[proteomics] add README (#333)

Add README for proteomics module

python/python/bystro/proteomics/README.md

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+# Proteomics README
+This module provides functionality for analyzing proteomics datasets, principally from Fragpipe
+software suite, in conjunction with genomic annotations provided by Bystro.
+
+## Proteomics Datasets
+The fundamental entity in a proteomics dataset is an _abundance matrix_, which records the measured
+protein abundances of a set of proteins in a set of samples.  Also included in the dataset is an
+_annotation matrix_, which records metadata pertaining to the samples.  Such datasets are
+represented within Bystro by the class `TandemMassTagDataset` in `fragpipe_tandem_mass_tag.py`.
+(The file `fragpipe_data_independent_analysis.py` provides similar functionality for Fragpipe DIA
+datasets.  Although there are many proteomics protocols with their own file formats, they are
+largely `sui generis` and we will constrain our discussion to Fragpipe TMT datasets below.  For
+further details see the [Fragpipe
+documentation](https://fragpipe.nesvilab.org/docs/tutorial_fragpipe_outputs.html)).
+
+## CLI
+The file `proteomics_cli.py` provides a CLI for uploading proteomics datasets to Bystro.  AFter
+authentication, the user may upload a dataset via the command:
+
+```
+python proteomics_cli.py upload-proteomics-dataset --protein-abundance-file PROTEIN_ABUNDANCE_FILE \
+                                                   --experiment-annotation-file EXPERIMENT_ANNOTATION_FILE
+                                                   [--dir DIR]
+```
+where `DIR` is an optional location for storage within Bystro.
+
+## Proteomics Listener
+The loading of proteomics datasets within Bystro is handled by the proteomics listener, a python
+service that is initialized during Bystro startup and listens to the `proteomics` beanstalkd tube
+for incoming `ProteomicsSubmission` messages.  Upon receipt of a `ProteomicsSubmission` message, the
+listener loads the file described in the submission payload and returns the result in a
+`ProteomicsResponse`.
+
+## Annotation Interface
+It's often desirable to analyze a proteomics dataset in conjunction with a genomic annotation file
+(of the same subjects) provided by Bystro.  The basic workflow of this joint analysis is as follows:
+
+1.  The user queries the annotation file with an arbitrary OpenSearch query in order to select a
+    subset of rows (i.e. variant records) from the annotation file.  For example, a user might
+    provide the query: "exonic (gnomad.genomes.af:<0.1 || gnomad.exomes.af:<0.1)" which means
+    "return all exonic variants where the allele frequency in gnomad.genomes or gnomad.exomes is
+    less than 10%".
+2.  The annotation interface returns a list of variant / sample pairs, i.e. for every valid variant
+    and every sample containing that variant, we return a record of the form: `(sample_id, chrom,
+    pos, ref, alt, gene_name, dosage)`.  This functionality is provided through the method
+    `get_annotation_result_from_query`.
+3.  The annotation query results are then joined to a given proteomics dataset on `sample_id` and
+    `gene_name`.  That is to say, the result will be a Pandas dataframe with the columns:
+    `(sample_id, chrom, pos, ref, alt, gene_name, dosage, protein_abundance)`, where
+    `protein_abundance` is the abundance of protein `gene_name` for sample `sample_id`.  This
+    functionality is provided through the method `join_annotation_result_to_proteomics_dataset`.
+
+Notes:
+1.  In general, a given subject's `sample_id` in the genomic annotation file may not be identical to
+    its `sample_id` in the proteomic dataset.  Instead, a subject may globally identified through a
+    `tracking_id`.  In that case, the user may provide two mappings:
+    `get_tracking_id_from_genomic_sample_id` and `get_tracking_id_from_proteomic_sample_id`, and
+    these helper functions will be used to canonicalize the `sample_ids` before joining the two
+    datasets.
+
+2.  We assume (in accordance with the datasets we have seen so far) that Fragpipe will map peptide
+    Uniprot IDs to HUGO gene name symbols, so that annotation gene names can be identified with
+    proteomics gene names in that namespace, but this may not always be the case.  Functionality to
+    map from Uniprot IDs to HUGO symbols and vice versa is described below.
+
+## Uniprot / HUGO symbol mapping
+	An additional module, `uniprot_id_gene_name_mapping.py`, is provided to convert Uniprot IDs to
+    gene names and vice versa.  This module provides two public methods,
+    `get_gene_names_from_uniprot_id` and `get_uniprot_ids_from_gene_name`.  (NB: this mapping is in
+    general many to many, hence each returns a list of cognates in the other namespace.)  These
+    mappings are populated by a data download from Uniprot, which can be run by the script
+    `scripts/get_uniprot_id_gene_name_mapping.py`.  Currently, this script only queries the human
+    proteome, but can be trivially amended to include all model organisms supported in
+    `bystro/config/*.mapping.yml`, or indeed all of Uniprot.
+
+
+
+
+