Reconstructing Genomes from Metagenomic Samples Using the RAST Binning Service (RBS)

The ability to reconstruct fairly complete genomes from metagenomic samples is almost certainly a key technology that will accelerate our characterization of the tree of life. A number of research groups have now generated valuable reconstructions of genomes from metagenomic samples, and this is having an impact on estimating genomic sequences for unculturable organisms. For background we particularly recommend

Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life.

Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, Hugenholtz P, Tyson GW. Nat Microbiol. 2017 PMID: 28894102

but there have been several other excellent efforts that have been reported over the last few years.

RBS is a server that grew out of the RAST annotation project; it is now supported and maintained as part of the PATRIC project. In this tutorial we will discuss what RBS does. For a discussion of how to use it see Using the PATRIC Metagenomic Binning Service.

The Server: an Overview

As input to the server, a user supplies a metagenomic sample in one of the following forms:

  1. two files containing paired-end reads,
  2. a file of contigs representing an assembled sample. Note that RBS is able to function without coverage data; however, if coverage information is present in the FASTA sequence identifier or comment, RBS will find it and use this information to improve the binning.

The output is a set of Genome Packages. Each genome package is a Rast Genome along with an Evaluation Report which estimates the quality of the RAST genome.

The goal is to reconstruct complete (or near-complete) genomes from the sample data. Reconstructing a complete genome including the large repeats is usually quite difficult, so we instead try to reconstruct the regions between large repeats.

It should be noted that the number of genomes that we expect to extract will depend on the samples we use as input. If we wish to explore this proposed technology, it would make sense to begin with a very limited collection of samples. On the other hand, if the goal were to extract as many new genomes as possible, one might select hundreds (or even thousands) of samples as input. You should carefully note that this document describes one approach to extracting genomes from samples. There are a number of approaches emerging, and it is likely that aspects of the process we describe may well turn out to be non-optimal. We do hope that you will explore the approach and have fun in the process.

Here then is basically how we build the reconstructed genomes.

Preparation: Construct a Representative Collection of a Universal Protein

We sought a functional role that satisfies the following criteria:

  1. Exactly one gene encodes the role in all (or at least most) prokaryotic genomes.
  2. A role encoded by a long gene is preferred.

We picked

Phenylalanyl-tRNA synthetase alpha chain (EC 6.1.1.20)

but there are other good choices. Once we selected a role, which we call the seed role, we constructed a blast database containing a representative collection of protein sequences that implement the seed role. A crude version of this database can be built using the PATRIC command line tools, described in Using the PATRIC Command-line Interface. The following sequence does the trick.

p3-echo "Phenylalanyl-tRNA synthetase alpha chain (EC 6.1.1.20)" | p3-find-features --attr genome_id,genome_name,patric_id,aa_sequence product | p3-tbl-to-fasta --comment=genome_id --comment=genome_name patric_id aa_sequence

The output is then filtered to exclude proteins from low-quality genomes. The resulting FASTA database currently contains over 80,000 proteins.

Step 1: For Each Sample, Construct a set of Contig Bins

The first step of the actual binning process is to find all instances of the seed role in the submitted sample. This is done using BLAST, comparing the sequences in each sample to a selected representative set of instances of the seed role. The tool filters out hits which do not adequately cover the known seed role instance. Similarly, it removes hits against contigs that have less that 4-fold average coverage or are less than 400 base pairs in length. Each hit that remains represents a bin that will eventually be expanded into a reconstructed genome. A bin is normally thought of as containing a single genome, but when the binning service cannot resolve two genomes, it may merge them into a single bin; such bins will usually receive a low quality score (see section 5 below), and should be set aside for further processing.

Step 2: For Each Sample, Compute a Set of Reference Genomes

At this point, RBS has computed a set of bins, where each bin conceptually contains a single seed role and the contig that contains that seed role. Our overall objective is to determine which contigs from the cross-assembly associated with the sample should be placed into each bin. That is, RBS needs to split the contig pool into subsets that go with each bin, and an extra set of contigs that could not be placed (there may be many of these coming from the non-abundant organisms included in the sample, as well as those contigs whose placement would be ambiguous). There are several possible strategies for placing contigs into bins. RBS uses reference genomes. This involves associating a known, sequenced reference genome with each of the bins. These reference genomes play a central role in the next step, which involves actually splitting up the pool of contigs in the sample.

So, how should we go about assigning a sequenced reference genome to each bin? RBS attempts to find a reference genome that is phylogenetically close to each bin. To be useful, the reference genome needs to be substantially closer to the genome represented by the bin than to any of the genomes represented by other bins. A blastn is used to compare the seed role instance in the bin contig to all of the seed roles from the PATRIC database as computed in the preparation step above. If the seed roles in two bins appear to belong to the same species, they are combined into a single bin.

Once we have chosen the reference genomes, we look for protein 12-mers that discriminate those genomes; that is, 12-mers which occur in one bin’s reference genomes but not in the reference genomes for any other bins. These discriminating kmers are put into a temporary database to be used in the next step.

Step 4: For Each Sample, Place Contigs Into Bins

Once reference genomes have been determined for each bin, we can partition the contigs from the sample into the bins. Each contig is examined for protein 12-mers in all 6 frames. In particular, we select for the discriminating kmers computed above. If a contig has 10 or more such kmers belonging to a single bin’s reference genomes, it is placed into that bin. In particular, a contig C should be copied into bin B if and only if the similarity of C against the contigs of the reference genomes for B exceeds the specified threshold (10 discriminating kmers), and it is greater than the similarity to other reference genomes. That is, C is put into the bin belonging to reference genome G if C is most similar to G and the similarity exceeds the threshold.

Step 5: Evaluate the Quality of Each Bin

At this point, each bin contains a set of contigs that have tentatively been labeled as coming from a single clonal population. There are numerous possible sources of error, so how might we evaluate the quality of a bin? Fortunately, several such tools exist. The most notable is checkM (which we have found extremely useful):

Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2014. Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research, 25: 1043-1055.

We also annotation the bin using PATRIC RAST, and perform a consistency check on the annotation as a second check on the quality. The consistency checking tool maintains a database of which functional roles tend to occur in the presence of others and which should not appear in the presence of others. This database is applied to the annotations from RAST to produce a coarse score (percentage of roles that are correctly present or absent) and a fine score (percentage of roles that are correctly absent or present in the correct number).

The RBS output displays the high-quality bins separately from the bins of more dubious quality.

Summary

In this document, we sketch out the operation of a tool for reconstructing thousands of genomes from metagenomic samples. There are several alternative plans being developed by the research community. Here is a brief summary of a plan implemented by a European team that included Bjorn Nielsen, Dusko Ehrlich and Peer Bork (see “Identification and Assembly of Genomes and Genetic Elements in Complex Metagenomic Samples Without Using Reference Genomes”).

DNA from a series of independent biological samples from microbial communities, here originating from the human gut microbiome, is extracted and shotgun sequenced. Genes assembled and identified in individual samples are then integrated to form a cross-sample, nonredundant gene catalog. The abundance profile of each gene in the catalog is assessed by counting the matching sequence reads in each sample. To facilitate co-abundance clustering of large gene catalogs, we used random seed genes as ‘baits’ for identifying groups of genes that correlate (PCC > 0.9) to the abundance profile of the bait genes. The fixed PCC distance threshold is called a canopy. To center the canopy on a co-abundance gene group (CAG), the median gene abundance profile of the genes within the original seed canopy (or subsequent canopies) is used iteratively to recapture a new canopy until it settles on a particular profile. The gene content of a settled canopy is named a metagenomic species (MGS) if it contains 700 or more genes. The smaller groups remain referred to as CAGs. Sequence reads from individual samples that map to the MGS genes and their contigs are then extracted and used to assembly a draft genome sequence for an MGS; we refer to this process as MGS-augmented genome assembly. The use of sample-specific sequence reads in the assemblies helps discriminate between closely related strains.

It seems likely that we will be able to harvest thousands of genomes from metagenomic samples. The number of potentially useful samples is growing exponentially, the desire to gain genomes for unculturable organisms is growing, and our ability to extract reconstructed genomes is improving. Further improvements to the existing algorithms will inevitably increase the fraction of bins that can be salvaged.