Biology subjects

Hong, C.

Publications and source records attributed to Hong, C..

4 recordsLinked to original sources

bioRxiv2018

Real-time non-invasive intracranial state estimation using unscented Kalman filter

Intracranial pressure (ICP) monitoring is desirable as a first-line measure to assist decision-making in cases of increased ICP. Clinically, non-invasive ICP monitoring is also required to avoid infection and hemorrhage in patients. The relationships among the arterial blood pressure (Pa), ICP, cerebral blood flow, and its velocity (QCBFv) measured by transcranial Doppler ultrasound measurement have been reported. However, real-time non-invasive ICP estimation using these modalities is less well documented. Here, we present a novel algorithm for real-time and non-invasive ICP monitoring with QCBFv and Pa, called direct-current (DC)-ICP. This technique is compared with invasive ICP for 11 traumatic-brain-injury patients admitted to Cheju Halla Hospital and Gangnam Severance Hospital from July 2017 to June 2018. The inter-subject correlation coefficient between true and estimate was 0.70. The AUCs of the ROCs for prediction of increased ICP for the DC-ICP methods are 0.816. Thus, QCBFv monitoring can facilitate reliable real-time ICP tracking with our novel DC-ICP algorithm, which can provide valuable information under clinical conditions.

neuroscience↗

bioRxiv2018

Divine: Prioritizing Genes for Rare Mendelian Disease in Whole Exome Sequencing Data

MotivationRecent studies showed that a phenotype-driven analysis of whole exome sequencing (WES) could provide more accurate and clinically relevant genetic variants.\n\nResultsWe develop a computational tool called Divine that integrates patients phenotype(s) and WES data with 30 prior biological knowledge (e.g., human phenotype ontology, gene ontology, pathway database, protein-protein interaction networks, pathogenicity by the amino acid change due to polymorphism, and hot-spot protein domains) to prioritize potential disease-causing genes. In a retrospective study with 22 real and four simulated data set, Divine ranks the same pathogenic genes confirmed by the original studies 5th on average out of a thousand of mutated genes and outperforms existing state-of-the-art methods.\n\nAvailabilityhttps://github.com/hwanglab/divine\n\nContacthwangt@ccf.org\n\nSupplementary informationSupplementary Document is attached at the end of the page.

bioinformatics↗

bioRxiv2017

Genomic footprints of activated telomere maintenance mechanisms in cancer

Cancers require telomere maintenance mechanisms for unlimited replicative potential. We dissected whole-genome sequencing data of over 2,500 matched tumor-control samples from 36 different tumor types to characterize the genomic footprints of these mechanisms. While the telomere content of tumors with ATRX or DAXX mutations (ATRX/DAXXtrunc) was increased, tumors with TERT modifications showed a moderate decrease of telomere content. One quarter of all tumor samples contained somatic integrations of telomeric sequences into non-telomeric DNA. With 80% prevalence, ATRX/DAXXtrunc tumors display a 3-fold enrichment of telomere insertions. A systematic analysis of telomere composition identified aberrant telomere variant repeat (TVR) distribution as a genomic marker of ATRX/DAXXtrunc tumors. In this clinically relevant subgroup, singleton TTCGGG and TTTGGG TVRs (previously undescribed) were significantly enriched or depleted, respectively. Overall, our findings provide new insight into the recurrent genomic alterations that are associated with the establishment of different telomere maintenance mechanisms in cancer.

genomics↗

bioRxiv2017

Unique genomic features and deeply-conserved functions of long non-coding RNAs in the Cancer LncRNA Census (CLC)

Long non-coding RNAs (lncRNAs) that drive tumorigenesis are a growing focus of cancer genomics studies. To facilitate further discovery, we have created the \"Cancer LncRNA Census\" (CLC), a manually-curated and strictly-defined compilation of lncRNAs with causative roles in cancer. CLC has two principle applications: first, as a resource for training and benchmarking de novo identification methods; and second, as a dataset for studying the fundamental properties of these genes.\n\nCLC Version 1 comprises 122 lncRNAs implicated in 29 distinct cancers. LncRNAs are included based on functional or genetic evidence for causative roles in cancer progression. All belong to the GENCODE reference annotation, to enable integration across projects and datasets. For each entry, the evidence type, biological activity (oncogene or tumour suppressor), source reference and cancer type are recorded. Supporting its usefulness, CLC genes are significantly enriched amongst de novo predicted driver genes from PCAWG. CLC genes are distinguished from other lncRNAs by a series of features consistent with biological function, including gene length, high expression and sequence conservation of both exons and promoters. We identify a trend for CLC genes to be co-localised with known protein-coding cancer genes along the human genome. Finally, by integrating data from transposon-mutagenesis functional screens, we show that mouse orthologues of CLC genes tend also to be cancer genes.\n\nThus CLC represents a valuable resource for research into long non-coding RNAs in cancer. Their evolutionary and genomic properties have implications for understanding disease mechanisms and point to conserved functions across ~80 million years of evolution.

bioinformatics↗