We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes
Summary
Researchers developed VEBA, a modular bioinformatics software suite that automates end-to-end metagenomic analysis for recovering and characterizing genomes of bacteria, microeukaryotes, and viruses from environmental samples, enabling new biological discoveries from existing datasets.
The VEBA software suite allows for the in silico recovery of microorganisms from all domains of life by integrating cutting edge algorithms in novel ways. VEBA fully integrates both end-to-end and task-specific metagenomic analysis in a modular architecture that minimizes dependencies and maximizes productivity. The contributions of VEBA to the metagenomics community includes seamless end-to-end metagenomics analysis but also provides users with the flexibility to perform specific analytical tasks. VEBA allows for the automation of several metagenomics steps and shows that new information can be recovered from existing datasets.
Sign in to start a discussion.
More Papers Like This
Unveiling the Microbial Realm with VEBA 2.0: A modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic, and viral multi-omics from either short- or long-read sequencing
Researchers introduced VEBA 2.0, an open-source bioinformatics software suite for analyzing complex microbial communities including bacteria, archaea, eukaryotes, and viruses from sequencing data. The tool enables comprehensive microbiome research, which is relevant to understanding how microbial communities interact with environmental contaminants like microplastics.
GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree search
Researchers developed GiantHunter, a tool using reinforcement learning and Monte Carlo tree search to accurately detect nucleocytoplasmic large DNA viruses (NCLDVs) in metagenomic data, addressing challenges posed by high genomic diversity and limited reference genomes in environmental samples.
The virtual microbiome: A computational framework to evaluate microbiome analyses
Researchers created virtual bacterial populations mimicking real microbiome ecology to test the accuracy of standard microbiome analysis pipelines, finding that gaps in genomic databases significantly compromise microbiome characterization reliability — an issue typically overlooked in microbiome studies.
The electronic tree of life (eToL): a net of long probes to characterize the microbiome from RNA-seq data
Researchers developed the electronic Tree of Life (eToL), a tool that uses long RNA probes to identify and characterize the full range of microbes living in a sample from standard RNA-sequencing data. This method broadens the ability to study the microbiome — the community of microorganisms in a given environment — with potential uses in diagnostics and environmental monitoring.
Novel bacterial lineages assembled from wastewater-impacted river metagenomes unveil ecosystem functions and risk of antibiotic resistance spread in the community
Researchers assembled novel bacterial lineages from metagenomes of wastewater-impacted river sediments, identifying previously undescribed microbial taxa with metabolic capabilities for plastic degradation and emerging contaminant breakdown.