PathRacer: Racing Profile HMM Paths on Assembly Graph

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Выдержка

Recently large databases containing profile Hidden Markov Models (pHMMs) emerged. These pHMMs may represent the sequences of antibiotic resistance genes, or allelic variations amongst highly conserved housekeeping genes used for strain typing, etc. The typical application of such a database includes the alignment of contigs to pHMM hoping that the sequence of gene of interest is located within the single contig. Such a condition is often violated for metagenomes preventing the effective use of such databases. We present PathRacer—a novel standalone tool that aligns profile HMM directly to the assembly graph (performing the codon translation on fly for amino acid pHMMs). The tool provides the set of most probable paths traversed by a HMM through the whole assembly graph, regardless whether the sequence of interested is encoded on the single contig or scattered across the set of edges, therefore significantly improving the recovery of sequences of interest even from fragmented metagenome assemblies.

Язык оригиналаанглийский
Название основной публикации6th International Conference on Algorithms for Computational Biology
РедакторыMiguel A. Vega-Rodríguez, Ian Holmes, Carlos Martín-Vide
ИздательSpringer
Страницы80-94
Число страниц15
ISBN (печатное издание)9783030181734
DOI
СостояниеОпубликовано - 25 мая 2019
Событие6th International Conference on Algorithms for Computational Biology, AlCoB 2019 - Berkeley, Соединенные Штаты Америки
Продолжительность: 28 мая 201930 мая 2019

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том11488 LNBI
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349

Конференция

Конференция6th International Conference on Algorithms for Computational Biology, AlCoB 2019
СтранаСоединенные Штаты Америки
ГородBerkeley
Период28/05/1930/05/19

Отпечаток

Hidden Markov models
Markov Model
Path
Genes
Graph in graph theory
Gene
Antibiotics
Amino acids
Probable
Amino Acids
Alignment
Recovery
Profile

Предметные области Scopus

  • Теоретические компьютерные науки
  • Компьютерные науки (все)

Цитировать

Shlemov, A., & Korobeynikov, A. (2019). PathRacer: Racing Profile HMM Paths on Assembly Graph. В M. A. Vega-Rodríguez, I. Holmes, & C. Martín-Vide (Ред.), 6th International Conference on Algorithms for Computational Biology (стр. 80-94). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 11488 LNBI). Springer. https://doi.org/10.1007/978-3-030-18174-1_6
Shlemov, Alexander ; Korobeynikov, Anton. / PathRacer : Racing Profile HMM Paths on Assembly Graph. 6th International Conference on Algorithms for Computational Biology. редактор / Miguel A. Vega-Rodríguez ; Ian Holmes ; Carlos Martín-Vide. Springer, 2019. стр. 80-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Recently large databases containing profile Hidden Markov Models (pHMMs) emerged. These pHMMs may represent the sequences of antibiotic resistance genes, or allelic variations amongst highly conserved housekeeping genes used for strain typing, etc. The typical application of such a database includes the alignment of contigs to pHMM hoping that the sequence of gene of interest is located within the single contig. Such a condition is often violated for metagenomes preventing the effective use of such databases. We present PathRacer—a novel standalone tool that aligns profile HMM directly to the assembly graph (performing the codon translation on fly for amino acid pHMMs). The tool provides the set of most probable paths traversed by a HMM through the whole assembly graph, regardless whether the sequence of interested is encoded on the single contig or scattered across the set of edges, therefore significantly improving the recovery of sequences of interest even from fragmented metagenome assemblies.",
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Shlemov, A & Korobeynikov, A 2019, PathRacer: Racing Profile HMM Paths on Assembly Graph. в MA Vega-Rodríguez, I Holmes & C Martín-Vide (ред.), 6th International Conference on Algorithms for Computational Biology. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), том. 11488 LNBI, Springer, стр. 80-94, Berkeley, Соединенные Штаты Америки, 28/05/19. https://doi.org/10.1007/978-3-030-18174-1_6

PathRacer : Racing Profile HMM Paths on Assembly Graph. / Shlemov, Alexander; Korobeynikov, Anton.

6th International Conference on Algorithms for Computational Biology. ред. / Miguel A. Vega-Rodríguez; Ian Holmes; Carlos Martín-Vide. Springer, 2019. стр. 80-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 11488 LNBI).

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

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Shlemov A, Korobeynikov A. PathRacer: Racing Profile HMM Paths on Assembly Graph. В Vega-Rodríguez MA, Holmes I, Martín-Vide C, редакторы, 6th International Conference on Algorithms for Computational Biology. Springer. 2019. стр. 80-94. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-18174-1_6