Table of Contents
SingleM is a tool to find the abundances of discrete operational taxonomic units (OTUs) directly from shotgun metagenome data, without heavy reliance on reference sequence databases. It is able to differentiate closely related species even if those species are from lineages new to science.
Where GraftM can give a taxonomic overview of your community e.g. proportion of a community from a particular taxonomic family, SingleM finds sequence-based OTUs from raw, untrimmed metagenomic reads.
This gives you the ability to answer questions such as:
An overview of your community can be obtained like so:
singlem pipe --sequences my_sequences.fastq.gz --otu_table otu_table.csv --threads 24
Please use raw metagenome reads, not quality trimmed reads. Quality trimming with e.g. Trimmomatic reads often makes them too short for SingleM to use. On the other hand, adapter trimming is unlikely to be detrimental.
The output table consists of columns:
gene sample sequence num_hits coverage taxonomy 4.21.ribosomal_protein_S19_rpsS my_sequences TGGTCGCGCCGTTCGACGGTCACTCCGGACTTCATCGGCCTACAGTTCGCCGTGCACATC 1 1.64 Root; d__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; o__Desulfuromonadales 4.21.ribosomal_protein_S19_rpsS my_sequences TGGTCGCGGCGCTCAACCATTCTGCCCGAGTTCGTCGGCCACACCGTGGCCGTTCACAAC 1 1.64 Root; d__Bacteria; p__Acidobacteria; c__Solibacteres; o__Solibacterales; f__Solibacteraceae; g__Candidatus_Solibacter; s__Candidatus_Solibacter_usitatus
Currently SingleM concentrates on 14 single copy marker genes to provide fine-grained differentiation of species that is independent of the copy-number variation issues that hamper 16S analyses. SingleM is reasonably fast and is quite scalable, although there is much room for improvement. On average, each of the 14 genes better differentiates closely related lineages than a typical 16S amplicon-based study.
Once an OTU table has been generated with the
pipe command, it can be further processed in various ways using
Create a Krona plot of the community. The following command generates a Krona file
my_krona.html which can be viewed in a web browser:
singlem summarise --input_otu_tables otu_table.csv --krona my_krona.html
Several OTU tables can be combined into one. Note that this is not necessary if the combined output is to be input again into summarise (or many other commands) - it is possible to just specify multiple input tables with
--input_otu_tables. To combine:
singlem summarise --input_otu_tables otu_table1.csv otu_table2.csv --output_otu_table combined.otu_table.csv
Cluster sequences, collapsing them into OTUs with less resolution, but with more robustness against sequencing error:
singlem summarise --input_otu_tables otu_table.csv --cluster --clustered_output_otu_table clustered.otu_table.csv
Rarefy a set of OTU tables so that each sample contains the same number of OTU sequences:
singlem summarise --input_otu_tables otu_table.csv other_samples.otu_table.csv --rarefied_output_otu_table rarefied.otu_table.csv --number_to_choose 100
Conversion to BIOM format for use with QIIME:
singlem summarise --input_otu_tables otu_table.csv other_samples.otu_table.csv --biom_prefix myprefix
This generates a BIOM table for each marker gene e.g.
As SingleM generates OTUs that are independent of taxonomy, they can be used as input to beta diversity methods known to be appropriate for the analysis of 16S amplicon studies, of which there are many. We recommend express beta diversity (EBD) as it implements many different metrics with a unified interface. For instance to calculate Bray-Curtis beta diversity, first convert your OTU table to unifrac file format using
singlem summarise. Note that this file format does not contain any phylogenetic information, even if the format is called 'unifrac'.
singlem summarise --input_otu_table otu_table.csv --unifrac_by_otu otu_table.unifrac
The above commands generates 14 different unifrac format files, one for each marker gene used in SingleM. At this point, you need to choose one table to proceed with. Hopefully, the choice matters little, but it might pay to use multiple tables and ensure that the results are consistent.
To calculate beta diversity that does not account for the phylogenetic relationships between the OTU sequences, use the EBD script
convertToEBD.py to convert the unifrac format into ebd format, and calculate the diversity metric:
convertToEBD.py otu_table.unifrac.4.12.ribosomal_protein_L11_rplK.unifrac otu_table.ebd ExpressBetaDiversity -s otu_table.ebd -c Bray-Curtis
Phylogenetic tree-based methods of calculating beta diversity can also be calculated, but
pipe must be used to generate a new OTU table using the
diamond_example taxonomy assignment method so that each OTU is assigned to a single leaf in the tree:
singlem pipe --sequences my_sequences.fastq.gz --otu_table otu_table.diamond_example.csv --threads 24 --assignment_method diamond_example
Then, use the
--unifrac_by_taxonomy flag to create a unifrac format file indexed by taxonomy identifier:
singlem summarise --otu_tables otu_table.diamond_example.csv --unifrac_by_taxonomy otu_table.diamond_example.csv convertToEBD.py otu_table.diamond_example.unifrac otu_table.diamond_example.ebd
Then, finally run
ExpressBetaDiversity using the
ExpressBetaDiversity -s otu_table.diamond_example.ebd -c Bray-Curtis -t <path_to_tree_in_singlem_package>
<path_to_tree_in_singlem_package> is the newick format file in the SingleM package used to find the OTU sequences. This path can be found using
It can be useful in some situations to search for sequences in OTU tables. For instance, you may ask "is the most abundant OTU or anything similar in samples B, C or D?" To answer this question make a SingleM database from sample B, C & D's OTU tables:
singlem makedb --otu_tables sample_B.csv sample_C.csv sample_D.csv --db_path sample_BCD.sdb
.sdb is the conventional file extension for SingleM databases. Then to query this database
singlem query --query_sequence TGGTCGCGGCGCTCAACCATTCTGCCCGAGTTCGTCGGCCACACCGTGGCCGTTCACAAC --db sample_BCD.sdb
SingleM can be used to determine how much of a community is represented in an assembly or represented by a set of genomes.
The assessment is carried out by comparing the set of OTU sequences in the assembly/genomes to those found from the raw metagenome reads. A similar metric can be estimated by the fraction of reads mapping to either the assembly or the genome, but the approach here is more flexible and has several advantages:
To assess how well a set of sequences represent a metagenome, first run
on both the genomes and the raw reads, and then use
singlem pipe --sequences raw.fq.gz --otu_table metagenome.otu_table.csv singlem pipe --sequences my_genomes/*.fasta --otu_table genomes.otu_table.csv singlem appraise --metagenome_otu_tables metagenome.otu_table.csv --genome_otu_tables genomes.otu_table.csv
One may also accommodate some sequence differences, with
output OTU tables of those sequences that match and those that do not (see
singlem appraise -h). Assessing assemblies is similar to assessing genomes,
except that when assessing bins duplicate markers from the same genome are
excluded as likely contamination.
An appraisal can also be represented visually, using
Each rectangle represents a single OTU sequence where its size represents its
abundance (the number of reads that OTU represents in the metagenome). Colours
represent 89% OTU clustering of these sequences (~genus level), with the
taxonomy of the most common sequence written out. Here we see that highly
abundant OTUs in SRR5040536 were assembled, but only 1 of the 3 abundant
Gallionellales OTUs has an associated bin. As is common, the highest abundance
lineages did not necessarily assemble and bin successfully. The marker
4.20.ribosomal_protein_S15P_S13e was chosen as the representative marker
because it has a representative fraction of OTUs binned, assembled and
The most straightforward way of installing SingleM is to use the GNU Guix package which is part of the ACE Guix package collection. This method installs not just the Python libraries required but the compiled bioinformatics tools needed as well. Once you have installed Guix, clone the ACE collection and install:
git clone https://github.com/Ecogenomics/ace-guix GUIX_PACKAGE_PATH=ace-guix guix package --install singlem
Beyond installing GNU Guix, super-user privileges are not required for installation.
A docker image generated from the Guix package is available on DockerHub. After installing Docker, run the following, replacing
[RELEASE_TAG] with a tag from https://hub.docker.com/r/wwood/singlem/tags:
docker pull wwood/singlem:[RELEASE_TAG]
If the sequence data to be analyzed is in the current working directory, SingleM can be used like so:
docker run -v `pwd`:`pwd` wwood/singlem:[RELEASE_TAG] pipe --sequences `pwd`/my.fastq.gz --otu_table `pwd`/my.otu_table.csv --threads 14
Conda support at this time is partial because some dependencies are not packaged for conda, and the following is not well tested, but it may aid your installation. After installing conda and setting up the bioconda and conda-forge channels,
conda create -n singlem nose python hmmer h5py matplotlib krona diamond orfm pplacer vsearch smafa tempdir biopython biom-format dendropy mfqe conda activate singlem pip install orator pip install extern pip install squarify pip install graftm pip install singlem
SingleM has migrated to Python 3. To install the Python libraries required:
pip install graftm pip install singlem
You may need super-user privileges.
SingleM also has the following non-Python dependencies:
Some dependencies of GraftM:
Yes. By default, SingleM builds OTU tables from ribosomal protein genes rather than 16S because this in general gives more strain-level resolution due to redundancy in the genetic code. If you are really keen on using 16S, then you can use SingleM with a 16S SingleM package (spkg). There is a repository of auxiliary packages at https://github.com/wwood/singlem_extra_packages including a 16S package that is suitable for this purpose. The resolution won't be as high taxonomically, and there are issues around copy number variation, but it could be useful to use 16S for various reasons e.g. linking it to an amplicon study or using the GreenGenes taxonomy. For now there's no 16S spkg that gets installed by default, you have to use the
--singlem_packages flag in
pipe mode pointing to a separately downloaded package - see https://github.com/wwood/singlem_extra_packages/blob/master/README.md.
There are two ways. It is possible to specify multiple input files to the
singlem pipe subcommand directly by space separating them. Alternatively
singlem pipe can be run on each sample and OTU tables combined using
singlem summarise. The results should be identical, though there are some performance trade-offs. For large numbers of samples (>100) it is probably preferable to run each sample individually or in smaller groups.