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Purpose: The presentation and etiology of cerebral palsy (CP) are heterogeneous. Diagnostic evaluation can be a prolonged and expensive process that might remain inconclusive. This study aimed to determine the diagnostic yield and impact on management of next-generation sequencing (NGS) in 50 individuals with atypical CP (ACP)., Methods: Patient eligibility criteria included impaired motor function with onset at birth or within the first year of life, and one or more of the following: severe intellectual disability, progressive neurological deterioration, other abnormalities on neurological examination, multiorgan disease, congenital anomalies outside of the central nervous system, an abnormal neurotransmitter profile, family history, brain imaging findings not typical for cerebral palsy. Previous assessment by a neurologist and/or clinical geneticist, including biochemical testing, neuroimaging, and chromosomal microarray, did not yield an etiologic diagnosis., Results: A precise molecular diagnosis was established in 65% of the 50 patients. We also identified candidate disease genes without a current OMIM disease designation. Targeted intervention was enabled in eight families (~15%)., Conclusion: NGS enabled a molecular diagnosis in ACP cases, ending the diagnostic odyssey, improving genetic counseling and personalized management, all in all enhancing precision medicine practices.

Many inborn errors of metabolism (IEMs) are amenable to treatment; therefore, early diagnosis and treatment is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, whole exome sequencing (WES) variant prioritization coupled with clinical and bioinformatics expertise is the primary method used to identify novel disease-causing variants; however, causation is often difficult to establish due to the number of plausible variants. Integrated analysis of untargeted metabolomics (UM) and WES or whole genome sequencing (WGS) data is a promising systematic approach for identifying disease-causing variants. In this review, we provide a literature-based overview of UM methods utilizing liquid chromatography mass spectrometry (LC-MS), and assess approaches to integrating WES/WGS and LC-MS UM data for the discovery and prioritization of variants causing IEMs. To embed this integrated -omics approach in the clinic, expansion of gene-metabolite annotations and metabolomic feature-to-metabolite mapping methods are needed.

Since Garrod's first description of alkaptonuria in 1902, and newborn screening for phenylketonuria introduced in the 1960s, P4 medicine (preventive, predictive, personalized, and participatory) has been a reality for the clinician serving patients with inherited metabolic diseases. The era of high-throughput technologies promises to accelerate its scale dramatically. Genomics, transcriptomics, epigenomics, proteomics, glycomics, metabolomics, and lipidomics offer an amazing opportunity for holistic investigation and contextual pathophysiologic understanding of inherited metabolic diseases for precise diagnosis and tailored treatment. While each of the -omics technologies is important to systems biology, some are more mature than others. Exome sequencing is emerging as a reimbursed test in clinics around the world, and untargeted metabolomics has the potential to serve as a single biochemical testing platform. The challenge lies in the integration and cautious interpretation of these big data, with translation into clinically meaningful information and/or action for our patients. A daunting but exciting task for the clinician; we provide clinical cases to illustrate the importance of his/her role as the connector between physicians, laboratory experts and researchers in the basic, computer, and clinical sciences. Open collaborations, data sharing, functional assays, and model organisms play a key role in the validation of -omics discoveries. Having all the right expertise at the table when discussing the diagnostic approach and individualized management plan according to the information yielded by -omics investigations (e.g., actionable mutations, novel therapeutic interventions), is the stepping stone of P4 medicine. Patient participation and the adjustment of the medical team's plan to his/her and the family's wishes most certainly is the capstone. Are you ready?

RepeatMasker is a popular software tool widely used in computational genomics to identify, classify, and mask repetitive elements, including low-complexity sequences and interspersed repeats. RepeatMasker searches for repetitive sequence by aligning the input genome sequence against a library of known repeats, such as Repbase. Here, we describe two Basic Protocols that provide detailed guidelines on how to use RepeatMasker, either via the Web interface or command-line Unix/Linux system, to analyze repetitive elements in genomic sequences. Sequence comparisons in RepeatMasker are usually performed by the alignment program cross_match, which requires significant processing time for larger sequences. An Alternate Protocol describes how to reduce the processing time using an alternative alignment program, such as WU-BLAST. Further, the advantages, limitations, and known bugs of the software are discussed. Finally, guidelines for understanding the results are provided.