Binning
A metagenomic sequence pipeline produces a collection of
reads, contigs, and genes. Associating these data with the organisms
from which they were derived is highly desirable for
the interpretation of the ecosystem. This process of association
between sequence data and contributing species (or higher level
taxonomic groups) is called binning or classification.
Analyses of datasets obtained using shotgun sequencing involve characterizing the taxonomic and functional diversity of a given environment by analyzing DNA fragments originating from the genomes of resident microbes. Existing binning methods can be classified into two categories, namely taxonomy dependent and taxonomy independent:
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Taxonomy depedent:
A majority of methods available for binning datasets obtained using shotgun sequencing belong to the taxonomy-dependent category. In these methods, the extent of ‘similarity’ of reads with sequences (in reference databases) or pre-computed models (built using sequences in reference databases) drives the assignment process. Based on the strategy used for comparing reads with sequences/pre-computed models, taxonomy-dependent methods can be sub-classified into alignment-based, composition-based and hybrid methods.
Here, we developed a novel taxonomy-dependent and alignment-free approach called MBMC (Metagenomic Binning by Markov Chains).
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Taxonomy indepedent:
Taxonomy independent methods simply group/bin reads in a given dataset based on their mutual similarity and do not involve a database comparison step.
Unsupervised methods usually bin reads based on three observations:- The k-mer frequency from reads of a genome is usually linearly proportional to that of the genome's abundance.
- Sufficiently long w-mers are usually unique in each genome.
- The short q-mer frequency distributions (or q-mer distributions in short) of individual sufficiently long reads sampled from the same genome or similar genomes are similar.
The following table shows a list of commonly used tools for metagenomic binning.
| Categoriy | Year | Tools | Short Descriptions | URL |
|---|---|---|---|---|
| Taxonomy-dependent methods | 2012 | AmphoraNet | The webserver implementation of the AMPHORA2 pipeline for metagenomic analysis of shotgun sequencing data. | AmphoraNet |
| 2008 | CARMA | A software pipeline for characterizing the taxonomic composition and genetic diversity of short-read metagenomes. | CARMA | |
| 2011 | ClaMS | A sequence composition-based classifier for metagenomic sequences | ClaMS | |
| 2010 | DiScRIBinATE | Distance Score Ratio for Improved Binning and Taxonomic Estimation. | DiScRIBinATE | |
| 2012 | Genometa | A Java based local bioinformatics program which allows rapid analysis of metagenomic short read datasets. | Genometa | |
| 2014 | KRAKEN | A system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies. | KRAKEN | |
| 2013 | LMAT | Designed to efficiently assign taxonomic labels to as many reads as possible in very large metagenomic datasets and report the taxonomic profile of the input sample. | LMAT | |
| 2010 | MARTA | This java-based software blasts each sequence that you provide it, and then looks for a consensus taxon among the top-hits returned from blast. | MARTA | |
| 2012 | MetaPhlAn | A computational tool for profiling the composition of microbial communities from metagenomic shotgun sequencing data. MetaPhlAn relies on unique clade-specific marker genes identified from 3,000 reference genomes. | MetaPhlAn | |
| 2011 | MetaPhyler | A taxonomic classifier for metagenomic shotgun reads, which uses phylogenetic marker genes as a taxonomic reference. | MetaPhyler | |
| 2010 | MG-RAST | An automated analysis platform for metagenomes providing quantitative insights into microbial populations based on sequence data. | MG-RAST | |
| 2010 | MLTreeMap | Analyzes DNA sequences and determines their most likely phylogenetic origin. | MLTreeMap | |
| 2014 | MyTaxa | A homology-based bioinformatics framework to classify metagenomic and genomic sequences. | MyTaxa | |
| 2012 | NBC | The Naïve Bayes Classification tool webserver for taxonomic classification of metagenomic reads. | NBC | |
| 2007 | PhyloPythia | Accurate phylogenetic classification of variable-length DNA fragments. | PhyloPythia | |
| 2012 | PhyloPythiaS | The Web Server for Taxonomic Assignment of Metagenome Sequences. | PhyloPythiaS | |
| 2009 | Phymm/PhymmBL | Phylogenetic Classification of Metagenomic Data with Interpolated Markov Models. | Phymm/PhymmBL | |
| 2010 | Pplacer | Places query sequences on a fixed reference phylogenetic tree to maximize phylogenetic likelihood or posterior probability according to a reference alignment. | Pplacer | |
| 2011 | ProViDE | A novel similarity based binning algorithm that uses a customized set of alignment parameter thresholds/ranges, specifically suited for the accurate taxonomic labelling of viral metagenomic sequences. | ProViDE | |
| 2011 | RAIphy | A semi-supervised metagenomic fragment classification program. | RAIphy | |
| 2012 | Sequedex | A signature-based method to classify the function and phylogeny of reads as short as 30 bp. | Sequedex | |
| 2009 | SOrt-ITEMS | Sequence orthology based approach for improved taxonomic estimation of metagenomic sequences. | SOrt-ITEMS | |
| 2011 | SPHINX | A hybrid binning approach that achieves high binning efficiency by utilizing both 'compositional' and 'similarity' features of the query sequence during the binning process. | SPHINX | |
| 2009 | TACOA | Software that can accurately predict the taxonomic origin of genomic fragments from metagenomic data sets by combining the advantages of the k -NN approach with a smoothing kernel function. | TACOA | |
| 2011 | TaxSOM | A tool for taxonomic classification of DNA fragments, as they are typically obtained in metagenome projects. | TaxSOM | |
| 2010 | Treephyler | A tool for fast taxonomic profiling of metagenomes. | Treephyler | |
| 2009 | WebCARMA | Taxonomic classification of metagenomic shotgun sequences. | WebCARMA | |
| 2013 | MEGAN5 | Interactively analyze and compare metagenomic and metatranscriptomic data, both taxonomically and functionally | MEGAN5 | |
| 2011 | ProViDE | A software tool for accurate estimation of viral diversity in metagenomic samples | ProViDE | |
| 2011 | PaPaRa | Parsimony-based Phylogeny-Aware Read alignment program | PaPaRa | |
| 2014 | MetaCluster-TA | A software for binning and annotating short paired-end reads. | MetaCluster-TA | |
| Taxonomy-independent methods | 2011 | AbundanceBin | An abundance-based tool for binning metagenomic sequences, such that the reads classified in a bin belong to species of identical or very similar abundances. | AbundanceBin |
| 2008 | CompostBin | A DNA-composition-based binning algorithm for classifying metagenomic reads. | CompostBin | |
| 2012 | MetaCluster 5.0 | MetaCluster5.0 is an unsupervised binning method. | MetaCluster 5.0 | |
| 2004 | TETRA | The standalone-programs can be used to calculate, how well tetranucleotide usage patterns in DNA sequences correlate. | TETRA |
There is no standard for the taxonomic classification of metagenome sequences. Also, taxonomic sequence classification can be error prone, in particular for habitats with a complex diversity or high proportions of as yet barely characterized taxa.
Reference:
1. Kunin V, Copeland A, Lapidus A, Mavromatis K, Hugenholtz P. A bioinformatician's guide to metagenomics. Microbiol Mol Biol Rev. 2008 Dec;72(4):557-78, Table of Contents. doi: 10.1128/MMBR.00009-08.
2. Mande SS, Mohammed MH, Ghosh TS. Classification of metagenomic sequences: methods and challenges. Brief Bioinform. 2012 Nov;13(6):669-81. doi: 10.1093/bib/bbs054. Epub 2012 Sep 8.
3. Wang Y, Leung HC, Yiu SM, Chin FY. MetaCluster 5.0: a two-round binning approach for metagenomic data for low-abundance species in a noisy sample. Bioinformatics. 2012 Sep 15;28(18):i356-i362. doi: 10.1093/bioinformatics/bts397.