DocumentCode :
3539749
Title :
Prediction of horizontal gene transfer in escherichia coil using machine learning
Author :
Sudasinghe, P.G. ; Wijesinghe, C.R. ; Weerasinghe, A.R.
Author_Institution :
Sch. of Comput., Univ. of Colombo, Colombo, Sri Lanka
fYear :
2013
fDate :
11-15 Dec. 2013
Firstpage :
118
Lastpage :
124
Abstract :
Horizontal Gene Transfer (HGT), also known as Lateral Gene Transfer is a process where an organism acquires genetic material from another organism without being a descendant of that organism. Horizontal gene transfer is said to be the predominant method of evolution in prokaryotic organisms. This study is focused on constructing a method that employs genome comparison and semi supervised learning to identify genes that are horizontally transferred to Escherichia coli 0157:H7 and attempting to find a link between these genes and other organisms that display pathogenic behaviour. E.coli 0157:H7 is compared to E.coli K-12 which is a harmless strain of the same organism. This comparison yields the set of genes that has not originated from the same ancestor (non-homologous) and is the possible cause of its pathogenic properties. A supervised self-organizing map was constructed to classify the non-homologous genes as either horizontally or vertically transferred. Most of the obtained horizontally transferred genes have shown a striking similarity to other pathological bacteria and Achaea. The results have indicated that, while it is possible to discern the mode of transfer of a gene based on compositional feature to a certain degree, it is better to combine several other features to further refine the findings.
Keywords :
biology computing; genetics; learning (artificial intelligence); microorganisms; pattern classification; self-organising feature maps; Achaea; E coli K-12; E coli O157:H7; Escherichia coli; genome comparison; horizontal gene transfer prediction; lateral gene transfer; machine learning; nonhomologous genes classification; pathogenic behaviour; pathogenic properties; pathological bacteria; prokaryotic organisms; semisupervised learning; supervised self-organizing map; Amino acids; Bioinformatics; Genomics; Microorganisms; Strain; Vegetation; Codon adaptation index; Escherichia coli; GC content; Horizontal gene transfer; Lateral Gene Transfer; Self-organizing map; Supervised Self-organizing map; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in ICT for Emerging Regions (ICTer), 2013 International Conference on
Conference_Location :
Colombo
Print_ISBN :
978-1-4799-1275-9
Type :
conf
DOI :
10.1109/ICTer.2013.6761165
Filename :
6761165
Link To Document :
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