Commit 16b622fa by Mustafa Tekpinar

Added evcouplings to the image for MSA generation.

parent 6c95c457
...@@ -4,5 +4,6 @@ ...@@ -4,5 +4,6 @@
example/.RData example/.RData
gemmeAnal.pyc gemmeAnal.pyc
__pycache__/* __pycache__/*
esgemme/__pycache__/
esgemme.egg-info/* esgemme.egg-info/*
.RData .RData
\ No newline at end of file
...@@ -36,6 +36,7 @@ apt-get install -y nano && \ ...@@ -36,6 +36,7 @@ apt-get install -y nano && \
apt-get install -y less && \ apt-get install -y less && \
apt-get install -y wget && \ apt-get install -y wget && \
apt-get install csh && \ apt-get install csh && \
apt-get install -y hmmer && \
apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
################################################################### ###################################################################
RUN wget https://github.com/soedinglab/hh-suite/releases/download/v3.3.0/hhsuite-3.3.0-AVX2-Linux.tar.gz RUN wget https://github.com/soedinglab/hh-suite/releases/download/v3.3.0/hhsuite-3.3.0-AVX2-Linux.tar.gz
...@@ -55,6 +56,17 @@ RUN pip3 install -e ./demust/ ...@@ -55,6 +56,17 @@ RUN pip3 install -e ./demust/
RUN Rscript -e 'install.packages("seqinr", repos="http://cran.us.r-project.org", dependencies=TRUE)' RUN Rscript -e 'install.packages("seqinr", repos="http://cran.us.r-project.org", dependencies=TRUE)'
#PLMC and EVcouplings installation for obtaining sequences.
RUN wget https://github.com/debbiemarkslab/plmc/archive/refs/heads/master.zip
RUN unzip master.zip
WORKDIR /home/tekpinar/research/lcqb/plmc-master
RUN make all-openmp32
RUN cp bin/plmc /usr/local/bin/
WORKDIR /home/tekpinar/research/lcqb
RUN rm -r master.zip plmc-master
RUN pip3 install install https://github.com/debbiemarkslab/EVcouplings/archive/develop.zip
# Build command # Build command
# sudo docker build -t tekpinar/esgemme-docker:v1.3.0 . # sudo docker build -t tekpinar/esgemme-docker:v1.3.0 .
# sudo docker login # sudo docker login
......
# Sample configuration file for evcouplings monomer protein prediction pipeline.
# This file determines all aspects of the computation:
# - which compute environment to use
# - which stages of the pipeline to run
# - what the settings for each of the stages are
# Minimal settings required before this configuration can be executed:
# - set your environment, paths to tools and databases (at the end of this file)
# - under "global", set prefix and sequence_id
# - run it! :)
# Configuration rules:
# 1) Global settings override settings for stages
# 2) Outputs of a stage are merged into "global" and fed into the input of subsequent stages
# (e.g., the alignment_file output of align will be used by the alignment_file input of couplings)
# 3) All settings are explicitly specified here. No hidden defaults in code.
# 4) Each stage is also passed the parameters in the "databases" and "tools" sections
pipeline: protein_monomer
# which stages of workflow to run. Uncomment downstream stages using # (however, no stage can be run before the previous
# stage has been run)
stages:
- align
# - couplings
# - compare
# - mutate
# - fold
# Global job settings (which protein, region). These will override settings of the same name in each of the stages.
# These are typically the settings you want to modify for each of your jobs, together with some settings in the align stage.
global:
# mandatory output prefix of the job (e.g. output/HRAS will store outputs in folder "output", using files prefixed with "HRAS")
prefix:
# mandatory sequence identifier (mandatory, even if sequence_file is given)
sequence_id:
# optional FASTA file with target sequence (if blank, will fetch try to fetch sequence_id from databases.sequence_download_url)
# if sequence_file is set, sequence_id must be defined, but can be arbitrary identifier (does not have to match ID in file)
sequence_file:
# cut to subregion of sequence (specify as list, e.g. [24, 286], leave blank for full sequence)
region:
# Clustering threshold for downweighting redudant sequences (Meff computation). E.g. 0.8 will cluster sequences
# at a 80% sequence identity cutoff
theta: 0.8
# number of cores to use. If running through evcouplings application, will be overriden by environment.cores
cpu:
# Specify multiple batch jobs (if empty, only a single job will be run). Each entry (e.g. b_0.75) will be appended to
# global.prefix to uniquely identify the subjob. Parameters for individual stages that should be overridden for each
# subjob have to be specified, for all other parameters jobs share the same values.
batch:
# _b0.75:
# align: {domain_threshold: 0.75, sequence_threshold: 0.75}
# _b0.3:
# align: {domain_threshold: 0.3, sequence_threshold: 0.3}
# Sequence alignment generation/processing.
align:
# standard: iterative sequence search and postprocessing using jackhmmer.
protocol: standard
# The following fields usually do not need to be set, since "global" defines them.
# prefix:
# sequence_id:
# sequence_file:
# region:
# theta:
# index of first residue in sequence_id / sequence_file. This can be used to renumber sequences that already have
# been cut to a subsequence
first_index: 1
# Use bitscore threshold instead of E-value threshold for sequence search
use_bitscores: True
# jackhmmer domain- and sequence-level inclusion thresholds.
# if use_bitscores is True:
# - floating point number will be interpreted as a relative bitscore threshold (bits/residue)
# - integer will be interpreted as an absolute bitscore threshold
# if use_bitscore is False:
# - mantissa-exponent string or float will be interpreted literally
# - integer will be interpreted as negative of the exponent (10 -> 1E-10)
domain_threshold: 0.5
sequence_threshold: 0.5
# number of jackhmmer iterations
iterations: 5
# sequence database (specify possible databases and paths in "databases" section below)
database: uniref100
# compute the redundancy-reduced number of effective sequences (M_eff) already in the alignment stage.
# To save compute time, this computation is normally carried out in the couplings stage
compute_num_effective_seqs: False
# Filter sequence alignment at this % sequence identity cutoff. Can be used to cut computation time in
# the couplings stage (e.g. set to 95 to remove any sequence that is more than 95% identical to a sequence
# already present in the alignment). If blank, no filtering. If filtering, HHfilter must be installed.
seqid_filter: 98
# Only keep sequences that align to at least x% of the target sequence (i.e. remove fragments)
minimum_sequence_coverage: 50
# Only include alignment columns with at least x% residues (rather than gaps) during model inference
minimum_column_coverage: 70
# Create a file with extracted annotation from UniRef/UniProt sequence FASTA headers
extract_annotation: True
cpu:
# set to True to turn of jackhmmer bias correction
nobias: False
# if align stage has been run previously, reuse the generated raw sequence alignment coming out of jackhmmer
reuse_alignment: True
# create checkpoint files of HMM and aligment after each iteration
checkpoints_hmm: False
checkpoints_ali: False
# Alternative protocol: reuse existing alignment and apply postprocessing to generate alignment that is consistent
# with pipeline requirements. Uncomment, and comment all values in align section above to enable the "existing" protocol
# protocol: existing
# prefix:
# # Path of input alignment. Alignment needs to contain region in form SEQID/start-end, or first_index must be set
# input_alignment:
# sequence_id:
# first_index:
# compute_num_effective_seqs: False
# theta:
# seqid_filter:
# minimum_sequence_coverage: 50
# minimum_column_coverage: 70
# extract_annotation: True
# Inference of evolutionary couplings from sequence alignment
couplings:
# current options:
# - standard (model inference using plmc)
# - mean_field (mean field direct coupling analysis, see below)
protocol: standard
# number of plmc iterations
iterations: 100
# specify custom alphabet as a string. Gap symbol must be first character
alphabet:
# Treat gaps as missing data during model inference
ignore_gaps: True
# strength of regularization on coupling parameters J
lambda_J: 0.01
# adjust for larger number of coupling parameters relative to number of fields h (multiply by model length and
# number of states)
lambda_J_times_Lq: True
# strength of regularization on fields h
lambda_h: 0.01
lambda_group:
scale_clusters:
# reuse ECs and model parameters, if this stage has been run before
reuse_ecs: True
# Sequence separation filter for generation of CouplingScores_longrange.csv table (i.e. to take out short-range
# ECs from table, only pairs with abs(i-j)>=min_sequence_distance will be kept.
min_sequence_distance: 6
# Post-inference scoring of ECs to derive probabilities
# Options are: skewnormal, normal, logistic_regression
scoring_model: logistic_regression
# Alternative protocol: compute couplings with mean field direct coupling analysis
# Uncomment, and comment all values in align section above to enable the mean_field protocol
# protocol: mean_field
# Options: cn, di, mi_apc, mi
# ec_score_type: cn
# Post-inference scoring of ECs to derive probabilities - only available if used_score == "cn"!
# Options are: skewnormal, normal, logistic_regression
# scoring_model: logistic_regression
# pseudo_count: 0.5
# alphabet:
# ignore_gaps: False
# reuse_ecs: True
# min_sequence_distance: 6
# Following input parameters will usually be overriden by "global" and outputs of "align" stage
# prefix:
# alignment_file:
# focus_sequence:
# focus_mode: True
# segments:
# cpu:
# theta:
# Compare ECs to known 3D structures
compare:
# Current options: standard
protocol: standard
# Following parameters will be usually overriden by global settings / output of previous stage
prefix:
sequence_id:
ec_file:
target_sequence_file:
# If True, find structures by sequence alignment against the PDB, otherwise identify structures using
# sequence_id and SIFTS database (sequence_id must be UniProt AC/ID in this case)
by_alignment: True
# Leave this parameter empty to use all PDB structures for given sequence_id, otherwise
# will be limited to the given IDs (single value or list). Important: note that this acts only as a filter on the
# structures found by alignment or in the SIFTS table (!)
pdb_ids:
# Limit number of structures and chains for comparison
max_num_structures: 10
max_num_hits: 25
# compare to multimer contacts (if multiple chains of the same sequence or its homologs are present in a structure)
compare_multimer: True
# settings for sequence alignment against PDB sequences using jackhmmer
# (additional settings like iterations possible, compare to align stage)
sequence_file:
first_index:
region:
alignment_min_overlap: 20
use_bitscores: True
domain_threshold: 0.1
sequence_threshold: 0.1
# Comparison and plotting settings
# Filter that defines which atoms will be used for distance calculations. If empty/None, no filter will be
# applied (resulting in the computation of minimum atom distances between all pairs of atoms). If setting to any
# particular PDB atom type, only these atoms will be used for the computation (e.g. CA will give C_alpha distances,
# CB will give C_beta distances, etc.)
atom_filter:
# Distance cutoff (Angstrom) for a true positive pair
distance_cutoff: 5
# Only long-range pairs with abs(i-j)>= min_sequence_distance will be used for CouplingScoresCompared_longrange.csv file
min_sequence_distance: 6
# Plot contact maps with ECs above these mixture model probability cutoffs
plot_probability_cutoffs: [0.90, 0.99]
# Plot fixed numbers of ECS. Integers will be interpreted as absolute numbers, floats as fractions of L (model length)
plot_lowest_count: 0.05
plot_highest_count: 1.0
plot_increase: 0.05
# Axis boundaries of contact map plot depending on range of ECs and structure.
# Options: union, intersection, ecs, structure, [start, end] (e.g. [100, 200])
boundaries: union
# scale sizes of EC dots in scatter plot based on strength of EC score
scale_sizes: True
# draw secondary structure on contact map plots
draw_secondary_structure: True
# draw structure and alignment/EC coverage information on contact map plots
draw_coverage: True
# print information about used PDB structures on contact map plots
print_pdb_information: True
# Alignment method to use to search the PDB Seqres database. Options: jackhmmer, hmmsearch
# Set to jackhmmer to search the PDB Seqres database using jackhmmer from the target sequence only (more stringent).
# Set to hmmsearch to search the PDB seqres database using an HMM built from the output monomer alignment (less stringent).
# Warning: searching by HMM may result in crystal structures from very distant homologs or even unrelated sequences.
pdb_alignment_method: jackhmmer
# Settings for Mutation effect predictions
mutate:
# Options: standard
protocol: standard
# predict the following dataset file (.csv file, mutants like A102V or A102V,K199W in column "mutant")
mutation_dataset_file:
# Inputs set by global stage and output of previous stages
# prefix:
# model_file:
# Settings for 3D structure prediction
fold:
# Options: standard
protocol: standard
# Options: cns_dgsa
engine: cns_dgsa
# Config file. If blank, default configuration (restraints.yml) in package will be used
folding_config_file:
# If True, limit 3D modeling only to that region of sequence that actually has sequence alignment coverage)
cut_to_alignment_region: True
# Method for secondary structure prediction (options: psipred, requires PSIPRED installation). Will be used
# to generate distance and dihedral angle restraints for local geometry.
sec_struct_method: psipred
# If secondary structure was already predicted in previous execution of configuration, reuse the file
reuse_sec_struct: True
# Instead of predicting secondary structure in pipeline, can specify a secondary structure file:
# Must be csv file with columns i (position), A_i (residue) and sec_struct_3state (secondary structure, H, E or C
# for helix, sheet or coil)
sec_struct_file:
# Do not use EC pairs as distance restraints that are geometrically incompatible with predicted secondary structure
filter_sec_struct_clashes: True
# Only use ECs with sequence distance abs(i-j) >= min_sequence_distance for folding
min_sequence_distance: 6
# Predict structures using all ECs above these probability cutoffs according to mixture model
fold_probability_cutoffs: [0.90, 0.99]
# Predict structures with selected number of ECs.
# Integers will be interpreted as absolute number of ECs, floats as a fraction of L (model length)
fold_lowest_count: 0.5
fold_highest_count: 1.3
fold_increase: 0.05
# number of trial structure to generate for each EC set
num_models: 10
# remove intermediate files created during folding and keep only final models
cleanup: True
# Inputs defined by "global" or previous stages
# prefix:
# ec_file:
# target_sequence_file:
# segments:
# remapped_pdb_files:
# cpu:
# These settings allow job status tracking using a database, and result collection in an archive
management:
# URI of database
database_uri:
# unique job identifier
job_name:
# add the following output files to results archive
archive: [target_sequence_file, statistics_file, alignment_file, frequencies_file, ec_file, ec_longrange_file,
model_file, enrichment_file, evzoom_file, enrichment_pml_files, ec_lines_pml_file, contact_map_files,
ec_compared_all_file, ec_compared_longrange_file, remapped_pdb_files, mutations_epistatic_pml_files,
mutation_matrix_file, mutation_matrix_plot_files, secondary_structure_pml_file, folding_ec_file,
folded_structure_files, folding_ranking_file, folding_comparison_file, folding_individual_comparison_files,
ec_lines_compared_pml_file, pdb_structure_hits_file, sec_struct_file]
# Delete the following output files after running the job if you don't need them, to save disk space.
# Note that this may jeopardize your ability to rerun parts of the job if intermediate files are missing.
# The following, deactivated default deletes the biggest output files.
# delete: [raw_alignment_file, model_file]
# Computational environment for batch jobs (using evcouplings command line application)
environment:
# current options for engine: lsf, local, slurm (for local, only set cores and leave all other fields blank)
# If your batch engine of choice (e.g. SGE, Torque) is not available yet, please consider contributing by
# implementing it and submitting a pull request!
# Note that "cores" will override the "cpu" parameter for "global"
engine:
queue:
cores: 6
memory:
time:
# Special setting for "local" engine to define number of workers running in parallel
# (note that "cores" has to be defined above to make sure each job only uses a defined
# number of cores). If not defined or None, will default to number of cores / cores per job;
# otherwise specify integer to limit number of workers (1 for serial execution of subjobs)
# parallel_workers: 1
# command that will be executed before running actual computation (can be used to set up environment)
configuration:
# Paths to databases used by evcouplings.
databases:
# Sequence databases (only download the ones you want to use). You can also specify arbitrary databases in FASTA format
# using a database name of your choice here)
uniprot: /n/groups/marks/databases/jackhmmer/uniprot/uniprot_current.o2.fasta
uniref100: /home/tekpinar/databases/uniref100.fasta
uniref90: /home/tekpinar/databases/uniref90.fasta
# URL do download sequences if sequence_file is not given. {} will be replaced by sequence_id.
sequence_download_url: http://rest.uniprot.org/uniprot/{}.fasta
# Directory with PDB MMTF structures (leave blank to fetch structures from web)
pdb_mmtf_dir:
# SIFTS mapping information. Point to file paths in an existing directory, and if these files do not exist, they will be
# automatically generated and saved at the given file path (this may take a while).
# Periodically delete these files to more recent versions of SIFTS are used.
sifts_mapping_table: /n/groups/marks/databases/SIFTS/pdb_chain_uniprot_plus_current.o2.csv
sifts_sequence_db: /n/groups/marks/databases/SIFTS/pdb_chain_uniprot_plus_current.o2.fasta
# Paths to external tools used by evcouplings. Please refer to README.md for installation instructions and which tools are required.
tools:
jackhmmer: /usr/bin/jackhmmer
plmc: /usr/local/bin/plmc
hmmbuild: /usr/bin/hmmbuild
hmmsearch: /usr/bin/hmmsearch
hhfilter: /home/tekpinar/research/lcqb/hhsuite/bin/hhfilter
psipred: /n/groups/marks/software/runpsipred_o2
cns: /n/groups/marks/pipelines/evcouplings/software/cns_solve_1.21/intel-x86_64bit-linux/bin/cns
maxcluster: /n/groups/marks/pipelines/evcouplings/software/maxcluster64bit
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