Biology subjects

Chong, L. C.

Publications and source records attributed to Chong, L. C..

6 recordsLinked to original sources

bioRxiv2026

Data-Driven Virus Discovery with Virushunter and Virusgatherer

We present Virushunter and Virusgatherer, a bipartite computational workflow for discovering viral genomes from unprocessed next-generation sequencing data. Virushunter conducts a profile Hidden Markov Model-based sequence homology search for viral hallmark protein-coding sequence reads to construct microcontigs, which are used as seeds for targeted viral genome assembly by Virusgatherer. We showcase the application of Virushunter and Virusgatherer through an RNA virus-wide screen in 3348 bat transcriptome sequencing experiments from the Sequence Read Archive. Many of the discovered viruses are likely prototypes of new putative virus species, including a novel alphacoronavirus identified in a South American velvety free-tailed bat and a novel rhabdovirus in a European barbastelle bat. The tools are implemented as a reproducible Snakemake pipeline, which is available at https://github.com/lauberlab/VirusHunterGatherer.

bioinformatics↗

bioRxiv2023

The spatially resolved tumor microenvironment predicts treatment outcome in relapsed/refractory Hodgkin lymphoma

PURPOSEAbout a third of relapsed or refractory classic Hodgkin lymphoma (r/r CHL) patients succumb to their disease after high-dose chemotherapy followed by autologous stem cell transplantation (HDC/ASCT). Here, we aimed to describe spatially resolved tumor microenvironment (TME) ecosystems to establish novel biomarkers associated with treatment failure in r/r CHL. METHODSWe performed imaging mass cytometry (IMC) on 169 paired primary diagnostic and relapse biopsies using a marker panel specific for CHL biology. For each cell type in the TME, we calculated a spatial score measuring the distance of nearest neighbor cells to the malignant Hodgkin Reed Sternberg cells within close interaction range. Spatial scores were used as features in prognostic model development for post-ASCT outcomes. RESULTSHighly multiplexed IMC data revealed shared TME patterns in paired diagnostic and early relapse/refractory CHL samples, whereas TME patterns were more divergent in pairs of diagnostic and late relapse samples. Integrated analysis of IMC and single cell RNA sequencing data identified unique architecture defined by CXCR5+ HRS cells and their strong spatial relationship with CXCL13+ macrophages in the TME. We developed a prognostic assay ( RHL4S) using four spatially resolved parameters, CXCR5+ HRS cells, PD1+CD4+ T cells, tumor-associated macrophages, and CXCR5+ B cells, which effectively separated patients into high-risk vs low-risk groups with significantly different post-ASCT outcomes. The RHL4S assay was validated in an independent r/r CHL cohort using a multicolor immunofluorescence assay. CONCLUSIONSWe identified the interaction of CXCR5+ HRS cells with ligand-expressing CXCL13+ macrophages as a prominent crosstalk axis in relapsed CHL. Harnessing this TME biology, we developed a novel prognostic model applicable to r/r CHL biopsies, RHL4S, opening new avenues for spatial biomarker development.

cancer biology↗

bioRxiv2022

Negligible peptidome diversity of SARS-CoV-2 and its higher taxonomic ranks

The unprecedented increase in SARS-CoV-2 sequence data limits the application of alignment-dependent approaches to study viral diversity. Herein, we applied our recently published UNIQmin, an alignment-free tool to study the protein sequence diversity of SARS-CoV-2 (sub-species) and its higher taxonomic lineage ranks (species, genus, and family). Only less than 0.5% of the reported SARS-CoV-2 protein sequences are required to represent the inherent viral peptidome diversity, which only increases to a mere [~]2% at the family rank. This is expected to remain relatively the same even with further increases in the sequence data. The findings have important implications in the design of vaccines, drugs, and diagnostics, whereby the number of sequences required for consideration of such studies is drastically reduced, short-circuiting the discovery process, while still providing for a systematic evaluation and coverage of the pathogen diversity.

bioinformatics↗

bioRxiv2022

UNIQmin, an alignment-free tool to study viral sequence diversity across taxonomic lineages: a case study of monkeypox virus

Sequence changes in viral genomes generate protein sequence diversity that enable viruses to evade the host immune system, hindering the development of effective preventive and therapeutic interventions. Massive proliferation of sequence data provides unprecedented opportunities to study viral adaptation and evolution. Alignment-free approach removes various restrictions, otherwise posed by an alignment-dependent approach for the study of sequence diversity. The publicly available tool, UNIQmin offers an alignment-free approach for the study of viral sequence diversity at any given rank of taxonomy lineage and is big data ready. The tool performs an exhaustive search to determine the minimal set of sequences required to capture the peptidome diversity within a given dataset. This compression is possible through the removal of identical sequences and unique sequences that do not contribute effectively to the peptidome diversity pool. Herein, we describe a detailed three-part protocol utilizing UNIQmin to generate the minimal set for the purpose of viral diversity analyses, alignment-free at any rank of the taxonomy lineage, using the latest global public health threat monkeypox virus (MPX) as a case study. This protocol enables systematic diversity study across the taxonomic lineage, which are much needed for our future preparedness of a viral epidemic, in particular when data is in abundance, freely available, and alignment is not an option.

bioinformatics↗

bioRxiv2021

iCn3D: from Web-based 3D Viewer to Structure Analysis Tool in Batch Mode

iCn3D was initially developed as a web-based 3D molecular viewer. It then evolved from visualization into a full-featured interactive structural analysis software. It became a collaborative research instrument through the sharing of permanent, shortened URLs that encapsulate not only annotated visual molecular scenes, but also all underlying data and analysis scripts in a FAIR manner. More recently, with the growth of structural databases, the need to analyze large structural datasets systematically led us to use Python scripts and convert the code to be used in Node.js scripts. We showed a few examples of Python scripts at https://github.com/ncbi/icn3d/tree/master/icn3dpython to export secondary structures or PNG images from iCn3D. Users just need to replace the URL in the Python scripts to export other annotations from iCn3D. Furthermore, any interactive iCn3D feature can be converted into a Node.js script to be run in batch mode, enabling an interactive analysis performed on one or a handful of protein complexes to be scaled up to analysis features of large ensembles of structures. Currently available Node.js analysis scripts examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode. This development will enable ensemble analyses on growing structural databases such as AlphaFold or RoseTTAFold on one hand and Electron Microscopy on the other. In this paper, we also review new features such as DelPhi electrostatic potential, 3D view of mutations, alignment of multiple chains, assembly of multiple structures by realignment, dynamic symmetry calculation, 2D cartoons at different levels, interactive contact maps, and use of iCn3D in Jupyter Notebook as described at https://pypi.org/project/icn3dpy.

bioinformatics↗

bioRxiv2017

BPG: Seamless, Automated and Interactive Visualization of Scientific Data

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

bioinformatics↗