caspredict 0.3.4

Automatic detection and subtyping of CRISPR-Cas operons

CasPredict

Detect CRISPR-Cas genes and arrays, and predict the subtype based on both Cas genes and CRISPR repeat sequence.

This software finds Cas genes with a large suite of HMMs, then groups these HMMs into operons, and predicts the subtype of the operons based on a scoring scheme. Furthermore, it finds CRISPR arrays with minced, and using a kmer-based machine learning approach (extreme gradient boosting trees) it predicts the subtype of the CRISPR arrays based on the consensus repeat. It then connects the Cas operons and CRISPR arrays, producing as output:

• CRISPR-Cas loci, with consensus subtype prediction based on both Cas genes (mostly) and CRISPR consensus repeats
• Orphan Cas operons, and their predicted subtype
• Orphan CRISPR arrays, and their predicted associated subtype

It includes the following subtypes:

• All the ones in the most recent Nature Reviews Microbiology: Evolutionary classification of CRISPR–Cas systems: a burst of class 2 and derived variants
• Updated type IV subtypes and variants based on: Type IV CRISPR–Cas systems are highly diverse and involved in competition between plasmids
• Type V-K: RNA-guided DNA insertion with CRISPR-associated transposases
• Transposon associated type I-F: Transposon-encoded CRISPR–Cas systems direct RNA-guided DNA integration

Table of contents

1. Quick start
2. Installation
3. CasPredict - How to
4. RepeatType - How to

Quick start

conda create -n caspredict -c conda-forge -c bioconda -c russel88 caspredict
conda activate caspredict
caspredict my.fasta my_output

Installation

Conda

It is advised to use miniconda or anaconda to install.

Create the environment with caspredict and all dependencies

conda create -n caspredict -c conda-forge -c bioconda -c russel88 caspredict

pip

If you have the dependencies (Python >= 3.8, HMMER >= 3.2, Prodigal >= 2.6, grep, sed) in your PATH you can install with pip

python -m pip install caspredict

When installing with pip, you need to download the database manually:

Coming soon...

CasPredict - How to

CasPredict takes as input a nucleotide fasta, and produces outputs with CRISPR-Cas predictions

Activate environment

conda activate caspredict

Run with a nucleotide fasta as input

caspredict genome.fa my_output

Use multiple threads

caspredict genome.fa my_output -t 20

Check the different options

caspredict -h

Output

• CRISPR_Cas.tab: CRISPR_Cas loci, with consensus subtype prediction
• cas_operons.tab: All certain Cas operons
• crisprs_all.tab: All CRISPR arrays
• crisprs_orphan.tab: Orphan CRISPRs (those not in CRISPR_Cas.tab)
• cas_operons_orphan.tab: Orphan Cas operons (those not in CRISPR_Cas.tab)
• cas_operons_putative.tab: Putative Cas operons, mostly false positives, but also some ambiguous and partial systems
• spacers.fa: Fasta file with all spacer sequences
• hmmer.tab: All HMM vs. ORF matches, raw unfiltered results
• arguments.tab: File with arguments given to CasPredict

Notes on output

The cas_operons.tab and cas_operons_putative.tab tables will only be produced if there are any Cas genes. The CRISPR_Cas.tab, cas_operons_orphan.tab, crispr_orphan.tab tables will only be produced if any Cas operons can be connected with any CRISPR arrays. If no Cas operons are present, or no CRISPR arrays are adjacent to any Cas operon, then all Cas operons (if any) are orphan, and all CRISPR arrays (if any) are orphan.

RepeatType - How to

With an input of CRISPR repeats (one per line, in a simple textfile) RepeatType will predict the subtype, based on the kmer composition of the repeat

Activate environment

conda activate caspredict

Run with a simple textfile, containing only CRISPR repeats (in capital letters), one repeat per line.

repeatType repeats.txt

Output

The script prints:

• Repeat sequence
• Predicted subtype
• Probability of prediction

Notes on output

• Predictions with probabilities below 0.75 are uncertain, and should be taken with a grain of salt.
• The classifier was only trained on the subtypes for which there were enough (>20) repeats. It can therefore only predict subtypes of repeats associated with the following subtypes:
  • I-A, I-B, I-C, I-D, I-E, I-F, I-G
  • II-A, II-B, II-C
  • III-A, III-B, III-C, III-D
  • IV-A1, IV-A2, IV-A3
  • V-A
  • VI-B
• This the accuracy per subtype (on an unseen test dataset):
  • I-A 0.60
  • I-B 0.90
  • I-C 0.98
  • I-D 0.47
  • I-E 1.00
  • I-F 0.99
  • I-G 0.83
  • II-A 0.94
  • II-B 1.00
  • II-C 0.89
  • III-A 0.89
  • III-B 0.49
  • III-C 0.60
  • III-D 0.28
  • IV-A1 0.79
  • IV-A2 0.78
  • IV-A3 0.98
  • V-A 0.77
  • VI-B 1.00