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The Shiny web server is available under the address: https://biogenies.info/PlastoGram.

PlastoGram uses HMMER software to predict thylakoid lumen proteins imported via Sec and Tat pathways with HMM profiles. You can install HMMER using the following instructions (adapted from HMMER documentation):

• installing with system packager

  % brew install hmmer               # OS/X, HomeBrew
  % port install hmmer               # OS/X, MacPorts
  % apt install hmmer                # Linux (Ubuntu, Debian...)
  % dnf install hmmer                # Linux (Fedora)
  % yum install hmmer                # Linux (older Fedora)
  % conda install -c bioconda hmmer  # Anaconda

• compiling from source

  % wget http://eddylab.org/software/hmmer/hmmer.tar.gz 
  % tar zxf hmmer.tar.gz
  % cd hmmer-3.3.2
  % ./configure --prefix /your/install/path
  % make
  % make check
  % make install
  % (cd easel; make install)

For more information about HMMER please visit HMMER website.

You can install the latest development version of the package:

if(!require("devtools")) install.packages("devtools")
devtools::install_github("https://github.com/BioGenies/PlastoGram/")

PlastoGram is a robust ensemble model for detailed and accurate prediction of subplastid localization and origin of analysed protein. PlastoGram classifies protein as nuclear- or plastid-encoded and predicts its most probable localization considering envelope, stroma, thylakoid membrane or thylakoid lumen. For the latter, also the import pathway (Sec or Tat) is predicted. We provide an additional function to differentiate nuclear-encoded inner and outer membrane proteins.

PlastoGram consists of eight lower-level models trained to tackle a specific labeling problem and a higher-level random forest model, which uses results from of lower-level models to provide the prediction of a protein localization and origin. The lower-level models used in the PlastoGram with their areas of competence are listed below:

• Nuclear_model - recognizes nuclear-encoded proteins
• Membrane_model - identifies membrane proteins
• N_E_vs_N_TM_model - differentiates between nuclear-encoded envelope proteins and nuclear-encoded thylakoid membrane proteins. Prediction values over 0.5 indicate envelope, whereas lower thylakoid membrane
• Plastid_membrane_model - distinguishes plastid-encoded proteins targeted to plastid inner and thylakoid membrane. Prediction values higher than 0.5 indicate inner membrane, whereas lower thylakoid membrane
• N_E_vs_N_S_model - differentiates nuclear-encoded proteins targeted to envelope from nuclear-encoded stromal proteins. Prediction values over 0.5 indicate envelope, whereas lower stroma
• Nuclear_membrane_model - distinguishes nuclear-encoded membrane proteins from all others
• Sec_model - identifies proteins targeted to the thylakoid lumen via Sec pathway
• Tat_model - recognizes proteins targeted to the thylakoid lumen via Tat pathway

We provide two versions of PlastoGram trained and evaluated on slightly different subsets of data. Two versions of data sets differed in a strategy of division into train-test and independent sets. In the holdout version, the independent set was created by randomly selecting 15% of sequences from each class, whereas the rest was used in cross-validation and to train the final model. In the partitioning version, division into train-test and independent sets was carried out using homology partitioning ensuring that between these sets there are no sequences with identity percent higher than 40%.

• PlastoGram H - model trained on the holdout data set, had better performance in the independent test but homology between training and independent sequences was not accounted for.
• PlastoGram P - model trained on the partitioning data set, had lower performance in the independent test but there were fewer test sequences and they were not homologous to those in the training set.

Suppopse you wish to predict localization of proteins which sequences are stored in sequence_file.fa located in the working directory using the holdout version of PlastoGram model.

# load the package
library(PlastoGram)

# read in sequences
sequences <- read_txt("sequence_file.fa")

# run predictions
predictions <- predict(PlastoGram_H, sequences)

# inspect results
# see predictions of origin and localization:
predictions[["Final_results"]]

# save predictions of origin and localization to a csv file named 'PlastoGram_predictions.csv'
write.csv(predictions[["Final_results"]], "PlastoGram_predictions.csv")

# get summary with number of proteins identified in each class
library(dplyr)
predictions[["Final_results"]] %>% 
  group_by(Localization) %>% 
  summarise(count = n())

# see predictions from lower-level models
predictions[["Lower_level_preds"]]

# see higher-level model predictions
predictions[["Higher_level_preds"]]

# see predictions of detailed localization for N_E proteins (OM or IM)
predictions[["OM_IM_preds"]]

Sidorczuk K., Gagat P., Kała J., Nielsen H., Pietluch F., Mackiewicz P., Burdukiewicz M. Prediction of protein subplastid localization and origin with PlastoGram. Sci Rep 13, 8365 (2023). https://doi.org/10.1038/s41598-023-35296-0

Code necessary to reproduce the whole analysis described in the article and leading to the final PlastoGram algorithm is available at https://github.com/BioGenies/PlastoGram-analysis.

For general questions or problems, please email Katarzyna Sidorczuk or Michal Burdukiewicz.

This work has been supported by the National Science Centre grant 2018/31/N/NZ2/01338 to K.S., the National Science Centre grant 2017/26/D/NZ8/00444 to P.G., and the National Science Centre grant 2019/35/N/NZ8/03366 to F.P. M. B. was supported by the Maria Zambrano grant funded by the European Union-NextGenerationEU.