4,762 research outputs found
On the Complexity of the Single Individual SNP Haplotyping Problem
We present several new results pertaining to haplotyping. These results
concern the combinatorial problem of reconstructing haplotypes from incomplete
and/or imperfectly sequenced haplotype fragments. We consider the complexity of
the problems Minimum Error Correction (MEC) and Longest Haplotype
Reconstruction (LHR) for different restrictions on the input data.
Specifically, we look at the gapless case, where every row of the input
corresponds to a gapless haplotype-fragment, and the 1-gap case, where at most
one gap per fragment is allowed. We prove that MEC is APX-hard in the 1-gap
case and still NP-hard in the gapless case. In addition, we question earlier
claims that MEC is NP-hard even when the input matrix is restricted to being
completely binary. Concerning LHR, we show that this problem is NP-hard and
APX-hard in the 1-gap case (and thus also in the general case), but is
polynomial time solvable in the gapless case.Comment: 26 pages. Related to the WABI2005 paper, "On the Complexity of
Several Haplotyping Problems", but with more/different results. This papers
has just been submitted to the IEEE/ACM Transactions on Computational Biology
and Bioinformatics and we are awaiting a decision on acceptance. It differs
from the mid-August version of this paper because here we prove that 1-gap
LHR is APX-hard. (In the earlier version of the paper we could prove only
that it was NP-hard.
Boosting Haplotype Inference with Local Search
Abstract. A very challenging problem in the genetics domain is to infer haplotypes from genotypes. This process is expected to identify genes affecting health, disease and response to drugs. One of the approaches to haplotype inference aims to minimise the number of different haplotypes used, and is known as haplotype inference by pure parsimony (HIPP). The HIPP problem is computationally difficult, being NP-hard. Recently, a SAT-based method (SHIPs) has been proposed to solve the HIPP problem. This method iteratively considers an increasing number of haplotypes, starting from an initial lower bound. Hence, one important aspect of SHIPs is the lower bounding procedure, which reduces the number of iterations of the basic algorithm, and also indirectly simplifies the resulting SAT model. This paper describes the use of local search to improve existing lower bounding procedures. The new lower bounding procedure is guaranteed to be as tight as the existing procedures. In practice the new procedure is in most cases considerably tighter, allowing significant improvement of performance on challenging problem instances.
Viral population estimation using pyrosequencing
The diversity of virus populations within single infected hosts presents a
major difficulty for the natural immune response as well as for vaccine design
and antiviral drug therapy. Recently developed pyrophosphate based sequencing
technologies (pyrosequencing) can be used for quantifying this diversity by
ultra-deep sequencing of virus samples. We present computational methods for
the analysis of such sequence data and apply these techniques to pyrosequencing
data obtained from HIV populations within patients harboring drug resistant
virus strains. Our main result is the estimation of the population structure of
the sample from the pyrosequencing reads. This inference is based on a
statistical approach to error correction, followed by a combinatorial algorithm
for constructing a minimal set of haplotypes that explain the data. Using this
set of explaining haplotypes, we apply a statistical model to infer the
frequencies of the haplotypes in the population via an EM algorithm. We
demonstrate that pyrosequencing reads allow for effective population
reconstruction by extensive simulations and by comparison to 165 sequences
obtained directly from clonal sequencing of four independent, diverse HIV
populations. Thus, pyrosequencing can be used for cost-effective estimation of
the structure of virus populations, promising new insights into viral
evolutionary dynamics and disease control strategies.Comment: 23 pages, 13 figure
From cheek swabs to consensus sequences : an A to Z protocol for high-throughput DNA sequencing of complete human mitochondrial genomes
Background: Next-generation DNA sequencing (NGS) technologies have made huge impacts in many fields of biological research, but especially in evolutionary biology. One area where NGS has shown potential is for high-throughput sequencing of complete mtDNA genomes (of humans and other animals). Despite the increasing use of NGS technologies and a better appreciation of their importance in answering biological questions, there remain significant obstacles to the successful implementation of NGS-based projects, especially for new users.
Results: Here we present an ‘A to Z’ protocol for obtaining complete human mitochondrial (mtDNA) genomes – from DNA extraction to consensus sequence. Although designed for use on humans, this protocol could also be used to sequence small, organellar genomes from other species, and also nuclear loci. This protocol includes DNA extraction, PCR amplification, fragmentation of PCR products, barcoding of fragments, sequencing using the 454 GS FLX platform, and a complete bioinformatics pipeline (primer removal, reference-based mapping, output of coverage plots and SNP calling).
Conclusions: All steps in this protocol are designed to be straightforward to implement, especially for researchers who are undertaking next-generation sequencing for the first time. The molecular steps are scalable to large numbers (hundreds) of individuals and all steps post-DNA extraction can be carried out in 96-well plate format. Also, the protocol has been assembled so that individual ‘modules’ can be swapped out to suit available resources
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