Patrick Flick

A parallel connectivity algorithm for de Bruijn graphs in metagenomic applications

Dramatic advances in DNA sequencing technology have made it possible to study microbial environments by direct sequencing of environmental DNA samples. Yet, due to the huge volume and high data complexity, current de novo assemblers cannot handle large metagenomic datasets or fail to perform assembly with acceptable quality. This paper presents the first parallel solution for decomposing the metagenomic assembly problem without compromising the post-assembly quality. We transform this problem into that of finding weakly connected components in the de Bruijn graph. We propose a novel distributed memory algorithm to identify the connected subgraphs, and present strategies to minimize the communication volume. We demonstrate the scalability of our algorithm on a soil metagenome dataset with 1.8 billion reads. Our approach achieves a runtime of 22 minutes using 1280 Intel Xeon cores for a 421 GB uncompressed FASTQ dataset. Moreover, our solution is generalizable to finding connected components in arbitrary undirected graphs.

Parallel distributed memory construction of suffix and longest common prefix arrays

Suffix arrays and trees are fundamental string data structures of importance to many applications in computational biology. Consequently, their parallel construction is an actively studied problem. To date, algorithms with best practical performance lack efficient worst-case run-time guarantees, and vice versa. In addition, much of the recent work targeted low core count, shared memory parallelization. In this paper, we present parallel algorithms for distributed memory construction of suffix arrays and longest common prefix (LCP) arrays that simultaneously achieve good worst-case run-time bounds and superior practical performance. Our algorithms run in O(Tsort(n, p) · log n) worst-case time where Tsort(n, p) is the run-time of parallel sorting. We present several algorithm engineering techniques that improve performance in practice. We demonstrate the construction of suffix and LCP arrays of the human genome in less than 8 seconds on 1,024 Intel Xeon cores, reaching speedups of over 110X compared to the best sequential suffix array construction implementation divsufsort.

Kmerind: A Flexible Parallel Library for K-mer Indexing of Biological Sequences on Distributed Memory Systems

Counting and indexing fixed length substrings, or

GOATOOLS: A Python library for Gene Ontology analyses

k

Parallel Construction of Suffix Trees and the All-Nearest-Smaller-Values Problem

k-mers, in biological sequences is a key step in many bioinformatics tasks including genome alignment and mapping, genome assembly, and error correction. While advances in next generation sequencing technologies have dramatically reduced the cost and improved latency and throughput, few bioinformatics tools can efficiently process the datasets at the current generation rate of 1.8 terabases per 3-day experiment from a single sequencer. We present Kmerind, a high performance parallel

GOATOOLS: Tools for Gene Ontology

k

An Adaptive Parallel Algorithm for Computing Connected Components

k-mer indexing library for distributed memory environments. The Kmerind library provides a set of simple and consistent APIs with sequential semantics and parallel implementations that are designed to be flexible and extensible. Kmerinds

An Adaptive Parallel Algorithm for Computing Connectivity.

k

Reprint of “A parallel connectivity algorithm for de Bruijn graphs in metagenomic applications”

k-mer counter performs similarly or better than the best existing

goatools: GOATOOLS v0.6.4

k