3,639 research outputs found
Sorting suffixes of a text via its Lyndon Factorization
The process of sorting the suffixes of a text plays a fundamental role in
Text Algorithms. They are used for instance in the constructions of the
Burrows-Wheeler transform and the suffix array, widely used in several fields
of Computer Science. For this reason, several recent researches have been
devoted to finding new strategies to obtain effective methods for such a
sorting. In this paper we introduce a new methodology in which an important
role is played by the Lyndon factorization, so that the local suffixes inside
factors detected by this factorization keep their mutual order when extended to
the suffixes of the whole word. This property suggests a versatile technique
that easily can be adapted to different implementative scenarios.Comment: Submitted to the Prague Stringology Conference 2013 (PSC 2013
GPU-Accelerated BWT Construction for Large Collection of Short Reads
Advances in DNA sequencing technology have stimulated the development of
algorithms and tools for processing very large collections of short strings
(reads). Short-read alignment and assembly are among the most well-studied
problems. Many state-of-the-art aligners, at their core, have used the
Burrows-Wheeler transform (BWT) as a main-memory index of a reference genome
(typical example, NCBI human genome). Recently, BWT has also found its use in
string-graph assembly, for indexing the reads (i.e., raw data from DNA
sequencers). In a typical data set, the volume of reads is tens of times of the
sequenced genome and can be up to 100 Gigabases. Note that a reference genome
is relatively stable and computing the index is not a frequent task. For reads,
the index has to computed from scratch for each given input. The ability of
efficient BWT construction becomes a much bigger concern than before. In this
paper, we present a practical method called CX1 for constructing the BWT of
very large string collections. CX1 is the first tool that can take advantage of
the parallelism given by a graphics processing unit (GPU, a relative cheap
device providing a thousand or more primitive cores), as well as simultaneously
the parallelism from a multi-core CPU and more interestingly, from a cluster of
GPU-enabled nodes. Using CX1, the BWT of a short-read collection of up to 100
Gigabases can be constructed in less than 2 hours using a machine equipped with
a quad-core CPU and a GPU, or in about 43 minutes using a cluster with 4 such
machines (the speedup is almost linear after excluding the first 16 minutes for
loading the reads from the hard disk). The previously fastest tool BRC is
measured to take 12 hours to process 100 Gigabases on one machine; it is
non-trivial how BRC can be parallelized to take advantage a cluster of
machines, let alone GPUs.Comment: 11 page
Parallel Wavelet Tree Construction
We present parallel algorithms for wavelet tree construction with
polylogarithmic depth, improving upon the linear depth of the recent parallel
algorithms by Fuentes-Sepulveda et al. We experimentally show on a 40-core
machine with two-way hyper-threading that we outperform the existing parallel
algorithms by 1.3--5.6x and achieve up to 27x speedup over the sequential
algorithm on a variety of real-world and artificial inputs. Our algorithms show
good scalability with increasing thread count, input size and alphabet size. We
also discuss extensions to variants of the standard wavelet tree.Comment: This is a longer version of the paper that appears in the Proceedings
of the IEEE Data Compression Conference, 201
Rust-Bio - a fast and safe bioinformatics library
We present Rust-Bio, the first general purpose bioinformatics library for the
innovative Rust programming language. Rust-Bio leverages the unique combination
of speed, memory safety and high-level syntax offered by Rust to provide a fast
and safe set of bioinformatics algorithms and data structures with a focus on
sequence analysis
MaxSSmap: A GPU program for mapping divergent short reads to genomes with the maximum scoring subsequence
Programs based on hash tables and Burrows-Wheeler are very fast for mapping
short reads to genomes but have low accuracy in the presence of mismatches and
gaps. Such reads can be aligned accurately with the Smith-Waterman algorithm
but it can take hours and days to map millions of reads even for bacteria
genomes. We introduce a GPU program called MaxSSmap with the aim of achieving
comparable accuracy to Smith-Waterman but with faster runtimes. Similar to most
programs MaxSSmap identifies a local region of the genome followed by exact
alignment. Instead of using hash tables or Burrows-Wheeler in the first part,
MaxSSmap calculates maximum scoring subsequence score between the read and
disjoint fragments of the genome in parallel on a GPU and selects the highest
scoring fragment for exact alignment. We evaluate MaxSSmap's accuracy and
runtime when mapping simulated Illumina E.coli and human chromosome one reads
of different lengths and 10\% to 30\% mismatches with gaps to the E.coli genome
and human chromosome one. We also demonstrate applications on real data by
mapping ancient horse DNA reads to modern genomes and unmapped paired reads
from NA12878 in 1000 genomes. We show that MaxSSmap attains comparable high
accuracy and low error to fast Smith-Waterman programs yet has much lower
runtimes. We show that MaxSSmap can map reads rejected by BWA and NextGenMap
with high accuracy and low error much faster than if Smith-Waterman were used.
On short read lengths of 36 and 51 both MaxSSmap and Smith-Waterman have lower
accuracy compared to at higher lengths. On real data MaxSSmap produces many
alignments with high score and mapping quality that are not given by NextGenMap
and BWA. The MaxSSmap source code is freely available from
http://www.cs.njit.edu/usman/MaxSSmap
Prospects and limitations of full-text index structures in genome analysis
The combination of incessant advances in sequencing technology producing large amounts of data and innovative bioinformatics approaches, designed to cope with this data flood, has led to new interesting results in the life sciences. Given the magnitude of sequence data to be processed, many bioinformatics tools rely on efficient solutions to a variety of complex string problems. These solutions include fast heuristic algorithms and advanced data structures, generally referred to as index structures. Although the importance of index structures is generally known to the bioinformatics community, the design and potency of these data structures, as well as their properties and limitations, are less understood. Moreover, the last decade has seen a boom in the number of variant index structures featuring complex and diverse memory-time trade-offs. This article brings a comprehensive state-of-the-art overview of the most popular index structures and their recently developed variants. Their features, interrelationships, the trade-offs they impose, but also their practical limitations, are explained and compared
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