You might want download our modified affy package, if you got error message "The affy package is not designed for this array type, Please use either the oligo or xps package." Our modified affy package is for analyzing Gene ST, Gene Exon or HTA20 in a traditional way. And clicking the link "Version #" would lead to download page for the version # Intro Part II of our adventure to warehouse a subset of DBbSNP's JSON data.25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1ĭatabase builds used to generate custom CDF. Part II of our tutorial picks up here! Warehousing DbSNP, Part II: Streaming and Parsing SNP Data We turn to iterators to help us decompress and stream the file contents through our parser. Uncompressed, the 200GB file is too big to load into memory. Our goal is to grab the frequency of each allele, and the clinically significant diseases that are influenced by the allele. Parsing the JSON DataĪs previously mentioned, each line of our file contains a JSON object, representing a RefSNP.Įach RefSNP JSON object holds all of the Alleles of the RefSNP in question, usually a single-nucleotide variation (i.e.
DBSNP DOWNLOAD SERIES
We will use these types in part II of our series to keep ourselves sane.
# DTO's RefSnpCopyFromData = namedtuple ( "RefSnpCopyFromData", ) RefSnpAllele = namedtuple ( "RefSnpAllele", ) RefSnpAlleleFreqStudy = namedtuple ( "RefSnpAlleleFreqStudy", ) RefSnpAlleleClinDisease = namedtuple ( "RefSnpAlleleClinDisease", ) Creating and configuring this database is outside the scope of this post, but if on a Mac, you can start by installing PostgreSQL with: To create these tables, I assume we’ve already created a local PostgreSQL database. Avoiding small redundancies like these allow for more malleable code, and less opportunity for bugs down the road. Note: We’re using Inheritance to DRY ( don’t repeat yourself) out the ref_snp_allele_clin_diseases and ref_snp_allele_freq_studies model definitions. size ( dbsnp_filename ) / ( 1024 ** 3 ), 2 ) print ( f "Filesize: ) class RefSnpAlleleFrequencyStudy ( RefSnpAlleleRelative, Base ): _tablename_ = 'ref_snp_allele_freq_studies' name = Column ( Text ) allele_count = Column ( Integer ) total_count = Column ( Integer ) class RefSnpClinicalDisease ( RefSnpAlleleRelative, Base ): _tablename_ = 'ref_snp_allele_clin_diseases' disease_name_csv = Column ( Text ) clinical_significance_csv = Column ( Text ) citation_list = Column ( ARRAY ( Integer, dimensions = 1 )) cwd ( "snp/.redesign/latest_release/JSON" ) size_gb = round ( ftp. Import ftplib dbsnp_filename = "" with open ( dbsnp_filename, "wb" ) as fp : ftp = ftplib. In a perfect, simpler world, the following few LOC would download our 1st file: You can easily turn your 23&Me Raw Data into insights from DbSNP using this ( free) API the client is found here on github. I gave a lightning talk at P圜on2018 about a live, deployed application of this database: The Snip API. Let’s begin with step 1: downloading refsnp_ as our case study. Using this compression ratio ( 1:20) as a rule-of-thumb, the uncompressed size of the entire dataset is 2.1TB…that’s a lot of data to process and warehouse. The uncompressed size of chromosome 1’s file is 162GB, with the compressed size being 8GB. The total size of this JSON payload is a whopping 104GB of gzipped data. Moving forward, NCBI plans to store 1 JSON file per chromosome, and these files can be found here.
DBSNP DOWNLOAD CODE
I’ve also sprinkled in Github Links above all code snippets, to navigate to each corresponding line & file. The entire codebase of this walkthrough is available here on github.
DBSNP DOWNLOAD HOW TO
This series of posts cover how to migrate DbSNP’s newly downloadable JSON files into a slimmed-down, relational database, containing population frequency data, associated pubmed IDs, and clinvar data for each SNP in the database. Menu Warehousing DbSNP, Part I: Downloading Chromosome 1 & Creating our Database
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