Biology subjects

Kwon, S. B.

Publications and source records attributed to Kwon, S. B..

7 recordsLinked to original sources

bioRxiv2024

Learning a Pairwise Epigenomic and Transcription Factor Binding Association Score Across the Human Genome

Identifying pairwise associations between genomic loci is an important challenge for which large and diverse collections of epigenomic and transcription factor (TF) binding data can potentially be informative. We therefore developed Learning Evidence of Pairwise Association from Epigenomic and TF binding data (LEPAE). LEPAE uses neural networks to quantify evidence of association for pairs of genomic windows from large-scale epigenomic and TF binding data along with distance information. We applied LEPAE using thousands of human datasets. We present evidence using additional data that LEPAE captures biologically meaningful pairwise relationships between genomic loci and expect LEPAE scores to be a resource.

bioinformatics↗

bioRxiv2021

A mammalian methylation array for profiling methylation levels at conserved sequences

Infinium methylation arrays are not available for the vast majority of non-human mammals. Moreover, even if species-specific arrays were available, probe differences between them would confound cross-species comparisons. To address these challenges, we developed the mammalian methylation array, a single custom array that measures 36k CpGs that are well conserved across mammalian species. We designed a set of probes on the array that can tolerate specific cross-species mutations. We annotate the array in over 200 species and report CpG island status and chromatin states in select species. Calibration experiments demonstrate the high fidelity in humans, rats, and mice. The mammalian methylation array has several strengths: it applies to all mammalian species even those that have not yet been sequenced, it provides deep coverage of conserved cytosines facilitating the development epigenetic biomarkers, and it increases the probability that biological insights gained in one species will translate to others.

bioinformatics↗

bioRxiv2020

Learning a genome-wide score of human-mouse conservation at the functional genomics level

Identifying genomic regions with functional genomic properties that are conserved between human and mouse is an important challenge in the context of mouse model studies. To address this, we take a novel approach and learn a score of evidence of conservation at the functional genomics level by integrating large-scale information in a compendium of epigenomic, transcription factor binding, and transcriptomic data from human and mouse. The computational method we developed to do this, Learning Evidence of Conservation from Integrated Functional genomic annotations (LECIF), trains a neural network, which is then used to generate a genome-wide score in human and mouse. The resulting LECIF score highlights human and mouse regions with shared functional genomic properties and captures correspondence of biologically similar human and mouse annotations even though it was not explicitly given such information. LECIF will be a resource for mouse model studies.

bioinformatics↗

bioRxiv2020

Single-nucleotide conservation state annotation of SARS-CoV-2 genome

Given the global impact and severity of COVID-19, there is a pressing need for a better understanding of the SARS-CoV-2 genome and mutations. Multi-strain sequence alignments of coronaviruses (CoV) provide important information for interpreting the genome and its variation. We apply a comparative genomics method, ConsHMM, to the multi-strain alignments of CoV to annotate every base of the SARS-CoV-2 genome with conservation states based on sequence alignment patterns among CoV. The learned conservation states show distinct enrichment patterns for genes, protein domains, and other regions of interest. Certain states are strongly enriched or depleted of SARS-CoV-2 mutations, which can be used to predict potentially consequential mutations. We expect the conservation states to be a resource for interpreting the SARS-CoV-2 genome and mutations.

genomics↗

bioRxiv2019

Identification and characterization of constrained non-exonic bases lacking predictive epigenomic and transcription factor binding annotations

Annotations of evolutionarily constraint provide important information for variant prioritization. Genome-wide maps of epigenomic marks and transcription factor binding provide complementary information for interpreting a subset of such prioritized variants. Here we developed the Constrained Non-Exonic Predictor (CNEP) to quantify the evidence of each base in the human genome being in a constrained non-exonic element from over 60,000 epigenomic and transcription factor binding features. We find that the CNEP score outperforms baseline and related existing scores at predicting constrained non-exonic bases from such data. However, a subset of such bases are still not well predicted by CNEP. We developed a complementary Conservation Signature Score by CNEP (CSS-CNEP) using conservation state and constrained element annotations that is predictive of those bases. Using human genetic variation, regulatory sequence motifs, mouse epigenomic data, and retrospectively considered additional human data we further characterize the nature of constrained non-exonic bases with low CNEP scores.

genomics↗

bioRxiv2019

EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences

The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the effcacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns.

bioinformatics↗

bioRxiv2018

Conversion of a soluble protein into a potent chaperone in vivo

Protein-folding assistance and aggregation inhibition by cellular factors are largely understood in the context of molecular chaperones. As an alternative and complementary model, we previously proposed that, in general, soluble cellular macromolecules including chaperones with large excluded volume and surface charges exhibit the intrinsic chaperone activity to prevent aggregation of their connected polypeptides, irrespective of the connection types, and thus to aid productive protein folding. As a proof of concept, we here demonstrated that a model soluble protein with an inactive protease domain robustly exerted chaperone activity toward various proteins harboring a short protease-recognition tag of 7 residues in Escherichia coli. The chaperone activity of this protein was similar or even superior to that of representative E. coli chaperones in vivo. Furthermore, in vitro refolding experiments confirmed the in vivo results. Our findings revealed that a soluble protein exhibits the intrinsic chaperone activity, which is manifested, upon binding to aggregation-prone proteins. This study gives new insights into the ubiquitous chaperoning role of cellular macromolecules in protein-folding assistance and aggregation inhibition underlying the maintenance of protein solubility and proteostasis in vivo.

biochemistry↗