Analysis of Data Complexity in Human DNA for Gene-Containing Zone Prediction†

Entropy

Ricardo E. Monge³  Juan L. Crespo¹  Carlos M. Travieso-González² 

[1] Escuela de Ingeniería Eléctrica, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, Código Postal 2060-San José, Costa Rica; E-Mail:;id="af1-entropy-17-01673">1Escuela de Ciencias de la Computación y de la Informática, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, Código Postal 2060-San José, Costa RicaCosta Rica

关键词: information complexity;    DNA;    genomic variability;    gene prediction;    nucleic acid sequence;   

DOI  :  10.3390/e17041673

来源: mdpi

【 摘 要 】

This study delves further into the analysis of genomic data by computing a variety of complexity measures. We analyze the effect of window size and evaluate the precision and recall of the prediction of gene zones, aided with a much larger dataset (full chromosomes). A technique based on the separation of two cases (gene-containing and non-gene-containing) has been developed as a basic gene predictor for automated DNA analysis. This predictor was tested on various sequences of human DNA obtained from public databases, in a set of three experiments. The first one covers window size and other parameters; the second one corresponds to an analysis of a full human chromosome (198 million nucleic acids); and the last one tests subject variability (with five different individual subjects). All three experiments have high-quality results, in terms of recall and precision, thus indicating the effectiveness of the predictor.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland