ArticleBMC bioinformatics2012
A comparative evaluation of sequence classification programs.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- MNBC: a multithreaded Minimizer-based Naïve Bayes Classifier for improved metagenomic sequence classification.Bioinformatics (Oxford, England) · 2024Article
- Characteristic Attribute Organization System (CAOS): Identifying Classification Rules Based on Phylogenetically Organized Sequences.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Profiling Walnut Fungal Pathobiome Associated with Walnut Dieback Using Community-Targeted DNA Metabarcoding.Plants (Basel, Switzerland) · 2023Article
- Identification of Antibiotic Resistance Proteins via MiCId's Augmented Workflow. A Mass Spectrometry-Based Proteomics Approach.Journal of the American Society for Mass Spectrometry · 2022Article
- Mismatch-tolerant, alignment-free sequence classification using multiple spaced seeds and multiindex Bloom filters.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Biodiversity inventory of the grey mullets (Actinopterygii: Mugilidae) of the Indo-Australian Archipelago through the iterative use of DNA-based species delimitation and specimen assignment methods.Evolutionary applications · 2020Article
- Robust Accurate Identification and Biomass Estimates of Microorganisms via Tandem Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2020Article
- Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data.Frontiers in genetics · 2020Article
- Environmental metagenome classification for constructing a microbiome fingerprint.Biology direct · 2019Article
- Exploring the diversity of Poaceae-infecting mastreviruses on Reunion Island using a viral metagenomics-based approach.Scientific reports · 2019Article
- MSC: a metagenomic sequence classification algorithm.Bioinformatics (Oxford, England) · 2019Article
- Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance.GigaScience · 2019Review
- An efficient and robust laboratory workflow and tetrapod database for larger scale environmental DNA studies.GigaScience · 2019Article
- Article
- Phylogenetic microbiota profiling in fecal samples depends on combination of sequencing depth and choice of NGS analysis method.PloS one · 2019Article
- Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2018Article
- Using pseudoalignment and base quality to accurately quantify microbial community composition.PLoS computational biology · 2018Article
- Article
- Overview of Virus Metagenomic Classification Methods and Their Biological Applications.Frontiers in microbiology · 2018Review
- Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.Genome biology · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA fundamental problem in modern genomics is to taxonomically or functionally classify DNA sequence fragments derived from environmental sampling (i.e., metagenomics). Several different methods have been proposed for doing this effectively and efficiently, and many have been implemented in software. In addition to varying their basic algorithmic approach to classification, some methods screen sequence reads for 'barcoding genes' like 16S rRNA, or various types of protein-coding genes. Due to the sheer number and complexity of methods, it can be difficult for a researcher to choose one that is well-suited for a particular analysis.
resultsWe divided the very large number of programs that have been released in recent years for solving the sequence classification problem into three main categories based on the general algorithm they use to compare a query sequence against a database of sequences. We also evaluated the performance of the leading programs in each category on data sets whose taxonomic and functional composition is known.
conclusionsWe found significant variability in classification accuracy, precision, and resource consumption of sequence classification programs when used to analyze various metagenomics data sets. However, we observe some general trends and patterns that will be useful to researchers who use sequence classification programs.
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