Conference Paper

Evolutionary placement of short sequence reads on multi-core architectures.

DOI: 10.1109/AICCSA.2010.5586973 Conference: The 8th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2010, Hammamet, Tunisia, May 16-19, 2010
Source: DBLP

ABSTRACT The application of high performance computing methods in bioinformatics becomes increasingly important because of the masses of data generated by novel short-read DNA sequencers. One important application of such short reads, is the analysis of microbial communities where the anonymous short reads need to be identified by sequence comparison to a set of reference sequences. This identification is required to analyze the microbial composition and biological diversity of the sample. We briefly introduce a new algorithm for evolutionary (phylogenetic) placement of short reads under the Maximum Likelihood criterion and implement it in RAxML. While this algorithm is significantly more accurate than plain pair-wise sequence comparison it can become highly compute-intensive when a typical number of 100,000 reads and more need to be placed into an existing phylogenetic tree. Therefore, we deploy multi-grain parallelism to improve parallel efficiency of this algorithm on 16-core and 32-core architectures. Via this multi-grain approach, we achieve parallel execution time improvements of 25% and super-linear speedups on 16 cores, as well as near-linear speedups and improvements exceeding 50% on 32-cores on two large real-world microbial datasets. Evolutionary placement of 100,000 reads into a tree with more than 4,000 taxa now only requires less than 2 hours of execution time on 32 cores.

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    ABSTRACT: We present an evolutionary placement algorithm (EPA) and a Web server for the rapid assignment of sequence fragments (short reads) to edges of a given phylogenetic tree under the maximum-likelihood model. The accuracy of the algorithm is evaluated on several real-world data sets and compared with placement by pair-wise sequence comparison, using edit distances and BLAST. We introduce a slow and accurate as well as a fast and less accurate placement algorithm. For the slow algorithm, we develop additional heuristic techniques that yield almost the same run times as the fast version with only a small loss of accuracy. When those additional heuristics are employed, the run time of the more accurate algorithm is comparable with that of a simple BLAST search for data sets with a high number of short query sequences. Moreover, the accuracy of the EPA is significantly higher, in particular when the sample of taxa in the reference topology is sparse or inadequate. Our algorithm, which has been integrated into RAxML, therefore provides an equally fast but more accurate alternative to BLAST for tree-based inference of the evolutionary origin and composition of short sequence reads. We are also actively developing a Web server that offers a freely available service for computing read placements on trees using the EPA.
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    ABSTRACT: Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets. This paper introduces pplacer, a software package for phylogenetic placement and subsequent visualization. The algorithm can place twenty thousand short reads on a reference tree of one thousand taxa per hour per processor, has essentially linear time and memory complexity in the number of reference taxa, and is easy to run in parallel. Pplacer features calculation of the posterior probability of a placement on an edge, which is a statistically rigorous way of quantifying uncertainty on an edge-by-edge basis. It also can inform the user of the positional uncertainty for query sequences by calculating expected distance between placement locations, which is crucial in the estimation of uncertainty with a well-sampled reference tree. The software provides visualizations using branch thickness and color to represent number of placements and their uncertainty. A simulation study using reads generated from 631 COG alignments shows a high level of accuracy for phylogenetic placement over a wide range of alignment diversity, and the power of edge uncertainty estimates to measure placement confidence. Pplacer enables efficient phylogenetic placement and subsequent visualization, making likelihood-based phylogenetics methodology practical for large collections of reads; it is freely available as source code, binaries, and a web service.
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    ABSTRACT: Selective sweep detection localizes targets of recent and strong positive selection by analyzing single nucleotide polymorphisms (SNPs) in intra-species multiple sequence alignments. Substantial advances in wet-lab sequencing technologies currently allow for generating unprecedented amounts of molecular data. The increasing number of sequences and number of SNPs in such large multiple sequence alignments cause prohibiting long execution times for population genetics data analyses that rely on selective sweep theory. To alleviate this problem, we have recently implemented fine- and coarse-grain parallel versions of our open-source tool OmegaPlus for selective sweep detection that is based on the ω statistic. A performance issue with the coarse-grain parallelization is that individual coarse-grain tasks exhibit significant run-time differences, and hence cause load imbalance. Here, we introduce a significantly improved multi-grain parallelization scheme which outperforms both the fine-grain as well as the coarse-grain versions of OmegaPlus with respect to parallel efficiency. The multi-grain approach exploits both coarse-grain and fine-grain operations by using available threads/cores that have completed their coarse-grain tasks to accelerate the slowest task by means of fine-grain parallelism. A performance assessment on real-world and simulated datasets showed that the multi-grain version is up to 39% and 64.4% faster than the coarse-grain and the fine-grain versions, respectively, when the same number of threads is used.
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