Modern Data Formats for Big Bioinformatics Data Analytics

TL;DR

This paper reviews various data formats used in big bioinformatics data analytics, emphasizing their impact on data processing efficiency and guiding researchers in selecting suitable formats for different tools and algorithms.

Contribution

It provides a comprehensive analysis of modern data formats for big bioinformatics data, aiding optimal data handling and tool compatibility.

Findings

Analysis of data formats used in bioinformatics tools

Comparison of data formats for efficiency in big data processing

Guidelines for selecting appropriate data formats

Abstract

Next Generation Sequencing (NGS) technology has resulted in massive amounts of proteomics and genomics data. This data is of no use if it is not properly analyzed. ETL (Extraction, Transformation, Loading) is an important step in designing data analytics applications. ETL requires proper understanding of features of data. Data format plays a key role in understanding of data, representation of data, space required to store data, data I/O during processing of data, intermediate results of processing, in-memory analysis of data and overall time required to process data. Different data mining and machine learning algorithms require input data in specific types and formats. This paper explores the data formats used by different tools and algorithms and also presents modern data formats that are used on Big Data Platform. It will help researchers and developers in choosing appropriate data format to be used for a particular tool or algorithm.