Genome-wide inference of ancestral recombination graphs

The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the "ancestral recombination graph" (ARG), a complete record of coalescence and recombination events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. Here, we introduce a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of n chromosomes conditional on an ARG of n-1 chromosomes, an operation we call "threading." Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the true posterior distribution and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. Preliminary results also indicate that our methods can be used to gain insight into complex features of human population structure, even with a noninformative prior distribution.Comment: 88 pages, 7 main figures, 22 supplementary figures. This version contains a substantially expanded genomic data analysi

Fat vs. thin threading approach on GPUs: application to stochastic simulation of chemical reactions

We explore two different threading approaches on a graphics processing unit (GPU) exploiting two different characteristics of the current GPU architecture. The fat thread approach tries to minimise data access time by relying on shared memory and registers potentially sacrificing parallelism. The thin thread approach maximises parallelism and tries to hide access latencies. We apply these two approaches to the parallel stochastic simulation of chemical reaction systems using the stochastic simulation algorithm (SSA) by Gillespie (J. Phys. Chem, Vol. 81, p. 2340-2361, 1977). In these cases, the proposed thin thread approach shows comparable performance while eliminating the limitation of the reaction system’s size

Molecular dynamics recipes for genome research

Molecular dynamics (MD) simulation allows one to predict the time evolution of a system of interacting particles. It is widely used in physics, chemistry and biology to address specific questions about the structural properties and dynamical mechanisms of model systems. MD earned a great success in genome research, as it proved to be beneficial in sorting pathogenic from neutral genomic mutations. Considering their computational requirements, simulations are commonly performed on HPC computing devices, which are generally expensive and hard to administer. However, variables like the software tool used for modeling and simulation or the size of the molecule under investigation might make one hardware type or configuration more advantageous than another or even make the commodity hardware definitely suitable for MD studies. This work aims to shed lights on this aspect

Parallel evolution strategy for protein threading.

A protein-sequence folds into a specific shape in order to function in its aqueous state. If the primary sequence of a protein is given, what is its three dimensional structure? This is a long-standing problem in the field of molecular biology and it has large implication to drug design and cure. Among several proposed approaches, protein threading represents one of the most promising technique. The protein threading problem (PTP) is the problem of determining the three-dimensional structure of a given but arbitrary protein sequence from a set of known structures of other proteins. This problem is known to be NP-hard and current computational approaches to threading are time-consuming and data-intensive. In this thesis, we proposed an evolution strategy (ES) based approach for protein threading (EST). We also developed two parallel approaches for the PTP problem and both are parallelizations of our novel EST. The first method, we call SQST-PEST (Single Query Single Template Parallel EST) threads a single query against a single template. We use ES to find the best alignment between the query and the template, and ES is parallelized. The second method, we call SQMT-PEST (Single Query Multiple Templates Parallel EST) to allow for threading a single query against multiple templates within reasonable time. We obtained better results than current comparable approaches, as well as significant reduction in execution time.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .I85. Source: Masters Abstracts International, Volume: 44-03, page: 1403. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Towards Structural Classification of Proteins based on Contact Map Overlap

A multitude of measures have been proposed to quantify the similarity between protein 3-D structure. Among these measures, contact map overlap (CMO) maximization deserved sustained attention during past decade because it offers a fine estimation of the natural homology relation between proteins. Despite this large involvement of the bioinformatics and computer science community, the performance of known algorithms remains modest. Due to the complexity of the problem, they got stuck on relatively small instances and are not applicable for large scale comparison. This paper offers a clear improvement over past methods in this respect. We present a new integer programming model for CMO and propose an exact B &B algorithm with bounds computed by solving Lagrangian relaxation. The efficiency of the approach is demonstrated on a popular small benchmark (Skolnick set, 40 domains). On this set our algorithm significantly outperforms the best existing exact algorithms, and yet provides lower and upper bounds of better quality. Some hard CMO instances have been solved for the first time and within reasonable time limits. From the values of the running time and the relative gap (relative difference between upper and lower bounds), we obtained the right classification for this test. These encouraging result led us to design a harder benchmark to better assess the classification capability of our approach. We constructed a large scale set of 300 protein domains (a subset of ASTRAL database) that we have called Proteus 300. Using the relative gap of any of the 44850 couples as a similarity measure, we obtained a classification in very good agreement with SCOP. Our algorithm provides thus a powerful classification tool for large structure databases

Parallelizing RRT on distributed-memory architectures

This paper addresses the problem of improving the performance of the Rapidly-exploring Random Tree (RRT) algorithm by parallelizing it. For scalability reasons we do so on a distributed-memory architecture, using the message-passing paradigm. We present three parallel versions of RRT along with the technicalities involved in their implementation. We also evaluate the algorithms and study how they behave on different motion planning problems