Shared Data Science Infrastructure for Genomics Data

Abstract

Background: Creating a scalable computational infrastructure to analyze the wealth of information contained in data repositories is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared data science infrastructures like Boa_g is needed to efficiently process and parse data contained in large data repositories. The main features of Boa_g are inspired from existing languages for data intensive computing and can easily integrate data from biological data repositories. Results: As a proof of concept, Boa for genomics, Boa_g, has been implemented to analyze RefSeq’s 153,848 annotation (GFF) and assembly (FASTA) file metadata. Boa_g provides a massive improvement from existing solutions like Python and MongoDB, by utilizing a domain-specific language that uses Hadoop infrastructure for a smaller storage footprint that scales well and requires fewer lines of code. We execute scripts through Boa_g to answer questions about the genomes in RefSeq. We identify the largest and smallest genomes deposited, explore exon frequencies for assemblies after 2016, identify the most commonly used bacterial genome assembly program, and address how animal genome assemblies have improved since 2016. Boa_g databases provide a significant reduction in required storage of the raw data and a significant speed up in its ability to query large datasets due to automated parallelization and distribution of Hadoop infrastructure during computations. Conclusions: In order to keep pace with our ability to produce biological data, innovative methods are required. The Shared Data Science Infrastructure, Boa_g, provides researchers a greater access to researchers to efficiently explore data in new ways. We demonstrate the potential of a the domain specific language Boa_g using the RefSeq database to explore how deposited genome assemblies and annotations are changing over time. This is a small example of how Boa_g could be used with large biological datasets.

Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data

“…Focusing on the similar kind of goal of collective program analysis [56,57] as well as PASS in terms of reducing the data that is sent to downstream analysis and using the semantics of task that is conducted during downstream analysis to reduce the data, can we determine task-dependent program similarity for inding analogous programs in the realm of data science? Another goal might be to realize PASS as part of the shared infrastructure such as Boa [3,6,13,14,25] and understand performance improvements and computation savings that might result from it.…”