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Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines

Improving Protein-Protein Interaction Pair Ranking with an Integrated Global Association Score

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Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines

Content Provider	ACM Digital Library
Author	Datta, Suprakash Ashlock, Wendy
Abstract	Side effect machines produce features for classifiers that distinguish different types of DNA sequences. They have the, as yet unexploited, potential to give insight into biological features of the sequences. We introduce several innovations to the production and use of side effect machine sequence features. We compare the results of using consensus sequences and genomic sequences for training classifiers and find that more accurate results can be obtained using genomic sequences. Surprisingly, we were even able to build a classifier that distinguished consensus sequences from genomic sequences with high accuracy, suggesting that consensus sequences are not always representative of their genomic counterparts. We apply our techniques to the problem of distinguishing two types of transposable elements, solo LTRs and SINEs. Identifying these sequences is important because they affect gene expression, genome structure, and genetic diversity, and they serve as genetic markers. They are of similar length, neither codes for protein, and both have many nearly identical copies throughout the genome. Being able to efficiently and automatically distinguish them will aid efforts to improve annotations of genomes. Our approach reveals structural characteristics of the sequences of potential interest to biologists.
Starting Page	1676
Ending Page	1689
Page Count	14
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2012.116
Volume Number	9
Issue Number	6
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-11-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bioinformatics,Genomics,Genetic algorithms,DNA,Accuracy,Humans,Correlation,side effect machines,Endogenous retroviruses,LTR retrotransposons,SINE elements,feature evaluation and selection,machine learning,evolutionary computing and genetic algorithms
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines

Evidence that a major class of mouse endogenous long terminal repeats (LTRs) resulted from recombination between exogenous retroviral LTRs and similar LTR-like elements (LTR-IS).

Evolutionary conservation, diversity and specificity of LTR-retrotransposons in flowering plants: insights from genome-wide analysis and multi-specific comparison

Fast algorithms for recognizing retroviruses

On side effect machines as a representation for evolutionary algorithms

Mobile genetic elements in protozoan parasites

Evidence that a major class of mouse endogenous long terminal repeats (LTRs) resulted from recombination between exogenous retroviral LTRs and similar LTR-like elements (LTR-IS).

Evolved Features for DNA Sequence Classification and Their Fitness Landscapes

Genomic Landscape of Long Terminal Repeat Retrotransposons (LTR-RTs) and Solo LTRs as Shaped by Ectopic Recombination in Chicken and Zebra Finch

Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines

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Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines

Evidence that a major class of mouse endogenous long terminal repeats (LTRs) resulted from recombination between exogenous retroviral LTRs and similar LTR-like elements (LTR-IS).

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Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines