Biology subjects

Huang, Y. S.

Publications and source records attributed to Huang, Y. S..

4 recordsLinked to original sources

bioRxiv2023

Performance comparison of Accucopy, Sequenza, and ControlFreeC

Copy number alterations (CNAs) are an important type of genomic aberrations. It plays an important role in tumor pathogenesis and progression of cancer. It is important to detect regions of the cancer genome where copy number changes occur, which may provide clues that drive cancer progression. Deep sequencing technology provides genomic data at single-nucleotide resolution and is considered a better technique for detecting CNAs. There are currently many CNA-detection algorithms developed for whole genome sequencing (WGS) data. However, their detection capabilities have not been systematically investigated. Therefore, we selected three algorithms: Accucopy, Sequenza, and ControlFreeC, and applied them to data simulated under different settings. The results indicate that: 1) the correct inference of tumor sample purity is crucial to the inference of CNAs. If the tumor purity is wrongly inferred, the CNA detection will fail. 2) Higher sequencing depth and abundance of CNAs can improve performance. 3) Under the settings tested (sequencing depth at 5X or 30X, purity from 0.1 to 0.9, existence of subclones or not), Accucopy is the best-performing algorithm overall. For coverage=5X samples, ControlFreeC requires tumor purity to be above 50% to perform well. Sequenza can only perform well in high-coverage and more-CNA samples.

bioinformatics↗

bioRxiv2023

eGADA: enhanced Genomic Alteration Detection Algorithm, a fast genomic segmentation algorithm

eGADA is an enhanced version of GADA, which is a fast segmentation algorithm utilizing the Sparse Bayesian Learning (or Relevance Vector Machine) technique from Tipping 2001. It can be applied to array intensity data, NGS sequencing coverage data, or any sequential data that displays characteristics of stepwise functions. Improvements by eGADA over GADA include: a) a customized Red-Black tree to expedite the final backward elimination step of GADA; b) code in C++, which is safer and better structured than C; c) use Boost libraries extensively to provide user-friendly help and commandline argument processing; d) user-friendly input and output formats; e) export a dynamic library eGADA.so (packaged via Boost.Python) that offers API to Python; f) other bug fixes/optimization. The code is published at https://github.com/polyactis/eGADA.

bioinformatics↗

bioRxiv2022

Processing UMI Datasets at High Accuracy and Efficiency with the Sentieon ctDNA Analysis Pipeline

Liquid biopsy enables identification of low allele frequency (AF) tumor variants and novel clinical applications such as minimum residual disease (MRD) monitoring. However, challenges remain, primarily due to limited sample volume and low read count of low-AF variants. Because of the low AFs, some clinically significant variants are difficult to distinguish from errors introduced by PCR amplification and sequencing. Unique Molecular Identifiers (UMIs) have been developed to further reduce base error rates and improve the variant calling accuracy, which enables better discrimination between background errors and real somatic variants. While multiple UMI-aware ctDNA analysis pipelines have been published and adopted, their accuracy and runtime efficiency could be improved. In this study, we present the Sentieon ctDNA pipeline, a fast and accurate solution for small somatic variant calling from ctDNA sequencing data. The pipeline consists of four core modules: alignment, consensus generation, variant calling, and variant filtering. We benchmarked the ctDNA pipeline using both simulated and real datasets, and found that the Sentieon ctDNA pipeline is more accurate than alternatives.

bioinformatics↗

bioRxiv2016

Genetic variation and gene expression across multiple tissues and developmental stages in a non-human primate

By analyzing multi-tissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalogue of expression quantitative trait loci (eQTLs) in a non-human primate model. This catalogue contains more genome-wide significant eQTLs, per sample, than comparable human resources, and reveals sex and age-related expression patterns. Findings include a master regulatory locus that likely plays a role in immune function, and a locus regulating hippocampal long non-coding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders.

genetics↗