Bioinformatics and Biology InsightsBioinformaticsPeer-reviewed journal article
D-graph clusters flaviviruses and β-coronaviruses according to their hosts, disease type and human cell receptors
Abstract
MotivationThere is a need for rapid and easy to use, alignment free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is a dynamic programming application developed to generate 2D-maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such "PD-graphs" show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities. ResultsPD-Graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors and disease phenotypes. PD-graphs separate the tick- from the mosquito-borne FV, clusters viruses that infect bats, camels, seabirds and humans separately and the clusters correlate with disease phenotype. The PD method segregates the {beta}-CoV spike proteins of SARS, SARS-CoV-2, and MERS sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence. Availability and implementationDGraph is written in Java, compatible with the Java 5 runtime or newer. Source code and executable is available from the GitHub website (https://github.com/bjmnbraun/DGraph/releases). Documentation for installation and use of the software is available from the Readme.md file at (https://github.com/bjmnbraun/DGraph). Contactbjmnbraun@gmail.com or webraun@utmb.edu Supplementary informationSupplementary information Table S1 and Fig. S1 are online available.
- assay: protein sequence
- research focus: clustering
- research focus: benchmarking
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- Citations
- 18
- FWCI
- 0.94
- Field percentile
- top 28%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2
Sravani Nanduri, Allison Black, … John Huddleston·Virus EvolutionPeer-reviewed·Jan 1, 2024
- VirusDIP: Virus Data Integration Platform
Lina Wang, Fengzhen Chen, … Xiaofeng Wei·bioRxiv·Jun 8, 2020
- Chaos Game Representation: An Alignment-Free Technique for Exploring Evolutionary Relationships of Protein Sequences
Priyasma Bhoumik, Austin L. Hughes·bioRxiv·Mar 5, 2018
- Enhanced genome annotation strategy provides novel insights on the phylogeny of Flaviviridae
A. de Bernardi Schneider, D. Jacob Machado, D. A. Janies·bioRxiv·Jun 18, 2019
- Complete assembly of a dengue virus type 3 genome from a recent genotype III clade by metagenomic sequencing of serum
C. Pattabiraman, M. Dias, … S. Krishna·Wellcome Open ResearchPeer-reviewed·Apr 23, 2018
- COVFlow: phylodynamics analyses of viruses from selected SARS-CoV-2 genome sequences
Gonché Danesh, Corentin Boennec, … Samuel Alizon·Peer Community JournalPeer-reviewed·Oct 16, 2023