ReviewComputational and structural biotechnology journal2017
Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics.
Review in Computational and structural biotechnology journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- Deployable high-fidelity metagenome binning at scale with QuickBin.Communications biology · 2026Article
- A review of computational approaches for metagenomics by long-read sequencing.Science China. Life sciences · 2026Review
- Article
- Calibrating tetranucleotide-frequency distances for metagenomic binning with right-skewed distribution models.Bioinformatics advances · 2026Article
- Metagenomic approaches for studying ubiquitous yet diverse nucleoid associated proteins in microbial communities: challenges and advances.World journal of microbiology & biotechnology · 2025Review
- Fluorescence-based spectrometric and imaging methods and machine learning analyses for microbiota analysis.Mikrochimica acta · 2025Review
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- Long-read metagenomics gives a more accurate insight into the microbiota of long-ripened gouda cheeses.Frontiers in microbiology · 2025Article
- OUTPOST: A comprehensive analysis software for whole-metagenome shotgun sequencing incorporating group stratification.iMetaOmics · 2024Article
- Solving genomic puzzles: computational methods for metagenomic binning.Briefings in bioinformatics · 2024Review
- Biofilm marker discovery with cloud-based dockerized metagenomics analysis of microbial communities.Briefings in bioinformatics · 2024Article
- AFITbin: a metagenomic contig binning method using aggregate l-mer frequency based on initial and terminal nucleotides.BMC bioinformatics · 2024Article
- DeepMicroClass sorts metagenomic contigs into prokaryotes, eukaryotes and viruses.NAR genomics and bioinformatics · 2024Article
- Current Trends and Challenges of Microbiome Research in Prostate Cancer.Current oncology reports · 2024Review
- Enhancing Clinical Utility: Utilization of International Standards and Guidelines for Metagenomic Sequencing in Infectious Disease Diagnosis.International journal of molecular sciences · 2024Review
- Unraveling metagenomics through long-read sequencing: a comprehensive review.Journal of translational medicine · 2024Review
- Effective binning of metagenomic contigs using contrastive multi-view representation learning.Nature communications · 2024Article
- MOBFinder: a tool for mobilization typing of plasmid metagenomic fragments based on a language model.GigaScience · 2024Article
- Antibiotic Resistance Profiling and Phylogenicity of Uropathogenic Bacteria Isolated from Patients with Urinary Tract Infections.Antibiotics (Basel, Switzerland) · 2023Article
- Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation.mSystems · 2023Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
One of main steps in a study of microbial communities is resolving their composition, diversity and function. In the past, these issues were mostly addressed by the use of amplicon sequencing of a target gene because of reasonable price and easier computational postprocessing of the bioinformatic data. With the advancement of sequencing techniques, the main focus shifted to the whole metagenome shotgun sequencing, which allows much more detailed analysis of the metagenomic data, including reconstruction of novel microbial genomes and to gain knowledge about genetic potential and metabolic capacities of whole environments. On the other hand, the output of whole metagenomic shotgun sequencing is mixture of short DNA fragments belonging to various genomes, therefore this approach requires more sophisticated computational algorithms for clustering of related sequences, commonly referred to as sequence binning. There are currently two types of binning methods: taxonomy dependent and taxonomy independent. The first type classifies the DNA fragments by performing a standard homology inference against a reference database, while the latter performs the reference-free binning by applying clustering techniques on features extracted from the sequences. In this review, we describe the strategies within the second approach. Although these strategies do not require prior knowledge, they have higher demands on the length of sequences. Besides their basic principle, an overview of particular methods and tools is provided. Furthermore, the review covers the utilization of the methods in context with the length of sequences and discusses the needs for metagenomic data preprocessing in form of initial assembly prior to binning.
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