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1. peaksat: an R package for ChIP-seq peak saturation analysis

   https://doi.org/10.1186/s12864-023-09109-7  

   Boyd, Joseph R. ; Gao, Cong ; Quinn, Kathleen ; Fritz, Andrew ; Stein, Janet ; Stein, Gary ; Glass, Karen ; Frietze, Seth ( January 2023 , BMC Genomics)

   Epigenomic profiling assays such as ChIP-seq have been widely used to map the genome-wide enrichment profiles of chromatin-associated proteins and posttranslational histone modifications. Sequencing depth is a key parameter in experimental design and quality control. However, due to variable sequencing depth requirements across experimental conditions, it can be challenging to determine optimal sequencing depth, particularly for projects involving multiple targets or cell types.

   We developed thepeaksatR package to provide target read depth estimates for epigenomic experiments based on the analysis of peak saturation curves. We appliedpeaksatto establish the distinctive read depth requirements for ChIP-seq studies of histone modifications in different cell lines. Usingpeaksat,we were able to estimate the target read depth required per library to obtain high-quality peak calls for downstream analysis. In addition,peaksatwas applied to other sequence-enrichment methods including CUT&RUN and ATAC-seq.

   peaksataddresses a need for researchers to make informed decisions about whether their sequencing data has been generated to an adequate depth and subsequently sufficient meaningful peaks, and failing that, how many more reads would be required per library.peaksatis applicable to other sequence-based methods that include calling peaks in their analysis.
2. Allele-specific NKX2-5 binding underlies multiple genetic associations with human electrocardiographic traits

   https://doi.org/10.1038/s41588-019-0499-3  

   Benaglio, Paola ; D’Antonio-Chronowska, Agnieszka ; Ma, Wubin ; Yang, Feng ; Young Greenwald, William W. ; Donovan, Margaret K. ; DeBoever, Christopher ; Li, He ; Drees, Frauke ; Singhal, Sanghamitra ; et al ( September 2019 , Nature Genetics)

   The cardiac transcription factor (TF) gene NKX2-5 has been associated with electrocardiographic (EKG) traits through genome-wide association studies (GWASs), but the extent to which differential binding of NKX2-5 at common regulatory variants contributes to these traits has not yet been studied. We analyzed transcriptomic and epigenomic data from induced pluripotent stem cell-derived cardiomyocytes from seven related individuals, and identified ~2,000 single-nucleotide variants associated with allele-specific effects (ASE-SNVs) on NKX2-5 binding. NKX2-5 ASE-SNVs were enriched for altered TF motifs, for heart-specific expression quantitative trait loci and for EKG GWAS signals. Using fine-mapping combined with epigenomic data from induced pluripotent stem cell–derived cardiomyocytes, we prioritized candidate causal variants for EKG traits, many of which were NKX2-5 ASE-SNVs. Experimentally characterizing two NKX2-5 ASE-SNVs (rs3807989 and rs590041) showed that they modulate the expression of target genes via differential protein binding in cardiac cells, indicating that they are functional variants underlying EKG GWAS signals. Our results show that differential NKX2-5 binding at numerous regulatory variants across the genome contributes to EKG phenotypes.
3. Interpretation of deep learning in genomics and epigenomics

   https://doi.org/10.1093/bib/bbaa177  

   Talukder, Amlan ; Barham, Clayton ; Li, Xiaoman ; Hu, Haiyan ( May 2021 , Briefings in Bioinformatics)

   Abstract Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu
4. Deep learning-based enhancement of epigenomics data with AtacWorks

   https://doi.org/10.1038/s41467-021-21765-5  

   Lal, Avantika ; Chiang, Zachary D. ; Yakovenko, Nikolai ; Duarte, Fabiana M. ; Israeli, Johnny ; Buenrostro, Jason D. ( December 2021 , Nature Communications)

   Abstract ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks, a deep learning toolkit to denoise sequencing coverage and identify regulatory peaks at base-pair resolution from low cell count, low-coverage, or low-quality ATAC-seq data. Models trained by AtacWorks can detect peaks from cell types not seen in the training data, and are generalizable across diverse sample preparations and experimental platforms. We demonstrate that AtacWorks enhances the sensitivity of single-cell experiments by producing results on par with those of conventional methods using ~10 times as many cells, and further show that this framework can be adapted to enable cross-modality inference of protein-DNA interactions. Finally, we establish that AtacWorks can enable new biological discoveries by identifying active regulatory regions associated with lineage priming in rare subpopulations of hematopoietic stem cells.
5. Association of CNVs with methylation variation

   https://doi.org/10.1038/s41525-020-00145-w  

   Shi, Xinghua ; Radhakrishnan, Saranya ; Wen, Jia ; Chen, Jin Yun ; Chen, Junjie ; Lam, Brianna Ashlyn ; Mills, Ryan E. ; Stranger, Barbara E. ; Lee, Charles ; Setlur, Sunita R. ( September 2020 , npj Genomic Medicine)

   Germline copy number variants (CNVs) and single-nucleotide polymorphisms (SNPs) form the basis of inter-individual genetic variation. Although the phenotypic effects of SNPs have been extensively investigated, the effects of CNVs is relatively less understood. To better characterize mechanisms by which CNVs affect cellular phenotype, we tested their association with variable CpG methylation in a genome-wide manner. Using paired CNV and methylation data from the 1000 genomes and HapMap projects, we identified genome-wide associations by methylation quantitative trait locus (mQTL) analysis. We found individual CNVs being associated with methylation of multiple CpGs and vice versa. CNV-associated methylation changes were correlated with gene expression. CNV-mQTLs were enriched for regulatory regions, transcription factor-binding sites (TFBSs), and were involved in long-range physical interactions with associated CpGs. Some CNV-mQTLs were associated with methylation of imprinted genes. Several CNV-mQTLs and/or associated genes were among those previously reported by genome-wide association studies (GWASs). We demonstrate that germline CNVs in the genome are associated with CpG methylation. Our findings suggest that structural variation together with methylation may affect cellular phenotype.