Welcome to cazomevolve’s documentation!

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cazomevolve

cazomevolve (“cazome-evolve”) is a Python3 package for the automated annotation and exploratory analysis of the CAZyme complements (CAZomes) for a set of species and/or genomes of interest.

Carbohydrate Active enZymes are a subset of proteins that generate, modify and/or degrade carbohydrates. These enzymes are pivotal in many biological processes, including energy metabolism, cell structure, signalling and pathogen recognition. Therefore, these enzymes are of significant biological and industrial interest.

CAZy (www.cazy.org) is the most comprehensive CAZyme database, grouping proteins by sequence similarity into CAZy families. Therefore, CAZy family annotations correspond to presumed shared mechanism and structural fold. cazomevolve using the CAZy family annotations to standardise summarising and capturing the range of CAZyme functions within a CAZome, which enables the comparison of the CAZomes across a set of genomes in order to identify groups of CAZy families of biological and/or industrial interest.

cazomevolve can also automate the process of exploring sequence diversity in an individual CAZy family or across a set of CAZy families of interest. This enables the systematic identification of CAZymes that are potnetially not yet functionally or structurally represented in CAZy, as well as exploring relationships across CAZy families.

Use cazomevolve to explore:

CAZome sizes:

  • Compare the number of CAZymes and CAZy families

  • Calculate the proportion of the proteome encompassed by the CAZome

  • Compute the CAZy family to CAZyme ratio

CAZy class frequencies:

  • Calculate the number of CAZymes per CAZy class

  • Plot a proportional area plot of CAZy class frequency broken down by CAZy class and user defined group deliniations (e.g. by genus or species)

CAZy families:

  • Explore sequence diversity within a set of CAZy families: * Run all-vs-all sequence comparison analyses * Cluster the sequences by degree of sequence similarity * Generate clustermaps of sequence identity, BLAST-score ratio, and coverage

  • Explore CAZy family frequencies: * Compute the number of CAZymes per CAZy family * Identify lineage or group specific families - e.g. genus or species specific families * Identify core CAZomes - families that appear in all genomes * Cluster genomes by CAZy family frequencies using hierarchical clustering: * Generate annotated clustermaps of CAZy family frequencies * Build a dendogram using distances calculated from the CAZome composition * Construct tanglegrams to compare CAZy family dendrogram to a ANI-dendrogram or phylogenetic tree

Always co-occurring families:

  • Identity CAZy families that are always present in the CAZome together

  • Find lineage or group specific groups of co-occurring families

  • Construct an upset plot of co-occurring families

  • Calculate the number of genomes each group of co-occurring families appear in

Principal component analysis (PCA):

  • Use PCA to idenify overal trends in the large and complex data set

  • Project genomes onto use selected principal components (PCs)

  • Construct loadings plots to explore correlation between CAZy families and PCs

  • Identify relationships between groups of CAZy families

  • Explore associations between CAZy families and lineage, phenotype, and niche adaptation

Co-evolving CAZy families:

  • Generate the input file tab delimited list of genomes and CAZy families required by [coinfinder]() (Whelan _et al._)

  • Optionally add taxonomic data to the tab delimited list, to include taxa in the coinfinder output

  • Reconstruct phylogenetic trees to be used as input by coinfinder: * Reconstruct a multi-gene phylogenetic tree using [RaxML-ng]() * Construct an ANI-based dendrogram

  • Use coinfinder to identify CAZy families that appear together in a genome more often than expected by lienage and chance

Example of use and application

An example of using cazomevovle to explore relationships and compare the CAZomes of a diverse set of bacteria, can be found within the Supplementary Information Hobbs et al., _Pectobacteriaceae_ repository.

This repository houses the code, data and analyses aurgmented and completed during the exploration of +700 _Pectobacteriaceae_ CAZomes, and where a potential association between the composition of the CAZome and the plant host range of these plant pathogens was identified.

Subcommand summary

cazomevolve is configured through a series of subcommands. Below is a list of these subcommands (excluding required and optional arguments) included in cazomevolve.

Explore CAZy family sequence diversity

get_fam_seqs - retrieve the protein sequences for all proteins in a CAZy family of interest.

run_fam_blast and run_fam_diamond - perform an all-versus-all sequence comparison analysis using BLAST or DIAMOND.

Use functions available in the cazomevolve.seq_diversity.explore module to explore the sequence diversity.

Annotate CAZomes

Download genomes

Use download_acc_genomes to download the genomes for the genomic version accessions listed in a plain text file.

Use download_genomes to download all genomes associated with a search term in the NCBI Assembly database. For example, retrieve all genomes associated with the term “_Pectobacteriaceae_” in NCBI to retrieve all _Pectobacteriaceae_ genomes.

Retrieve CAZy annotations

Build a local CAZyme database containing all CAZyme records from the CAZy database using build_cazy_db. Then extract the CAZy family annotations from the local CAZyme for annotated protein seqences in a set of assembly files (specifically the proteome FASTA files) using get_cazy_cazymes.

Get dbCAN predicted CAZy family annotations

Use run_dbcan to automate running dbCAN (versions 2, 3, or 4) over a set of proteome FASTA file. Parse the output from dbCAN to extract the consensus CAZy family annotations (i.e. annotations that at leasst two tools agree upon) using get_dbcan_cazymes.

Explore and compare CAZomes

Import and implement functions available in the cazomevolve.cazome.explore module to explore and compare:

  • CAZome sizes

  • CAZy class distributions

  • CAZy family frequencies

  • Lineage specific CAZy families

  • Groups of CAZy families that always co-occur together

  • Perform Principal Component Analysis (PCA) to explore trends across the data set

Installation

The easiest way to install cazomevolve is via PyPi.

pip install cazomevolve

Alternatively, cazomevolve can be installed from source:

git clone https://github.com/HobnobMancer/cazomevolve.git
pip install -e cazomevolve/.

Documentation

Contents:

Citing and citations

If you use cazomevolve in your work _please_ cite our work (including the provided DOI), as well as the specfic version of the tool you used. This is not only helpful to us as the developers to get our work out into the world, but it is also essential for the reproducibility and integrity of scientific research.

Citation:

Hobbs, Emma. E. M., Gloster, Tracey, M., Pritchard, Leighton (2023) cazomevolve, _GitHub_. DOI: 10.5281/zenodo.6614827

cazomevovle depends on a number of tools. To recognise the contributions that the authors and developers have made, please also cite the following:

CAZy:

cazomevolve uses the CAZy family classifications establised and curated by the CAZy database.

Elodie Drula and others, The carbohydrate-active enzyme database: functions and literature, Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D571–D577, https://doi.org/10.1093/nar/gkab1045

cazy_webscraper:

Hobbs, E. E. M., Gloster, T. M., and Pritchard, L. (2022) ‘cazy_webscraper: local compilation and interrogation of comprehensive CAZyme datasets’, bioRxiv, https://doi.org/10.1101/2022.12.02.518825

For additional citations when using cazy_webscraper, see the cazy_webscraper documentation.

ncbi-genome-download:

Blin et al. (2017) ncbi-genome-download, https://github.com/kblin/ncbi-genome-download

dbCAN:

dbCAN version 2:

Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, Busk PK, Xu Y, Yin Y. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018 Jul 2;46(W1):W95-W101. doi: 10.1093/nar/gky418. PMID: 29771380; PMCID: PMC6031026.

dbCAN version 3:

If using dbCAN version 3, cite the publication for version 2 as well as eCAMI:

Xu J, Zhang H, Zheng J, Dovoedo P, Yin Y. eCAMI: simultaneous classification and motif identification for enzyme annotation. Bioinformatics. 2020 Apr 1;36(7):2068-2075. doi: 10.1093/bioinformatics/btz908. PMID: 31794006.

dbCAN version 4:

Zheng J, Ge Q, Yan Y, Zhang X, Huang L, Yin Y. dbCAN3: automated carbohydrate-active enzyme and substrate annotation. Nucleic Acids Res. 2023 May 1:gkad328. doi: 10.1093/nar/gkad328. Epub ahead of print. PMID: 37125649.

BLAST Score Ratio:

Rasko DA, Myers GS, Ravel J. Visualization of comparative genomic analyses by BLAST score ratio. BMC Bioinformatics. 2005 Jan 5;6:2. doi: 10.1186/1471-2105-6-2. PMID: 15634352; PMCID: PMC545078.

DIAMOND

Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015 Jan;12(1):59-60. doi: 10.1038/nmeth.3176. Epub 2014 Nov 17. PMID: 25402007.

BLAST

Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. Journal of molecular biology. 1990;215(3):403–10.

Explore CAZy family sequence diversity and CAZomes:

cazomevolve uses several packages to visualise and interrogate the dataset.

  • Waskom, M. L. (2021) seaborn: statistical data visualization, Journal of Open Source Software, 6(60), pp. 3021

  • Virtanen et al. (2020), SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nature Methods, 17, pp.261-272

  • Pedregosa et al., (2011), Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, pp.2825-2830.

Development and issues

If there are additional features you wish to be added, you have problems with the tool, or would like to contribute, please raise an issue at the GitHub repository.