Computational Strategies for Scalable Genomics Analysis

Abstract

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

Whole Genome Alignment: Methods, Challenges, and Future Directions

“…This necessitates the development of even more scalable and efficient algorithms that can cope with the volume and complexity of modern genomic datasets. Also, the potential of parallel and distributed computing in enhancing the efficiency of WGA has been recognized, but the cost, resources, and expertise required for these approaches limit their adoption [75].…”

A review of deep learning applications in human genomics using next-generation sequencing data

“…In the 1980s, the initial deep learning architecture was constructed on artificial neural networks (ANNs) [ 100 ], but the actual power of deep learning developed outward in 2006 [ 101 , 102 ]. Since then, deep learning has been functional in various arenas involving genomics, bioinformatics, drug discovery, automated speech detection, image recognition and natural language processing [ 6 , 13 , 103 ].…”

Preparing for Emerging Zoonotic Viruses

“…Moreover, there has been a vast increase of (digital) data in the past decade that has great potential to be used for zoonotic disease prediction, early detection and control ( Becker et al ., 2019 ; Masri et al ., 2019 ). This is not only caused by the shift of traditional diagnostics to multi-analyte technologies such as next generation sequencing, but also by the large increase of for example environmental, financial and social media information collection ( Simonsen et al ., 2016 ; Shi and Wang, 2019 ). However, the use of this “Big Data” with different data types of multiple origins also makes the analysis much more challenging ( Khoury and Ioannidis, 2014 ).…”