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Use of a neural network to predict normalized signal strengths from a DNA-sequencing microarray

A microarray DNA sequencing experiment for a molecule of N bases produces a 4xN data matrix, where for each of the N positions each quartet comprises the signal strength of binding of an experimental DNA to a reference oligonucleotide affixed to the microarray, for the four possible bases (A, C, G,...

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Autores principales: Chilaka, Charles, Carr, Steven, Shalaby, Nabil, Banzhaf, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5651225/
https://www.ncbi.nlm.nih.gov/pubmed/29081611
http://dx.doi.org/10.6026/97320630013313
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author Chilaka, Charles
Carr, Steven
Shalaby, Nabil
Banzhaf, Wolfgang
author_facet Chilaka, Charles
Carr, Steven
Shalaby, Nabil
Banzhaf, Wolfgang
author_sort Chilaka, Charles
collection PubMed
description A microarray DNA sequencing experiment for a molecule of N bases produces a 4xN data matrix, where for each of the N positions each quartet comprises the signal strength of binding of an experimental DNA to a reference oligonucleotide affixed to the microarray, for the four possible bases (A, C, G, or T). The strongest signal in each quartet should result from a perfect complementary match between experimental and reference DNA sequence, and therefore indicate the correct base call at that position. The linear series of calls should constitute the DNA sequence. Variation in the absolute and relative signal strengths, due to variable base composition and other factors over the N quartets, can interfere with the accuracy and (or) confidence of base calls in ways that are not fully understood. We used a feed-forward back-propagation neural network model to predict normalized signal intensities of a microarray-derived DNA sequence of N = 15,453 bases. The DNA sequence was encoded as n-gram neural input vectors, where n = 1, 2, and their composite. The data were divided into training, validation, and testing sets. Regression values were >99% overall, and improved with increased number of neurons in the hidden layer, and in the composition n-grams. We also noticed a very low mean square error overall which transforms to a high performance value.
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spelling pubmed-56512252017-10-27 Use of a neural network to predict normalized signal strengths from a DNA-sequencing microarray Chilaka, Charles Carr, Steven Shalaby, Nabil Banzhaf, Wolfgang Bioinformation Hypothesis A microarray DNA sequencing experiment for a molecule of N bases produces a 4xN data matrix, where for each of the N positions each quartet comprises the signal strength of binding of an experimental DNA to a reference oligonucleotide affixed to the microarray, for the four possible bases (A, C, G, or T). The strongest signal in each quartet should result from a perfect complementary match between experimental and reference DNA sequence, and therefore indicate the correct base call at that position. The linear series of calls should constitute the DNA sequence. Variation in the absolute and relative signal strengths, due to variable base composition and other factors over the N quartets, can interfere with the accuracy and (or) confidence of base calls in ways that are not fully understood. We used a feed-forward back-propagation neural network model to predict normalized signal intensities of a microarray-derived DNA sequence of N = 15,453 bases. The DNA sequence was encoded as n-gram neural input vectors, where n = 1, 2, and their composite. The data were divided into training, validation, and testing sets. Regression values were >99% overall, and improved with increased number of neurons in the hidden layer, and in the composition n-grams. We also noticed a very low mean square error overall which transforms to a high performance value. Biomedical Informatics 2017-09-30 /pmc/articles/PMC5651225/ /pubmed/29081611 http://dx.doi.org/10.6026/97320630013313 Text en © 2017 Biomedical Informatics http://creativecommons.org/licenses/by/3.0/ This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
spellingShingle Hypothesis
Chilaka, Charles
Carr, Steven
Shalaby, Nabil
Banzhaf, Wolfgang
Use of a neural network to predict normalized signal strengths from a DNA-sequencing microarray
title Use of a neural network to predict normalized signal strengths from a DNA-sequencing microarray
title_full Use of a neural network to predict normalized signal strengths from a DNA-sequencing microarray
title_fullStr Use of a neural network to predict normalized signal strengths from a DNA-sequencing microarray
title_full_unstemmed Use of a neural network to predict normalized signal strengths from a DNA-sequencing microarray
title_short Use of a neural network to predict normalized signal strengths from a DNA-sequencing microarray
title_sort use of a neural network to predict normalized signal strengths from a dna-sequencing microarray
topic Hypothesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5651225/
https://www.ncbi.nlm.nih.gov/pubmed/29081611
http://dx.doi.org/10.6026/97320630013313
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