東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Machine Learning and Data-Parallel P...

Bzhalava, Zurab,

Linked to FindBook

Google Book

Amazon

博客來

Machine Learning and Data-Parallel Processing for Viral Metagenomics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning and Data-Parallel Processing for Viral Metagenomics // Zurab Bzhalava.
Author:	Bzhalava, Zurab,
Description:	1 electronic resource (58 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Epstein-Barr virus. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28421720
ISBN:	9798744402808

Machine Learning and Data-Parallel Processing for Viral Metagenomics /
Bzhalava, Zurab,

Machine Learning and Data-Parallel Processing for Viral Metagenomics /Zurab Bzhalava. - 1 electronic resource (58 pages)

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

More than 2 million cancer cases around the world each year are caused by viruses. In addition, there are epidemiological indications that other cancer-associated viruses may also exist. However, the identification of highly divergent and yet unknown viruses in human biospecimens is one of the biggest challenges in bio-informatics. Modern-day Next Generation Sequencing (NGS) technologies can be used to directly sequence biospecimens from clinical cohorts with unprecedented speed and depth. These technologies are able to generate billions of bases with rapidly decreasing cost but current bioinformatics tools are inefficient to effectively process these massive datasets. Thus, the objective of this thesis was to facilitate both the detection of highly divergent viruses among generated sequences as well as large-scale analysis of human metagenomic datasets.To re-analyze human sample-derived sequences that were classified as being of "unknown" origin by conventional alignment-based methods, we used a methodology based on profile Hidden Markov Models (HMM) which can capture evolutionary changes by using multiple sequence alignments. We thus identified 510 sequences that were classified as distantly related to viruses. Many of these sequences were homologs to large viruses such as Herpesviridae and Mimiviridae but some of them were also related to small circular viruses such as Circoviridae. We found that bioinformatics analysis using viral profile HMM is capable of extending the classification of previously unknown sequences and consequently the detection of viruses in biospecimens from humans.Different organisms use synonymous codons differently to encode the same amino acids. To investigate whether codon usage bias could predict the presence of virus in metagenomic sequencing data originating from human samples, we trained Random Forest and Artificial Neural Networks based on Relative Synonymous Codon Usage (RSCU) frequency. Our analysis showed that machine learning techniques based on RSCU could identify putative viral sequences with area under the ROC curve of 0.79 and provide important information for taxonomic classification.For identification of viral genomes among raw metagenomic sequences, we developed the tool ViraMiner, a deep learning-based method which uses Convolutional Neural Networks with two convolutional branches. Using 300 base-pair length sequences, ViraMiner achieved 0.923 area under the ROC curve which is considerably improved performance in comparison with previous machine learning methods for virus sequence classification. The proposed architecture, to the best of our knowledge, is the first deep learning tool which can detect viral genomes on raw metagenomic sequences originating from a variety of human samples.To enable large-scale analysis of massive metagenomic sequencing data we used Apache Hadoop and Apache Spark to develop ViraPipe, a scalable parallel bio-informatics pipeline for viral metagenomics. Comparing ViraPipe (executed on 23 nodes) with the sequential pipeline (executed on a single node) was 11 times faster in the metagenome analysis. The new distributed workflow contains several standard bioinformatics tools and can scale to terabytes of data by accessing more computer power from the nodes.To analyze terabytes of RNA-seq data originating from head and neck squamous cell carcinoma samples, we used our parallel bioinformatics pipeline ViraPipe and the most recent version of the HPV sequence database.

English

ISBN: 9798744402808Subjects--Topical Terms:

2050670
Epstein-Barr virus.

Machine Learning and Data-Parallel Processing for Viral Metagenomics /
LDR:04845nmm a22003853i 4500 001 2437605
005 20251124054945.5
006 m o d
007 cr|nu||||||||
008 261011s2020 miu||||||m |||||||eng d
020 $a 9798744402808
035 $a (MiAaPQD)AAI28421720
035 $a (MiAaPQD)Karolinska_1061647028
035 $a AAI28421720
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Bzhalava, Zurab, $e author. $3 3828653
245 1 0 $a Machine Learning and Data-Parallel Processing for Viral Metagenomics / $c Zurab Bzhalava.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 1 electronic resource (58 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisors: Dillner, Joakim; Sundstrom, Karin; Bala, Piotr.
502 $b Ph.D. $c Karolinska Institutet (Sweden) $d 2020.
520 $a More than 2 million cancer cases around the world each year are caused by viruses. In addition, there are epidemiological indications that other cancer-associated viruses may also exist. However, the identification of highly divergent and yet unknown viruses in human biospecimens is one of the biggest challenges in bio-informatics. Modern-day Next Generation Sequencing (NGS) technologies can be used to directly sequence biospecimens from clinical cohorts with unprecedented speed and depth. These technologies are able to generate billions of bases with rapidly decreasing cost but current bioinformatics tools are inefficient to effectively process these massive datasets. Thus, the objective of this thesis was to facilitate both the detection of highly divergent viruses among generated sequences as well as large-scale analysis of human metagenomic datasets.To re-analyze human sample-derived sequences that were classified as being of "unknown" origin by conventional alignment-based methods, we used a methodology based on profile Hidden Markov Models (HMM) which can capture evolutionary changes by using multiple sequence alignments. We thus identified 510 sequences that were classified as distantly related to viruses. Many of these sequences were homologs to large viruses such as Herpesviridae and Mimiviridae but some of them were also related to small circular viruses such as Circoviridae. We found that bioinformatics analysis using viral profile HMM is capable of extending the classification of previously unknown sequences and consequently the detection of viruses in biospecimens from humans.Different organisms use synonymous codons differently to encode the same amino acids. To investigate whether codon usage bias could predict the presence of virus in metagenomic sequencing data originating from human samples, we trained Random Forest and Artificial Neural Networks based on Relative Synonymous Codon Usage (RSCU) frequency. Our analysis showed that machine learning techniques based on RSCU could identify putative viral sequences with area under the ROC curve of 0.79 and provide important information for taxonomic classification.For identification of viral genomes among raw metagenomic sequences, we developed the tool ViraMiner, a deep learning-based method which uses Convolutional Neural Networks with two convolutional branches. Using 300 base-pair length sequences, ViraMiner achieved 0.923 area under the ROC curve which is considerably improved performance in comparison with previous machine learning methods for virus sequence classification. The proposed architecture, to the best of our knowledge, is the first deep learning tool which can detect viral genomes on raw metagenomic sequences originating from a variety of human samples.To enable large-scale analysis of massive metagenomic sequencing data we used Apache Hadoop and Apache Spark to develop ViraPipe, a scalable parallel bio-informatics pipeline for viral metagenomics. Comparing ViraPipe (executed on 23 nodes) with the sequential pipeline (executed on a single node) was 11 times faster in the metagenome analysis. The new distributed workflow contains several standard bioinformatics tools and can scale to terabytes of data by accessing more computer power from the nodes.To analyze terabytes of RNA-seq data originating from head and neck squamous cell carcinoma samples, we used our parallel bioinformatics pipeline ViraPipe and the most recent version of the HPV sequence database.
546 $a English
590 $a School code: 0658
650 4 $a Epstein-Barr virus. $3 2050670
650 4 $a Human papillomavirus. $3 3682467
650 4 $a Neural networks. $3 677449
650 4 $a Hepatitis B. $3 860674
690 $a 0800
710 2 $a Karolinska Institutet (Sweden). $e degree granting institution. $3 3815330
720 1 $a Dillner, Joakim $e degree supervisor.
720 1 $a Sundstrom, Karin $e degree supervisor.
720 1 $a Bala, Piotr $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 83-02B.
790 $a 0658
791 $a Ph.D.
792 $a 2020
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28421720