ORI
ORI (Oxford nanopore Reads Identification) is a software allowing, from long nanopore reads, to identify the bacterial strains present in a sample.
Install / Use
/learn @gsiekaniec/ORIREADME
<img src="img/ORI.png" alt="ORI" width="3000"/>
ORI (Oxford nanopore Reads Identification) is a software using long nanopore reads to identify bacteria present in a sample at the strain level.
There are two sub-parts in the ORI program: (1) the creation of the index containing the reference genomes of the interest species and (2) the query of this index with long reads from Nanopore sequencing in order to identify the strain(s).
The index is based on the structure implemented in HowDeSBT [1] modified in order to use qgrams (word from spaced seeds) instead of kmers.
As previously said, we replaced kmers by qgrams. To get qgrams we use spaced seeds which introduce don’t care positions in kmers that won’t be disturbed by sequencing errors. To select the best seed pattern for classification of long reads we used the iedera software [2][3]. The best seed for our classification tools seems to be the following: size 15 and weight 13, 111111001111111. This seed is then applied to words of size 15 resulting in qgrams used instead of kmers.
The preconstructed indexes for Streptococcus thermophilus strains are available in the ORI github in the directory: preconstructed_indexes directory of ORI.
<sub>1. Robert S Harris and Paul Medvedev, Improved representation of sequence bloom trees, Bioinformatics, btz662 <10.1093/bioinformatics/btz662>
<sub>2. Noe L., Best hits of 11110110111: model-free selection and parameter-free sensitivity calculation of spaced seeds, Algorithms for Molecular Biology, 12(1). 2017 http://doi.org/10.1186/s13015-017-0092-1
<sub>3. Kucherov G., Noe L., Roytberg, M., A unifying framework for seed sensitivity and its application to subset seeds, Journal of Bioinformatics and Computational Biology, 4(2):553-569, 2006 http://doi.org/10.1142/S0219720006001977
Installation
The easiest way to install ORI is through conda.
<details><summary><strong>From Conda</strong></summary>conda create -p ori_env
conda activate ori_env
conda install -c gsiekaniec -c conda-forge ori
How does it work ?
The seedfile.txt can be found in the seed directory of ORI.
You can also create the seedfile.txt in your chosen repertory:
cd path/to/repertory
touch seedfile.txt
echo "111111001111111" > seedfile.txt
I) First step: create your own index
:warning: Caution: reference genomes must be in .fasta or .fna and in the same directory.
If you have the fasta files distributed in several subfolders, you have to redirect them to a single directory.
Repertory containing fasta files (must be the current directory) to run the following scripts:
cd path/to/the/fasta/files/repertory
1) Create the bloom filters (.bf) for each genome
As the last quantification step requires the size of the genomes, it is preferable to calculate it when we have our genomes:
ORI.py length -g path/to/the/genomes -o length.txt
| Parameters | Description | Required | |----------|:-------------:|------:| | -g/--genomes | path to the repertory containing genome (.fna or .fasta). | Yes | | -o/--outfile | output file containing length of each genome. | No. Default: length.txt | | -fpr/--false_positive_rate | false positive percent (between 0 and 1) wanted for the bloom filter containing the largest genomes. | No. Default: 0.01 | | -s/--seed_size | size of the spaced seed you want to use (not the weight). | No. Default: None |
</details>The ORI.py length step also allows to calculate an effective size for the bloom filters. Use the -seed_size and -false_positive_rate options . The bloom filter size is given in the bf_min_size.txt file.
Then we create the bloom filters for all genomes:
howdesbt makebfQ --k=15 --qgram=path/to/seedfile.txt --bits=0.25G *.fasta
| Parameters | Description | |----------|:-------------:| | --k | length of the seed given in --qgram. You may modify your seedfile.txt and this parameter. | | --qgram | file containing the used spaced seed. It's a text file containing the seed used on the first line (here 111111001111111). | | --bits | size of bloom filters. A size that is too small will give too many false positives to be usable and a size that is too large will take up too much space and greatly increase the computation times. |
</details>Then we get the names of the bf (bloom filter) files used to create the tree:
ls *.bf > leafname
Now that the bloom filters are created it is no longer necessary to keep the fastas files. If it is not necessary to keep them, they can be deleted to save space. In addition, if the fasta files cannot be completely downloaded on the machine due to lack of space, it is possible to download them little by little and create the filters as you go by deleting the fasta files once in the form of a filter (.bf).
2) If you want to cluster closely related strains to identify a fine cluster of strains rather than a mixed list of single strains:
howdesbt distance --list=leafname
| Parameters | Description | |----------|:-------------:| | --list | list of the bloom filters names (one per line). |
</details>:warning: Caution: the threshold parameter t depends on the genetic proximity of your strains (based on Hamming distances matrix). To adapt this parameter to your bacterial species, just launch the following command once (ORI.py threshold). It gives a figure threshold.png as output containing the distribution of the distances between the strains of the index (distances are multiplied by 1e05 in the figure). More generally, if your cluster of strains is too large and gives you to many possibilities of identification, try a lower t value (e.g. i know that the strains number 205, 51 and 55 are really closed on a phylogenic tree, but a bit farther away to strains 54 and 78; if a threshold of 0.0002 (default value) gives you a cluster containing the 5 strains, you can lower to -t 0.0001 to obtained two separated clusters).
ORI.py threshold -m hamming_matrix.tsv -t 0.0002
| Parameters | Description | Required | |----------|:-------------:|------:| | -m/--matrix | path to the hamming distance matrix. It's the output of the first howdesbt distance | Yes | | -t/--threshold | threshold that we want to set to merge close genomes. Be careful not to set this threshold too high or too low. Floating number between 0 and 1. | No. Default: 0.0002 |
</details>The default 0.0002 value is the value used to merge Streptococcus thermophilus strains using filters of size 0.5G. This value must be modified in the case of using another species and/or another filter size.
Once you have defined your own t value, merge your strains in adapted clusters:
howdesbt distance --list=leafname --threshold=0.0002 --matrix=hamming_matrix.bin --merge
| Parameters | Description | |----------|:-------------:| | --list | list of the bloom filters names (one per line). | | --matrix | path to the hamming distance matrix. It's the output of the first howdesbt distance | | --threshold | hamming distance threshold between bloom filter for merging them. Floating number between 0 and 1. | | --merge | merge maximal cliques ? |
</details>ORI.py clean_merge -n leafname -r path/to/repository/with/bf/files -o list_number_file.txt
| Parameters | Description | Required | |----------|:-------------:|------:| | -n/--names | list of the bloom filters names (one per line). | Yes | | -r/--repository | repertory containing the bloom filter file (.bf). | Yes | | -o/--outfile | output file. Out file containing one id number, one genome name and the corresponding number of sequence per line. | Yes |
</details>ls *.bf > leafname_merge
Since the genomes of some strains have been merged, the size of these clusters must also be recalculated:
ORI.py merge_length -b leafname_merge -l length.txt -c list_number_file.txt -o merge_length.txt
| Parameters | Description | Required | |----------|:-------------:|------:| | -b/--bflist | list of the bloom filters names (one per line). | Yes | | -l/--lengthfile | file containing length of each genome. It's the output of ORI.py length (default : length.txt). | Yes | | -c/--correspondance | file containing correspondance between numbers and genomes. It's the output of ORI.py clean_merge (list_number_file.txt). | Yes | | -o/--outfile | output file containing length of each genome or genomes cluster. | No. Default: length_merge.txt |
</details>3) Create the tree
To run these commands you must be in the directory containing the .bf files.
howdesbt cluster --list=leafname/or/leafname_merge --tree=union.sbt --nodename=node{number} --cull
| Parameters | Description | |----------|:-------------:| | --list | list of the bloom filters names (one per line). | | --tree | name for tree toplogy file. | | --nodename | filename template for internal tree nodes this must contain the substring {number}. | | --cull | remove nodes from the binary tree; remove those for which saturation of determined is more than 2 standard deviations. |
</details>howdesbt build --howde --tree=union.sbt --outtree=howde.sbt
| Parameters | Description | |----------|:-------------:| | --howde | create tre
