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

Abstract

Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.

Keywords: Deep learning applications; Disease variants; Epigenomics; Gene expression; Human genomics; NGS; Pharmacogenomics; Variant calling.

Fig. 1

Timeline of implementing deep learning algorithms in genomics. This timeline plot demonstrated the delay of implementing DL tools in genomics; for example, both (LSTM) and (BLSTM) algorithms have been invented in 1997 and the first genomic application was implemented in 2015. Similar observations are for the rest of the deep learning algorithms (Table 6)

Fig. 2

Deep learning applications in genomics. This figure represents the application of deep learning tools in five major subareas of genomics. One example deep learning tool and underlying network architecture has been shown for each of the genomic subareas, and its input data type and the predictive output were mentioned briefly. Each bar plot depicts the frequency of most used deep learning algorithms underlying deep learning tools in that subarea of genomics (Tables 1, 2, 3, 4, 5)