Biology subjects

Cifello, J.

Publications and source records attributed to Cifello, J..

4 recordsLinked to original sources

bioRxiv2024

CHARMER: detecting and harmonizing high-confidence chromatin interactions across tissues and Hi-C protocols

MotivationChromatin conformation capture experiments (CCC), such as Hi-C and Capture Hi-C (CHiC) work to elucidate the three-dimensional organization of the genome and the underlying epigenetic regulatory structures within. CCC experiments produce large amounts of FASTQ sequencing data with a substantial amount of technical noise and require sophisticated computational pipelines in order to extract meaningful results. Large-scale CCC data repositories like 4D Nucleome and ENCODE mostly provide raw contact information but lack annotated, statistically significant interaction data suitable for downstream genetic and genomic analyses. ResultsHere, we present CHARMER, an end-to-end pipeline integrated across multiple CCC assay types (HiC, CHiC) which generates statistically significant, harmonized, queryable, chromatin interactions in a consistent BED-like format across cell/tissue types and CCC assays. AvailabilityCHARMER is freely available at https://bitbucket.org/wanglab-upenn/CHARMER and harmonized chromatin interaction data will be available in the upcoming version of the FILER database (https://lisanwanglab.org/FILER).

bioinformatics↗

bioRxiv2024

BTS: scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across omics data

MotivationSummary statistics from genome-wide association studies (GWAS) are widely used in fine-mapping and colocalization analyses to identify causal variants and their enrichment in functional contexts, such as affected cell types and genomic features. With the expansion of functional genomic (FG) datasets, which now include hundreds of thousands of tracks across various cell and tissue types, it is critical to establish scalable algorithms integrating thousands of diverse FG annotations with GWAS results. ResultsWe propose BTS (Bayesian Tissue Score), a novel, highly efficient algorithm uniquely designed for 1) identifying affected cell types and functional elements (context-mapping) and 2) fine-mapping potentially causal variants in a context-specific manner using large collections of cell type-specific FG annotation tracks. BTS leverages GWAS summary statistics and annotation-specific Bayesian models to analyze genome-wide annotation tracks, including enhancers, open chromatin, and histone marks. We evaluated BTS on GWAS summary statistics for immune and cardiovascular traits, such as Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Coronary Artery Disease (CAD). Our results demonstrate that BTS is over 100x more efficient in estimating functional annotation effects and context-specific variant fine-mapping compared to existing methods. Importantly, this large-scale Bayesian approach prioritizes both known and novel annotations, cell types, genomic regions, and variants and provides valuable biological insights into the functional contexts of these diseases. Availability and implementationDocker image is available at https://hub.docker.com/r/wanglab/bts with pre-installed BTS R package (https://bitbucket.org/wanglab-upenn/BTS-R) and BTS GWAS summary statistics analysis pipeline (https://bitbucket.org/wanglab-upenn/bts-pipeline).

bioinformatics↗

bioRxiv2023

hipFG: High-throughput harmonization and integration pipeline for functional genomics data

SummaryPreparing functional genomic (FG) data with diverse assay types and file formats for integration into analysis workflows that interpret genome-wide association and other studies is a significant and time-consuming challenge. Here we introduce hipFG, an automatically customized pipeline for efficient and scalable normalization of heterogenous FG data collections into standardized, indexed, rapidly searchable analysis-ready datasets while accounting for FG datatypes (e.g., chromatin interactions, genomic intervals, quantitative trait loci). Availability and ImplementationhipFG is freely available at https://bitbucket.org/wanglab-upenn/hipFG. Docker container is available at https://hub.docker.com/r/wanglab/hipfg. Contactlswang@pennmedicine.upenn.edu Supplementary informationSupplementary data are available as BioRxiv supplemental files.

bioinformatics↗

bioRxiv2021

Langevin dynamics simulation of protein dynamics in nanopores at microsecond timescales

With rapid advancement in the fields of nanopore analysis of protein, it has become imperative to develop modeling framework for understanding the protein dynamics in nanopores. Such modeling framework should include the effects of electro-osmosis, as it plays significant role during protein translocation in confinement. Currently, the molecular dynamics simulations that include the hydrodynamic effects are limited to a timescale of few 100 ns. These simulations give insight about important events like protein unfolding which occurs in this timescale. But many electrophoresis experiments are limited by a detector resolution of ~ 2.5 s. Analytical theory has been used to interpret protein dynamics at such large timescale. There is a need for molecular modeling of more complex environment and protein shapes which cannot be accounted for by analytical theory. We have developed a framework to study globular protein dynamics in nanopores by using langevin dynamics on a rigid body model of protein and the hydrodynamics is accounted by analytical theory for simple cylindrical nanopore geometry. This framework has been applied to study the dynamics of Ubiquitin translocation in SiNx nanopore by Nir et al26. They have reported 7 times decrease in average dwell time of the protein inside the nanopore in response to a small change in pH from 7.0 to 7.2 and the modification of protein charge was attributed for such drastic change. Closer examination using our simulation revealed that the electro-osmotic effects originating due to very small change in the surface electrostatic potential of the nanopore could lead to such a drastic change in protein dynamics.

biophysics↗