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### DART-ID configuration
### =========================
# List the input files here, or define them on the command line
# when running the tool.
# for example, dart_id -i /path/to/input1.txt /path/to/input2.txt
input:
- /path/to/dat/FP061A/evidence.txt
- /path/to/dat/FP062ABCD/evidence.txt
- /path/to/dat/FP063/evidence.txt
- /path/to/dat/FP064AG/evidence.txt
# Folder to output to. The updated evidence file, as well as the
# optional parameters files and figures will be deposited here
# this can also be specified on the command line. e.g.:
# -o /path/to/output/folder
#output: /path/to/output/folder
# Print diagnostic figures, as well as an HTML file that allows
# for quick browsing
print_figures: true
## Input Type Options
## ==========================
# column names of the input file
# as of now all input files have to be the same format
# change these as the input file changes,
# e.g., when a different search engine or search engine configuration is used
col_names:
# These four columns are required. This program will not work without them.
# Sequence can be the canonical amino-acid sequence,
# or the modified/annotated sequence, as provided by the search engine
sequence: Modified sequence
# The name of the raw/spectrum file, or a unique identifier for each
# mass-spec run
raw_file: Raw file
# The retention/elution time of the ion, in minutes
# This can also be in seconds, just make sure you update the priors in
# model to reflect this change.
retention_time: Retention time
# The error probability of the peptide-spectrum-match. can be provided
# by the search engine or by a separate program, e.g., Percolator
pep: PEP
# optional columns, that would be used for filtering or figure generation
# Used to (optionally) append the ion charge state to the peptide sequence,
# so that peptides with different charge states are treated as different
# peptide species.
charge: Charge
# Used to run the Fido protein inference algorithm
leading_protein: Leading razor protein
proteins: Proteins
# The base peak width, i.e., the time range between when an ion
# first elutes to when it last elutes. Use this as a quality score
# in order to filter out poorly retained ions.
retention_length: Retention length
# Unused
#intensity: Intensity
#leading_gene: ~
#genes: ~
#exclude: ~
#exp_id: ~
#peptide_id: ~
## PSM Filters
## ======================
# Filters are used to exclude certain observations (PSMs) from
# the alignment process.
# Remove/comment-out filters from this list that you do not want to have.
filters:
# Filter out entire raw files, especially if they are of a different run-time,
# or if the LC for that experiment was problematic. The "expr" field is a regular
# expression that will be checked against all raw files in the input.
#- name: exclude_filename
# expr: PS06[1-3][AB]|PS064F
# Same as above, but as a whitelist instead of a blacklist
#- name: include_filename
# expr: 2018A
# Provide an exclusion list of UniProt IDs. Any PSM matching this
# list will be filtered out
# Either a file, with UniProt IDs separated by line breaks, can be
# specified with the "file" field, or
# a list of UniProt IDs can be provided in the "list" field
#- name: uniprot_exclusion
# file: /path/to/list_of_uniprot_ids.txt
# list:
# - or_you_could
# - list_uniprot_ids_here
# - P36578
# - Q99797
# Filter out contaminants as marked by the search engine
# The "tag" option is the pattern used to filter out PSMs
- name: contaminant
tag: CON__
# Filter out decoys as marked by the search engine
# The "tag" option is the pattern used to filter out PSMs
- name: decoy
tag: REV__
# Filter out PSMs by the retention length, which is defined
# by some search engines as the time at which this spectra is first
# observed, to the time this spectra is last observed
#
# If "dynamic" is set to true, then the threshold is a fraction of
# the maximum RT for that raw file (i.e., the run-time). A value of 0.01
# denotes that the threshold will be 1% of the total run-time of the experiment.
- name: retention_length
dynamic: true
value: 0.01667
# Filter out PSMs by their RT ranges in each experiment. This behavior is
# similar but not exactly the same as the "retention_length" filter.
#
# If "dynamic" is set to true, then the threshold is a fraction of
# the maximum RT for that raw file (i.e., the run-time). A value of 0.01
# denotes that the threshold will be 1% of the total run-time of the experiment.
- name: smears
dynamic: true
value: 0.03333
### =======================
### !! ADVANCED SETTINGS !!
### =======================
# Only edit the following settings if you understand their effects
# Please refer to config_annotated.yaml for detailed descriptions for
# each configuration field
# Level of verbosity. Higher numbers = printing more information
# 0 = ERROR
# 1 = WARNING (default)
# 2 = INFO
# 3 = DEBUG
# verbose: 1
## Input
## ==========================
# Column delimiter of the input files. i.e., ',' for CSV, '\t' for tabular
# sep: \t
# The input data is loaded in with pandas, and it doesn't like
# some columns being mostly empty. This needs to be set to false
# for input formats like MaxQuant.
# low_memory: false
# Instead of running a new STAN alignment, use a set of parameters
# from a previous run. The folder needs to include the three files
# outputted from a run with the "save_params" option on, and this run
# needs to be run with the exact same filters as that previous run.
# (exp_params.txt, peptide_params.txt, pair_params.txt)
# params_folder: /path/to/output_folder_from_prev_run
## Alignment Options
## ==========================
# Which alignment model to use
# Options: 'linear', 'two_piece_linear', 'two_piece_linear_laplace'
# model: 'two_piece_linear_laplace'
# add charge of ion onto the sequence, so that sequences ionized
# with different charge states will be aligned separately.
#
# Sometimes peptide sequences will form chemical adducts on column
# that can reflect on the charge received by the peptide during the
# ioniziation process, and aligning differently charged peptides can
# account for these chromatographic changes
# add_charge_to_sequence: false
# Number of iterations to run when generating priors
# If the average error when generating priors is too high,
# or prohibitive for STAN, then increase these to get more accurate priors
# prior_iters: 10
# Number of iterations to run for STAN. If STAN is consistently hitting
# its iteration limit without reaching an optima it is happy with,
# then increase this number
# stan_iters: 20000
## Advanced Alignment Options
# Minimum value for mu, a canonical retention time (RT) for a peptide
# mu_min: 1
# Amount to distort RTs when calculating priors. If STAN is erroring out
# because the priors are already too close to the optima, then consider
# slowly increasing this value to give STAN more room to iterate.
# rt_distortion: 0
# Advanced STAN parameters (with cmdstan: https://mc-stan.org/users/interfaces/cmdstan),
# for the LBFGS optimization algorithm
# we recommend leaving these at their defaults.
# Line search step size for first iteration
# init_alpha: 0.001
# Convergence tolerance on absolute changes in objective function value
# tol_obj: 1.e-12
# Convergence tolerance on relative changes in objective function value
# tol_rel_obj: 10000
# Convergence tolerance on the norm of the gradient
# tol_grad: 1.e-8
# Convergence tolerance on the relative norm of the gradient
# tol_rel_grad: 10000000
# Convergence tolerance on changes in parameter value
# tol_param: 1.e-8
# Amount of history to keep for L-BFGS
# history_size: 5
## Update Options
## ==========================
# parametric, parametric_mixture, non-parametric
# bootstrap_method: 'parametric_mixture'
# bootstrap_iters: 50
# mean, median, weighted_mean
# mu_estimation: 'median'
## Protein Inference Options
## ==========================
# Run protein inference on the newly updated PSMs with the Fido framework
# https://noble.gs.washington.edu/proj/fido
# Paper in J. Proteome Research: http://dx.doi.org/10.1021/pr100594k
# Most, if not all, parameters described below are also described in detail
# on the Fido website and by the helper tips for the command-line verison of Fido.
# To run protein inference, set this flag to true
# run_pi: true
# Parameters derived from a parameter search and optimizing over an objective
# that minimizes selecting false positives.
# Parameters listed below are the default for fido. Leave these, or specify them,
# to skip the parameter searching step.
# Comment out these three parameters to search for the best set of 3 parameters and
# then run protein inference with those.
# pi_gamma: 0.5
# pi_alpha: 0.1
# pi_beta: 0.01
# Log2 of maximum number of subgraph connected states. Graphs with more states
# than this threshold will be pruned. Increasing this number increases run-time,
# by a lot!
# pi_connected_protein_thresh: 14
# Clean up the peptide sequence string, by removing adjacent amino acids,
# modifications, and also switching isoleucine to leucine.
# pi_clean_peptide_name: false
# Default behavior is to cut all PSMs except for the highest scoring one,
# for each peptide, in order to simplify the graph. Set this to true to include
# all PSMs
# pi_use_all_psms: false
# Use protein group level inference
# pi_group_proteins: false
# Prune low-scoring PSMs from the graph before the main pruning procedure.
# The threshold in this case is 1e-2 (PEP > 0.99)
# pi_prune_low_scores: true
# Accuracy of the parameter selection. This will be ignored if pi_gamma, pi_alpha,
# and pi_beta are provided, as the selection will not be performed in the first place.
# 1 = best / slower (uses entire data file)
# 2 = relaxed / faster (uses 300 observations)
# 3 = sloppy / very fast (uses 100 observations)
# pi_parameter_accuracy: 3
# Proteins in the "Proteins" column are assumed to be protein IDs in a string,
# separated by a delimiter, which is specified here:
# i.e., the delimiter is ';' if the "Proteins" string is:
# "Protein1;Protein2;Protein3;Protein4"
# pi_protein_delimiter: ';'
# A substring that delineates decoy proteins. In the case of MaxQuant,
# all decoy proteins are prepended with the string "REV__"
# pi_decoy_tag: 'REV__'
## Output
## ==========================
# Save the parameters outputted by STAN into three text files.
# Use the "params_folder" option in a future run to use these
# parameters instead of running the alignment procedure again.
# save_params: true
# Default behavior is to only append two columns, the new PEP
# and the updated PEP. Set this to true to get many more columns
# added on.
# add_diagnostic_cols: false
# Overwrite the original PEP column with the updated PEP, and save
# the original PEP to the 'Spectra PEP' column.
# Useful for workflows that rely on the PEP column
# overwrite_pep: false
# Remove PSMs that have an FDR (q-value) below this value.
# 0.01 corresponds to selecting PSMs at an FDR of 1%
# psm_fdr_threshold: 0.01
# Remove PSMs that have an associated protein FDR (q-value) below this value.
# 0.01 corresponds to selecting proteins at an FDR of 1%
# protein_fdr_threshold: 0.01
# If providing multiple input files, combine them all into one
# tabular file and save it.
# save_combined_output: true
# The name of the combined output file.
# combined_output_name: ev_updated.txt
# If providing separate input files, then save the output files separately
# as well. This can be used in conjunction with 'save_combined_output'
# save_separate_output: false
# Save the separate output files into the same folder where they originally
# came from. **WARNING** this program does not check to see if it will overwrite
# an existing file. Please choose the options below carefully to avoid overwriting
# your original data!
# save_in_input_folder: false
# The suffix and extension of each of the separate output files.
# For example, if one of the inputs was "evidence.txt",
# the output would be "evidence_updated.txt"
# output_suffix: _updated
# output_ext: .txt
# Save logging messages to file?
# log_file: true
## Filters
## ==========================
# Lower threshold of PEP. PSMs with PEP higher than this value will not be
# considered for the alignment process
# These PSMs can still have their confidence updated, as long as there are
# PSMs of the same sequence that have PEP below this value
# pep_threshold: 0.5
# Peptide sequences need to be observed in at least this number of experiments,
# at a PEP below the pep_threshold, in order to participate in the alignment process
# num_experiments: 3
# Minimum number of confident PSMs per experiment, in order to participate in RT alignment
# If an experiment has less than this number of confident PSMs, then all of its
# PSMs will be excluded from the RT alignment process
# min_psms_per_experiment: 50
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