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Mining biological complexity: Cross ...

Yale University.

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Mining biological complexity: Cross integration of large-scale metagenomics, environmental, and chemical datasets.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mining biological complexity: Cross integration of large-scale metagenomics, environmental, and chemical datasets./
Author:	Gianoulis, Tara Ann.
Description:	164 p.
Notes:	Advisers: Mark Gerstein; Michael Snyder.
Contained By:	Dissertation Abstracts International70-06B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3362153
ISBN:	9781109209778

Mining biological complexity: Cross integration of large-scale metagenomics, environmental, and chemical datasets.
Gianoulis, Tara Ann.

Mining biological complexity: Cross integration of large-scale metagenomics, environmental, and chemical datasets. - 164 p.

Advisers: Mark Gerstein; Michael Snyder.

Thesis (Ph.D.)--Yale University, 2009.

Annotation of complete genomes, community analysis of entire ecosystems (metagenomics), and comparative analysis of regulatory networks from multiple species, each of these experiments is emblematic of the high throughput data that is radically altering the scientific landscape. Moreover, so-called next generation sequencing has significantly increased the scope of questions being asked through sequencing making it crucial to understand how to interpret, decode, and integrate sequence data. Although each assay can provide only snapshots of the genes or proteins, through integration of multiple features across different conditions, time points, and species, the goal is to extract the dynamics from these static images and derive their emergent properties. Current integration schemas are constrained to single dimensional features and do not have the flexibility to integrate features not centered solely on genes or proteins. Here, we have developed a new type of integration, cross integration, where the goal is to integrate not to stack gene and protein features in a single dimension but to build spanning relationships (cross patterns) across multidimensional ones. We showed that fusing geography and metagenomics could illuminate microbial adaptations to environmental differences. We identified a number of metabolic components that co-vary with specific environmental features, which we term a metabolic footprint. Further, we speculate that analysis of these environmental dynamics could be used as a sensitive biosensor to detect chemical or other environmental perturbations. In addition, we developed a new formalism both to express and define cross integration and apply it to chemogenomics data. In this manner, we were able to identify cross patterns between properties of drugs and their protein targets. Some of these were intuitive, such as the mirroring of physicochemical properties between drug and target, and others were subtler such as sensitivities to both environmental stress response and particular drug properties. Mining such biological complexity requires a robust infrastructure and new computational models. We have explored several methods to uncover subtle, indirect relationships between multidimensional features; many exciting discoveries remain.

ISBN: 9781109209778Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Mining biological complexity: Cross integration of large-scale metagenomics, environmental, and chemical datasets.
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245 1 0 $a Mining biological complexity: Cross integration of large-scale metagenomics, environmental, and chemical datasets.
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500 $a Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: .
502 $a Thesis (Ph.D.)--Yale University, 2009.
520 $a Annotation of complete genomes, community analysis of entire ecosystems (metagenomics), and comparative analysis of regulatory networks from multiple species, each of these experiments is emblematic of the high throughput data that is radically altering the scientific landscape. Moreover, so-called next generation sequencing has significantly increased the scope of questions being asked through sequencing making it crucial to understand how to interpret, decode, and integrate sequence data. Although each assay can provide only snapshots of the genes or proteins, through integration of multiple features across different conditions, time points, and species, the goal is to extract the dynamics from these static images and derive their emergent properties. Current integration schemas are constrained to single dimensional features and do not have the flexibility to integrate features not centered solely on genes or proteins. Here, we have developed a new type of integration, cross integration, where the goal is to integrate not to stack gene and protein features in a single dimension but to build spanning relationships (cross patterns) across multidimensional ones. We showed that fusing geography and metagenomics could illuminate microbial adaptations to environmental differences. We identified a number of metabolic components that co-vary with specific environmental features, which we term a metabolic footprint. Further, we speculate that analysis of these environmental dynamics could be used as a sensitive biosensor to detect chemical or other environmental perturbations. In addition, we developed a new formalism both to express and define cross integration and apply it to chemogenomics data. In this manner, we were able to identify cross patterns between properties of drugs and their protein targets. Some of these were intuitive, such as the mirroring of physicochemical properties between drug and target, and others were subtler such as sensitivities to both environmental stress response and particular drug properties. Mining such biological complexity requires a robust infrastructure and new computational models. We have explored several methods to uncover subtle, indirect relationships between multidimensional features; many exciting discoveries remain.
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