A rust classifier based on probminhash and HNSW for microbial genomes

gsearch is the new name of the crate archaea. It stands for genomic search.

This package (currently in development) compute probminhash signature of bacteria and archaea (or virus and fungi) genomes and stores the id of bacteria and probminhash signature in a Hnsw structure for searching of new request genomes.

This package is developped by Jean-Pierre Both (https://github.com/jean-pierreBoth) for the software part and Jianshu Zhao (https://github.com/jianshu93) for the genomics part. We also created a mirror of this repo on GitLab (https://gitlab.com/Jianshu_Zhao/archaea), just in case Github service is not available in some region, e.g, China.

Sketching of genomes/tohnsw

The sketching and database is done by the module tohnsw.

The sketching of reference genomes can take some time (one or 2 hours for ~65,000 bacterial genomes of NCBI for parameters giving a correct quality of sketching). Result is stored in 2 structures: - A Hnsw structure storing rank of data processed and corresponding sketches. - A Dictionary associating each rank to a fasta id and fasta filename.

The Hnsw structure is dumped in hnswdump.hnsw.graph and hnswdump.hnsw.data The Dictionary is dumped in a json file seqdict.json

Requests

For requests the module request is being used. It reloads the dumped files, hnsw and seqdict related takes a list of fasta files containing requests and for each fasta file dumps the asked number of nearest neighbours.

Usage

Simple case for install:

Pre-built binaries will be available on release page (https://github.com/jean-pierreBoth/gsearch/releases/tag/v0.0.12) for major platforms (no need to install but just download and make it executable). We recommend you use the linux one (GSearchLinuxx86-64intel-mkl-static.zip ) for linux system in this release page for convenience because the only dependency is GCC (Recent Linux version does not allow static compiling of GCC libraries like libc.so.6). For macOS, we recommend the universal binary (https://github.com/jean-pierreBoth/gsearch/releases/download/v0.0.12/GSearchdarwin_universal.zip) for any macOS platform (x86-64 or arm64).

Otherwise it is possible to install/compile by yourself (see install section)

```bash

get the binary for linux (make sure you have recent Linux installed with GCC, e.g., Ubuntu 18.0.4 or above):

wget https://github.com/jean-pierreBoth/gsearch/releases/download/v0.0.12/GSearchLinuxx86-64intel-mkl-static.zip unzip GSearchLinuxx86-64intel-mkl-static.zip

get the binary for macOS:

wget https://github.com/jean-pierreBoth/gsearch/releases/download/v0.0.12/GSearchdarwinuniversal.zip unzip GSearchdarwinuniversal.zip

make it excutable (changed it accordingly on macOS)

chmod a+x ./GSearchLinuxx86-64_intel-mkl-static/*

put it under your system/usr bin directory (/usr/local/bin/ as an example) where it can be called:

mv ./GSearchLinuxx86-64_intel-mkl-static/* /usr/local/bin/

check install

tohnsw -h request -h

check install MacOS, you may need to change the system setup to allow external binary to run by type the following first and use your admin password:

sudo spctl --master-disable

and then

tohnsw -h request -h

request neighbours for each genomes (fna, fasta, faa et.al. are supported) in querydirnt or aa using pre-built database:

wget http://enve-omics.ce.gatech.edu/data/publicgsearch/GTDBr207hnswgraph.tar.gz tar xzvf ./GTDBr207hnsw_graph.tar.gz

get test data, we provide 2 genomes at nt, AA and universal gene level

wget https://github.com/jean-pierreBoth/gsearch/releases/download/v0.0.12/testdata.tar.gz tar xzvf ./testdata.tar.gz

cd ./GTDBr207hnsw_graph/nucl

request neighbors for nt genomes (here -n is how many neighbors you want to return for each of your query genome)

request -b ./ -r ../../testdata/querydir_nt -n 50

or request neighbors for aa genomes (predicted by Prodigal or FragGeneScanRs)

cd ./GTDBr207hnswgraph/prot request -b ./ -r ../../testdata/querydiraa -n 50 --aa

or request neighbors for aa universal gene (extracted by hmmer according to hmm files from gtdb, we also provide one in release page)

cd ./GTDBr207hnswgraph/universal request -b ./ -r ../../testdata/querydiruniversal_aa -n 50 --aa

Building database. database is huge in size, users are welcome to download gtdb database here: (https://data.ace.uq.edu.au/public/gtdb/data/releases/release207/207.0/genomicfilesreps/gtdbgenomesrepsr207.tar.gz) and here (https://data.ace.uq.edu.au/public/gtdb/data/releases/release207/207.0/genomicfilesreps/gtdbproteinsaareps_r207.tar.gz)

build database given genome file directory, fna.gz was expected. L for nt and .faa or .faa.gz for --aa. Limit for k is 32 (15 not work due to compression), for s is 65535 (u16) and for n is 255 (u8)

tohnsw -d dbdirnt -s 12000 -k 16 --ef 1600 -n 128 tohnsw -d dbdiraa -s 12000 -k 7 --ef 1600 -n 128 --aa

When there are new genomes after comparing with the current database (GTDB v207, e.g. ANI < 95% with any genome after searcing, corresponding to 0.9850 ProbMinHash distance), those genomes can be added to the database:

must run in the existing database file folder

cd ./GTDBr207hnsw_graph/nucl

old .graph,.data and all .json files will be updated to the new one. Then the new one can be used for requesting as an updated database

tohnsw -d dbdirnt (new genomes directory) -s 12000 -k 16 --ef 1600 -n 128 --add

or add at the amino acid level:

cd ./GTDBr207hnswgraph/prot tohnsw -d dbdir_nt (new genomes directory in AA format predicted by prodigal/FragGeneScanRs) -s 12000 -k 16 --ef 1600 -n 128 --aa --add ```

Output explanation

gsearch.answers is the default output file in your current directory.
For each of your genome in the query_dir, there will be requested N nearest genomes found and sorted by distance (smallest to largest).
If one genome in the query does not exist in the output file, meaning at this level (nt or aa), there is no such nearest genomes in the database (or distant away from the best hit in the database), you may then go to amino acid level or universal gene level.

Dependencies, features and Installation

Features

• hnswrs relies on the crate simdeez to accelerate distance computation. On intel you can build hnswrs with the feature simdeez_f

• annembed relies on openblas so you must choose between the features "annembedopenblas-static" , "annembedopenblas-system" or "annembed_intel-mkl". You may need to install gcc, gfortran and make. This can be done using the --features option as explained below, or by modifying the features section in Cargo.toml. In that case just fill in the default you want.

• kmerutils provides a feature "withzmq". This feature can be used to store compressed qualities on a server and run requests. It is not necessary in this crate.

Install

First install Rust tools

bash curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

gsearch installation and compilation from Crates.io (Recommended)

• simple installation, with annembed enabled would be with intel-mkl

bash cargo install gsearch --features="annembed_intel-mkl"

or with a system installed openblas:

bash cargo install gsearch --features="annembed_openblas-system" - On MacOS, which requires dynamic library link (you have to install openblas first and then xz, the MacOS/Darwin binary provided also requires this): (note that openblas install lib path is different on M1 MACs).
So you need to run:

bash brew install openblas xz echo 'export LDFLAGS="-L/usr/local/opt/openblas/lib"' >> ~/.bash_profile echo 'export CPPFLAGS="-I/usr/local/opt/openblas/include"' >> ~/.bash_profile echo 'export PKG_CONFIG_PATH="/usr/local/opt/openblas/lib/pkgconfig"' >> ~/.bash_profile source ~/.bash_profile cargo install gsearch --features="annembed_openblas-system"

• Intel:
  You can enable simd instruction with the feature hnswrs/simdeezf.
  Using openblas instead of intel-mkl you would run:
  bash cargo install gsearch --features="annembed_openblas-system" --features="hnsw_rs/simdeez_f"

gsearch installation from the most recent version from github

• direct installation from github:
  bash cargo install gsearch --features="annembed_intel-mkl" --git https://github.com/jean-pierreBoth/gsearch

• download and compilation: ```bash git clone https://github.com/jean-pierreBoth/gsearch cd gsearch

  build

cargo build --release --features="annembed_openblas-static"

on MacOS, which requires dynamic library link:

cargo build --release --features="annembed_openblas-system" ```

Documentation generation

Html documentation can be generated by running (example for someone using the "annembed_openblas-system" feature):

bash cargo doc --features="annembed_openblas-system" --no-deps --open

Then install FragGeneScanRs:

bash cargo install --git https://gitlab.com/Jianshu_Zhao/fraggenescanrs

Some hints in case of problem (including installing/compiling on ARM64 CPUs) are given here

Pre-built databases

We provide pre-built genome/proteome database graph file for bacteria/archaea, virus and fungi. Proteome database are based on genes for each genome, predicted by FragGeneScanRs (https://gitlab.com/JianshuZhao/fraggenescanrs) for bacteria/archaea/virus and GeneMark-ES version 2 (http://exon.gatech.edu/GeneMark/licensedownload.cgi) for fungi.

• Bacteria/archaea genomes are the newest version of GTDB database (https://gtdb.ecogenomic.org), which defines a bacterial speces at 95% ANI. Note that GSearch can also run for even higher resolution species database such as 99% ANI.
• Virus data base are based on the JGI IMG/VR database newest version (https://genome.jgi.doe.gov/portal/IMGVR/IMGVR.home.html), which also define a virus OTU (vOTU) at 95% ANI.
• Fungi database are based on the entire RefSeq fungal genomes (retrived via the MycoCosm website), we dereplicated and define a fungal speices at 99.5% ANI.
• All three pre-built databases are available here:http://enve-omics.ce.gatech.edu/data/gsearch

References

1. Jianshu Zhao, Jean Pierre Both, Luis M. Rodriguez-R and Konstantinos T. Konstantinidis, 2022. GSearch: Ultra-Fast and Scalable Microbial Genome Search by combining Kmer Hashing with Hierarchical Navigable Small World Graphs. bioRxiv 2022:2022.2010.2021.513218. biorxiv.