GET scoreset¶
get a scoreset from MaveDB via the API¶
To begin, import the modeules below.
import attr, os
from pprint import PrettyPrinter
from mavetools.client.client import Client
from mavetools.models.scoreset import ScoreSet
Pretty printer is used to format the output nicely.
pp = PrettyPrinter(indent=2)
Here your base_url is set to localhost, http://127.0.0.1:8000/api/. This default funcionality is what you would want to use when working with a local instance of MaveDB (e.g., a development branch). If working with production mavedb you would set base url to https://www.mavedb.org/api/.
In the cell below, comment out the base_url you will not be using.
base_url = 'http://127.0.0.1:8000/api/'
#base_url = 'https://www.mavedb.org/api/'
Set experiment_urn to match the scoreset you want to get.
scoreset_urn = 'urn:mavedb:00000001-a-1'
Next, you will need an auth_token to make POST requests to MaveDB. If you have one, substitute it in the example provided below. If you need one, please follow these instructions:
1. go to https://www.mavedb.org
2. login using your ORCID ID
3. go to settings
4. generate new auth token
5. copy auth token and pase it in the auth_token field below
# this is an example of what your auth_token should look like
auth_token = 'R2skRbpBD3Rsf5dNHoQxDZevdEE74T5lCKMFyBhBwwPFH4ZfTrxDz7TZ0kbFLtEZ'
Here you instantiate the Client object. The Client object is the object by which the POST request is performed. The client object is instantiated with the value of base_url provided earlier, so make sure that is up-to-date. If base_url does not exist, base_url is defaulted to localhost, http://127.0.0.1:8000/api/.
client = Client(base_url, auth_token=auth_token) if base_url else Client(auth_token=auth_token)
GET the model instance by passing the model type (Scoreset, in this instance) and the scoreset_urn as arguments to the get_model_istance funtion that operates on the Client object. This will GET the model instance (resource) from the server via the approprate API endpoint.
scoreset = client.get_model_instance(ScoreSet, scoreset_urn)
Now, display the results!
pp.pprint(attr.asdict(scoreset))
{ 'abstract_text': 'Although we now routinely sequence human genomes, we can '
'confidently identify only a fraction of the sequence '
'variants that have a functional impact. Here, we developed '
'a deep mutational scanning framework that produces '
'exhaustive maps for human missense variants by combining '
'random codon mutagenesis and multiplexed functional '
'variation assays with computational imputation and '
'refinement. We applied this framework to four proteins '
'corresponding to six human genes: UBE2I (encoding SUMO E2 '
'conjugase), SUMO1 (small ubiquitin-like modifier), TPK1 '
'(thiamin pyrophosphokinase), and CALM1/2/3 (three genes '
'encoding the protein calmodulin). The resulting maps '
'recapitulate known protein features and confidently '
'identify pathogenic variation. Assays potentially amenable '
'to deep mutational scanning are already available for 57% '
'of human disease genes, suggesting that DMS could '
'ultimately map functional variation for all human disease '
'genes. rn'
'rn'
'See [Weile *et al.* '
'2017](http://msb.embopress.org/content/13/12/957)',
'approved': None,
'contributors': ['0000-0003-1628-9390'],
'count_columns': ['hgvs_nt', 'hgvs_splice', 'hgvs_pro'],
'created_by': '0000-0003-1628-9390',
'creation_date': '2018-06-26',
'current_version': 'urn:mavedb:00000001-a-1',
'data_usage_policy': '',
'dataset_columns': None,
'doi_ids': [],
'experiment': 'urn:mavedb:00000001-a',
'extra_metadata': {},
'is_meta_analysis': False,
'keywords': [ {'text': 'DMS-BarSeq'},
{'text': 'E2'},
{'text': 'sumoylation'},
{'text': 'imputation'},
{'text': 'DMS-TileSeq'},
{'text': 'complementation'}],
'last_child_value': None,
'licence': { 'link': 'https://creativecommons.org/licenses/by/4.0/',
'long_name': 'CC BY 4.0 (Attribution)',
'short_name': 'CC BY 4.0',
'version': '4.0'},
'method_text': '##Scoring procedure:rn'
'DMS-BarSeq and DMS-TileSeq reads were processed using the '
'[dmsPipeline](https://bitbucket.org/rothlabto/dmspipeline) '
'software. Briefly, Barseq read counts were used to establish '
'relative frequencies of each strain at each timepoint and '
'converted to estimates of absolute frequencies using OD '
'measurement data. Absolute counts were used to establish '
'growth curves from which fitness parameters were estimated '
'and then normalized to 0-1 scale where 0 corresponds to null '
'controls and 1 corresponds to WT controls. Meanwhile, '
'TileSeq read counts were used to establish relative allele '
'frequencies in each condition. Non-mutagenized control '
'counts were subtracted from counts (as estimates of '
'sequencing error). log ratios of selection over '
'non-selection counts were calculated. The resulting TileSeq '
'fitness values were then rescaled to the distribution of the '
'BarSeq fitness scores. Fitness scores were joined using '
'confidence-weighted averages. Random-Forest base machine '
'learning was used to impute missing values and refine '
'low-confidence measurements, based on intrinsic, structural, '
'and biochemical features.rn'
'rn'
'See [Weile *et al.* '
'2017](http://msb.embopress.org/content/13/12/957) for more '
'details.rn'
'rn'
'## Additional columns:rn'
'* exp.score = experimental score from the joint '
'DMS-BarSeq/DMS-TileSeq screensrn'
'* exp.sd = standard deviation of the experimental scorern'
'* df = degrees of freedom (number of replicates contributing '
'to the experimental score)rn'
'* pred.score = machine-learning predicted score',
'modification_date': '2019-08-08',
'modified_by': '0000-0003-1628-9390',
'next_version': None,
'previous_version': None,
'private': None,
'publish_date': '2018-06-26',
'pubmed_ids': [ { 'dbname': 'PubMed',
'dbversion': None,
'identifier': '29269382',
'url': 'http://www.ncbi.nlm.nih.gov/pubmed/29269382'}],
'replaces': None,
'score_columns': [ 'hgvs_nt',
'hgvs_splice',
'hgvs_pro',
'score',
'sd',
'se',
'exp.score',
'exp.sd',
'df',
'pred.score'],
'short_description': 'A joint Deep Mutational Scan of the human SUMO E2 '
'conjugase UBE2I using functional complementation in '
'yeast, combining DMS-BarSeq and DMS-TileSeq data, '
'followed by machine-learning-based imputation and '
'refinement.',
'sra_ids': None,
'target': { 'ensembl': { 'dbname': 'Ensembl',
'dbversion': None,
'identifier': 'ENSG00000103275',
'offset': 0,
'url': 'http://www.ensembl.org/id/ENSG00000103275'},
'name': 'UBE2I',
'reference_maps': [ { 'genome': { 'assembly_identifier': { 'dbname': 'GenomeAssembly',
'dbversion': None,
'identifier': 'GCF_000001405.26',
'url': 'http://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26'},
'organism_name': 'Homo sapiens',
'short_name': 'hg38'}}],
'reference_sequence': { 'sequence': 'ATGTCGGGGATCGCCCTCAGCAGACTCGCCCAGGAGAGGAAAGCATGGAGGAAAGACCACCCATTTGGTTTCGTGGCTGTCCCAACAAAAAATCCCGATGGCACGATGAACCTCATGAACTGGGAGTGCGCCATTCCAGGAAAGAAAGGGACTCCGTGGGAAGGAGGCTTGTTTAAACTACGGATGCTTTTCAAAGATGATTATCCATCTTCGCCACCAAAATGTAAATTCGAACCACCATTATTTCACCCGAATGTGTACCCTTCGGGGACAGTGTGCCTGTCCATCTTAGAGGAGGACAAGGACTGGAGGCCAGCCATCACAATCAAACAGATCCTATTAGGAATACAGGAACTTCTAAATGAACCAAATATCCAAGACCCAGCTCAAGCAGAGGCCTACACGATTTACTGCCAAAACAGAGTGGAGTACGAGAAAAGGGTCCGAGCACAAGCCAAGAAGTTTGCGCCCTCATAA',
'sequence_type': 'dna'},
'refseq': { 'dbname': 'RefSeq',
'dbversion': None,
'identifier': 'NM_003345',
'offset': 159,
'url': 'http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=NM_003345'},
'scoreset': 'urn:mavedb:00000001-a-1',
'type': 'Protein coding',
'uniprot': { 'dbname': 'UniProt',
'dbversion': None,
'identifier': 'P63279',
'offset': 0,
'url': 'http://purl.uniprot.org/uniprot/P63279'}},
'title': 'UBE2I imputed & refined',
'urn': 'urn:mavedb:00000001-a-1',
'variant_count': 3180}