Repeat expansions are responsible for over 40 monogenic disorders, and undoubtedly more pathogenic repeat expansions remain to be discovered. Existing methods for detecting repeat expansions in short-read sequencing data require predefined repeat catalogs. Recent discoveries emphasize the need for methods that do not require pre-specified candidate repeats. To address this need, we introduce ExpansionHunter Denovo, an efficient catalog-free method for genome-wide repeat expansion detection. Analysis of real and simulated data shows that our method can identify large expansions of 41 out of 44 pathogenic repeats, including nine recently reported non-reference repeat expansions not discoverable via existing methods.

Copy number variations (CNVs) are implicated across many neurodevelopmental disorders (NDDs) and contribute to their shared genetic etiology. Multiple studies have attempted to identify shared etiology among NDDs, but this is the first genome-wide CNV analysis across autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), and obsessive-compulsive disorder (OCD) at once. Using microarray (Affymetrix CytoScan HD), we genotyped 2,691 subjects diagnosed with an NDD (204 SCZ, 1,838 ASD, 427 ADHD and 222 OCD) and 1,769 family members, mainly parents. We identified rare CNVs, defined as those found in <0.1% of 10,851 population control samples. We found clinically relevant CNVs (broadly defined) in 284 (10.5%) of total subjects, including 22 (10.8%) among subjects with SCZ, 209 (11.4%) with ASD, 40 (9.4%) with ADHD, and 13 (5.6%) with OCD. Among all NDD subjects, we identified 17 (0.63%) with aneuploidies and 115 (4.3%) with known genomic disorder variants. We searched further for genes impacted by different CNVs in multiple disorders. Examples of NDD-associated genes linked across more than one disorder (listed in order of occurrence frequency) are NRXN1, SEH1L, LDLRAD4, GNAL, GNG13, MKRN1, DCTN2, KNDC1, PCMTD2, KIF5A, SYNM, and long non-coding RNAs: AK127244 and PTCHD1-AS. We demonstrated that CNVs impacting the same genes could potentially contribute to the etiology of multiple NDDs. The CNVs identified will serve as a useful resource for both research and diagnostic laboratories for prioritization of variants.

Motivation: Kinase-mediated phosphorylation is the central mechanism of post-translational modification to regulate cellular responses and phenotypes. Signaling defects associated with protein phosphorylation are linked to many diseases, particularly cancer. Characterizing protein kinases and their substrates enhances our ability to understand and treat such diseases and broadens our knowledge of signaling networks in general. While most or all protein kinases have been identified in well-studied eukaryotes, the sites that they phosphorylate have been only partially elucidated. Experimental methods for identifying phosphorylation sites are resource intensive, so the ability to computationally predict potential sites has considerable value. Results: Many computational techniques for phosphorylation site prediction have been proposed, most of which are available on the web. These techniques differ in several ways, including the machine learning technique used; the amount of sequence information used; whether or not structural information is used in addition to sequence information; whether predictions are made for specific kinases or for kinases in general; and sources of training and testing data. This review summarizes, categorizes and compares the available methods for phosphorylation site prediction, and provides an overview of the challenges that are faced when designing predictors and how they have been addressed. It should therefore be useful both for those wishing to choose a phosphorylation site predictor for their particular biological application, and for those attempting to improve upon established techniques in the future. Contact:brett.trost@usask.ca