Md Pavel Mahmud

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MOTIVATION Mapping billions of reads from next generation sequencing experiments to reference genomes is a crucial task, which can require hundreds of hours of running time on a single CPU even for the fastest known implementations. Traditional approaches have difficulties dealing with matches of large edit distance, particularly in the presence of frequent(More)
Bayesian computations with Hidden Markov Models (HMMs) are often avoided in practice. Instead, due to reduced running time, point estimates – maximum likelihood (ML) or maximum a posterior (MAP) – are obtained and observation sequences are segmented based on the Viterbi path, even though the lack of accuracy and dependency on starting points of the local(More)
BACKGROUND Hidden Markov Models (HMM) are often used for analyzing Comparative Genomic Hybridization (CGH) data to identify chromosomal aberrations or copy number variations by segmenting observation sequences. For efficiency reasons the parameters of a HMM are often estimated with maximum likelihood and a segmentation is obtained with the Viterbi(More)
1 Abstract As high-throughput sequencers become standard equipment outside of sequencing centers, there is an increasing need for efficient methods for pre-processing and primary analysis. While a vast literature proposes methods for HTS data analysis, we argue that significant improvements can still be gained by exploiting expensive pre-processing steps(More)
Since the genomics era has started in the '70s, microarray technologies have been extensively used for biological applications such as gene expression profiling, copy number variation (CNV) or Single Neucleotide Polymorphism (SNP) detection. To analyze microarray data, numerous statistical and algorithmic techniques have been developed over the last two(More)
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