Héctor Martínez

Acceleration of short and long DNA read mapping without loss of accuracy using suffix array

HPG Aligner applies suffix arrays for DNA read mapping. This implementation produces a highly sensitive and extremely fast mapping of DNA reads that scales up almost linearly with read length. The approach presented here is faster (over 20× for long reads) and more sensitive (over 98% in a wide range of read lengths) than the current state-of-the-art mappers. HPG Aligner is not only an optimal alternative for current sequencers but also the only solution available to cope with longer reads and growing throughputs produced by forthcoming sequencing technologies. Availability and implementation: https://github.com/opencb/hpg-aligner. Contact: jdopazo@cipf.es or imedina@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Highly sensitive and ultrafast read mapping for RNA-seq analysis

As sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available.

A dynamic pipeline for RNA sequencing on multicore processors

We present a concurrent algorithm for mapping short and long RNA sequences on multicore processors. Our solution processes the data, initially stored on disk, in batches of reads which are passed between the consecutive stages of a pipeline. A major operational reorganization of the original static pipeline, combined with a complete reimplementation based on POSIX threads, renders a dissociated execution between threads and stages/task types, so that threads can compute any type of pending task resulting in a dynamic pipeline. The experiments on a multicore platform reveal that this reorganization yields significantly higher performance, specially for architectures equipped with a small to moderate number of cores. As an additional contribution, our experiments also reveal that the use of 16-nucleotide (nt) seeds during the one of the stages of the pipeline, instead of the 15-nt length that was proposed originally, yields a remarkable reduction in the execution time of the global alignment process while maintaining the sensitivity of the algorithm.

Applying the European Spatial Development Perspective in Low-density Regions: A Methodology Based on Mobility and Labour Market Structure

The paper reflects on how ESDP (European Spatial Development Perspective) principles can be applied in territories with weak population patterns in quantitative terms. The ESDP defines a functional urban area (FUA) as the influence area of a city and sets a minimum threshold of 15,000 inhabitants for the city and 40,000 for the entire FUA. These thresholds are taken as guidelines to explore the concept of functional regions, adding more information from several sources. Hence the paper starts under the normative background given by EU spatial policy and proposes a methodology of analysis combining several techniques, including an application for the Castilla–La Mancha autonomous region (ES42 in NUTS 2). The approaches used in the method proposed include data from mobility, commuting, accessibility and qualitative analyses of services. The outcome shows how ESDP principles could be applied in practice in places with low-density settlement.

Concurrent and Accurate Short Read Mapping on Multicore Processors

We introduce a parallel aligner with a work-flow organization for fast and accurate mapping of RNA sequences on servers equipped with multicore processors. Our software, HPG Aligner SA (HPG Aligner SA is an open-source application. The software is available at http://www.opencb.org/), exploits a suffix array to rapidly map a large fraction of the RNA fragments (reads), as well as leverages the accuracy of the Smith-Waterman algorithm to deal with conflictive reads. The aligner is enhanced with a careful strategy to detect splice junctions based on an adaptive division of RNA reads into small segments (or seeds), which are then mapped onto a number of candidate alignment locations, providing crucial information for the successful alignment of the complete reads. The experimental results on a platform with Intel multicore technology report the parallel performance of HPG Aligner SA , on RNA reads of 100-400 nucleotides, which excels in execution time/sensitivity to state-of-the-art aligners such as TopHat 2+Bowtie 2, MapSplice, and STAR.

A framework for genomic sequencing on clusters of multicore and manycore processors

The advances in genomic sequencing during the past few years have motivated the development of fast and reliable software for DNA/RNA sequencing on current high performance architectures. Most of these efforts target multicore processors, only a few can also exploit graphics processing units, and a much smaller set will run in clusters equipped with any of these multi-threaded architecture technologies. Furthermore, the examples that can be used on clusters today are all strongly coupled with a particular aligner. In this paper we introduce an alignment framework that can be leveraged to coordinately run any “single-node” aligner, taking advantage of the resources of a cluster without having to modify any portion of the original software. The key to our transparent migration lies in hiding the complexity associated with the multi-node execution (such as coordinating the processes running in the cluster nodes) inside the generic-aligner framework. Moreover, following the design and operation in our Message Passing Interface (MPI) version of HPG Aligner RNA BWT, we organize the framework into two stages in order to be able to execute different aligners in each one of them. With this configuration, for example, the first stage can ideally apply a fast aligner to accelerate the process, while the second one can be tuned to act as a refinement stage that further improves the global alignment process with little cost.

Accelerating FaST-LMM for Epistasis Tests

We introduce an enhanced version of FaST-LMM that maintains the sensitivity of this software when applied to identify epistasis interactions while delivering an acceleration factor that is close to 7.5\(\times \) on a server equipped with a state-of-the-art graphics coprocessor. This performance boost is obtained from the combined effects of integrating a dictionary for faster storage of the test results; a re-organization of the original FaST-LMM Python code; and off-loading of compute-intensive parts to the graphics accelerator.

FaST-LMM for Two-Way Epistasis Tests on High-Performance Clusters

We introduce a version of the epistasis test in FaST-LMM for clusters of multithreaded processors. This new software maintains the sensitivity of the original FaST-LMM while delivering acceleration that is close to linear on 12-16 nodes of two recent platforms, with respect to improved implementation of FaST-LMM presented in an earlier work. This efficiency is attained through several enhancements on the original single-node version of FaST-LMM, together with the development of a message passing interface (MPI)-based version that ensures a balanced distribution of the workload as well as a multigraphics processing unit (GPU) module that can exploit the presence of multiple GPUs per node.

Dynamic reconfiguration of noniterative scientific applications: A case study with HPG aligner

Several studies have proved the benefits of job malleability, that is, the capacity of an application to adapt its parallelism to a dynamically changing number of allocated processors. The most remarkable advantages of executing malleable jobs as part of a high performance computer workload are the throughput increase and the more efficient utilization of the underlying resources. Malleability has been mostly applied to iterative applications where all the processes execute the same operations over different sets of data and with a balanced per process load. Unfortunately, not all scientific applications adhere to this process-level malleable job structure. There are scientific applications which are either noniterative or present an irregular per process load distribution. Unlike many other reconfiguration tools, the Dynamic Management of Resources Application Programming Interface (DMR API) provides the necessary flexibility to make malleable these out-of-target applications. In this article, we study the particular case of using the DMR API to generate a malleable version of HPG aligner, a distributed-memory noniterative genomic sequencer featuring an irregular communication pattern among processes. Through this first conversion of an out-of-target application to a malleable job, we both illustrate how the DMR API may be used to convert this type of applications into malleable and test the benefits of this conversion in production clusters. Our experimental results reveal an important reduction of the malleable HPG aligner jobs completion time compared to the original HPG aligner version. Furthermore, HPG aligner malleable workloads achieve a greater throughput than their fixed counterparts.