Biology subjects

Kelly, S. T.

Publications and source records attributed to Kelly, S. T..

3 recordsLinked to original sources

bioRxiv2022

HiCUP-Plus: a fast open-source pipeline for accurately processing large scale Hi-C sequence data

Hi-C is an unbiased genome-wide assay to study 3D chromosome conformation and gene-regulation. The HiCUP pipeline is an open-source tool to process Hi-C from massively parallel sequencing while accounting for biases specific to the restriction enzyme digests used. It is an excellent solution tailored to analyse this technique, however the latest aligner supported by the current release is Bowtie2. To improve the computational performance and mapping accuracy when using the HiCUP pipeline, we have modified it to optionally call the HiSAT2 and Dragen aligners. This allows using the HiCUP pipeline with 3rd party aligners, including the commercially-licensed high performance Dragen aligner. The HiCUP+ pipeline is modified extensively to be compatible with Dragen outputs while ensuring that the same results as the original pipeline can be reproduced with the Bowtie or Bowtie2 aligners. Using the highly accurate HiSAT2 or Dragen aligners produces larger outputs with a higher proportion of uniquely mapped read pairs. It is therefore feasible to leverage the reduced compute-time of Dragen to reduce compute costs and turnaround-time without compromising quality of results. The HiCUP pipeline and Dragen both compute rich summary information.

bioinformatics↗

bioRxiv2021

UniverSC: a flexible cross-platform single-cell data processing pipeline

Single-cell RNA-sequencing analysis to quantify RNA molecules in individual cells has become popular owing to the large amount of information one can obtain from each experiment. We have developed UniverSC (https://github.com/minoda-lab/universc), a universal single-cell processing tool that supports any UMI-based platform. Our command-line tool enables consistent and comprehensive integration, comparison, and evaluation across data generated from a wide range of platforms.

bioinformatics↗

bioRxiv2020

graphsim: An R package for simulating gene expression data from graph structures of biological pathways

Transcriptomic analysis is used to capture the molecular state of a cell or sample in many biological and medical applications. In addition to identifying alterations in activity at the level of individual genes, understanding changes in the gene networks that regulate fundamental biological mechanisms is also an important objective of molecular analysis. As a result, databases that describe biological pathways are increasingly uesad to assist with the interpretation of results from large-scale genomics studies. Incorporating information from biological pathways and gene regulatory networks into a genomic data analysis is a popular strategy, and there are many methods that provide this functionality for gene expression data. When developing or comparing such methods, it is important to gain an accurate assessment of their performance. Simulation-based validation studies are frequently used for this. This necessitates the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This procedure has been released as the graphsim R package on CRAN and GitHub (https://github.com/TomKellyGenetics/graphsim) and is compatible with any graph structure that can be described using the igraph package. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression.

bioinformatics↗