Högskolan i Skövde

his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Detection and analysis of megasatellites in the human genome using in silico methods
University of Skövde, School of Humanities and Informatics.
2005 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

Megasatellites are polymorphic tandem repetitive sequences with repeat-units longer than or equal to 1000 base pairs. The novel algorithm Megasatfinder predicts megasatellites in the human genome. A structured method of analysing the algorithm is developed and conducted. The analysis method consists of six test scenarios. Scripts are created, which execute the algorithm using various parameter settings. Three nucleotide sequences are applied; a real sequence extracted from the human genome and two random sequences, generated using different base probabilities. Usability and accuracy are investigated, providing the user with confidence in the algorithm and its output. The results indicate that Megasatfinder is an excellent tool for the detection of megasatellites and that the generated results are highly reliable. The results of the complete analysis suggest alterations in the default parameter settings, presented as user guidelines, and state that artificially generated sequences are not applicable as models for real DNA in computational simulations.

Place, publisher, year, edition, pages
Skövde: Institutionen för kommunikation och information , 2005. , p. 81
Keywords [en]
Genomic variation, repetitive sequences, tandem repeats, polymorphism, satellite DNA, megasatellites, Megasatfinder, in silico prediction, algorithm analysis method.
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:his:diva-961OAI: oai:DiVA.org:his-961DiVA, id: diva2:3386
Presentation
(English)
Uppsok
bio-/geovetenskap
Supervisors
Available from: 2008-03-07 Created: 2008-03-07 Last updated: 2009-05-11

Open Access in DiVA

fulltext(3754 kB)307 downloads
File information
File name FULLTEXT01.psFile size 3754 kBChecksum MD5
f53e15b094faf38b20d8bd1949e12c961ac4b7196eaabb86da76ac9ba419df38c147b8cb
Type fulltextMimetype application/postscript
fulltext(731 kB)396 downloads
File information
File name FULLTEXT02.pdfFile size 731 kBChecksum SHA-512
f42ae550461446192ac8c35042fe0600e7a0c9cd2ff79198516b3813a7d7b134c3ac806307af7462a72c49b30143148e8b51944a0a9b46adf4924fe9ef052fad
Type fulltextMimetype application/pdf

By organisation
School of Humanities and Informatics
Bioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar
Total: 706 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 774 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf