PcG-finder is a computational pipeline to identify Polycomb-group (PcG) proteins homologous in eukaryotes. PcG-finder can be run using any set of sequence data, as long as sequences are in fasta format. PcG-finder is a python script to apply pipeline employs hmmer (jackhmmer) protein search and eggNOG mapper to find a robust protein homologous within a big protein database. Finally, the selected protein searched for the domain architecture using hmmerscan. All results will be reported in a tab-delimited file.
PcG-finder can be used to process sequences downloaded directly from GenBank/NCBI local sequence data (e.g. sequences not downloaded from GenBank such as unpublished data), or a combination of both. PcG-finder process total predicted proteins based on genomics or transcriptomics data. PcG-finder offers the option to select a different PcGs’ sub-units [E(z), Su(z)12, ESC, or NURF55]; to allow users to find the homologous of each sub-unit separately. Also, PcG-finder offers a multi-threading option for fast and short-time computing.
PcG-finder is scalable and can be used to process a variety of datasets, ranging from small datasets (one species) to large datasets with thousands of proteins. PcG-finder was intended to be objective and repeatable and provides a meaningful output at every step to help guide user decisions.
PcG-finder is described in more detail in the following publication:
Sharaf A, Vijayanathan M, Oborník M, Mozgová I (2022) Phylogenetic profiling resolves early emergence of PRC2 and illuminates its functional core. Life Science Alliance 5(7) e202101271. https://doi.org/10.26508/lsa.202101271
A visual overview of the major steps in PcG-finder is shown below:
PcG-finder is written in Python (compatible with 3.0 or more). The required dependencies will be downloaded and executed during the installation of PcG-finder as a Python package or library. PcG-finder directory must be used as a working directory, then the input file(s) must be transferred to the PcG-finder directory but the output folder can be elsewhere. PcG-finder requires a 40 GB free-space to download the necessary databases. There are only two external dependencies that should be installed before using PcGs-finder:
• HMMER: biosequence analysis using profile hidden Markov model.
http://hmmer.org • eggnog-mapper. https://github.com/eggnogdb/eggnog-mapper
Helpful installation instructions for these dependencies can be found on the previous links.
PcG-finder scripts can be run using Mac OSX (10.10+) and Linux, and can also work with Windows using a program like Cygwin. PcG-finder can install using the following command:
./setup.py OR python3 setup.py
PcG-finder can be run using following command:
./PcGs_finder.py [-h] -i INFILE -pg PCG -o OUTFOLDER [-t THREADS] (it's 10 by default)
PcG-finder can be tested for the first time through the available test data (Athaliana.fasta; the total predicted protein of Arabidopsis thaliana).
for E(z) sub-unit: ./PcGs_finder.py -i Athaliana.fasta -pg ezh -o TEST
for Su(z)12 sub-unit: ./PcGs_finder.py -i Athaliana.fasta -pg suz12 -o TEST
for ESC sub-unit: ./PcGs_finder.py -i Athaliana.fasta -pg esc -o TEST
for NURF55 sub-unit: ./PcGs_finder.py -i Athaliana.fasta -pg p55 -o TEST