Biology subjects

Kuksa, P. P.

Publications and source records attributed to Kuksa, P. P..

8 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

Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes

Progressive supranuclear palsy (PSP) is a rare Parkinsonian disorder characterized by problems with movement, balance, cognition, and other symptoms. PSP differs from Alzheimers disease (AD) and other neurodegenerative diseases displaying abnormal forms of the microtubule-associated protein tau ("tauopathies") by the presence of pathology not only in neurons, but also in astrocytes and oligodendrocytes. Genetic contributors may mediate these differences, however much of PSP genetics remains unexplained. Here we conducted the largest genome-wide association study (GWAS) of PSP to date including 2,779 cases (2,595 neuropathologically-confirmed) and 5,584 controls and identified six independent PSP susceptibility loci with genome-wide significant (p < 5x10-8) associations including five known (MAPT, MOBP, STX6, RUNX2, SLCO1A2) and one novel locus (C4A). Integration with cell type-specific epigenomic annotations revealed a unique oligodendrocytic signature that distinguishes PSP from AD and Parkinsons disease. Candidate PSP risk gene prioritization using expression quantitative trait loci (eQTLs) identified oligodendrocyte-specific effects on gene expression in half of the genome-wide significant loci, as well as an association with elevated C4A expression in bulk brain tissue which may be driven by increased C4A copy number in PSP cases. Finally, histological studies demonstrated abnormal tau aggregates in oligodendrocytes that colocalize with C4 (complement) deposition. Integrating GWAS with functional studies including epigenomic and eQTL analyses, we identified potential causal roles for variation in MOBP, STX6, RUNX2, SLCO1A2, and C4A in the pathogenesis of PSP.

neuroscience↗

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↗

bioRxiv2020

SparkINFERNO: A scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants

SummaryWe report SparkINFERNO (Spark-based INFERence of the molecular mechanisms of NOn-coding genetic variants), a scalable bioinformatics pipeline characterizing noncoding GWAS association findings. SparkINFERNO prioritizes causal variants underlying GWAS association signals and reports relevant regulatory elements, tissue contexts, and plausible target genes they affect. To achieve this, the SparkINFERNO algorithm integrates GWAS summary statistics with large-scale collection of functional genomics datasets spanning enhancer activity, transcription factor binding, expression quantitative trait loci, and other functional datasets across more than 400 tissues and cell types. Scalability is achieved by an underlying API implemented using Apache Spark and Giggle-based genomic indexing. We evaluated SparkINFERNO on large GWAS studies and show that SparkINFERNO is more than 60-times efficient and scales with data size and amount of computational resources. AvailabilitySparkINFERNO runs on clusters or a single server with Apache Spark environment, and is available at https://bitbucket.org/wanglab-upenn/SparkINFERNO or https://hub.docker.com/r/wanglab/spark-inferno. Contactlswang@pennmedicine.upenn.edu

bioinformatics↗

bioRxiv2019

HIPPIE2: a method for fine-scale identification of physically interacting chromatin regions

Most regulatory chromatin interactions are mediated by various transcription factors (TFs) and involve physically-interacting elements such as enhancers, insulators, or promoters. To map these elements and interactions, we developed HIPPIE2 which analyzes raw reads from high-throughput chromosome conformation (Hi-C) experiments to identify fine-scale physically-interacting regions (PIRs). Unlike standard genome binning approaches (e.g., 10K-1Mbp bins), HIPPIE2 dynamically calls physical locations of PIRs with better precision and higher resolution based on the pattern of restriction events and relative locations of interacting sites inferred from the sequencing readout.\n\nWe applied HIPPIE2 to in situ Hi-C datasets across 6 human cell lines (GM12878, IMR90, K562, HMEC, HUVEC, NHEK) with matched ENCODE and Roadmap functional genomic data. HIPPIE2 detected 1,042,738 distinct PIRs across cell lines, with high resolution (average PIR length of 1,006bps) and high reproducibility (92.3% in GM12878 replicates). 32.8% of PIRs were shared among cell lines. PIRs are enriched for epigenetic marks (H3K27ac, H3K4me1) and open chromatin, suggesting active regulatory roles. HIPPIE2 identified 2.8M significant intrachromosomal PIR-PIR interactions, 27.2% of which were enriched for TF binding sites. 50,608 interactions were enhancer-promoter interactions and were enriched for 33 TFs (31 in enhancers/29 in promoters), several of which are known to mediate DNA looping/long-distance regulation. 29 TFs were enriched in >1 cell line and 4 were cell line-specific. These findings demonstrate that the dynamic approach used in HIPPIE2 (https://bitbucket.com/wanglab-upenn/HIPPIE2) characterizes PIR-PIR interactions with high resolution and reproducibility.

bioinformatics↗

bioRxiv2018

Fast Parallel Algorithm for Large Fractal Kinetic Models with Diffusion

Chemical kinetic simulations are usually based on the law of mass action that applies to behavior of particles in solution. Molecular interactions in a crowded medium as in a cell, however, are not easily described by such conventional mathematical treatment. Fractal kinetics is emerging as a novel method for simulating kinetic reactions in such an environment. To date, there has not been a fast, efficient, and, more importantly, parallel algorithm for such computations. Here, we present an algorithm with several novel features for simulating large (with respect to size and time scale) fractal kinetic models. We applied the fractal kinetic technique and our algorithm to a canonical substrate-enzyme model with explicit phase-separation in the product, and achieved a speed-up of up to 8 times over previous results with reasonably tight bounds on the accuracy of the simulation. We anticipate that this technique and algorithm will have important applications to simulation of intra-cell biochemical reactions with complex dynamic behavior.

biophysics↗

bioRxiv2017

INFERNO - INFERring the molecular mechanisms of NOncoding genetic variants

The majority of variants identified by genome-wide association studies (GWAS) reside in the noncoding genome, where they affect regulatory elements including transcriptional enhancers. We propose INFERNO (INFERring the molecular mechanisms of NOncoding genetic variants), a novel method which integrates hundreds of diverse functional genomics data sources with GWAS summary statistics to identify putatively causal noncoding variants underlying association signals. INFERNO comprehensively infers the relevant tissue contexts, target genes, and downstream biological processes affected by causal variants. We apply INFERNO to schizophrenia GWAS data, recapitulating known schizophrenia-associated genes including CACNA1C and discovering novel signals related to transmembrane cellular processes.

bioinformatics↗