Bacterial DNA sequence compression models using artificial neural networks
type: Item
instance of: scholarly article
title: Bacterial DNA sequence compression models using artificial neural networks
zbMATH Open document ID: 1361.92006
author: Manuel J. Duarte
author: Armando J. Pinho
published in: Entropy
publication date: 2016-05-10T00:00:00Z
review text: Summary: It is widely accepted that the advances in DNA sequencing techniques have contributed to an unprecedented growth of genomic data. This fact has increased the interest in DNA compression, not only from the information theory and biology points of view, but also from a practical perspective, since such sequences require storage resources. Several compression methods exist, and particularly, those using finite-context models (FCMs) have received increasing attention, as they have been proven to effectively compress DNA sequences with low bits-per-base, as well as low encoding/decoding time-per-base. However, the amount of run-time memory required to store high-order finite-context models may become impractical, since a context-order as low as 16 requires a maximum of \(17.2\times 10^9\) memory entries. This paper presents a method to reduce such a memory requirement by using a novel application of artificial neural networks (ANN) to build such probabilistic models in a compact way and shows how to use them to estimate the probabilities. Such a system was implemented, and its performance compared against state-of-the art compressors, such as XM-DNA (expert model) and FCM-Mx (mixture of finite-context models) , as well as with general-purpose compressors. Using a combination of order-10 FCM and ANN, similar encoding results to those of FCM, up to order-16, are obtained using only 17 megabytes of memory, whereas the latter, even employing hash-tables, uses several hundreds of megabytes.
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 92D10
Mathematics Subject Classification ID: 94A17
zbMATH DE Number: 6578338
zbMATH Keywords: compression
zbMATH Keywords: finite-context models
zbMATH Keywords: Markov models
zbMATH Keywords: neural nets
describes a project that uses: PRMLT
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e15093435
OpenAlex ID: W2065504303
Wikidata QID: Q111518074
cites work: A universal algorithm for sequential data compression
cites work: A new challenge for compression algorithms: Genetic sequences
DOI: 10.3390/E15093435
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description: scientific article; zbMATH DE number 6578338