Biology subjects

Spill, Y. G.

Publications and source records attributed to Spill, Y. G..

3 recordsLinked to original sources

bioRxiv2023

MethyLasso: a segmentation approach to analyze DNA methylation patterns and identify differentially methylation regions from whole-genome datasets

DNA methylation is an epigenetic mark involved in the regulation of gene expression and patterns of DNA methylation anticorrelates with chromatin accessibility and transcription factor binding. DNA methylation can be profiled at the single cytosine resolution in the whole genome and has been performed in many cell types and conditions. Computational approaches are then essential to study DNA methylation patterns in a single condition or capture dynamic changes of DNA methylation levels across conditions. Towards this goal, we developed MethyLasso, a new approach based on the segmentation of DNA methylation data, that enables the identification of low-methylated regions (LMRs), unmethylated regions (UMRs), DNA methylation valleys (DMVs) and partially methylated domains (PMDs) in a single condition as well as differentially methylated regions (DMRs) between two conditions. We performed a rigorous benchmarking comparing existing approaches by evaluating the number, size, level of DNA methylation, boundaries, CpG content and coverage of the regions using several real datasets as well as the sensitivity and precision of the approaches using simulated data and show that MethyLasso performs best overall. MethyLasso is freely available at https://github.com/abardet/methylasso.

bioinformatics↗

bioRxiv2021

Focal DNA hypo-methylation in cancer is mediated by transcription factors binding

Aberrant DNA methylation has emerged as a hallmark of cancer cells and profiling their epigenetic landscape has widely been carried out in many types of cancer. However, the mechanisms underlying changes in DNA methylation remain elusive. Transcription factors, initially thought to be repressed from binding by DNA methylation, have recently emerged as potential drivers of DNA methylation patterns. Here we perform a rigorous bioinformatic analysis integrating the massive amount of data available from The Cancer Genome Atlas to identify transcription factors driving aberrant DNA methylation. We predict TFs known to be involved in cancer as well as novel candidates to drive hypo-methylated regions such as FOXA1 and GATA3 in breast cancer, FOXA1 and TWIST1 in prostate cancer and NFE2L2 in lung cancer. We also predict TFs that lead to hyper-methylated regions upon TF loss such as EGR1 in several cancer types. Finally, we validate experimentally that FOXA1 and GATA3 mediate hypo-methylated regions in breast cancer cells. Our work shows the importance of TFs as upstream regulators shaping DNA methylation patterns in cancer.

cancer biology↗

bioRxiv2017

Binless normalization of Hi-C data provides significant interaction and difference detection independently of resolution

3C-like experiments, such as 4C or Hi-C, have been fundamental in understanding genome organization. Thanks to these technologies, it is now known, for example, that Topologically Associating Domains (TADs) and chromatin loops are implicated in the dynamic interplay of gene activation and repression, and their disruption can have dramatic effects on embryonic development. To make their detection easier, scientists have endeavored into deeper sequencing to mechanically increase the chances to detect weaker signals such as chromatin loops. Part of this mindset can be attributed to the limitations of existing software: the analysis of Hi-C experiments is both statistically and computationally demanding. Here, we devise a new way to represent Hi-C data, which leads to a more detailed classification of paired-end reads and, ultimately, to a new normalization and interaction detection method. Unlike any other, Binless is resolution-agnostic, and adapts to the quality and quantity of available data. We demonstrate its capacities to call interactions and differences and make the software freely available.

bioinformatics↗