DNA Modification Patterns Filtering and Analysis Using DNAModAnnot.

Mapping DNA modifications at the base resolution is now possible at the genome level thanks to advances in sequencing technologies. Long-read sequencing data can be used to identify modified base patterns. However, the downstream analysis of Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT) data requires the integration of genomic annotation and comprehensive filtering to prevent the accumulation of artifact signals. We present in this chapter, a linear workflow to fully analyze modified base patterns using the DNA Modification Annotation (DNAModAnnot) package. This workflow includes a thorough filtering based on sequencing quality and false discovery rate estimation and provides tools for a global analysis of DNA modifications. Here, we provide an application example of this workflow with PacBio data and guide the user by explaining expected outputs via a fully integrated Rmarkdown script. This protocol is presented with tips showing how to adapt the provided code for annotating epigenomes of any organism according to the user needs.

Identification of differentially methylated regions in rare diseases from a single-patient perspective.

BACKGROUND: DNA methylation (5-mC) is being widely recognized as an alternative in the detection of sequence variants in the diagnosis of some rare neurodevelopmental and imprinting disorders. Identification of alterations in DNA methylation plays an important role in the diagnosis and understanding of the etiology of those disorders. Canonical pipelines for the detection of differentially methylated regions (DMRs) usually rely on inter-group (e.g., case versus control) comparisons. However, these tools might perform suboptimally in the context of rare diseases and multilocus imprinting disturbances due to small cohort sizes and inter-patient heterogeneity. Therefore, there is a need to provide a simple but statistically robust pipeline for scientists and clinicians to perform differential methylation analyses at the single patient level as well as to evaluate how parameter fine-tuning may affect differentially methylated region detection. RESULT: We implemented an improved statistical method to detect differentially methylated regions in correlated datasets based on the Z-score and empirical Brown aggregation methods from a single-patient perspective. To accurately assess the predictive power of our method, we generated semi-simulated data using a public control population of 521 samples and investigated how the size of the control population, methylation difference, and region size affect DMR detection. In addition, we validated the detection of methylation events in patients suffering from rare multi-locus imprinting disturbance and evaluated how this method could complement existing tools in the context of clinical diagnosis. CONCLUSION: In this study, we present a robust statistical method to perform differential methylation analysis at the single patient level and describe its optimal parameters to increase DMRs identification performance. Finally, we show its diagnostic utility when applied to rare disorders.

Integration of multimodal data in the developing tooth reveals candidate regulatory loci driving human odontogenic phenotypes.

Human odontogenic aberrations such as abnormal tooth number and delayed tooth eruption can occur as a symptom of rare syndromes or, more commonly, as nonsyndromic phenotypes. These phenotypes can require extensive and expensive dental treatment, posing a significant burden. While many dental phenotypes are heritable, most nonsyndromic cases have not been linked to causal genes. We demonstrate the novel finding that common sequence variants associated with human odontogenic phenotypes are enriched in developmental craniofacial enhancers conserved between human and mouse. However, the bulk nature of these samples obscures if this finding is due to the tooth itself or the surrounding tissues. We therefore sought to identify enhancers specifically active in the tooth anlagen and quantify their contribution to the observed genetic enrichments. We systematically identified 22,001 conserved enhancers active in E13.5 mouse incisors using ChIP-seq and machine learning pipelines and demonstrated biologically relevant enrichments in putative target genes, transcription factor binding motifs, and in vivo activity. Multi-tissue comparisons of human and mouse enhancers revealed that these putative tooth enhancers had the strongest enrichment of odontogenic phenotype-associated variants, suggesting a role for dysregulation of tooth developmental enhancers in human dental phenotypes. The large number of these regions genome-wide necessitated prioritization of enhancer loci for future investigations. As enhancers modulate gene expression, we prioritized regions based on enhancers' putative target genes. We predicted these target genes and prioritized loci by integrating chromatin state, bulk gene expression and coexpression, GWAS variants, and cell type resolved gene expression to generate a prioritized list of putative odontogenic phenotype-driving loci active in the developing tooth. These genomic regions are of particular interest for downstream experiments determining the role of specific dental enhancer:gene pairs in odontogenesis.

DNAModAnnot: a R toolbox for DNA modification filtering and annotation.

MOTIVATION: Long-read sequencing technologies can be employed to detect and map DNA modifications at the nucleotide resolution on a genome-wide scale. However, published software packages neglect the integration of genomic annotation and comprehensive filtering when analyzing patterns of modified bases detected using Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT) data. Here, we present DNA Modification Annotation (DNAModAnnot), a R package designed for the global analysis of DNA modification patterns using adapted filtering and visualization tools. RESULTS: We tested our package using PacBio sequencing data to analyze patterns of the 6-methyladenine (6mA) in the ciliate Paramecium tetraurelia, in which high 6mA amounts were previously reported. We found P. tetraurelia 6mA genome-wide distribution to be similar to other ciliates. We also performed 5-methylcytosine (5mC) analysis in human lymphoblastoid cells using ONT data and confirmed previously known patterns of 5mC. DNAModAnnot provides a toolbox for the genome-wide analysis of different DNA modifications using PacBio and ONT long-read sequencing data. AVAILABILITY AND IMPLEMENTATION: DNAModAnnot is distributed as a R package available via GitHub (https://github.com/AlexisHardy/DNAModAnnot). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.