Bacterial population assay via k-mer analysis extended abstract

D. Papamichail; S. S. Skiena; D. Van Der Lelie; S. R. McCorkle

doi:10.1142/9781860947322_0030

Bacterial population assay via k-mer analysis extended abstract

D. Papamichail
, S. S. Skiena
, D. Van Der Lelie
, S. R. McCorkle

Computer Science

Stony Brook University
Brookhaven National Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Identifying and assaying the relative abundance of members of complex microbial communities is an important problem in ecology. Sandberg et al. ¹¹ investigated the usage of genomic signatures to provide high identification percentages from short sequence samples. In this paper we present an improved naive Bayesian classification method using conditional probabilities, which can be used to classify unsequenced bacterial species, as well as identify and predict the frequency of the dominant species in mixed microbial populations.

Original language	English
Title of host publication	Proceedings of the 3rd Asia-Pacific Bioinformatics Conference, APBC 2005
Publisher	Imperial College Press
Pages	299-308
Number of pages	10
ISBN (Print)	1860944779, 9781860944772
DOIs	https://doi.org/10.1142/9781860947322_0030
State	Published - 2005
Event	3rd Asia-Pacific Bioinformatics Conference, APBC 2005 - Singapore, Singapore Duration: Jan 17 2005 → Jan 21 2005

Publication series

Name	Series on Advances in Bioinformatics and Computational Biology
Volume	1
ISSN (Print)	1751-6404

Conference

Conference	3rd Asia-Pacific Bioinformatics Conference, APBC 2005
Country/Territory	Singapore
City	Singapore
Period	01/17/05 → 01/21/05

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10.1142/9781860947322_0030

Cite this

Papamichail, D., Skiena, S. S., Van Der Lelie, D., & McCorkle, S. R. (2005). Bacterial population assay via k-mer analysis extended abstract. In Proceedings of the 3rd Asia-Pacific Bioinformatics Conference, APBC 2005 (pp. 299-308). (Series on Advances in Bioinformatics and Computational Biology; Vol. 1). Imperial College Press. https://doi.org/10.1142/9781860947322_0030

@inproceedings{32fcbfa9ad1f4354a7a891789e34d1f4,

title = "Bacterial population assay via k-mer analysis extended abstract",

abstract = "Identifying and assaying the relative abundance of members of complex microbial communities is an important problem in ecology. Sandberg et al. 11 investigated the usage of genomic signatures to provide high identification percentages from short sequence samples. In this paper we present an improved naive Bayesian classification method using conditional probabilities, which can be used to classify unsequenced bacterial species, as well as identify and predict the frequency of the dominant species in mixed microbial populations.",

author = "D. Papamichail and Skiena, \{S. S.\} and \{Van Der Lelie\}, D. and McCorkle, \{S. R.\}",

year = "2005",

doi = "10.1142/9781860947322\_0030",

language = "English",

isbn = "1860944779",

series = "Series on Advances in Bioinformatics and Computational Biology",

publisher = "Imperial College Press",

pages = "299--308",

booktitle = "Proceedings of the 3rd Asia-Pacific Bioinformatics Conference, APBC 2005",

note = "3rd Asia-Pacific Bioinformatics Conference, APBC 2005 ; Conference date: 17-01-2005 Through 21-01-2005",

}

Papamichail, D, Skiena, SS, Van Der Lelie, D & McCorkle, SR 2005, Bacterial population assay via k-mer analysis extended abstract. in Proceedings of the 3rd Asia-Pacific Bioinformatics Conference, APBC 2005. Series on Advances in Bioinformatics and Computational Biology, vol. 1, Imperial College Press, pp. 299-308, 3rd Asia-Pacific Bioinformatics Conference, APBC 2005, Singapore, Singapore, 01/17/05. https://doi.org/10.1142/9781860947322_0030

Bacterial population assay via k-mer analysis extended abstract. / Papamichail, D.; Skiena, S. S.; Van Der Lelie, D. et al.
Proceedings of the 3rd Asia-Pacific Bioinformatics Conference, APBC 2005. Imperial College Press, 2005. p. 299-308 (Series on Advances in Bioinformatics and Computational Biology; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Identifying and assaying the relative abundance of members of complex microbial communities is an important problem in ecology. Sandberg et al. 11 investigated the usage of genomic signatures to provide high identification percentages from short sequence samples. In this paper we present an improved naive Bayesian classification method using conditional probabilities, which can be used to classify unsequenced bacterial species, as well as identify and predict the frequency of the dominant species in mixed microbial populations.

AB - Identifying and assaying the relative abundance of members of complex microbial communities is an important problem in ecology. Sandberg et al. 11 investigated the usage of genomic signatures to provide high identification percentages from short sequence samples. In this paper we present an improved naive Bayesian classification method using conditional probabilities, which can be used to classify unsequenced bacterial species, as well as identify and predict the frequency of the dominant species in mixed microbial populations.

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U2 - 10.1142/9781860947322_0030

DO - 10.1142/9781860947322_0030

M3 - Conference contribution

AN - SCOPUS:84857000796

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SN - 9781860944772

T3 - Series on Advances in Bioinformatics and Computational Biology

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BT - Proceedings of the 3rd Asia-Pacific Bioinformatics Conference, APBC 2005

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ER -

Bacterial population assay via k-mer analysis extended abstract

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