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Your search keyword '"Son K. Pham"' showing total 14 results
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14 results on '"Son K. Pham"'

12. ExSPAnder: a universal repeat resolver for DNA fragment assembly

Author

Irina Vasilinetc, Pavel A. Pevzner, Anton Korobeynikov, Sergey Nurk, Tatiana Krivosheeva, Andrey D. Prjibelski, Alexey Gurevich, Alla Lapidus, Son K. Pham, and Anton Bankevich

Subjects
Statistics and Probability, Staphylococcus aureus, Repetitive Sequences, Sequence assembly, Hybrid genome assembly, Computational biology, Biology, Biochemistry, Genome, Assemblers, Fragment (logic), Actinomycetales, Humans, Genomic library, Molecular Biology, Gene Library, Repetitive Sequences, Nucleic Acid, Genetics, High-Throughput Nucleotide Sequencing, DNA, Sequence Analysis, DNA, Original Papers, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Resolver, comic_books, Ismb 2014 Proceedings Papers Committee, Sequence Analysis, comic_books.character, Algorithms, Genome, Bacterial
Abstract

Next-generation sequencing (NGS) technologies have raised a challenging de novo genome assembly problem that is further amplified in recently emerged single-cell sequencing projects. While various NGS assemblers can use information from several libraries of read-pairs, most of them were originally developed for a single library and do not fully benefit from multiple libraries. Moreover, most assemblers assume uniform read coverage, condition that does not hold for single-cell projects where utilization of read-pairs is even more challenging. We have developed an exSPAnder algorithm that accurately resolves repeats in the case of both single and multiple libraries of read-pairs in both standard and single-cell assembly projects. Availability and implementation: http://bioinf.spbau.ru/en/spades Contact: ap@bioinf.spbau.ru

Published
2014

13. Pathset graphs: a novel approach for comprehensive utilization of paired reads in genome assembly

Author

Glenn Tesler, Dmitry Antipov, Max A. Alekseyev, Pavel A. Pevzner, Son K. Pham, and Alexander Sirotkin

Subjects
Theoretical computer science, Sequence assembly, Biology, Machine learning, computer.software_genre, Genome, Contig Mapping, De Bruijn graph, symbols.namesake, Histogram, Databases, Genetic, Genetics, Escherichia coli, Molecular Biology, RECOMB 2012: Part 3 of 3Guest Editor: Benny ChorResearch Articles, Contig, business.industry, Sequence Analysis, DNA, Data structure, Graph, Computational Mathematics, Computational Theory and Mathematics, Modeling and Simulation, symbols, Artificial intelligence, business, computer, Algorithms
Abstract

One of the key advances in genome assembly that has led to a significant improvement in contig lengths has been improved algorithms for utilization of paired reads (mate-pairs). While in most assemblers, mate-pair information is used in a post-processing step, the recently proposed Paired de Bruijn Graph (PDBG) approach incorporates the mate-pair information directly in the assembly graph structure. However, the PDBG approach faces difficulties when the variation in the insert sizes is high. To address this problem, we first transform mate-pairs into edge-pair histograms that allow one to better estimate the distance between edges in the assembly graph that represent regions linked by multiple mate-pairs. Further, we combine the ideas of mate-pair transformation and PDBGs to construct new data structures for genome assembly: pathsets and pathset graphs.

Published
2012

14. Pagerank based clustering of hypertext document collections

Author

Danil Nemirovsky, Son K. Pham, Vladimir Dobrynin, Elena Smirnova, Konstantin Avrachenkov, Models for the performance analysis and the control of networks (MAESTRO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), St Petersburg State University (SPbU), University of California [San Diego] (UC San Diego), University of California, and University of California (UC)

Subjects
Clustering high-dimensional data, DBSCAN, Fuzzy clustering, Computer science, Single-linkage clustering, Correlation clustering, Conceptual clustering, 02 engineering and technology, computer.software_genre, 01 natural sciences, 010305 fluids & plasmas, law.invention, Biclustering, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], PageRank, CURE data clustering algorithm, law, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Cluster analysis, Brown clustering, Information retrieval, Graph partition, Directed graph, Document clustering, Data stream clustering, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Canopy clustering algorithm, FLAME clustering, Affinity propagation, 020201 artificial intelligence & image processing, Data mining, Hierarchical clustering of networks, computer
Abstract

International audience; Clustering hypertext document collection is an important task in Information Retrieval. Most clustering methods are based on document content and do not take into account the hyper-text links. Here we propose a novel PageRank based clustering (PRC) algorithm which uses the hypertext structure. The PRC algorithm produces graph partitioning with high modularity and coverage. The comparison of the PRC algorithm with two content based clustering algorithms shows that there is a good match between PRC clustering and content based clustering.

Published
2008
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