2,594 research outputs found
Minimum error correction-based haplotype assembly: considerations for long read data
The single nucleotide polymorphism (SNP) is the most widely studied type of
genetic variation. A haplotype is defined as the sequence of alleles at SNP
sites on each haploid chromosome. Haplotype information is essential in
unravelling the genome-phenotype association. Haplotype assembly is a
well-known approach for reconstructing haplotypes, exploiting reads generated
by DNA sequencing devices. The Minimum Error Correction (MEC) metric is often
used for reconstruction of haplotypes from reads. However, problems with the
MEC metric have been reported. Here, we investigate the MEC approach to
demonstrate that it may result in incorrectly reconstructed haplotypes for
devices that produce error-prone long reads. Specifically, we evaluate this
approach for devices developed by Illumina, Pacific BioSciences and Oxford
Nanopore Technologies. We show that imprecise haplotypes may be reconstructed
with a lower MEC than that of the exact haplotype. The performance of MEC is
explored for different coverage levels and error rates of data. Our simulation
results reveal that in order to avoid incorrect MEC-based haplotypes, a
coverage of 25 is needed for reads generated by Pacific BioSciences RS systems.Comment: 17 pages, 6 figure
Exploring single-sample SNP and INDEL calling with whole-genome de novo assembly
Motivation: Eugene Myers in his string graph paper (Myers, 2005) suggested
that in a string graph or equivalently a unitig graph, any path spells a valid
assembly. As a string/unitig graph also encodes every valid assembly of reads,
such a graph, provided that it can be constructed correctly, is in fact a
lossless representation of reads. In principle, every analysis based on
whole-genome shotgun sequencing (WGS) data, such as SNP and insertion/deletion
(INDEL) calling, can also be achieved with unitigs.
Results: To explore the feasibility of using de novo assembly in the context
of resequencing, we developed a de novo assembler, fermi, that assembles
Illumina short reads into unitigs while preserving most of information of the
input reads. SNPs and INDELs can be called by mapping the unitigs against a
reference genome. By applying the method on 35-fold human resequencing data, we
showed that in comparison to the standard pipeline, our approach yields similar
accuracy for SNP calling and better results for INDEL calling. It has higher
sensitivity than other de novo assembly based methods for variant calling. Our
work suggests that variant calling with de novo assembly be a beneficial
complement to the standard variant calling pipeline for whole-genome
resequencing. In the methodological aspects, we proposed FMD-index for
forward-backward extension of DNA sequences, a fast algorithm for finding all
super-maximal exact matches and one-pass construction of unitigs from an
FMD-index.
Availability: http://github.com/lh3/fermi
Contact: [email protected]: Rev2: submitted version with minor improvements; 7 page
Algorithmic approaches for the single individual haplotyping problem
Since its introduction in 2001, the Single Individual Haplotyping problem has received an ever-increasing attention from the scientific community. In this paper we survey, in the form of an annotated bibliography, the developments in the study of the problem from its origin until our days
Variable neighborhood search for solving the DNA fragment assembly problem
The fragment assembly problem consists in the building of the DNA sequence from several hundreds (or even, thousands) of fragments obtained by biologists in the laboratory. This is an important task in any genome project, since the accuracy of the rest of the phases depends of the result of this stage.
In addition, real instances are very large and therefore, the efficiency is also a very important issue in the design of fragment assemblers. In this paper, we propose two Variable Neighborhood Search variants for solving the DNA fragment assembly problem. These algorithms are specifically adapted for the problem being the difference between them the optimization orientation (fitness function).
One of them maximizes the Parsons’s fitness function (which only considers the overlapping among the fragments) and the other estimates the variation in the number of contigs during a local search movement, in order to minimize the number of contigs. The results show that doesn’t exist a direct relation between these functions (even in several cases opposite values are generated) although for the tested instances, both variants allow to find similar and very good results but the second option reduces significatively the consumed-time.VIII Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI
Computational Molecular Biology
Computational Biology is a fairly new subject that arose in response to the computational problems posed by the analysis and the processing of biomolecular sequence and structure data. The field was initiated in the late 60's and early 70's largely by pioneers working in the life sciences. Physicists and mathematicians entered the field in the 70's and 80's, while Computer Science became involved with the new biological problems in the late 1980's. Computational problems have gained further importance in molecular biology through the various genome projects which produce enormous amounts of data. For this bibliography we focus on those areas of computational molecular biology that involve discrete algorithms or discrete optimization. We thus neglect several other areas of computational molecular biology, like most of the literature on the protein folding problem, as well as databases for molecular and genetic data, and genetic mapping algorithms. Due to the availability of review papers and a bibliography this bibliography
NGS Based Haplotype Assembly Using Matrix Completion
We apply matrix completion methods for haplotype assembly from NGS reads to
develop the new HapSVT, HapNuc, and HapOPT algorithms. This is performed by
applying a mathematical model to convert the reads to an incomplete matrix and
estimating unknown components. This process is followed by quantizing and
decoding the completed matrix in order to estimate haplotypes. These algorithms
are compared to the state-of-the-art algorithms using simulated data as well as
the real fosmid data. It is shown that the SNP missing rate and the haplotype
block length of the proposed HapOPT are better than those of HapCUT2 with
comparable accuracy in terms of reconstruction rate and switch error rate. A
program implementing the proposed algorithms in MATLAB is freely available at
https://github.com/smajidian/HapMC
A hybrid genetic algorithm and inver over approach for the travelling salesman problem
This article posted here with permission of the IEEE - Copyright @ 2010 IEEEThis paper proposes a two-phase hybrid approach for the travelling salesman problem (TSP). The first phase is based on a sequence based genetic algorithm (SBGA) with an embedded local search scheme. Within the SBGA, a memory is introduced to store good sequences (sub-tours) extracted from previous good solutions and the stored sequences are used to guide the generation of offspring via local search during the evolution of the population. Additionally, we also apply some techniques to adapt the key parameters based on whether the best individual of the population improves or not and maintain the diversity. After SBGA finishes, the hybrid approach enters the second phase, where the inver over (IO) operator, which is a state-of-the-art algorithm for the TSP, is used to further improve the solution quality of the population. Experiments are carried out to investigate the performance of the proposed hybrid approach in comparison with several relevant algorithms on a set of benchmark TSP instances. The experimental results show that the proposed hybrid approach is efficient in finding good quality solutions for the test TSPs.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/1
The application of artificial intelligence techniques to a sequencing problem in the biological domain
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