BackgroundBiotin–thiamine‐responsive basal ganglia disease (BTBGD) is an autosomal recessive neurometabolic disorder mostly presented in children. The disorder is described as having subacute encephalopathy with confusion, dystonia, and dysarthria triggered by febrile illness that leads to neuroregression and death if untreated. Using biotin and thiamine at an early stage of the disease can lead to significant improvement.MethodsBTBGD is a treatable disease if diagnosed at an early age and has been frequently reported in Saudi population. Keeping this in mind, the current study screened 3000 Saudi newborns for the SLC19A3 gene mutations using target sequencing, aiming to determine the carrier frequency in Saudi Population and whether BTBGD is a good candidate to be included in the newborn‐screened disorders.ResultsUsing targeted gene sequencing, DNA from 3000 newborns Saudi was screened for the SLC19A3 gene mutations using standard methods. Screening of the SLC19A3 gene revealed a previously reported heterozygous missense mutation (c.1264A>G (p.Thr422Ala) in six unrelated newborns. No probands having homozygous pathogenic mutations were found in the studied cohort. The variant has been frequently reported previously in homozygous state in Saudi population, making it a hot spot mutation. The current study showed that the carrier frequency of SLC19A3 gene mutation is 1 of 500 in Saudi newborns.ConclusionFor the first time in the literature, we determined the carrier frequency of SLC19A3 gene mutation in Saudi population. The estimated prevalence is too rare in Saudi population (at least one in million); therefore, the data are not in favor of including such very rare disorders in newborn screening program at population level. However, a larger cohort is needed for a more accurate estimate.

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.