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Title: High-throughput sequencing analysis pipeline
Author: Mozere, M.
ISNI:       0000 0004 7231 1027
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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High-throughput sequencing methods were developed to increase the productivity of processing data from genomic DNA. Sequencing platforms are generating massive amounts of genetic variation data which makes it difficult to pinpoint a small subset of functionally important variants. The focus has now shifted from generating sequences to searching for the critical differences that separate normal variants from disease ones. Our High-throughput Sequencing Analysis Pipeline (HSAP) is a multistep analysis software designed to annotate and filter variants in a top-down fashion from Variant Calling Format (VCF) files in order to find disease causing variants in the patients. It is designed in Linux medium and is composed of a collection of interacting task-specific modules written in different programming languages (such as Python, C++) and shell scripts. Each module is designed to perform a specific task, such as: annotate variants with their functional characterisation, zygosity status, allele frequencies within population; filter variants depending on the inherited disease model, read depth, call quality, physical location and other criteria. The output is added to the universal VCF format file, which contains annotated and filtered genomic variants. The pipeline was verified by identifying/confirming a specific disease-causing mutation for a single-gene disorder. HSAP is designed as an open-source locally self-contained bootable software that uses only information from publicly available databases. It has a user-friendly offline web-interface that allows to select different modules and chain them together to create unique filtering arrangements in order to adapt the pipeline as needed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available