore systems. We show that ParDRe is up to 27.29 times faster than Fulcrum (a representative state-of-the-art tool) on a platform with two 8-core Sandy-Bridge processors. [ABSTRACT FROM AUTHOR]