README.txt

Scripts for "The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs" by Axelle Marchant, Angel F Cisneros, Alexandre K Dubé, Isabelle Gagnon-Arsenault, Diana Ascencio, Honey A. Jain, Simon Aubé, Chris Eberlein, Daniel Evans-Yamamoto, Nozomu Yachie & Christian R Landry. https://doi.org/10.7554/eLife.46754.


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#	Scripts for RNAseq folder	#
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These scripts are used to analyze the RNAseq data.

1.cleaning_RNAseq.sh
Reads were cleaned using cutadapt (Martin, 2011). We removed the first 12 bp, trimmed the poly-A tail from the 3’ end, trimmed low-quality ends using a cutoff of 15 (phred quality + 33) and discarded reads shorter than 30 bp. The number of reads before and after cleaning can be found in Table S5. Raw sequences can be downloaded under the NCBI BioProject ID PRJNA480398.

2.create_gff_increasing_gene_window_with_3utr.R
Because we used a 3'mRNA-Seq Library, reads mapped largely to 3'UTRs. We increased the window of annotated genes in the SGD annotation (saccharomyces_cerevisiae_R64-2-1_20150113.gff version) using the UTR annotation from (Nagalakshmi et al., 2008).

3.alignment_and_count.sh
Cleaned reads were aligned on the reference genome of S288c from SGD (S288C_reference_genome_R64-2-1_20150113.fsa version) using bwa (Li and Durbin, 2009). Based on the reference genes-UTR annotation, the number of mapped reads per genes was estimated using htseq-count of the Python package HTSeq (Anders et al., 2015) and reported in Table S5.


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#	Scripts for ancestral tests folder     					#
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These scripts are used to analyze the plasmid-based PCA experiments for orthologous proteins.

001_imageAnalysis.R
Images of PCA plates were analyzed using gitter (Wagih and Parts, 2014) to quantify colony sizes. (Data from PCA pictures can be shared upon request)

002_Filtering.R
Filtering of colonies flagged as irregular by gitter (as S or S, C flags) or that did not grow on the last diploid selection step or on DMSO medium were considered as missing data. We considered only bait-prey pairs with at least four replicates and used the median of colony sizes as PCA signal. 

003_Zscores_and_figuresOK.R
The sizes of colonies were converted to z-scores using the mean and standard deviation of the background distribution (Zs = (PCAs - μb)/sdb). This script is also used to generate Figures 2B and 2C.

theme_Publication.R
A ggplot theme developed by Byron Jaeger used to format the plots.


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#	Scripts_for_interaction_HM_and_HET_data_analysis folder		#
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These scripts are used to analyze the PCA and HM and HET data from literature and database.

Other data files used for this analysis include:
- BIOGRID-ORGANISM-Saccharomyces_cerevisiae_S288c-3.5.166.tab2.txt: Data on yeast protein-protein interactions available at https://thebiogrid.org/
- Ihmels_expression_matrix.csv: Supplementary materials from (Ihmels et al. 2004) available at https://academic.oup.com/bioinformatics/article/20/13/1993/241660
- cc_NN.txt: Data on cell localization available at http://thecellmap.org/

0.gitter.R
Images of PCA plates were analyzed using gitter (Wagih and Parts, 2014) to quantify colony sizes. (Data from PCA pictures can be shared upon request)

1.create_dataframe.R
We generated a big dataframe from all output gitter data files and reported tagged proteins for each colony.

2.Filter.R
Colonies flagged as irregular by gitter (as S or S, C flags) or that did not grow on the last diploid selection step or on DMSO medium were considered as missing data. We considered only bait-prey pairs with at least four replicates and used the median of colony sizes as PCA signal. The data was finally filtered based on the completeness of paralogous pairs so we could test HMs and HETs systematically. 

3.Threshold.R
The distribution of PCA scores was modeled per duplication type (SSD and WGD) and per interaction tested (HM or HET) with the normalmixEM function (default parameters) available in the R mixtools package. The background signal on MTX was used as a null distribution to which interactions were compared. Colony sizes were converted to z-scores using the mean and standard deviation of the background distribution (Zs = (PCAs - μb)/sdb). PPI were considered as detected if Zs of the bait-prey pair was greater than 2.5.

4.Complete_HM.HET_with_literature_and_databases.R
To complement our experimental data, we extracted HM and HET of paralogs published in BioGRID version BIOGRID-3.5.166 and data from literature (Stynen et al., 2018; Kim et al., 2019). Output are organized by pair.

5.HM_compiled_data.R
To complement our experimental data, we extracted HM and HET of paralogs and singletons published in BioGRID version BIOGRID-3.5.166 and data from literature (Stynen et al., 2018; Kim et al., 2019). Output are organized ORF per ORF.

6.HM_comparison_among_groups_of_genes.R
Comparison of HM proportion among singletons and duplicates proteins.

7.HM_HET_expression_duplication.R
Analysis of factors that could be implicated into the HM and HET maintenance or deletion.
Particularly, we focused on duplication mechanisms and expression. It returns a summary table of HM / HET repartition among duplicates: TableS1

8.Main_Figures.R
Script to generate main figures from 6.HM_compiled_data.R and 8.HM_HET_expression_duplication.R scripts output

9.Sup_figures.R
Script to generate supplementary figures from 6.HM_compiled_data.R and 8.HM_HET_expression_duplication.R scripts output


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#	Scripts_for_interface_conservation_yeast folder			#
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These scripts are used to analyze sequence conservation at the regions of yeast paralogs that participate in the binding interfaces of HM or HET of paralogs.

001_extract_sequences.py 
This script extracts a series of sequences from a file. In this case, it was used to extract the sequences of duplicated genes from the file with the reference proteome.

002_alignments.sh 
This is the script that performs the alignments to the whole set of sequences from the PDB downloaded on September 21st, 2017. The sequences from the whole PDB (pdb_seqres.txt) can be downloaded from https://www.rcsb.org/pdb/download/download.do.

003_filter_alignments.R 
This script looks at the alignment results of the duplicated sequences to the PDB and formats them as a table.

004_download_PDB.sh 
This script receives the ID of a PDB structure and downloads it. It was used to download the PDB structures that were matched to duplicate proteins in our data set.

005_gather_PDB_info.ipynb
Given an alignment of protein sequences to PDB chains, pairs of paralogs, and downloaded files with the asymmetrical unit, this script gathers lists of PDB structures have subunits that match the proteins. It returns a table that matches proteins to PDB structures and their paralogs, as well as a table with the best structures that represent each of the observed complexes. This is a Jupyter notebook. 005_gather_PDB_info.py is a Python-formatted version of the same code.

006_format_structural_data.R
This script does some additional formatting of the tables obtained from the previous step. The tables from this script are designed to be integrated into Table S3. The table with the best structures selects the best crystal structures available for each of the identified complexes to use them in the interface conservation analysis.

007_interface_conservation_analysis.ipynb
This is the main script that looks for sequence conservation in interfaces. It uses the 001_generate_bio_assembly.py script and code from the 004.1_call_interfaces.py script from the scripts_for_simulations folder. It performs all the alignments and matches proteins to their corresponding PhylomeDB phylogenies (phylome_0007 was used for this analysis). This is a Jupyter notebook. 007_interface_conservation_analysis.py is a Python-formatted version of the same code.

008_pdb2fasta_interfaces.sh
This is a modified version of Pierre Poulain's pdb2fasta.sh script available at https://bitbucket.org/pierrepo/pdb2fasta/. It saves PDB files as FASTA formatted files with interface residues in lowercase and the rest of the sequence in uppercase.

009_interface_counter.ipynb
This is a Jupyter notebook that takes the FASTA formatted interface files and returns per chain counts of total residues and interface residues. This output was later integrated to table S13. 009_interface_counter.py is a Python-formatted version of the same code.

010_figure_2G_2H.R
This script produces Figures 2G and 2H from the processed files in the Data folder.


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#       Scripts_for_interface_conservation_human folder                 #
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These scripts are used to analyze sequence conservation at the regions of human paralogs that participate in the binding interfaces of HM or HET of paralogs.

001_extract_longest_isoforms.py
This script extracts the longest isoforms for each gene in the list of paralogs from the human reference proteome. The human reference proteome can be downloaded from:
http://useast.ensembl.org/info/data/ftp/index.html.

002_alignments.sh
This script performs the alignments to the whole set of sequences from the PDB downloaded on September 21st, 2017.

003_filter_alignments.R
This script looks at the alignments and filters the matches for each protein. It can be executed as follows:
Rscript 003_filter_alignments.R Data/PDB_matches_human_paralogs.aln Data/human_paralog_pair_list <output_folder> 0

004_download_PDB.sh
This script receives PDB IDs to download them. It is used to download the structures that matched the paralogs from the output of the previous step. These structures are later referred to in the code as pdb_folder.

005_gather_PDB_info_human.ipynb
Given an alignment of protein sequences to PDB chains, pairs of paralogs, and downloaded files with the asymmetrical unit, this script gathers lists of PDB structures have subunits that match the proteins. It returns a table that matches proteins to PDB structures and their paralogs, as well as a table with the best structures that represent each of the observed complexes. This is a Jupyter notebook. 005_gather_PDB_info.py is a Python-formatted version of the same code.

006_interface_conservation_analysis_human.ipynb
This is the main script that looks for sequence conservation in interfaces. It uses the 001_generate_bio_assembly.py script and code from the 004_call_interfaces.py script from the scripts_for_simulations folder. It performs all the alignments and matches proteins to their corresponding PhylomeDB phylogenies (phylome_0076 was used for this analysis). This is a Jupyter notebook. 006_interface_conservation_analysis.py is a Python-formatted version of the same code.

007_pdb2fasta_interfaces.sh
This is a modified version of Pierre Poulain's pdb2fasta.sh script available at https://bitbucket.org/pierrepo/pdb2fasta/. It saves PDB files as FASTA formatted files with interface residues in lowercase and the rest of the sequence in uppercase.

008_interface_counter.py
This script takes the FASTA formatted interface files and returns per chain counts of total residues and interface residues. This output was later integrated to the human structures in Table S13.

009_figure2_suppl6.R
This script produces Figure 2-figure supplement 6 from the processed files in the Data folder.


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#	Scripts_for_simulations folder					#
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These scripts are used to run simulations of the evolution of protein complexes under the different selection scenarios.

001_generate_bio_assembly.py
Given a file with the asymmetric unit in a PDB file, it does the calculations necessary to obtain a given biological assembly from REMARK 350. Output files are PDB formatted and chains are renamed starting with chain A in the output file.

002_insertion_residues_alternative_coordinates.py
This script looks for insertion residues and alternative coordinates in biological assembly files. Insertion residues are just included in the numbering. Residues with alternative coordinates are saved with the coordinates with the higher occupancy. Output files are PDB-formatted.

003_foldx_repair_slurm.sh
This script takes a biological assembly and applies the FoldX Repair function for energy minimization. It is written to submit a job to a SLURM manager.

004_call_interfaces.py
This script is used to call interface residues from a biological assembly. Interfaces are called according to Tsai's based on distance to the other chain (Tsai et al., 1996. Crit Rev Biochem Mol Biol). Only interfaces obtained by the distance definition were used for further analyses because of the general overlap of residues. Output files are PDB formatted with the b-factor replaced by the region identified with the following code:
        - 0.00 non-interfaces (distance)
        - 0.75 nearby (distance)
        - 1.00 contacting (distance)

This script depends on the helper functions from:
        - call_interfaces_helper.py: helper functions for parsing PDB coordinates.

005.1_simulations_admin.sh
This is the admin script that performs the simulations with FoldX. It is designed to work with SLURM and parallelize replicates, with the results organized in folders numbered by replicates and the successive substitutions. It prepares a script to submit the job to SLURM with each of the replicates as arguments. It depends on the following scripts:
	- 005.2_foldx_coevol_config.sh: This is a helper script that is used to load parameters set by the user.
	- 005.3_simulations_main.sh: This is the main loop of the simulations. It manages the output FoldX files by organizing them in folders and calls the other scripts.
	- 005.4_substitution_generator.py: This is the script that generates random substitutions based on a transition matrix for randomly chosen residues.
	- 005.5_apply_selection.R: This is the script that applies the selection function to the resulting mutants. 

006_gather_simulations.sh
This script looks at the results of a simulation run and gathers them with a tabular format in a folder named "Final_results" inside the main folder of the simulation run.

007_tools_simulations_figures_4_5.R
This script loads some functions that allow the analyses of the simulations after they are gathered by the previous script. It then uses these functions to produce Figures 4 and its supplements (2 to 4), as well as Figure 5 and its supplements (1 to 3).


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#       Scripts_for_pfam_similarity folder                              #
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This script is used to analyze the similarity of Pfam domain annotations between paralogs.

001_pfam_similarity.R
This is the script that does the analysis of the pfam domain annotation similarities. It produces Figure 3-figure supplement 1.