Genome-wide variant calling in reanalysis of exome sequencing data uncovered a pathogenic TUBB3 variant.

Almost half of all individuals affected by intellectual disability (ID) remain undiagnosed. In the Solve-RD project, exome sequencing (ES) datasets from unresolved individuals with (syndromic) ID (n = 1,472 probands) are systematically reanalyzed, starting from raw sequencing files, followed by genome-wide variant calling and new data interpretation. This strategy led to the identification of a disease-causing de novo missense variant in TUBB3 in a girl with severe developmental delay, secondary microcephaly, brain imaging abnormalities, high hypermetropia, strabismus and short stature. Interestingly, the TUBB3 variant could only be identified through reanalysis of ES data using a genome-wi…

Additional file 1 of Q-nexus: a comprehensive and efficient analysis pipeline designed for ChIP-nexus

Supplementary figures and tables. The following additional data are available with the online version of this paper. Additional data file 1 contains an explanatory figure for duplication levels as well as figures and tables for additional analyses including duplication rate plots, examples for mapping artifacts, 5â end coverage around motif centered binding sites, cross-correlation plots, qfrag-length distributions, scatterplots of signal scores of overlapping peaks and corresponding IDR plots, as well as two tables containing the total numbers of overlapping peaks and overlapping peaks with IDR â ¤ 0.01 for all pairs of biological replicates. (PDF 3840 kb)

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

International audience; No abstract available

SNV-InDel working group: Results and lessons learned from the analysis of 22,035 exomes and genomes from 6 European reference networks

Absolute of Relative? A New Approach to Building Feature Vectors For Emotion Tracking In Music

It is believed that violation of or conformity to expectancy when listening to music is one of the main sources of musical emotion. To address this, we test a new way of building feature vectors and representing features within the vector for the machine learning approach to continuous emotion tracking systems. Instead of looking at the absolute values for specific features, we concentrate on the average value of that feature across the whole song and the difference between that and the absolute value for a particular sample. To test this “relative” representation, we used a corpus of popular music with continuous labels on the arousalvalence space. The model consists of a Support Vector Re…