Nowadays, big data analytics in genomics is an emerging topic. In fact, the big amount of genomics data originated by emerging Next-Generation Sequencing (NGS) techniques require more and more fast and sophisticated algorithms. In this context, deep learning is re-emerging as a possible approach capable to speed up the DNA sequencing process. In this paper, we specifically discuss such a trend. In particular, starting from an analysis of the interest of the Internet community in both NGS and deep learning, we present a taxonomic analysis highlighting the major software solutions based on deep learning algorithms available for each specific NGS application field. In the end, we discuss future challenges in the perspective of Cloud computing services aimed at deep learning based solutions for NGS.
Why Deep Learning Is Changing the Way to Approach NGS Data Processing: a Review
Celesti, Antonio
;Villari, Massimo
2018-01-01
Abstract
Nowadays, big data analytics in genomics is an emerging topic. In fact, the big amount of genomics data originated by emerging Next-Generation Sequencing (NGS) techniques require more and more fast and sophisticated algorithms. In this context, deep learning is re-emerging as a possible approach capable to speed up the DNA sequencing process. In this paper, we specifically discuss such a trend. In particular, starting from an analysis of the interest of the Internet community in both NGS and deep learning, we present a taxonomic analysis highlighting the major software solutions based on deep learning algorithms available for each specific NGS application field. In the end, we discuss future challenges in the perspective of Cloud computing services aimed at deep learning based solutions for NGS.Pubblicazioni consigliate
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