Project description:Study purpose: to explore the entire spectrum of proteomic and genomic changes (amongst others) involved in diseases and in healthy/control populations. The Study is designed to discover biomarkers, develop and validate diagnostic assays, instruments and therapeutics as well as other medical research. Specifically, researchers may analyze proteins, RNA, DNA copy number changes, including large and small (1,000-100,000 kb) scale rearrangements, transcription profiles, epigenetic modifications, sequence variation, and sequence in both diseased tissue and case-matched germline DNA from Subjects.
Project description:We investigated the features of the genomic rearrangements in a cohort of 50 male individuals with proteolipid protein 1 (PLP1) copy number gain events who were ascertained with Pelizaeus-Merzbacher disease (PMD; MIM: 312080). Genomic rearrangements in PMD individuals with PLP1 copy number gain events were investigated by high-density customized array and breakpoint junction sequence analysis. Analysis of these data enabled the spectrum and relative distribution of the underlying genomic mutational signatures to be delineated. Genomic rearrangements in PMD individuals with PLP1 copy number gain events were investigated by high-density customized array and breakpoint junction sequence analysis.
Project description:Sequence-to-function neural networks learn cis-regulatory sequence rules driving many types of genomic data. Interpreting these models to relate the sequence rules to underlying biological processes remains challenging, especially for complex genomic readouts such as MNase-seq, which maps nucleosome occupancy but is confounded by experimental bias. Here, we introduce pairwise influence by sequence attribution (PISA), which uses attribution to combinatorially decode which bases contributed to the readout at a specific genomic coordinate. PISA visualizes the effects of transcription factor motifs, detects undiscovered motifs with complex contribution patterns, and reveals experimental biases. By learning the bias for MNase-seq, PISA enables unprecedented nucleosome prediction models. These models allow the de novo discovery of nucleosome-positioning motifs and reveal the basis of Micro-C chromatin domain boundaries through systematic motif perturbations. Finally, these models allow the design of sequences with altered nucleosome configurations. These results show that PISA is a versatile tool that expands our ability to train and interpret sequence-to-function neural networks on genomics data and understand the underlying cis-regulatory code.
Project description:Neisseria meningitidis is a major cause of bacterial meningitis and septicemia worldwide. Seven new serogroup C meningococci were isolated from two provinces of China in January, 2006. Their PorA VR types were P1.20, 9. Multilocus sequence typing results indicated that they all belonged to ST-7. It is a new serogroup C N. meningitidis sequence type clone identified in China. Here we also present the results of a genomic comparison of these isolates with other 15 N. meningitidis serogroup A and B isolates, which belonged to ST-7, based on comparative genomic hybridization analysis. The data described here would be helpful to monitor the spread of this new serogroup C meningococci sequence type clone in China and worldwide. Keywords: comparative genomic hybridization