Identifying Bacterial Strains from Sequencing Data

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKirjan luku tai artikkeliTieteellinenvertaisarvioitu

Kuvaus

Environmental and clinical settings can host a wide variety of both bacterial species and strains in a single colony but accurate identification of the organisms is difficult. We describe BIB, a probabilistic method for estimating the relative abundances of species or strains contained in mixed samples analyzed by short read high-throughput sequencing. By grouping closely related strains together in clusters, the BIB pipeline is capable of estimating the relative abundances of the clusters contained in a sequencing sample.
Alkuperäiskielienglanti
OtsikkoData Mining for Systems Biology : Methods and Protocols
ToimittajatHiroshi Mamitsuka
Sivumäärä7
JulkaisupaikkaNew York, NY
KustantajaHumana press
Julkaisupäivä2018
Painos2
Sivut1-7
ISBN (painettu)978-1-4939-8560-9
ISBN (elektroninen)978-1-4939-8561-6
DOI - pysyväislinkit
TilaJulkaistu - 2018
OKM-julkaisutyyppiA3 Kirjan tai muun kokoomateoksen osa

Julkaisusarja

NimiMethods in Molecular Biology
KustantajaHumana Press
Vuosikerta1807
ISSN (painettu)1064-3745
ISSN (elektroninen)1940-6029

Tieteenalat

  • 112 Tilastotiede
  • 1183 Kasvibiologia, mikrobiologia, virologia

Lainaa tätä

Mäklin, T., Corander, J., & Honkela, A. (2018). Identifying Bacterial Strains from Sequencing Data. teoksessa H. Mamitsuka (Toimittaja), Data Mining for Systems Biology: Methods and Protocols (2 toim., Sivut 1-7). (Methods in Molecular Biology; Vuosikerta 1807). New York, NY: Humana press. https://doi.org/10.1007/978-1-4939-8561-6_1
Mäklin, Tommi ; Corander, Jukka ; Honkela, Antti . / Identifying Bacterial Strains from Sequencing Data. Data Mining for Systems Biology: Methods and Protocols. Toimittaja / Hiroshi Mamitsuka. 2. toim. New York, NY : Humana press, 2018. Sivut 1-7 (Methods in Molecular Biology).
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Mäklin, T, Corander, J & Honkela, A 2018, Identifying Bacterial Strains from Sequencing Data. julkaisussa H Mamitsuka (Toimittaja), Data Mining for Systems Biology: Methods and Protocols. 2 toim, Methods in Molecular Biology, Vuosikerta 1807, Humana press, New York, NY, Sivut 1-7. https://doi.org/10.1007/978-1-4939-8561-6_1

Identifying Bacterial Strains from Sequencing Data. / Mäklin, Tommi; Corander, Jukka ; Honkela, Antti .

Data Mining for Systems Biology: Methods and Protocols. toim. / Hiroshi Mamitsuka. 2. toim. New York, NY : Humana press, 2018. s. 1-7 (Methods in Molecular Biology; Vuosikerta 1807).

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKirjan luku tai artikkeliTieteellinenvertaisarvioitu

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AU - Corander, Jukka

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N2 - Environmental and clinical settings can host a wide variety of both bacterial species and strains in a single colony but accurate identification of the organisms is difficult. We describe BIB, a probabilistic method for estimating the relative abundances of species or strains contained in mixed samples analyzed by short read high-throughput sequencing. By grouping closely related strains together in clusters, the BIB pipeline is capable of estimating the relative abundances of the clusters contained in a sequencing sample.

AB - Environmental and clinical settings can host a wide variety of both bacterial species and strains in a single colony but accurate identification of the organisms is difficult. We describe BIB, a probabilistic method for estimating the relative abundances of species or strains contained in mixed samples analyzed by short read high-throughput sequencing. By grouping closely related strains together in clusters, the BIB pipeline is capable of estimating the relative abundances of the clusters contained in a sequencing sample.

KW - 112 Statistics and probability

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Mäklin T, Corander J, Honkela A. Identifying Bacterial Strains from Sequencing Data. julkaisussa Mamitsuka H, toimittaja, Data Mining for Systems Biology: Methods and Protocols. 2 toim. New York, NY: Humana press. 2018. s. 1-7. (Methods in Molecular Biology). https://doi.org/10.1007/978-1-4939-8561-6_1