On the Use of Binary Trees for DNA Hydroxymethylation Analysis

González, César; Pérez, Manuel Asensi; Orduña, Juan M.; Chaves, Javier F; García, Ana-Bárbara

doi:10.1007/978-3-319-65482-9_37

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…In this implementation (González et al, 2017), workers use an ordered binary tree for each chromosome in the genome, in order to keep track of the count of methylated and hydroxymethylated bases in the BAM files. Each node of the tree represents a base in the mC map.…”

Section: Implementation Based On Binary Treesmentioning

confidence: 99%

“…The design of HPG-HMapper includes the consideration of two different implementations, one of them based on binary trees (González et al, 2017) and the other one based on linear arrays. The performance evaluation results show that the most efficient solution is the implementation based on linear arrays, which allows fast read and update operations while keeping a small memory footprint.…”

Section: Introductionmentioning

confidence: 99%

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HPG-HMapper: A DNA hydroxymethylation analysis tool

González

Pérez

Orduña

et al. 2019

The International Journal of High Performance Computing Applica

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DNA methylation (mC) and hydroxymethylation (hmC) can significantly affect the normal human development, as well as health and disease status. hmC studies require not only specific treatment of DNA, but also software tools for their analysis. However, there are no software tools capable of analyzing DNA hmC currently. In this article, we propose HPG-HMapper, a parallel software tool for analyzing the DNA hmC data obtained by ten-eleven translocation–assisted bisulfite sequencing. This tool takes as input data the output files of mC aligner tools, and it yields mC maps and the accounting of methylated and hydroxymethylated bases on each chromosome. The design of this tool includes the consideration of different approaches, one of them based on binary trees and the other one based on bit arrays. The performance evaluation results show that a hybrid implementation is the most efficient option, allowing fast read and update operations while keeping a small memory footprint.

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Section: Implementation Based On Binary Treesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HPG-HMapper: A DNA hydroxymethylation analysis tool

González

Pérez

Orduña

et al. 2019

The International Journal of High Performance Computing Applica

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“…However, all these software tools yield the results as text files which usually have sizes of tens of Gigabytes, and follow the Sequence Alignment/Map (SAM) format or the Binary Alignment Map (BAM) format. Other tools like HPG-HMapper ( [9,10]) uses the methylation information of each base for each read present in the BAM files to build a DNA methylation map which gives information about the methylation level for each base of the reference genome. This map is yielded as one csv file for each chromosome in the species.…”

Section: Introductionmentioning

confidence: 99%

“…HPG-DHunter is the third and last module of HPG-Msuite (https://grev-uv.github. io/), whose two first modules are HPG-Methyl ( [7,8]) and HPG-HMapper( [9,10]). This comprehensive suite carries out the whole process of methylation (and/or hydroxymethylation) analysis of fastq files coming from different samples, mapping the reads on the reference genome and building a methylation map, and detecting and visualizing DMRs between the different samples.…”

Section: Introductionmentioning

confidence: 99%

“…The first module is HPG-Methyl ( [7,8]), which takes fastq files as input and produces bam or sam files with the methylation and alignment data. The second module is HPG-HMapper ( [9,10]), which takes the bam/sam files as input data and produces one csv file for each chromosome and strand. Finally, HPG-DHunter takes the csv files (one for each chromosome and strand) and builds a…”

Section: Introductionmentioning

confidence: 99%

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HPG-DHunter: an ultrafast, friendly tool for DMR detection and visualization

et al. 2020

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Background Software tools for analyzing DNA methylation do not provide graphical results which can be easily identified, but huge text files containing the alignment of the samples and their methylation status at a resolution of base pairs. There have been proposed different tools and methods for finding Differentially Methylated Regions (DMRs) among different samples, but the execution time required by these tools is large, and the visualization of their results is far from being interactive. Additionally, these methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance when used with real datasets, probably due to the different approaches they use for DMR identification. Thus, a tool which automatically detects DMRs among different samples and interactively visualizes DMRs at different scales (from a bunch to ten of millions of DNA locations) can be the key for shortening the DNA methylation analysis process in many studies. Results In this paper, we propose a software tool based on the wavelet transform. This mathematical tool allows the fast automatic DMR detection by simple comparison of different signals at different resolution levels. Also, it allows an interactive visualization of the DMRs found at different resolution levels. The tool is publicly available at https://grev-uv.github.io/, and it is part of a complete suite of tools which allow to carry out the complete process of DNA alignment and methylation analysis, creation of methylation maps of the whole genome, and the detection and visualization of DMRs between different samples. Conclusions The validation of the developed software tool shows similar concordance with other well-known and extended tools when used with real and synthetic data. The batch mode of the tool is capable of automatically detecting the existing DMRs for half (twelve) of the human chromosomes between two sets of six samples (whose.csv files after the alignment and mapping procedures have an aggregated size of 108 Gigabytes) in around three hours and a half. When compared to other well-known tools, HPG-DHunter only requires around 15% of the execution time required by other tools for detecting the DMRs.

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