Rick Masonbrink

Rick Masonbrink

I am fortunate to work as a research scientist in the Genome Informatics Facility at Iowa State University, where I am surrounded by highly skilled bioinformaticians and constantly exposed to new ideas and approaches. This dynamic environment fosters my passion for science and my curiosity to keep learning and growing. With the support of the USDA, I have had the opportunity to create workshops and tutorials, and I also assist our faculty and industry collaborators through fee-for-service work. Over the years, I’ve been involved in a wide range of projects, developing and applying bioinformatics pipelines tailored to specific needs. I have also gained considerable experience in Hi-C scaffolding and genome annotation. I view this continuous exchange of ideas as key to my growth as both a researcher and a contributing member of the broader scientific community.

DeepGOPlus is a bioinformatics tool designed for protein function prediction using deep learning. It combines convolutional neural networks with sequence-based features to predict Gene Ontology (GO) terms for proteins. This tool is particularly useful for large-scale genome annotation and proteome analysis, though a webserver is currently available for smaller requests. Frequently, genome annotation projects will produce tens of thousands of genes, and depending upon the model status of your chosen species, many of these genes may lack significant homology to anything available in current databases. This software is not a replacement for identifying ontology terms using interproscan, but an alternative for novel gene candidates.

Software used in this tutorial

  • Kulmanov, M., & Hoehndorf, R. (2020). DeepGOPlus: Improved protein function prediction from sequence. Bioinformatics, 36(2), 422–429. https://doi.org/10.1093/bioinformatics/btz595
  • Buchfink, B., Xie, C., & Huson, D. H. (2015). Fast and sensitive protein alignment using DIAMOND. Nature Methods, 12(1), 59–60. https://doi.org/10.1038/nmeth.3176
  • Github Repository

Installation of DeepGOPlus and prerequisite software

Create conda environment in python 3.7.9

  • create conda environment
  • activate conda environment
  • create python virtual environment
  • upgrade pip and setuptools
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ml miniconda3
conda create -n deepgoplus_env python=3.7.9

#if you havent done this previously you'll need to run this to use conda
conda init
source ~/.bashrc

#activate the environment
conda activate deepgoplus_env

#create and actiavte a python virtual environment
python3 -m venv DeepGoPlus_pyenv
source DeepGoPlus_pyenv/bin/activate

#upgrade pip and setuptools
pip install --upgrade pip setuptools

Download the repository

  • Download the repository
  • install the requirements
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git clone https://github.com/bio-ontology-research-group/deepgoplus.git
cd deepgoplus/
pip install -r requirements.txt
pip install deepgoplus

Download the metadata and training datasets

  • download datasets
  • copy metadata to data folder
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wget http://deepgoplus.bio2vec.net/data/data.tar.gz

#automatically unpacks into data folder if within deepgoplus
tar -zxvf data.tar.gz

cp -rf metadata/ data/.

Install Diamond BLAST

  • download tar ball
  • add to PATH variable
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wget http://github.com/bbuchfink/diamond/releases/download/v2.1.9/diamond-linux64.tar.gz
tar xzf diamond-linux64.tar.gz

#add to .bashrc and source or run in terminal for temporary access
export PATH="/work/gif3/masonbrink/Software/:$PATH"

Run the database update.py script

In order for this to run properly, I had to modify the update.py script to get around the check for the uniprot header. This header is not required and is not found in the dataset we downloaded.

  • Release information
    • Current version is 1.0.21
      • Gene Ontology released on 2024-09-08
      • SwissProt data with version 2024_05.

The following lines need to be modified in the update.py script.

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#allow check for header to pass
if last_release_version == new_release_version:

to

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#allow check for header to pass
if last_release_version != new_release_version:

Once this has been updated we can run

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python update.py

Run DeepGoPlus analysis

  • Download some fun data from a plant parasitic nematode (Ditylenchus dipsaci)
  • Format the data
  • Run DeepGoPlus
  • Format the output
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wget https://ftp.ebi.ac.uk/pub/databases/wormbase/parasite/releases/WBPS19/species/ditylenchus_dipsaci/PRJNA498219/ditylenchus_dipsaci.PRJNA498219.WBPS19.protein.fa.gz

# this will save formatting steps later, reducing the fasta header to just the basic name (first column).
less ditylenchus_dipsaci.PRJNA498219.WBPS19.protein.fa |awk '{print $1}' >Cleanditylenchus_dipsaci.PRJNA498219.WBPS19.protein.fa

# use the same threshold as the online database
ml miniconda3; conda activate deepgoplus_env; source bin/activate ; deepgoplus -if Cleanditylenchus_dipsaci.PRJNA498219.WBPS19.protein.fa -dr data/ -of results_DDipsaci -t 0.3

# format the output
awk 'NF>3' results_DDipsaci |sed 's/\t/#/1' |sed 's/\t/,/g' |sed 's/#/\t/g' >DeepGoPlusResults.tab
Gene ID GO Terms
jg20583 GO:0110165-0.355, GO:0005575-0.361
jg20588 GO:0110165-0.349, GO:0005575-0.357
jg16733 GO:0003674-0.567, GO:0005488-0.500, GO:0005515-0.377, GO:0005575-0.725, GO:0005622-0.613, GO:0005737-0.575, GO:0005886-0.331, GO:0007154-0.330, GO:0008150-0.727, GO:0009987-0.583, GO:0016020-0.499, GO:0023052-0.332, GO:0043226-0.400, GO:0043227-0.329, GO:0043229-0.359, GO:0043231-0.311, GO:0050789-0.481, GO:0050794-0.450, GO:0050896-0.393, GO:0051716-0.344, GO:0065007-0.494, GO:0071944-0.352, GO:0110165-0.720
jg20471 GO:0110165-0.387, GO:0005575-0.396
jg2136 GO:0003674-0.503, GO:0005488-0.369, GO:0005515-0.349, GO:0005575-0.788, GO:0005622-0.688, GO:0005737-0.635, GO:0005886-0.434, GO:0008104-0.443, GO:0008150-0.742, GO:0009987-0.687, GO:0016020-0.654, GO:0016043-0.382, GO:0032991-0.303, GO:0033036-0.444, GO:0033365-0.308, GO:0043226-0.566, GO:0043227-0.528, GO:0043229-0.555, GO:0043231-0.503, GO:0045184-0.435, GO:0051179-0.510, GO:0051234-0.500, GO:0051641-0.465, GO:0051668-0.362, GO:0070727-0.443, GO:0071840-0.387, GO:0071944-0.435, GO:0072657-0.359, GO:0090150-0.353, GO:0110165-0.783
jg12535 GO:0110165-0.345, GO:0005575-0.345
jg2135 GO:0110165-0.395, GO:0005575-0.397, GO:0005622-0.365, GO:0016020-0.309, GO:0043229-0.329, GO:0043226-0.337
jg2134 GO:0110165-0.369, GO:0005575-0.370, GO:0005622-0.343
jg12533 GO:0110165-0.360, GO:0005575-0.364, GO:0016020-0.329
jg14715 GO:0110165-0.370, GO:0005575-0.383, GO:0005622-0.305
jg21581 GO:0110165-0.380, GO:0005575-0.380
jg2133 GO:0003674-0.732, GO:0005215-0.637, GO:0005575-0.637, GO:0005622-0.327, GO:0005737-0.327, GO:0005886-0.602, GO:0006810-0.737, GO:0006811-0.616, GO:0006812-0.598, GO:0008150-0.948, GO:0008324-0.354, GO:0009987-0.771, GO:0015075-0.362, GO:0015095-0.319, GO:0015318-0.575, GO:0015693-0.555, GO:0016020-0.603, GO:0022857-0.637, GO:0022890-0.566, GO:0030001-0.589, GO:0034220-0.616, GO:0046873-0.340, GO:0050896-0.334, GO:0051179-0.737, GO:0051234-0.737, GO:0051716-0.307, GO:0055085-0.679, GO:0071944-0.602, GO:0098655-0.596, GO:0098660-0.607, GO:0098662-0.595, GO:0110165-0.637, GO:1903830-0.445
jg12534 GO:0110165-0.394, GO:0005575-0.396, GO:0005622-0.314

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