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parameters_blueprint.yml
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parameters_blueprint.yml
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# global options must be defined
global:
# Required: Path to a Fasta file
sequences_file: /path/to/sequences.fasta
# Required: String for output
prefix: my_embeddings
## Optional: file manager
# file_manager: [*filesystem]
## Optional: remap index simple (not via md5, this is not encouraged)
# simple_remapping: [True, *False]
## Stages are executed in sequential order as they are outlined in this file.
## Stage names must be different!! If not: they will overwrite each_other
## The same stage type (e.g. embed,..) can be executed multiple types.
## Dependencies for a stage are defined in the dependencies parameter
## This config file includes options for initializing classes and options specific to the protocol
## Options notation:
## *: denotes the default option
## @: denotes that the file or directory will be downloaded and stored locally if not provided
stage_0:
type: align
# Required: which protocol to use.
# Options: mmseqs_search, deepblast
protocol: mmseqs_search
# Required for mmseqs_search: search sequence to search against (can be FASTA, mmseqs db or mmseqs profile)
search_sequences_file: /path/to/a/sequence/db.fasta
# search_sequences_directory: /path/to/a/sequence/db/
# search_profiles_directory: /path/to/a/profiles/db/
# Optional for mmseqs_search: convert alignment to profile
# convert_to_profiles: [True,*False]
# Optional for mmseqs_search: alignment parameters (check bio_embeddings/align/mmseqs2.py#13 for available options)
# mmseqs_search_options:
# num_iterations: 5
# sensitivity: 7.5
# minimum_sequence_identity: 0.2
# maximum_number_of_prefilter_sequences: 100
# alignment_output: True
stage_1:
type: embed
# Required: which embedder to use
# Options: seqvec, prottrans_albert_bfd, prottrans_bert_bfd, prottrans_t5_bfd, prottrans_t5_uniref50,
# prottrans_t5_xl_u50, prottrans_xlnet_uniref100, cpcprot, esm, esm1b, esm1v, plus_rnn, unirep, bepler
protocol: prottrans_t5_xl_u50
# Optional: reduce embeddings to fixed size, per-protein. Comment out if not needed.
# Note that you can always compute the reduced embeddings from the full embeddings
# but some further stages (e.g. unsupervised extract) need this option
reduce: True
# Optional: discard per amino acid embeddings.
# Setting this parameter to True will disable storing full size embeddings (per amino acid).
# This parameter only works in combination with `reduce: True` or `embeddings_transformer_function`.
# discard_per_amino_acid_embeddings: [True, *False]
# Optional/Advanced: apply a transformation on the per-amino-acid embeddings
# === This is an advanced parameter ===
# This parameter will be "eval"-uated. It must be a callable. Most likely, you'll want to define lambda functions.
# The input of the function is a per-amino-acid embedding (aka. an np array of shape n_layers*embedding_dimension*sequence_length).
# You can use numpy functions via "np".
# The result of the transformation will be stored in the transformed_embeddings_file.
# The pipeline *WILL NOT* check file size of the prospective transformed_embeddings_file.
# This parameter can be used in conjunction with `discard_per_amino_acid_embeddings`.
# Two examples:
# - "lambda x: x[0].mean(0)" --> for SeqVec, this will return the mean pooled embedding of the first layer
# - "lambda x: x.max(0)" --> for ProtTrans-BERT-BFD, this will return the max pooled embedding, instead of mean pooled
# embeddings_transformer_function:
## Mandatory for esm1v: This defines which of the five models of the ensemble will be used
# ensemble_id: [1,2,3,4,5]
#### Optional parameters to instantiate classes
### Optional for protocol: seqvec
# weights_file: @/path/to/file
# options_file: @/path/to/file
## The following parameter sets an upper bound on total AA to include when embedding many sequences.
## Adjust this parameter if CUDA runs out of memory! The default (15.000) works for a 1080 with 8GB RAM.
# max_amino_acids: [*15000]
## The following parameters sets the amount of AA to include in a batch before writing to disk.
### Optional for protocol: fasttext, word2vec, glove, esm
# model_file: @/path/to/file
### Optional for protocol: albert, bert, xlnet
# model_directory: @/path/to/directory
### Optional for protocol: seqvec, prottrans_albert_bfd, prottrans_bert_bfd, prottrans_xlnet_uniref100, esm
## Set the following parameter to calculate embeddings on a specific device or a specific GPU on multi-GPU hosts.
## The default, "cuda", runs on the default GPU. You can switch to CPU with "cpu" or select a GPU on
## multi-GPU systems with "cuda:0" or "cuda:1" etc.
## See https://pytorch.org/docs/stable/tensor_attributes.html?highlight=device#torch.torch.device
## for a complete list
## unirep will use the cpu by default; Follow https://github.com/google/jax#pip-installation
## to run it on the gpu
# device: cuda
## Save numbers with lower precision, so that they take only about half the
## storage space (replace float32 with float16).
## This makes predictions based on the embeddings less exact.
# half_precision: [true, *false]
## For prottrans_t5_bfd, prottrans_t5_uniref50 and prottrans_t5_xl_u50 only:
# Use the model in half precision mode (float16)
## We recommend activating this with T5, since tested GPU (Quadro RTX 3000) reduces memory consumption
## from 12GB to 7GB while the effect in benchmarks is negligible (±0.1 percentages points in different sets,
## generally below standard error)
# half_precision_model: [true, *false]
stage_2:
type: project
# Required: which projection algorithm to use
# Options: tsne, umap, pb_tucker
protocol: tsne
# Either depend on an embedding stage with reduced embeddings
depends_on: stage_1
# or define mapping and reduced embedding file:
# reduced_embeddings_file: path/to/reduced_embeddings_file.h5
# mapping_file: path/to/mapping_file.csv
### Optional for protocol: tsne
# n_iter: *15000
# perplexity: *6
# n_jobs: *-1
### Optional for protocol: umap
# min_dist: *0.6
# spread: *1
# n_neighbors: *15
### Optional for protocol: tsne and umap
# metric: *'cosine'
# n_components: *3
# random_state: *420
# verbose: *1
## Optional with pb_tucker: Postprocess reduced embeddings with tucker, which is a contrastive
## learning model trained to distinguish CATH superfamilies. It reduces the dimensionality from 1024 to 128.
# model_file: @/path/to/file
stage_3:
type: mutagenesis
## Required: Which language model to use
## Options: protbert_bfd_mutagenesis
protocol: protbert_bfd_mutagenesis
## Optional: temperature for softmax, see https://arxiv.org/abs/1503.02531
# temperature: [1*]
## Optional: Since we're running ProtBert, the common ProtBert options explained in `extract` are supported
# model_directory: @/path/to/directory
# device: cuda
# half_precision: [true, *false]
# half_precision_model: [true, *false]
stage_4:
type: visualize
## Required: which graph to render
## Options: plotly, plot_mutagenesis
protocol: plotly
## For plotly: Either depend on a project stage with projected embeddings file
depends_on: stage_2
## or define projected_reduced_embeddings_file:
# projected_reduced_embeddings_file: path/to/projected_reduced_embeddings_file.h5
## For plot_mutagenesis: Either depend on a mutagenesis stage
# depends_on: stage_3
## or define residue_probabilities_file:
# residue_probabilities_file: path/to/residue_probabilities_file.csv
## Optional for plotly: csv file with annotations
## csv must have header: identifier, label
# annotation_file: path/to/annotation_file.csv
## Optional for plotly: hide proteins for which there is no annotation in the annotation file (only relevant if annotation file is provided)
# display_unknown: [False, *True]
## Optional for plotly: set to True if in annotation_file identifiers correspond to sequence MD5 hashes
## if set to False (default), mapping will be performed on original identifiers.
## Where missing or duplicate, will be ignored
# merge_via_index: [True, *False]
## Optional for plotly: 2D vs 3D plot
# n_components: [2,*3]
stage_5:
type: extract
## Required: which method to use.
## Current options:
## - seqvec_from_publication (Uses models evaluated in https://doi.org/10.1186/s12859-019-3220-8 )
## - bert_from_publication (Uses models evaluated in https://doi.org/10.1101/2020.07.12.199554 )
## - t5_xl_u50_from_publication (Uses models evaluated in https://doi.org/10.1101/2020.07.12.199554 )
## - unsupervised (Uses concepts presented in https://github.com/Rostlab/goPredSim )
protocol: t5_xl_u50_from_publication
## The supervised extract (bert_from_publication and seqvec_from_publication) needs
## the full embeddings, i.e. `discard_per_amino_acid_embeddings` must be false in the
## embed stage, which is the default. For the unsupervised extract, you need the
## reduced embeddings, i.e. set `reduce: True`.
## Instead of an embed stage, you can also provide a file manually, either supply for unsupervised:
# reduced_embeddings_file:
## or for bert_from_publication and seqvec_from_publication
# embeddings_file:
depends_on: stage_2
## Optional for protocol: seqvec_from_publication, bert_from_publication,
## will be downloaded if not supplied
# secondary_structure_checkpoint_file: path/to/checkpoint.pt
# subcellular_location_checkpoint_file: path/to/checkpoint.pt
## Required for protocol: unsupervised
# reference_embeddings_file: path/to/embeddings.hd5
# reference_annotations_file: path/to/annotation_file.csv
## Optional for protocol: unsupervised
## The following two options refer to the pairwise_distance function of scikit-learn 0.23.2
## https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html#sklearn.metrics.pairwise_distances
# n_jobs: [1*]
# metric: [euclidean*]
## The following will define how many neighbours to consider to transfer annotations.
## k = 1 means transfer the annotations from the nearest neighbor
## k > 1 will result in merging the annotations of all k > 1 neighbors onto the target embedding
# k_nearest_neighbours: [1*]
## The following informs the pipeline whether you want to keep the pairwise distance matrix file.
## This is a CSV containing pairwise distances between query and reference embeddings using your metric.
## The file can become quite big (e.g. UniProt Human (query) vs. SwissProt (reference) results in 45GB)
## By default, this file will be discarded, but you can decide to keep it to perform other calculations.
# keep_pairwise_distances_matrix_file: [*False]