Dataset Information

A broad survey of DNA sequence data simulation tools.

ABSTRACT: In silico DNA sequence generation is a powerful technology to evaluate and validate bioinformatics tools, and accordingly more than 35 DNA sequence simulation tools have been developed. With such a diverse array of tools to choose from, an important question is: Which tool should be used for a desired outcome? This question is largely unanswered as documentation for many of these DNA simulation tools is sparse. To address this, we performed a review of DNA sequence simulation tools developed to date and evaluated 20 state-of-art DNA sequence simulation tools on their ability to produce accurate reads based on their implemented sequence error model. We provide a succinct description of each tool and suggest which tool is most appropriate for the given different scenarios. Given the multitude of similar yet non-identical tools, researchers can use this review as a guide to inform their choice of DNA sequence simulation tool. This paves the way towards assessing existing tools in a unified framework, as well as enabling different simulation scenario analysis within the same framework.

SUBMITTER: Alosaimi S

PROVIDER: S-EPMC7030445 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A broad survey of DNA sequence data simulation tools.

Alosaimi Shatha S Bandiang Armand A van Biljon Noelle N Awany Denis D Thami Prisca K PK Tchamga Milaine S S MSS Kiran Anmol A Messaoud Olfa O Hassan Radia Ismaeel Mohammed RIM Mugo Jacquiline J Ahmed Azza A Bope Christian D CD Allali Imane I Mazandu Gaston K GK Mulder Nicola J NJ Chimusa Emile R ER

Briefings in functional genomics 20200101 1

In silico DNA sequence generation is a powerful technology to evaluate and validate bioinformatics tools, and accordingly more than 35 DNA sequence simulation tools have been developed. With such a diverse array of tools to choose from, an important question is: Which tool should be used for a desired outcome? This question is largely unanswered as documentation for many of these DNA simulation tools is sparse. To address this, we performed a review of DNA sequence simulation tools developed to ...[more]

PMID: 31867604

Similar Datasets

Project description:BackgroundViruses, including bacteriophages, are important components of environmental and human associated microbial communities. Viruses can act as extracellular reservoirs of bacterial genes, can mediate microbiome dynamics, and can influence the virulence of clinical pathogens. Various targeted metagenomic analysis techniques detect viral sequences, but these methods often exclude large and genome integrated viruses. In this study, we evaluate and compare the ability of nine state-of-the-art bioinformatic tools, including Vibrant, VirSorter, VirSorter2, VirFinder, DeepVirFinder, MetaPhinder, Kraken 2, Phybrid, and a BLAST search using identified proteins from the Earth Virome Pipeline to identify viral contiguous sequences (contigs) across simulated metagenomes with different read distributions, taxonomic compositions, and complexities.ResultsOf the tools tested in this study, VirSorter achieved the best F1 score while Vibrant had the highest average F1 score at predicting integrated prophages. Though less balanced in its precision and recall, Kraken2 had the highest average precision by a substantial margin. We introduced the machine learning tool, Phybrid, which demonstrated an improvement in average F1 score over tools such as MetaPhinder. The tool utilizes machine learning with both gene content and nucleotide features. The addition of nucleotide features improves the precision and recall compared to the gene content features alone.Viral identification by all tools was not impacted by underlying read distribution but did improve with contig length. Tool performance was inversely related to taxonomic complexity and varied by the phage host. For instance, Rhizobium and Enterococcus phages were identified consistently by the tools; whereas, Neisseria prophage sequences were commonly missed in this study.ConclusionThis study benchmarked the performance of nine state-of-the-art bioinformatic tools to identify viral contigs across different simulation conditions. This study explored the ability of the tools to identify integrated prophage elements traditionally excluded from targeted sequencing approaches. Our comprehensive analysis of viral identification tools to assess their performance in a variety of situations provides valuable insights to viral researchers looking to mine viral elements from publicly available metagenomic data.

Dataset Information

A broad survey of DNA sequence data simulation tools.

Publications

A broad survey of DNA sequence data simulation tools.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets