scholarly journals DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning

2019 ◽  
Vol 35 (22) ◽  
pp. 4586-4595 ◽  
Author(s):  
Peng Ni ◽  
Neng Huang ◽  
Zhi Zhang ◽  
De-Peng Wang ◽  
Fan Liang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Deep Learning ◽  
Bisulfite Sequencing ◽  
Homo Sapiens ◽  
Accurate Method ◽  
Supplementary Information ◽  
Nanopore Sequencing ◽  
Methylation State ◽  
State Prediction ◽  
Genome Level

Abstract Motivation The Oxford Nanopore sequencing enables to directly detect methylation states of bases in DNA from reads without extra laboratory techniques. Novel computational methods are required to improve the accuracy and robustness of DNA methylation state prediction using Nanopore reads. Results In this study, we develop DeepSignal, a deep learning method to detect DNA methylation states from Nanopore sequencing reads. Testing on Nanopore reads of Homo sapiens (H. sapiens), Escherichia coli (E. coli) and pUC19 shows that DeepSignal can achieve higher performance at both read level and genome level on detecting 6 mA and 5mC methylation states comparing to previous hidden Markov model (HMM) based methods. DeepSignal achieves similar performance cross different DNA methylation bases, different DNA methylation motifs and both singleton and mixed DNA CpG. Moreover, DeepSignal requires much lower coverage than those required by HMM and statistics based methods. DeepSignal can achieve 90% above accuracy for detecting 5mC and 6 mA using only 2× coverage of reads. Furthermore, for DNA CpG methylation state prediction, DeepSignal achieves 90% correlation with bisulfite sequencing using just 20× coverage of reads, which is much better than HMM based methods. Especially, DeepSignal can predict methylation states of 5% more DNA CpGs that previously cannot be predicted by bisulfite sequencing. DeepSignal can be a robust and accurate method for detecting methylation states of DNA bases. Availability and implementation DeepSignal is publicly available at https://github.com/bioinfomaticsCSU/deepsignal. Supplementary information Supplementary data are available at bioinformatics online.

scholarly journals DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning

2018 ◽  
Author(s):  
Peng Ni ◽  
Neng Huang ◽  
Feng Luo ◽  
Jianxin Wang
Keyword(s):  
Dna Methylation ◽  
Deep Learning ◽  
Nanopore Sequencing ◽  
Methylation Data ◽  
Learning Method ◽  
Methylation State ◽  
E Coli ◽  
Laboratory Techniques ◽  
Oxford Nanopore ◽  
Genome Level

AbstractThe Oxford Nanopore sequencing enables to directly detect methylation sites in DNA from reads without extra laboratory techniques. In this study, we develop DeepSignal, a deep learning method to detect DNA methylated sites from Nanopore sequencing reads. DeepSignal construct features from both raw electrical signals and signal sequences in Nanopore reads. Testing on Nanopore reads of pUC19, E. coli and human, we show that DeepSignal can achieve both higher read level and genome level accuracy on detecting 6mA and 5mC methylation comparing to previous HMM based methods. Moreover, DeepSignal achieves similar performance cross different methylation bases and different methylation motifs. Furthermore, DeepSignal can detect 5mC and 6mA methylation states of genome sites with above 90% genome level accuracy under just 5X coverage using controlled methylation data.

scholarly journals MinION nanopore sequencing provides similar methylation estimates to Sanger bisulfite sequencing in the TRPA1 promoter region

2021 ◽  
Author(s):  
Sara Gombert ◽  
Kirsten Jahn ◽  
Hansi Pathak ◽  
Alexandra Burkert ◽  
Gunnar Schmidt ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Gold Standard ◽  
Promoter Region ◽  
Bisulfite Sequencing ◽  
Nanopore Sequencing ◽  
Methylation Data ◽  
New Approaches

Bisulfite sequencing has long been considered the gold standard for measurement of DNA methylation at single CpG resolution. In the meantime, several new approaches have been developed, which are regarded as less error-prone. Since these errors were shown to be sequence-specific, we aimed to verify the methylation data of a particular region of the TRPA1 promoter obtained from our previous studies. For this purpose, we compared methylation rates obtained via direct bisulfite sequencing and nanopore sequencing. Thus, we were able to confirm our previous findings to a large extent.

scholarly journals The Hi-Culfite assay reveals relationships between chromatin contacts and DNA methylation state

2018 ◽  
Author(s):  
Elena K. Stamenova ◽  
Neva C. Durand ◽  
Olga Dudchenko ◽  
Muhammad S. Shamim ◽  
Su-Chen Huang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Open Chromatin ◽  
Whole Genome ◽  
Methylation State ◽  
Whole Genome Bisulfite Sequencing ◽  
Context Sensitive ◽  
Genome Bisulfite Sequencing

AbstractHi-Culfite, a protocol combining Hi-C and whole-genome bisulfite sequencing (WGBS), determines chromatin contacts and DNA methylation simultaneously. Hi-Culfite also reveals relationships that cannot be seen when the two assays are performed separately. For instance, we show that loci associated with open chromatin exhibit context-sensitive methylation: when their spatial neighbors lie in closed chromatin, they are much more likely to be methylated.

scholarly journals LuxUS: DNA Methylation Analysis Using Generalized Linear Mixed Model with Spatial Correlation

2019 ◽  
Author(s):  
Viivi Halla-aho ◽  
Harri Lähdesmäki
Keyword(s):  
Dna Methylation ◽  
Spatial Correlation ◽  
Mixed Model ◽  
Bisulfite Sequencing ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Statistical Testing ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Bisulfite Sequencing Data

AbstractMotivationDNA methylation is an important epigenetic modification, which has multiple functions. DNA methylation and its connections to diseases have been extensively studied in recent years. It is known that DNA methylation levels of neighboring cytosines are correlated and that differential DNA methylation typically occurs rather as regions instead of individual cytosine level.ResultsWe have developed a generalized linear mixed model, LuxUS, that makes use of the correlation between neighboring cytosines to facilitate analysis of differential methylation. LuxUS implements a likelihood model for bisulfite sequencing data that accounts for experimental variation in underlying biochemistry. LuxUS can model both binary and continuous covariates, and mixed model formulation enables including replicate and cytosine random effects. Spatial correlation is included to the model through a cytosine random effect correlation structure. We show with simulation experiments that by utilizing the spatial correlation we gain more power to the statistical testing of differential DNA methylation. Results with real bisulfite sequencing data set show that LuxUS is able to detect biologically significant differentially methylated cytosines.AvailabilityThe tool is available at https://github.com/hallav/LuxUS.Supplementary informationSupplementary data are available at bioRxiv.

Using local alignment to enhance single-cell bisulfite sequencing data efficiency

2019 ◽  
Vol 35 (18) ◽  
pp. 3273-3278 ◽  
Author(s):  
Peng Wu ◽  
Yan Gao ◽  
Weilong Guo ◽  
Ping Zhu
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Bisulfite Sequencing ◽  
Critical Issue ◽  
Supplementary Information ◽  
Local Alignment ◽  
Sequencing Data ◽  
Read Mapping ◽  
Bisulfite Sequencing Data ◽  
Data Efficiency

Abstract Motivation Single-cell bisulfite sequencing (BS-seq) techniques have been developed for DNA methylation heterogeneity detection and studies with limited materials. However, the data deficiency such as low read mapping ratio is still a critical issue. Results We comprehensively characterize single-cell BS-seq data and reveal chimerical molecules to be the major source of alignment failures. These chimerical molecules are produced by recombination of genomic proximal sequences with microhomology regions (MR) after bisulfite conversion. In addition, we find DNA methylation within MR is highly variable, suggesting the necessity of removing these regions to accurately estimate DNA methylation levels. We further develop scBS-map to perform quality control and local alignment of bisulfite sequencing data, chimerical molecule determination and MR removal. Using scBS-map, we show remarkable increases in uniquely mapped reads, genomic coverage and number of CpG sites, and recover more functional elements with precise DNA methylation estimation. Availability and implementation The scBS-map software is freely available at https://github.com/wupengomics/scBS-map. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Latest techniques to study DNA methylation

2019 ◽  
Vol 63 (6) ◽  
pp. 639-648 ◽  
Author(s):  
Quentin Gouil ◽  
Andrew Keniry
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Dna Degradation ◽  
Regions Of Interest ◽  
Nanopore Sequencing ◽  
Short Read ◽  
Long Read ◽  
Base Modifications ◽  
Genomic Regions ◽  
Derivatives Of

Abstract Bisulfite sequencing is a powerful technique to detect 5-methylcytosine in DNA that has immensely contributed to our understanding of epigenetic regulation in plants and animals. Meanwhile, research on other base modifications, including 6-methyladenine and 4-methylcytosine that are frequent in prokaryotes, has been impeded by the lack of a comparable technique. Bisulfite sequencing also suffers from a number of drawbacks that are difficult to surmount, among which DNA degradation, lack of specificity, or short reads with low sequence diversity. In this review, we explore the recent refinements to bisulfite sequencing protocols that enable targeting genomic regions of interest, detecting derivatives of 5-methylcytosine, and mapping single-cell methylomes. We then present the unique advantage of long-read sequencing in detecting base modifications in native DNA and highlight the respective strengths and weaknesses of PacBio and Nanopore sequencing for this application. Although analysing epigenetic data from long-read platforms remains challenging, the ability to detect various modified bases from a universal sample preparation, in addition to the mapping and phasing advantages of the longer read lengths, provide long-read sequencing with a decisive edge over short-read bisulfite sequencing for an expanding number of applications across kingdoms.

scholarly journals LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation

2020 ◽  
Vol 36 (17) ◽  
pp. 4535-4543
Author(s):  
Viivi Halla-aho ◽  
Harri Lähdesmäki
Keyword(s):  
Dna Methylation ◽  
Spatial Correlation ◽  
Mixed Model ◽  
Bisulfite Sequencing ◽  
Linear Mixed Model ◽  
Epigenetic Modification ◽  
Random Effect ◽  
Generalized Linear Mixed Model ◽  
Statistical Testing ◽  
Supplementary Information

Abstract Motivation DNA methylation is an important epigenetic modification, which has multiple functions. DNA methylation and its connections to diseases have been extensively studied in recent years. It is known that DNA methylation levels of neighboring cytosines are correlated and that differential DNA methylation typically occurs rather as regions instead of individual cytosine level. Results We have developed a generalized linear mixed model, LuxUS, that makes use of the correlation between neighboring cytosines to facilitate analysis of differential methylation. LuxUS implements a likelihood model for bisulfite sequencing data that accounts for experimental variation in underlying biochemistry. LuxUS can model both binary and continuous covariates, and mixed model formulation enables including replicate and cytosine random effects. Spatial correlation is included to the model through a cytosine random effect correlation structure. We show with simulation experiments that using the spatial correlation, we gain more power to the statistical testing of differential DNA methylation. Results with real bisulfite sequencing dataset show that LuxUS is able to detect biologically significant differentially methylated cytosines. Availability and implementation The tool is available at https://github.com/hallav/LuxUS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals HAM-TBS: High accuracy methylation measurements via targeted bisulfite sequencing

2017 ◽  
Author(s):  
Simone Röh ◽  
Tobias Wiechmann ◽  
Susann Sauer ◽  
Maik Ködel ◽  
Elisabeth B. Binder ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Cost Effective ◽  
Accurate Method ◽  
High Accuracy ◽  
Ctcf Binding ◽  
Stress System ◽  
Robust Detection ◽  
Targeted Bisulfite Sequencing ◽  
Effective Manner

AbstractBackgroundThe ability to accurately and efficiently measure DNA methylation is vital to advance the understanding of this mechanism and its contribution to common diseases. Here, we present a highly accurate method to measure methylation using bisulfite sequencing (termed HAM-TBS). This novel method is able to assess DNA methylation in multiple samples with high accuracy in a cost-effective manner. We developed this assay for the FKBP5 locus, an important gene in the regulation of the stress system and previously linked to stress-related disorders, but the method is applicable to any locus of interest.ResultsHAM-TBS enables multiplexing of up to 96 samples spanning a region of ~10 kb using the llumina MiSeq. It incorporates a triplicate bisulfite conversion step, pooled target enrichment via PCR, PCR-free library preparation and a minimum coverage of 1,000x. Furthermore, we designed and validated a targeted panel to specifically assess regulatory regions within the FKBP5 locus including the transcription start site, topologically associated domain boundaries, intergenic and proximal enhancers as well as glucocorticoid receptor and CTCF binding sites that are not covered in commercially available DNA methylation arrays.ConclusionsHAM-TBS represents a highly accurate, medium-throughput sequencing approach for robust detection of DNA methylation changes in specific target regions.

scholarly journals Distinct epigenetic signatures between adult-onset and late-onset depression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hirotaka Yamagata ◽  
Hiroyuki Ogihara ◽  
Koji Matsuo ◽  
Shusaku Uchida ◽  
Ayumi Kobayashi ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Clinical Symptoms ◽  
Late Onset ◽  
Adult Onset ◽  
Blood Biomarkers ◽  
Major Depressive ◽  
Genome Wide ◽  
Healthy Control ◽  
Epigenetic Signatures

AbstractThe heterogeneity of major depressive disorder (MDD) is attributed to the fact that diagnostic criteria (e.g., DSM-5) are only based on clinical symptoms. The discovery of blood biomarkers has the potential to change the diagnosis of MDD. The purpose of this study was to identify blood biomarkers of DNA methylation by strategically subtyping patients with MDD by onset age. We analyzed genome-wide DNA methylation of patients with adult-onset depression (AOD; age ≥ 50 years, age at depression onset < 50 years; N = 10) and late-onset depression (LOD; age ≥ 50 years, age at depression onset ≥ 50 years; N = 25) in comparison to that of 30 healthy subjects. The methylation profile of the AOD group was not only different from that of the LOD group but also more homogenous. Six identified methylation CpG sites were validated by pyrosequencing and amplicon bisulfite sequencing as potential markers for AOD in a second set of independent patients with AOD and healthy control subjects (N = 11). The combination of three specific methylation markers achieved the highest accuracy (sensitivity, 64%; specificity, 91%; accuracy, 77%). Taken together, our findings suggest that DNA methylation markers are more suitable for AOD than for LOD patients.

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