What Exactly Is Metagenomics Binning?

Metagenomics binning involves clustering sequences into groups that reflect taxonomic classifications such as species, genus, or higher ranks. There are primarily two approaches to metagenomics binning: reference-based and reference-free methods. Reference-based techniques align sequences against established reference genome databases to identify their taxonomic affiliations. In contrast, reference-free methods rely solely on the sequence data itself, categorizing them into unlabelled groups.

This discussion will concentrate on reference-free binning methods, which can be further categorized into three types:

1. Composition-based binning
2. Abundance-based binning
3. A combination of composition and abundance-based binning

Composition-based Binning Tools

These tools utilize the compositional attributes of sequences, typically represented by oligonucleotide composition. Oligonucleotides are defined as continuous sequences of a small number of nucleotides, or k-mers, where 'k' refers to the length of the sequence. The oligonucleotide composition tends to be stable within microbial species but varies between different species. By representing sequences as oligonucleotide frequency vectors, various machine learning techniques can be applied to cluster similar sequences.

Notable tools in this category include:

• TETRASCIMM

For further reading on analyses using composition-based techniques, check out:

• Composition-based Clustering of Metagenomic Sequences

  A deep dive into clustering methods based on oligonucleotide composition.

  towardsdatascience.com

• How Similar is COVID-19 to Previously Discovered Coronaviruses

  A comparative analysis of composition profiles across coronavirus genomes.

  towardsdatascience.com

Abundance-based Binning

In metagenomic samples, species can be found in varying abundances. Some may appear in high numbers, while others may be relatively scarce. The coverage of sequences in these samples can reflect the abundance of the species they belong to. Abundance-based binning tools utilize this coverage data to group sequences with similar abundances.

Examples of these tools include:

• AbundanceBin
• Canopy

When dealing with species that have closely related nucleotide compositions, distinguishing between them using composition-based methods can be challenging. To address this, methods that combine both composition and abundance data have been developed.

Tools in this combined category include:

• MaxBin
• MetaWatt
• SolidBin
• MetaBC-LR

Other Innovative Approaches

Beyond the aforementioned methods, researchers have introduced additional tools that leverage extra information. Some noteworthy examples include:

• BMC3C: Utilizes codon information
• COCACOLA: Employs linkage information from paired-end reads
• d2S Bin: Refines binning results by adjusting sequences based on dissimilarity
• GraphBin: Enhances binning outcomes using connection data from the assembly graph (which I authored)

I hope you find this article informative, especially for those new to bioinformatics and metagenomics. I encourage you to experiment with these tools and assess their effectiveness. Relevant research articles are linked throughout, providing access to the software for your exploration.

Thank you for reading!

Cheers.