Frequency Analysis Techniques for Identification of Viral Genetic Data

ABSTRACT Environmental metagenomic samples and samples obtained as an attempt to identify a pathogen associated with the emergence of a novel infectious disease are important sources of novel microorganisms. The low costs and high throughput of sequencing technologies are expected to allow for the g...

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Autores principales:	Vladimir Trifonov, Raul Rabadan
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2010
Materias:	Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/c2dc93535d4a4736a7f72cb7336ffe45
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spelling	oai:doaj.org-article:c2dc93535d4a4736a7f72cb7336ffe452021-11-15T15:38:15ZFrequency Analysis Techniques for Identification of Viral Genetic Data10.1128/mBio.00156-102150-7511https://doaj.org/article/c2dc93535d4a4736a7f72cb7336ffe452010-08-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.00156-10https://doaj.org/toc/2150-7511ABSTRACT Environmental metagenomic samples and samples obtained as an attempt to identify a pathogen associated with the emergence of a novel infectious disease are important sources of novel microorganisms. The low costs and high throughput of sequencing technologies are expected to allow for the genetic material in those samples to be sequenced and the genomes of the novel microorganisms to be identified by alignment to those in a database of known genomes. Yet, for various biological and technical reasons, such alignment might not always be possible. We investigate a frequency analysis technique which on one hand allows for the identification of genetic material without relying on alignment and on the other hand makes possible the discovery of nonoverlapping contigs from the same organism. The technique is based on obtaining signatures of the genetic data and defining a distance/similarity measure between signatures. More precisely, the signatures of the genetic data are the frequencies of k-mers occurring in them, with k being a natural number. We considered an entropy-based distance between signatures, similar to the Kullback-Leibler distance in information theory, and investigated its ability to categorize negative-sense single-stranded RNA (ssRNA) viral genetic data. Our conclusion is that in this viral context, the technique provides a viable way of discovering genetic relationships without relying on alignment. We envision that our approach will be applicable to other microbial genetic contexts, e.g., other types of viruses, and will be an important tool in the discovery of novel microorganisms. IMPORTANCE Multiple factors contribute to the emergence of novel infectious diseases. Implementation of effective measures against such diseases relies on the rapid identification of novel pathogens. Another important source of novel microorganisms is environmental metagenomic samples. The low costs and high throughput of sequencing technologies provide a method for the identification of novel microorganisms by sequence alignment. There are several obstacles to this method, as follows: our knowledge of biology is biased by an anthropomorphic view, microbial genomic material could be a minuscule fraction of the sample, the sequencing and enrichment technologies can be a source of errors and biases, and finally, microbes have high diversity and high evolutionary rates. As a result, novel microorganisms could have very low genetic similarity to already known genomes, and the identification by alignment could be computationally prohibitive. We investigate a frequency analysis technique which allows for the identification of novel genetic material without relying on alignment.Vladimir TrifonovRaul RabadanAmerican Society for MicrobiologyarticleMicrobiologyQR1-502ENmBio, Vol 1, Iss 3 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microbiology QR1-502
spellingShingle	Microbiology QR1-502 Vladimir Trifonov Raul Rabadan Frequency Analysis Techniques for Identification of Viral Genetic Data
description	ABSTRACT Environmental metagenomic samples and samples obtained as an attempt to identify a pathogen associated with the emergence of a novel infectious disease are important sources of novel microorganisms. The low costs and high throughput of sequencing technologies are expected to allow for the genetic material in those samples to be sequenced and the genomes of the novel microorganisms to be identified by alignment to those in a database of known genomes. Yet, for various biological and technical reasons, such alignment might not always be possible. We investigate a frequency analysis technique which on one hand allows for the identification of genetic material without relying on alignment and on the other hand makes possible the discovery of nonoverlapping contigs from the same organism. The technique is based on obtaining signatures of the genetic data and defining a distance/similarity measure between signatures. More precisely, the signatures of the genetic data are the frequencies of k-mers occurring in them, with k being a natural number. We considered an entropy-based distance between signatures, similar to the Kullback-Leibler distance in information theory, and investigated its ability to categorize negative-sense single-stranded RNA (ssRNA) viral genetic data. Our conclusion is that in this viral context, the technique provides a viable way of discovering genetic relationships without relying on alignment. We envision that our approach will be applicable to other microbial genetic contexts, e.g., other types of viruses, and will be an important tool in the discovery of novel microorganisms. IMPORTANCE Multiple factors contribute to the emergence of novel infectious diseases. Implementation of effective measures against such diseases relies on the rapid identification of novel pathogens. Another important source of novel microorganisms is environmental metagenomic samples. The low costs and high throughput of sequencing technologies provide a method for the identification of novel microorganisms by sequence alignment. There are several obstacles to this method, as follows: our knowledge of biology is biased by an anthropomorphic view, microbial genomic material could be a minuscule fraction of the sample, the sequencing and enrichment technologies can be a source of errors and biases, and finally, microbes have high diversity and high evolutionary rates. As a result, novel microorganisms could have very low genetic similarity to already known genomes, and the identification by alignment could be computationally prohibitive. We investigate a frequency analysis technique which allows for the identification of novel genetic material without relying on alignment.
format	article
author	Vladimir Trifonov Raul Rabadan
author_facet	Vladimir Trifonov Raul Rabadan
author_sort	Vladimir Trifonov
title	Frequency Analysis Techniques for Identification of Viral Genetic Data
title_short	Frequency Analysis Techniques for Identification of Viral Genetic Data
title_full	Frequency Analysis Techniques for Identification of Viral Genetic Data
title_fullStr	Frequency Analysis Techniques for Identification of Viral Genetic Data
title_full_unstemmed	Frequency Analysis Techniques for Identification of Viral Genetic Data
title_sort	frequency analysis techniques for identification of viral genetic data
publisher	American Society for Microbiology
publishDate	2010
url	https://doaj.org/article/c2dc93535d4a4736a7f72cb7336ffe45
work_keys_str_mv	AT vladimirtrifonov frequencyanalysistechniquesforidentificationofviralgeneticdata AT raulrabadan frequencyanalysistechniquesforidentificationofviralgeneticdata
_version_	1718427850960273408

Frequency Analysis Techniques for Identification of Viral Genetic Data

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