Biology subjects

Lai, Y.

Publications and source records attributed to Lai, Y..

58 records · Page 4Linked to original sources

bioRxiv2018

An Empirical Bayes Approach for the Identification of Long-range Chromosomal Interaction from Hi-C Data

Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the 'true' interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a smoothed EM algorithm, and the empirical null by discrete curve fitting. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can better identify peaks than Fit-Hi-C in terms of accuracy, biological interpretability, and the consistency across biological replicates.

bioinformatics↗

bioRxiv2018

Temporal Gene Coexpression Network Analysis Using A Low-rank plus Sparse Framework

Various gene network models with distinct physical nature have been widely used in biological studies. For temporal transcriptomic studies, the current dynamic models either ignore the temporal variation in the network structure or fail to scale up to a large number of genes due to severe computational bottlenecks and sample size limitation. On the other hand, correlation-based gene networks are more computationally more affordable, but have not been properly extended to gene expression time-course data.\n\nWe propose Temporal Gene Coexpression Network (TGCN) for the transcriptomic time-course data. The mathematical nature of TGCN is the joint modeling of multiple covariance matrices across time points using a \"low-rank plus sparse\" framework, in which the network similarity across time points is explicitly modeled in the low-rank component. Using both simulations and a real data application, we showed that TGCN improved the covariance estimation loss and identified more robust and interpretable gene modules.

bioinformatics↗

bioRxiv2018

HERC2 promotes BLM and WRN to suppress G-quadruplex DNA

BLM and WRN are RecQ DNA helicases essential for genomic stability. Here we demonstrate that HERC2, a HECT E3 ligase, is critical for their functions to suppress G-quadruplex (G4) DNA. HERC2 interacts with BLM, WRN, and replication protein A (RPA) complexes during S-phase of the cell cycle. Depletion of HERC2 dissociates RPA from BLM and WRN complexes and significantly increases G4 formation. Triple depletion revealed that HERC2 has an epistatic relationship with BLM and WRN in their G4- suppressing function. In vitro, HERC2 releases RPA onto single-stranded DNA (ssDNA), rather than anchoring onto RPA-coated ssDNA. CRISPR/Cas9-mediated deletion of the catalytic ubiquitin-binding site of HERC2 causes RPA accumulation in the helicase complexes and increases G4, indicating an essential role for E3 activity in G4 suppression. Both HERC2 depletion and E3 inactivation sensitize cells to the G4-interacting compounds, telomestatin and pyridostatin. Overall, HERC2 is a master regulator of G4 suppression and affects the sensitivity of cells to G4 stabilizers.

molecular biology↗

bioRxiv2017

Enabling Precision Medicine via standard communication of NGS provenance, analysis, and results

A personalized approach based on a patients or pathogens unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to FAIR guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet lab procedures to computational methods. The BioCompute framework (https://osf.io/zm97b/) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCO) offer that standard, and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the \"Open-Stand.org principles for collaborative open standards development\". By communication of high-throughput sequencing studies using a BCO, regulatory agencies (e.g., FDA), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next generation sequencing workflow exchange, reporting, and regulatory reviews.

scientific communication and education↗