東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Combining Metagenomics with Structural Bioinformatics Reveals the Selective Pressures Driving Protein Evolution in Globally-Prevalent Microbial Populations.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Combining Metagenomics with Structural Bioinformatics Reveals the Selective Pressures Driving Protein Evolution in Globally-Prevalent Microbial Populations./
作者:	Kiefl, Evan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	178 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Microbiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29167213
ISBN:	9798834013907

Combining Metagenomics with Structural Bioinformatics Reveals the Selective Pressures Driving Protein Evolution in Globally-Prevalent Microbial Populations.
Kiefl, Evan.

Combining Metagenomics with Structural Bioinformatics Reveals the Selective Pressures Driving Protein Evolution in Globally-Prevalent Microbial Populations. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 178 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--The University of Chicago, 2022.

This item must not be sold to any third party vendors.

Microbes play important roles in disease, human health, and climate change. Understanding how environmental selective forces shape their evolution underpins our ability to prevent, promote, and engineer their behavior. The genetic diversity of microbial populations can be quantified with metagenomics, however, such diversity represents the outcome of both stochastic and selective forces, making it difficult to identify whether variants are maintained by adaptive, neutral, or purifying processes. This is partly due to the reliance on gene sequences to interpret variants, which disregards the physical properties of three-dimensional gene products that define the functional landscape on which selection acts. Although it is understood that the accuracy of sequence-based evolutionary models improves by integrating structural information of the encoded protein, including structural bioinformatics into metagenomic analyses is hampered by the absence of computational tools that allow researchers to seamlessly integrate these traditionally distinct data types. In my dissertation, I bridge this disconnect by developing anvi'o structure, a computational tool for the analysis and visualization of metagenomic sequence variants in the context of predicted protein structures and binding sites. Taking a marine microbial population as a model system, I illustrate how structure-informed analyses yield insight into the evolutionary relationship between microbes and their environments that can only be learned by combining metagenomics with structural biology. Overall, my work sheds light on how environments induce selective pressures that in turn impact the genetic diversity of populations, and provides a software tool that enables the community to employ similar analyses on different microbial systems.

ISBN: 9798834013907Subjects--Topical Terms:

536250
Microbiology.
Subjects--Index Terms:

Evolution

Combining Metagenomics with Structural Bioinformatics Reveals the Selective Pressures Driving Protein Evolution in Globally-Prevalent Microbial Populations.
LDR:02973nmm a2200349 4500 001 2348999
005 20220920134642.5
008 241004s2022 ||||||||||||||||| ||eng d
020 $a 9798834013907
035 $a (MiAaPQ)AAI29167213
035 $a AAI29167213
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kiefl, Evan. $0 (orcid)0000-0002-6473-0921 $3 3688383
245 1 0 $a Combining Metagenomics with Structural Bioinformatics Reveals the Selective Pressures Driving Protein Evolution in Globally-Prevalent Microbial Populations.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 178 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Eren, A. Murat.
502 $a Thesis (Ph.D.)--The University of Chicago, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Microbes play important roles in disease, human health, and climate change. Understanding how environmental selective forces shape their evolution underpins our ability to prevent, promote, and engineer their behavior. The genetic diversity of microbial populations can be quantified with metagenomics, however, such diversity represents the outcome of both stochastic and selective forces, making it difficult to identify whether variants are maintained by adaptive, neutral, or purifying processes. This is partly due to the reliance on gene sequences to interpret variants, which disregards the physical properties of three-dimensional gene products that define the functional landscape on which selection acts. Although it is understood that the accuracy of sequence-based evolutionary models improves by integrating structural information of the encoded protein, including structural bioinformatics into metagenomic analyses is hampered by the absence of computational tools that allow researchers to seamlessly integrate these traditionally distinct data types. In my dissertation, I bridge this disconnect by developing anvi'o structure, a computational tool for the analysis and visualization of metagenomic sequence variants in the context of predicted protein structures and binding sites. Taking a marine microbial population as a model system, I illustrate how structure-informed analyses yield insight into the evolutionary relationship between microbes and their environments that can only be learned by combining metagenomics with structural biology. Overall, my work sheds light on how environments induce selective pressures that in turn impact the genetic diversity of populations, and provides a software tool that enables the community to employ similar analyses on different microbial systems.
590 $a School code: 0330.
650 4 $a Microbiology. $3 536250
650 4 $a Bioinformatics. $3 553671
650 4 $a Evolution & development. $3 3172418
653 $a Evolution
653 $a Metagenomics
653 $a Protein
690 $a 0410
690 $a 0715
690 $a 0412
710 2 $a The University of Chicago. $b Biophysical Sciences. $3 3286007
773 0 $t Dissertations Abstracts International $g 83-12B.
790 $a 0330
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29167213