Dataset Information

Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data.

ABSTRACT: DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read bisulfite sequencing and long-read PacBio sequencing have inherent limitations to detect DNA modifications. Here, using raw electric signals of Oxford Nanopore long-read sequencing data, we design DeepMod, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) to detect DNA modifications. We sequence a human genome HX1 and a Chlamydomonas reinhardtii genome using Nanopore sequencing, and then evaluate DeepMod on three types of genomes (Escherichia coli, Chlamydomonas reinhardtii and human genomes). For 5mC detection, DeepMod achieves average precision up to 0.99 for both synthetically introduced and naturally occurring modifications. For 6mA detection, DeepMod achieves ~0.9 average precision on Escherichia coli data, and have improved performance than existing methods on Chlamydomonas reinhardtii data. In conclusion, DeepMod performs well for genome-scale detection of DNA modifications and will facilitate epigenetic analysis on diverse species.

SUBMITTER: Liu Q

PROVIDER: S-EPMC6547721 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data.

Liu Qian Q Fang Li L Yu Guoliang G Wang Depeng D Xiao Chuan-Le CL Wang Kai K

Nature communications 20190604 1

DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read bisulfite sequencing and long-read PacBio sequencing have inherent limitations to detect DNA modifications. Here, using raw electric signals of Oxford Nanopore long-read sequencing data, we design DeepMod, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) to detect DNA modifications. We sequence a human genome HX1 and ...[more]

PMID: 31164644

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Project description:Transposables elements (TEs) contribute to both structural and functional dynamics of most eukaryotic genomes. Because of their propensity to densely populate plant and animal genomes, the precise estimation of the impact of transposition on genomic diversity has been considered as one of the main challenges of today's genomics. The recent development of NGS (next generation sequencing) technologies has open new perspectives in population genomics by providing new methods for high throughput detection of Transposable Elements-associated Structural Variants (TEASV). However, these have relied on Illumina platform that generates short reads (up to 350 nucleotides). This limitation in size of sequence reads can cause high false discovery rate (FDR) and therefore limit the power of detection of TEASVs, especially in the case of large, complex genomes. The newest sequencing technologies, such as Oxford Nanopore Technologies (ONT) can generate kilobases-long reads thus representing a promising tool for TEASV detection in plant and animals.We present the results of a pilot experiment for TEASV detection on the model plant species Arabidopsis thaliana using ONT sequencing and show that it can be used efficiently to detect TE movements. We generated a ~0.8X genome coverage of a met1-derived epigenetic recombinant inbred line (epiRIL) using a MinIon device with R7 chemistry. We were able to detect nine new copies of the LTR-retrotransposon Evadé (EVD). We also evidenced the activity of the DNA transposon CACTA, CAC1.Even at a low sequence coverage (0.8X), ONT sequencing allowed us to reliably detect several TE insertions in Arabidopsis thaliana genome. The long read length allowed a precise and un-ambiguous mapping of the structural variations caused by the activity of TEs. This suggests that the trade-off between read length and genome coverage for TEASV detection may be in favor of the former. Should the technology be further improved both in terms of lower error rate and operation costs, it could be efficiently used in diversity studies at population level.

Dataset Information

Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data.

Publications

Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data.

Similar Datasets

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