Boosting the Detection of Transposable Elements Using Machine Learning

被引：0

作者：

Loureiro, Tiago ^{[1
]}

Camacho, Rui ^{[2
]}

Vieira, Jorge ^{[3
]}

Fonseca, Nuno A. ^{[4
,5
]}

机构：

[1] DEI and Faculdade de Engenharia, Universidade do Porto, Porto

[2] DEI and Faculdade de Engenharia and LIAAD-INESCTEC, Universidade do Porto, Porto

[3] IBMC - Instituto de Biologia Molecular e Celular and Universidade do Porto, Porto

[4] EMBL Outstation, European Bioinformatics Institute (EBI), Hinxton, Cambridge

[5] CRACS-INESCTEC, Porto

来源：

Advances in Intelligent Systems and Computing | 2013年 / 222卷

关键词：

Genomics; Machine Learning; Transposable Elements;

D O I：

10.1007/978-3-319-00578-2_12

中图分类号：

学科分类号：

摘要：

Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single one achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning classifiers. © Springer International Publishing Switzerland 2013.

引用

页码：85 / 91

页数：6

共 17 条

[11]

Llorns C., Futami R., Bezemer D., Moya A., Gypsy: Database (gydb) of mobile genetic elements, Nucleic Acids Research, 36, pp. 38-46, (2008)

[12]

Lisch D., How important are transposons for plant evolution?, Nat. Rev. Genet, 14, 1, pp. 49-61, (2013)

[13]

McQuilton P., Pierre E., Thurmond J., Flybase 101 - the basics of navigating flybase, Nucleic Acids Research, 40, pp. 706-714, (2012)

[14]

Green P., Smit A.F.A., Hubley R., RepeatMasker Open-3.0

[15]

Kent W., Blat the blast-like alignment tool, Genome Research, 12, (2002)

[16]

Witten I.H., Frank E., Data Mining: Practical Machine Learning Tools and Techniques, (2005)

[17]

Xu Z., Wang H., LTR_FINDER: An efficient tool for the prediction of full-length LTR retro-transposons, Nucleic Acids Research, 35, SUPPL. 2, (2007)

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