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Kollector : transcript-informed targeted de novo assembly of gene loci Kucuk, Muhammet Erdi

Abstract

The information stored in nucleotide sequences is of critical importance for modern biological and medical research. However, in spite of considerable advancements in sequencing and computing technologies, de novo assembly of whole eukaryotic genomes is still a time-consuming task that requires a significant amount of computational resources and expertise, and remains beyond the reach of many researchers. One solution to this problem is restricting the assembly to a portion of the genome, which is typically a small region of interest. Genes are the most obvious choice for this kind of targeted assembly approach, as they contain the most relevant biological information, which can be acted upon downstream. Here we present Kollector, a targeted assembly pipeline that assembles genic regions using the information from the transcript sequences. Kollector not just enables researchers to take advantage of the rapidly expanding transcriptome data, but is also scalable to large eukaryotic genomes. These features make Kollector a valuable addition to the current crop of targeted assembly tools, a fact we demonstrate by comparing Kollector to the state-of-the-art. Furthermore, we show that by localizing the assembly problem, Kollector can recover sequences that cannot be reconstructed by a whole genome de novo assembly approach. Finally, we also demonstrate several use cases for Kollector, ranging from comparative genomics to viral strain detection.

Item Metadata

Title
Kollector : transcript-informed targeted de novo assembly of gene loci
Creator
Kucuk, Muhammet Erdi
Publisher
University of British Columbia
Date Issued
2017
Description
The information stored in nucleotide sequences is of critical importance for modern biological and medical research. However, in spite of considerable advancements in sequencing and computing technologies, de novo assembly of whole eukaryotic genomes is still a time-consuming task that requires a significant amount of computational resources and expertise, and remains beyond the reach of many researchers. One solution to this problem is restricting the assembly to a portion of the genome, which is typically a small region of interest. Genes are the most obvious choice for this kind of targeted assembly approach, as they contain the most relevant biological information, which can be acted upon downstream. Here we present Kollector, a targeted assembly pipeline that assembles genic regions using the information from the transcript sequences. Kollector not just enables researchers to take advantage of the rapidly expanding transcriptome data, but is also scalable to large eukaryotic genomes. These features make Kollector a valuable addition to the current crop of targeted assembly tools, a fact we demonstrate by comparing Kollector to the state-of-the-art. Furthermore, we show that by localizing the assembly problem, Kollector can recover sequences that cannot be reconstructed by a whole genome de novo assembly approach. Finally, we also demonstrate several use cases for Kollector, ranging from comparative genomics to viral strain detection.
Genre
Thesis/Dissertation
Type
Text
Language
eng
Date Available
2017-04-24
Provider
Vancouver : University of British Columbia Library
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
DOI
10.14288/1.0345632
URI
http://hdl.handle.net/2429/61362
Degree
Master of Science - MSc
Program
Bioinformatics
Affiliation
Science, Faculty of
Degree Grantor
University of British Columbia
Graduation Date
2017-05
Campus
UBCV
Scholarly Level
Graduate
Rights URI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository
DSpace

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Attribution-NonCommercial-NoDerivatives 4.0 International