Embedded Files
IIT Roorkee
IIT Roorkee
SMTrackR: bioRxiv preprint
SMTrackR: bioRxiv preprint
Single-molecule assays like NOMe-seq, dSMF, and Nanopore are superior to DNase-seq and ATAC-seq as they do not destroy DNA. Thus, they enable quantification of all three, i.e., protein-free, Transcription Factor-bound, and histone-complex-bound states. But a user-friendly tool to visualize and quantify such states is lacking. Here, we present SMTrackR, a Bioconductor package to visualize protein-DNA binding states on individual sequenced DNA molecules. SMTrackR queries the single-molecule footprint database we built and hosted at Galaxy Server. It comprises BigBed files generated from NOMe-seq, dSMF, and Nanopore (SMAC-seq) datasets. SMTrackR exploits UCSC REST API to query a BigBed file and plot footprint heatmap categorized in different binding states, as well as report their occupancies. Additionally, this package generates a Gviz-enabled script to visualize these single molecules on gene tracks.
Please see the bioRxiv preprint here.
Cooperative binding between distant transcription factors is a hallmark of active enhancers
We showed that cooperative binding of TFs to DNA is a common phenomenon at active enhancers. And, the distances between bound TFs suggest nucleosome-mediated cooperativity, meaning that the TFs bind synergistically to evict nucleosomes and keep enhancers open for their function. Dual Enzyme Single Molecule Footprinting (dSMF)data played a key role in addressing the problem. Briefly, CpG and GpC methyltransferases are used to methylate accessible cytosines on chromatinized DNA. Thus, a DNA-bound protein leaves a footprint that can be captured on individual sequenced DNA molecules. Please read the article here and its coverage in the preview.
Transcription factor–nucleosome dynamics from plasma cfDNA identifies ER-driven states in breast cancer
The roles of transcription factors in driving cancers can not be underestimated. In particular, steroid receptor-positive cancers have low tumor mutation burden and are primarily regulated by Transcription Factors. We showed that we can monitor Estrogen-Receptor binding in tumors using plasma-derived cell-free DNA. Interestingly, the TF-Nucleosome dynamics helped us purify selection on potential loci informative of separating tumors from healthy. We extensively harnessed TCGA ATAC-seq and other publicly available data to demonstrate the potential of cfDNA-derived information. Please read about the work here.
Systematic prediction of DNA shape changes due to CpG methylation explains epigenetic effects on protein–DNA binding
My graduate study centered on understanding protein-DNA binding specificity in the context of a chemical modification on DNA known as CpG methylation. I developed a method, methyl-DNAshape, that predicts structural features of methylated DNA in a high-throughput manner. A statistical machine learning model, developed with methyl-DNA shape-derived features, explained the DNA shape-readout insight for Deoxyribonuclease I (DNase I). By analyzing the effect of methylation on DNA minor-groove width, we also explained the possible mechanism of reduced binding of human Pbx-Hox heterodimers to DNA sequences with methylated CpG at certain offsets. This work, in conjunction with a study by Kribelbauer et al. (2017), led to a better mechanistic understanding of Pbx-Hox DNA binding. The figure below is one of the main outcomes from this work.
Page updated
Google Sites
Report abuse