singlem microbial_fraction

The SingleM microbial_fraction ('SMF') subcommand estimates the fraction of reads in a metagenome that are microbial, compared to everything else e.g. eukaryote- or phage-derived. Here we define 'microbial' as either bacterial or archaeal, including their plasmids.

SingleM microbial_fraction also estimates the average genome size (AGS) of microbial cells in the sample.

The main conceptual advantage of this method over other tools is that it does not require reference sequences of the non-microbial genomes that may be present (e.g. those of an animal host). Instead, it uses a SingleM taxonomic profile of the metagenome to "add up" the components of the community which are microbial. The remaining components are non-microbial e.g. host, diet, or phage.

Roughly, the number of microbial bases is estimated by summing the genome sizes of each species/taxon after multiplying by their coverage in the taxonomic profile generated by SingleM pipe. The microbial fraction is then calculated as the ratio of microbial bases to the total metagenome size.

One current limitation of the approach relates to multi-copy plasmids. In microbial_fraction, the genome size of each microbial species is estimated as the sum of the chromosome and plasmid sizes, since these are the sequences available for each genome. However, in a metagenome, a species' plasmid may occur in multiple copies per cell (e.g. if the plasmid is 'high copy number'). SingleM microbial_fraction does not account for plasmid copy number, leading to an underestimation of the microbial fraction when plasmids are multi-copy. However, we consider this to be a minor issue, since plasmids are typically small compared to chromosomes. The average genome size estimate is unaffected by this limitation since by definition each plasmid counts only once regardless of its copy number.

To run microbial_fraction, first run pipe on your metagenome.

$ singlem pipe --forward SRR9841429_1.fastq.gz --reverse SRR9841429_2.fastq.gz --threads 32 -p SRR9841429.profile

Then run microbial_fraction on the profile.

singlem microbial_fraction --forward SRR9841429_1.fastq.gz --reverse SRR9841429_2.fastq.gz -p SRR9841429.profile >SRR9841429.smf.tsv

The output you get is a tab-separate values file containing:

sample	bacterial_archaeal_bases	metagenome_size	read_fraction	average_bacterial_archaeal_genome_size	warning
SRR9841429_1	2059301599	2648177782	77.76	3446415

So, for this sample, 77.76% of the reads are estimated to be microbial, and the average microbial genome size is 3.4 Mbp.

Not that the read_fraction column is a percentage, i.e. 0.3 is 0.3%, not 30%. The warning column is empty if no warning was emitted.

This tool can help prioritise samples for deeper sequencing, forecast how much sequencing is required for a given set of samples, and identify problematic steps in genome-resolved metagenomic pipelines.

The method is least reliable in simple communities consisting of a small number of species that are missing from the reference database. The main challenge in these cases is that the genome sizes of novel species are hard to estimate accurately. Multiplying a coverage from the taxonomic profile against an uncertain genome length equals an uncertain number of bases assigned as microbial.

To detect such situations SingleM microbial_fraction emits a warning when a sample's microbial_fraction estimate could under- or overestimate the read fraction. Specifically, the warning is emitted when the 3 highest abundance lineages not classified to the species level would change the estimated read fraction of the sample by >2% if their genome size is halved or doubled. Microbial fractions are also capped at 100% since values greater than this are impossible, but the original value can be recovered from the output if you calculate bacterial_archaeal_bases / metagenome_size.

OPTIONS

INPUT

-p, --input-profile INPUT_PROFILE

Input taxonomic profile file [required]

READ INFORMATION [1+ ARGS REQUIRED]

-1, --forward, --reads, --sequences sequence_file [sequence_file ...]

nucleotide read sequence(s) (forward or unpaired) to be searched. Can be FASTA or FASTQ format, GZIP-compressed or not. These must be the same ones that were used to generate the input profile.

-2, --reverse sequence_file [sequence_file ...]

reverse reads to be searched. Can be FASTA or FASTQ format, GZIP-compressed or not. These must be the same reads that were used to generate the input profile.

--input-metagenome-sizes INPUT_METAGENOME_SIZES

TSV file with 'sample' and 'num_bases' as a header, where sample matches the input profile name, and num_reads is the total number (forward+reverse) of bases in the metagenome that was analysed with 'pipe'. These must be the same reads that were used to generate the input profile.

DATABASE

--taxon-genome-lengths-file TAXON_GENOME_LENGTHS_FILE

TSV file with 'rank' and 'genome_size' as headers [default: Use genome lengths from the default metapackage]

--metapackage METAPACKAGE

Metapackage containing genome lengths [default: Use genome lengths from the default metapackage]

OTHER OPTIONS

--accept-missing-samples

If a sample is missing from the input-metagenome-sizes file, skip analysis of it without croaking.

--output-tsv OUTPUT_TSV

Output file [default: stdout]

--output-per-taxon-read-fractions OUTPUT_PER_TAXON_READ_FRACTIONS

Output a fraction for each taxon to this TSV [default: D o not output anything]

OTHER GENERAL OPTIONS

--debug

output debug information

--version

output version information and quit

--quiet

only output errors

--full-help

print longer help message

--full-help-roff

print longer help message in ROFF (manpage) format

AUTHORS

Ben J. Woodcroft, Centre for Microbiome Research, School of Biomedical Sciences, Faculty of Health, Queensland University of Technology
Samuel Aroney, Centre for Microbiome Research, School of Biomedical Sciences, Faculty of Health, Queensland University of Technology
Raphael Eisenhofer, Centre for Evolutionary Hologenomics, University of Copenhagen, Denmark
Rossen Zhao, Centre for Microbiome Research, School of Biomedical Sciences, Faculty of Health, Queensland University of Technology