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The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We provide pre-computed AI-Driver scores for all possible human missense variants (http://aidriver.maolab.org/) to identify driver mutations in the sea of somatic mutations discovered by personal cancer sequencing. We believe that AI-Driver together with pre-computed database will play vital important roles in the human cancer studies, such as identification of driver mutation in personal cancer genomes, discovery of targeting sites for cancer therapeutic treatments and prediction of tumor biomarkers for early diagnosis by liquid biopsy.

Genetic etiology of congenital/infantile nystagmus remains largely unknown. This study aimed to identify genomic mutations in patients with infantile nystagmus and an associated disease network. Patients with inherited and sporadic infantile nystagmus were recruited for whole-exome and Sanger sequencing. β-Mannosidase activities were measured. Gene expression, protein–protein interaction, and nystagmus-associated lysosomal storage disease (LSD) genes were analyzed. A novel heterozygous mutation (c.2013G>A; p.R638H) of MANBA, which encodes lysosomal β-mannosidase, was identified in patients with autosomal-dominant nystagmus. An additional mutation (c.2346T>A; p.L749H) in MANBA was found by screening patients with sporadic nystagmus. MANBA was expressed in the pretectal nucleus of the developing midbrain, known to be involved in oculomotor and optokinetic nystagmus. Functional validation of these mutations demonstrated a significant decrease of β-mannosidase activities in the patients as well as in mutant-transfected HEK293T cells. Further analysis revealed that nystagmus is present in at least 24 different LSDs involving the brain. This is the first identification of MANBA mutations in patients with autosomal-dominant nystagmus, suggesting a new clinical entity. Because β-mannosidase activities are required for development of the oculomotor nervous system, our findings shed new light on the role of LSD-associated genes in the pathogenesis of infantile nystagmus. Genet Med 17 12, 971–979.

Reduced representation bisulfite sequencing (RRBS) is a powerful method of DNA methylome profiling that can be applied to single cells. However, no previous report has described how PCR-based duplication-induced artifacts affect the accuracy of this method when measuring DNA methylation levels. For quantifying the effects of duplication-induced artifacts on methylome profiling when using ultra-trace amounts of starting material, we developed a novel method, namely quantitative RRBS (Q-RRBS), in which PCR-induced duplication is excluded through the use of unique molecular identifiers (UMIs). By performing Q-RRBS on varying amounts of starting material, we determined that duplication-induced artifacts were more severe when small quantities of the starting material were used. However, through using the UMIs, we successfully eliminated these artifacts. In addition, Q-RRBS could accurately detect allele-specific methylation in absence of allele-specific genetic variants. Our results demonstrate that Q-RRBS is an optimal strategy for DNA methylation profiling of single cells or samples containing ultra-trace amounts of cells.

Introduction: We describe a 10-year-old Chinese boy with features of Charcot–Marie–Tooth disease (CMT) and Duchenne muscular dystrophy (DMD). Methods: Case report. Results: Weakness and mild sensory loss in the distal extremities, pes cavus, and nerve conduction findings suggested demyelinating neuropathy, while moderate calf pseudohypertrophy, proximal muscle weakness, a myopathic pattern on electromyography, and deficiency of dystrophin immunohistochemical staining on muscle biopsy indicated DMD. Genetic testing revealed a large deletion spanning exon 50 in the gene coding for dystrophin and duplications in the gene coding for peripheral myelin protein 22. Conclusions: This is an interesting and very rare case of CMT type 1A comorbid with DMD. This results in an unusual phenotype and rapid deterioration of motor function. Usage of both target region capture and next generation sequencing is a powerful tool for predicting precisely the range of the large DNA fragment deletion in DMD. Muscle Nerve 48: 979–983, 2013

High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution. However, there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data. The challenges arise, at least in part, from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data. Here, we present an algorithm, termed Bycom, a new Bayesian model that can perform methylcytosine calling with high accuracy. Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy. Bycom performance was compared with the performance of Lister, the method most widely used to identify methylcytosines from bisulfite sequencing data. The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels. Bycom also showed higher sensitivity and specificity for low methylation level samples (, Author Summary High-throughput bisulfite sequencing (BS-seq) has advanced tremendously the study of DNA methylation and the determination of methylcytosines at single-base resolution. In BS-seq data analysis, sequencing errors, incomplete bisulfite conversion, and cell heterozygosis affect the accuracy of methylcytosine detection in quite a major way. Simple filtering methods using predefined thresholds have proved to have extremely low efficiency. The commonly used Lister uses binomial distribution to overcome the impacts of non-conversion rate and sequencing errors, but the impact of the cell heterozygosis is not considered. Here, we present Bycom, a novel algorithm based on the Bayesian inference model. To improve the accuracy of methylcytosine calling, Bycom considers sequencing errors, non-conversion rate, and cell heterozygosis integratively to identify methylcytosines from BS-seq data. We evaluated the performance of Bycom using different kinds of BS-seq data. Our results demonstrated that Bycom identified methylcytosines more accurately than Lister, especially in BS-seq data with extremely low genome-wide methylation levels.