This readme contains a description of the steps to obtain the main results of:
G.A. Merino, J. Raad, L.A. Bugnon, C. Yones, L. Kamenetzky, J. Claus, F. Ariel, D.H. Milone, G. Stegmayer,
“Novel SARS-CoV-2 encoded small RNAs in the passage to humans,” Bioinformatics, 2020,
https://doi.org/10.1093/bioinformatics/btaa1002
The following sections contain the steps and the libraries used in each of them. All datasets and libraries are open sourced and free to use. If you use any of the following in your pipelines, please cite them properly.
Several intermediate results are provided in this repository, thus you don’t need to run the whole pipeline.
sarscov2-mirna-discovery
│
├── genomes
│ └── NC_045512.2_Wuhan-Hu-1.fasta -> The complete SARS-CoV2 isolate Wuhan-Hu-1 genome.
│
├── sequences
│ ├── sars-cov2_hairpins.fasta -> Hairpin sequences extracted from NC_045512.2_Wuhan-Hu-1.
│ ├── sars-cov2_hairpins.fold -> sars-cov2_hairpins.fasta and its folding structure.
│ ├── pre-miRNAs_virus.fasta -> Hairpin sequences extracted from known virus pre-miRNAs.
│ ├── pre-miRNAs_virus.fold -> pre-miRNAs_virus.fasta and its folding structure.
│ └── unlabeled_hairpins.fold -> Hairpin sequences and its folding structure extracted from
| the human genome, sampled from sequences that do not code
| pre-miRNAs. Provided by external storage.
│
├── features
│ ├── sars-cov2_hairpins.csv -> Features extracted from sars-cov2_hairpins.fasta.
│ ├── pre-miRNAs_virus.csv -> Features extracted from pre-miRNAs_virus.fasta.
│ └── unlabeled_hairpins.csv -> Features extracted from unlabeled_hairpins.fasta.
| Provided by external storage.
│
├── models -> Trained models.
│
├── predictions -> Predictions on SARS-CoV2 hairpin sequences by each model.
│
├── src
| ├── train_pre-miRNA_models.ipynb -> Source code to train the models.
| ├── predict_pre-miRNAs.ipynb -> Source code to use the models to predict on SARS-CoV2 sequences.
| ├── link_DE.R -> Script for differential expression analysis.
| └── link_Figs.R -> Notebook to generate manuscript figures
|
└── matures -> Mature miRNAs sequences found.
SARS-CoV-2 genome was obtained from the NCBI GenBank on March 2020
These .fasta files can be also found in this repository, in the genomes/ directory.
The SARS-CoV-2 genome .fasta file is cut in overlapping windows using the Hextractor R package. Follow the link instructions to install, and run the main script with the following parameters:
HextractoR(NC_045512.2_Wuhan-Hu-1.fasta, min_valid_nucleotides = 500, window_size = 600,
window_step = 100, only_sloop = T, min_length = 60, min_bp = 16, margin_bp = 6,
blast_evalue = 0.005, identity_threshold = 90, nthreads = 4, nworks = 4, filter_files = { })This script generates a file with several hairpin-like sequences and its corresponding folding structure prediction.
To model the positive class, well known pre-miRNAs from viruses hairpins were extracted from mirbase. These sequences were folded with ViennaRNA. After installation:
RNAfold --noPS --infile=pre-miRNAs_virus.fasta --outfile=pre-miRNAs_virus.foldAdditional sequences were used to train the mirDNN. A set of 1M hairpin-like sequences from the human genome, which are not pre-miRNAs, were used to model the negative set. The folding structure (unlabeled_hairpins.fold) and features (unlabeled_hairpins.csv) for these sequences can be downloaded from this external repository. You will only need to download the unlabeled_hairpins.fold if you want to train the mirDNN. This data and further details are available in:
L.A. Bugnon, C. Yones, J. Raad, D.H. Milone, G. Stegmayer,
“Genome-wide hairpins datasets of animals and plants for novel miRNA prediction”
Data in Brief, Vol. 25, 2019, https://doi.org/10.1016/j.dib.2019.104209.
At this point, the sequence and folding prediction for each hairpin-like sequence in SARS-CoV-2, the known virus pre-miRNAs and some human non-pre-miRNA sequences are ready.
In order to extract meaningful features from the sequences, the miRNAfe package was used. Once installed, you can run the feature extraction with:
miRNAfe('sars-cov2_hairpins.fasta', 'config/Example_prediction.yaml');The .yaml file is provided with the package. This will be repeated for all the other .fasta files.
The training notebook is provided with instructions to train the OC-SVM, deeSOM and mirDNN classifiers. Doing so may take several hours.
If you want to directly predict the sequences, you can use the trained models that are provided in models/ as explained in the following section.
The prediction notebook is provided with instructions to use the trained models to rank the SARS-CoV-2 sequences (a high rank means a higher chance of being a pre-miRNA).
Once mature miRNAs were identified by combining MatureBayes predictions and the small RNA-seq reads profiles, their sequences (provided in matures/) must be submitted to Diana MR MicroT and miRDB (Custom prediction) for predicting human gene targets.
Expression data from RNA-seq experiments involving Calu3 cell-cultures infected with SARS-CoV-2 was downloaded from GEO-NCBI GSE148729. The differential expression script is provided with all the instructions to identify the set of differentially expressed genes. Following these instructions, the list of deregulated targets and the set of those potentially being silenced by the predicted viral miRNAs can be obtained.
The candidate sequence found in the previous point are aligned using the BLAST web platform against the following coronaviruses:
- HKU1 (NCBI Reference Sequence DQ415897.1),
- OC43 (NCBI Reference Sequence KY983585.1),
- Bat-CoV RF1 (NCBI Reference Sequence DQ412042.1),
- Bat-CoV RATG13 (NCBI Reference Sequence MN996532.1),
- Malayan Pangolin (NCBI Reference Sequence MT072864.1),
- SARS-CoV (NCBI Reference Sequence AY278741.1),
- MERS-CoV (NCBI Reference Sequence MN120514.1),
- Mice coronavirus (NCBI Reference Sequence GB KF294357.1),
- Ferret coronavirus (NCBI Reference Sequence 1264898).
Select the mature miRNA previously extracted from the candidate sequence in the previous point.
Perform the alignments with MUSCLE with ClustalW type output format and default settings.
The Figures notebook is provided with all the instructions to generate Figures 2, 3 and 4.