Biology subjects

Seyed Abolfazl Motahari

Publications and source records attributed to Seyed Abolfazl Motahari.

4 recordsLinked to original sources

bioRxiv2016

GW-CALL: Accurate Genome-Wide Variant Caller

The main challenge in reliable variant calling using DNA reads is to extract information from reads mappable to multiple locations on the reference genome. Conventional approaches ignore these reads and rely on reads mappable uniquely to the reference genome. These approaches fail to perform satisfactorily in variant calling within repeat regions which are abundant in many species including homo sapiens. This, in turn, lowers the reliability of any downstream analysis including poor performance in genome-wide association studies. GW-CALL, a fast and accurate variant caller, is proposed. GW-CALL exploits information of all reads in a genome-wide decision making process. In particular, it partitions the genome into several independent regions called clusters and incorporates an efficient algorithm to use all reads belonging to a cluster in calling variants within that cluster.\n\nAvailabilityGW-CALL is implemented in C++ and is freely available at URL: brl.ce.sharif.edu/gwcall.

Bioinformatics↗

bioRxiv2016

Statistical Association Mapping of Population-Structured Genetic Data

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the disease-causing sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease causal factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability distribution of the model parameters. We have implemented our proposed framework on a software package whose performance is extensively evaluated on a number of synthetic datasets, and compared to some of the well-known existing methods such as STRUCTURE. It has been shown that in extreme scenarios, up to 10 - 15% of improvement in the inference accuracy is achieved with a moderate increase in computational complexity.

Bioinformatics↗

bioRxiv2016

Breaking Lander-Waterman’s Coverage Bound

Lander-Watermans coverage bound establishes the total number of reads required to cover the whole genome of size G bases. In fact, their bound is a direct consequence of the well-known solution to the coupon collectors problem which proves that for such genome, the total number of bases to be sequenced should be O (G ln G). Although the result leads to a tight bound, it is based on a tacit assumption that the set of reads are first collected through a sequencing process and then are processed through a computation process, i.e., there are two different machines: one for sequencing and one for processing. In this paper, we present a significant improvement compared to Lander-Watermans result and prove that by combining the sequencing and computing processes, one can re-sequence the whole genome with as low as O(G) sequenced bases in total. Our approach also dramatically reduces the required computational power for the combined process. Simulation results are performed on real genomes with different sequencing error rates. The results support our theory predicting the log G improvement on coverage bound and corresponding reduction in the total number of bases required to be sequenced.

Bioinformatics↗

bioRxiv2016

Meta-aligner: Long-read alignment based on genome statistics

Fast and accurate alignment of long-reads plays an important role in reducing the overall cost of long-read sequencing. In this paper, we propose Meta-aligner, an efficient and accurate long-read aligner that exploits the statistics of reference genome to improve performance in terms of reducing time complexity and achieving significantly higher recall for very noisy and long reads. The first step of algorithm adopts well-known short-read aligners in order to rapidly align a large fraction of reads through a progressive process of aligning read fragments to the reference genome. In the second phase, the remaining reads are handled by simultaneous alignment of all read fragments and a decision making process which exploits the overall information provided by the corresponding mapped fragments. By using this procedure, significant performance improvement is attained in comparison with traditional schemes in the case of PacBio long-reads.

Bioinformatics↗