41,737 research outputs found
Aligning Multiple Sequences with Genetic Algorithm
The alignment of biological sequences is a crucial
tool in molecular biology and genome analysis. It helps to build
a phylogenetic tree of related DNA sequences and also to predict
the function and structure of unknown protein sequences by
aligning with other sequences whose function and structure is
already known. However, finding an optimal multiple sequence
alignment takes time and space exponential with the length or
number of sequences increases. Genetic Algorithms (GAs) are
strategies of random searching that optimize an objective
function which is a measure of alignment quality (distance) and
has the ability for exploratory search through the solution space
and exploitation of current results
Genetic Sequence Matching Using D4M Big Data Approaches
Recent technological advances in Next Generation Sequencing tools have led to
increasing speeds of DNA sample collection, preparation, and sequencing. One
instrument can produce over 600 Gb of genetic sequence data in a single run.
This creates new opportunities to efficiently handle the increasing workload.
We propose a new method of fast genetic sequence analysis using the Dynamic
Distributed Dimensional Data Model (D4M) - an associative array environment for
MATLAB developed at MIT Lincoln Laboratory. Based on mathematical and
statistical properties, the method leverages big data techniques and the
implementation of an Apache Acculumo database to accelerate computations
one-hundred fold over other methods. Comparisons of the D4M method with the
current gold-standard for sequence analysis, BLAST, show the two are comparable
in the alignments they find. This paper will present an overview of the D4M
genetic sequence algorithm and statistical comparisons with BLAST.Comment: 6 pages; to appear in IEEE High Performance Extreme Computing (HPEC)
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BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments
Advances in sequencing techniques have led to exponential growth in
biological data, demanding the development of large-scale bioinformatics
experiments. Because these experiments are computation- and data-intensive,
they require high-performance computing (HPC) techniques and can benefit from
specialized technologies such as Scientific Workflow Management Systems (SWfMS)
and databases. In this work, we present BioWorkbench, a framework for managing
and analyzing bioinformatics experiments. This framework automatically collects
provenance data, including both performance data from workflow execution and
data from the scientific domain of the workflow application. Provenance data
can be analyzed through a web application that abstracts a set of queries to
the provenance database, simplifying access to provenance information. We
evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree
assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a
RASopathy analysis workflow. We analyze each workflow from both computational
and scientific domain perspectives, by using queries to a provenance and
annotation database. Some of these queries are available as a pre-built feature
of the BioWorkbench web application. Through the provenance data, we show that
the framework is scalable and achieves high-performance, reducing up to 98% of
the case studies execution time. We also show how the application of machine
learning techniques can enrich the analysis process
Regulatory motif discovery using a population clustering evolutionary algorithm
This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences
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Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants.
De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy
Rapid Sequence Identification of Potential Pathogens Using Techniques from Sparse Linear Algebra
The decreasing costs and increasing speed and accuracy of DNA sample
collection, preparation, and sequencing has rapidly produced an enormous volume
of genetic data. However, fast and accurate analysis of the samples remains a
bottleneck. Here we present DRAGenS, a genetic sequence identification
algorithm that exhibits the Big Data handling and computational power of the
Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear
algebra and statistical properties to increase computational performance while
retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield
speed and precision tradeoffs, with applications in biodefense and medical
diagnostics. The DRAGenS analysis algorithm is tested over several
datasets, including three utilized for the Defense Threat Reduction Agency
(DTRA) metagenomic algorithm contest
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