Taxonomic Classification of Bacteria in Shotgun Metagenomic Samples

Visa enkel post
dc.contributor.authorGold Rodal, Iris
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerKristiansson, Erik
dc.contributor.supervisorAspelin, Oscar
dc.date.accessioned2023-12-06T14:10:42Z
dc.date.available2023-12-06T14:10:42Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractThe aim was to investigate taxonomic classification and removal of human host DNA in the context of a bioinformatic analysis pipeline for screening of pathogens. The examination was carried out using simulated short-read sequenced shotgun metagenomic samples. It was found that a majority of human origin DNA could be separated from bacteria using the K-mer based read classifier Kraken 2 with a custom built human only reference database. Effects on taxonomic classification performance were surveyed for variations in sample composition, parameter settings of the taxonomic classifier and reference database composition. Maintaining both high precision and recall for species level taxonomic classification of metagenomic samples was challenging for limited computational resources. A one-size-fits-all approach to taxonomic classification of any shotgun metagenomic sample would be near impossible with the tested K-mer based classifiers (Kraken 2 and Bracken) and instead specialized pipeline tracks optimized for different expected range of species, sequencing depth and abundance distributions could be a solution.
dc.identifier.coursecodeMVEX60
dc.identifier.urihttp://hdl.handle.net/20.500.12380/307420
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectMetagenome taxonomic classification, shotgun metagenomics, WGS, Kraken 2, Bracken, taxonomic classifier, host removal.
dc.titleTaxonomic Classification of Bacteria in Shotgun Metagenomic Samples
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
Master_Thesis_Iris_Gold_Rodal_2023.pdf
Storlek:
2.89 MB
Format:
Adobe Portable Document Format
Beskrivning:
Ladda ner
License bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
license.txt
Storlek:
2.35 KB
Format:
Item-specific license agreed upon to submission
Beskrivning:
Ladda ner
Samling
Examensarbeten för masterexamen