Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonom- ically annotate its reads to identify the species it contains. Taxonomic analysis of microbial communities requires reads clustering, a process referred to as binning. The major problems of metagenomics reads bin- ning are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, and sequencing errors. In this paper we present MetaProb 2 an unsupervised binning method based on reads assembly and probabilistic k-mers statistics. The novelties of MetaProb 2 are the use of minimizers to efficiently assemble reads into unitigs and a community detection algorithm based on graph modularity to cluster unitigs and to detect representative unitigs. The effectiveness of MetaProb 2 is demonstrated in both simulated and synthetic datasets in comparison with state-of-art binning tools such as MetaProb, Abun- danceBin, Bimeta and MetaCluster.
Download this repo using
git clone https://github.com/frankandreace/metaprob2.gitIn order to work, MetaProb2 needs 3 pieces of sotfware:
- Minimap2;
You can also use:
git clone https://github.com/lh3/minimap2 && (cd minimap2 && make)- Miniasm;
You can also use:
git clone https://github.com/lh3/miniasm && (cd miniasm && make)- MetaProb;
Go to MetaProb/Release/ and then use: make all. Be aware that the original version of MetaProb that can be found on Bitbucket is no longer woring nor mantained. Use the one present in this directory.
You need gcc and zlib to install Minimap2 and Miniasm; You also need Boost and Eingen libraries to use MetaProb. You should move the two folders in /usr/local/include before installing MetaProb.
You need to install scikit-network for python3.
Please follow the guides provided at the links above to correctly install the tools.
MetaProb2 is a metagemomic binning tool that uses mapping and assembly software together with a novel metagenomic community detection script to improve the results of MetaProb, source code available here.
We hereby provide the python3 and shell scripts to make the pipeline work without much effort.
The usage of the 3 different softwares is well explained at the liks provided above. For non-custom usage, you can simply downlaod the softwares, the libraries, the 2 python3 scripts provided in this repository and the shell script.
Please set all the tools path in METAPROB2.sh to make it work properly. You can understand all the parameters by calling ./METAPROB2.sh -h .
Usage example:
./METAPROB2 -s[number of expected species/genera]-c[minimum chain score] -t[number of cores to be used]-l <fasta/fastq input file> <output files name> All minimap2, miniasm and metaprob parameters are tune to make the algorithms work as better as possible. You can change them anytime but please check the manuals before:
1.Minimap2
2.Miniasm
3.MetaProb
File accepted have the following structure:
Structure file .fna example:
>IDENTIFICATION
ATAATTGGCAAGTGTTTTAGTCTTAGAGAGATTCTCTAAGTCTAACTTGAACTCAATTTGGAATCATTTCCCAATTTTTA
In this version, paired-end reads are passed to the algorithm in two separeted files and then merged into one with all the reads in the first file followed by all the reads in the second one. Since we use an all vs all overlap tool, if you have 2 files with paired end reads, please use "file generator" script. If you have an interleaved fasta file, please put ALL first end reads first (.1) and second end reads after (.2). The input file should look like this (if,for example, your sample has 100 paired end reads):
read1.1
read2.1
...
read100.1
read1.2
read2.2
...
read100.2
So we raccomend to control the paired-end read if they are paired in the correct manner.
During the process the tool will generate several additional files. 1.The script file_generator creates a fasta file that has in the first half all the .1 sequences and in the second half all the .2 sequences.
2.Minimap2 outputs paf files. You can learn more here.
3.Miniasm outputs gfa files. You can lear more here.
4.The script community_detection generates a fasta file that will be used as input for metaprob. It contains the representatives of each identified cluster together with isolated unitigs and the reads that have not been grouped by minimap2. It provides also a csv with all the reads contained in each cluster and all the reads contained in each isolated unitig.
5.MetaProb gives as output a csv with the grouped sequences (during the first phase) and a csv with the final cluster for every input sequence.
If you want to test the tool with some synthetic data, you can try with the same used in the paper.
#Test File
##Single-End Reads:
Single-End_dataset_part1
Single-End_dataset_part2
##Paired-End Reads split file
Paired-End_split_dataset_part01
Paired-End_split_dataset_part02
Paired-End_split_dataset_part03
Paired-End_split_dataset_part04
Paired-End_split_dataset_part05
Paired-End_split_dataset_part06
Paired-End_split_dataset_part07
Paired-End_split_dataset_part08
Paired-End_split_dataset_part09
Paired-End_split_dataset_part10
If you encounter bugs or have further questions or requests, you can raise an issue at the issue page. You can also contact Francesco Andreace at francesco.andreace@unipd.it .
If you use MetaProb 2 in your work, please cite:
Andreace F., Pizzi C., Comin M. (2021) MetaProb 2: Improving Unsupervised Metagenomic Binning with Efficient Reads Assembly Using Minimizers. In Computational Advances in Bio and Medical Sciences. ICCABS 2020. Lecture Notes in Computer Science, vol 12686, pp 15-25. https://doi.org/10.1007/978-3-030-79290-9_2
Francesco Andreace, Cinzia Pizzi, and Matteo Comin. "MetaProb 2: Metagenomic Reads Binning Based on Assembly Using Minimizers and K-Mers Statistics". Journal of Computational Biology 2021 28:11, 1052-1062. http://doi.org/10.1089/cmb.2021.0270