Big data handled with new BC|TILING module
We are proud to announce a new module for our BC|GENOME system for massively parallel computation that removes bottlenecks from handling of massive data amounts.
The BC|TILING module uses a highly compressed data format suitable for parallel computation and can be added to the existing platform to drastically increase data handling efficiency and reduce storage space requirements. The module stores genetic variant data derived from imputation and sequencing. The name “tiling” indicates the way the genotype data matrix is indexed and stored as compressed “tiles” of fixed size, enabling scalable, distributed data storage and analysis.
BC|TILING has been designed to work in the most extensive studies with a target of 1,000,000 subjects x 100,000,000 markers but without any upper or lower limits. The module increases data handling efficiency and reduces required storage space even with smaller amounts of data and does not require any investments into new hardware.
With this new system, handling of big data becomes faster than ever before. The ideal hardware consists of a HPC cluster with a large distributed file system that can be used as a permanent storage for genotype data. BC|TILING also enables starting with smaller storage and calculation capacity, utilizing new capacity as data amounts grow – the performance can be scaled linearly by adding new computation units.
BC|TILING removes bottlenecks from data import and retrieval, pre-processing and creating input for analysis. Pre-processing data for distributed analysis is now lightweight and fast. Data can be retrieved and exported quickly for selected markers or subjects in both marker-major formats (e.g. VCF, Oxford .gen, PLINK binary) and subject-major formats (e.g. PLINK ped, Merlin, Linkage, MaCH).
This new module was developed to increase productivity in data analysis and ensure the scalability of data analysis workflows with ever growing data amounts. We are committed to making sure that our customers achieve faster results and want to make their lives easier by eliminating bottlenecks and manual data management.
Table 1: BC|TILING storage space savings. Comparison is most relevant between SQL storage (BC|GENE), compressed data storage using BCD (also in BC|SNPmax), and tiled storage.
|Subjects||Variants/ subjects||Subjects x SNPs||SQL||BC|GENOME (BCD)||BC|TILING (dosage)||BC|TILING (VCF*)|
|10,000||100 M||1,000 B||88,000 GB||3,500 GB||375 GB||127 GB|
|100,000||100 M||10,000 B||880,000 GB||35,000 GB||3,750 GB||1,269 GB|
|1,000,000||100 M||100,000 B||8,800,000 GB||350,000 GB||37,500 GB||12,690 GB|
*This number is for VCF file with genotypes only. When storing additional info, disk consumption increases.
In addition to substantial storage savings, BC|TILING facilitates a highly efficient parallel data analysis process. In practice this means that a larger number of calculation nodes can be used for data analysis decreasing the processing time. The following table illustrates the performance increments for a small dataset which can be processed using all methods. For larger datasets, the performance benefit of BC|TILING will become even more substantial.
Table 2: BC|TILING performance (PLINK association analysis using single server with 4 CPU cores and large external calculation resource). Time includes segmentation, file format conversion, PLINK analysis and result collection.
|Dataset size (genotypes/ variants)||SQL||BC|GENOME||BC|TILING|
|2.2 B||2 hour 45 min||7 minutes||1 min 30 sec|
|22 B||N/A||N/A||8 min 50 sec|
|222 B||N/A||N/A||21 min 30 sec|