The ENCODE project is an international consortium with a goal of cataloguing all the functional elements in the human genome. The ENCODE Data Coordination Center (DCC) at the University of California, Santa Cruz serves as the central repository for ENCODE data. In this role, the DCC offers a collection of high-throughput, genome-wide data generated with technologies such as ChIP-Seq, RNA-Seq, DNA digestion and others. This data helps illuminate transcription factor-binding sites, histone marks, chromatin accessibility, DNA methylation, RNA expression, RNA binding and other cell-state indicators. It includes sequences with quality scores, alignments, signals calculated from the alignments, and in most cases, element or peak calls calculated from the signal data. Each data set is available for visualization and download via the UCSC Genome Browser (http://genome.ucsc.edu/). ENCODE data can also be retrieved using a metadata system that captures the experimental parameters of each assay. The ENCODE web portal at UCSC (http://encodeproject.org/) provides information about the ENCODE data and links for access.
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http://dx.doi.org/10.1093/nar/gkq1017 | DOI Listing |
JCO Glob Oncol
January 2025
University of Oxford, Oxford, United Kingdom.
Purpose: Epstein-Barr virus (EBV)-positive Burkitt lymphoma (BL) affects children in sub-Saharan Africa, but diagnosis via tissue biopsy is challenging. We explored a liquid biopsy approach using targeted next-generation sequencing to detect the -immunoglobulin (-Ig) translocation and EBV DNA, assessing its potential for minimally invasive BL diagnosis.
Materials And Methods: The panel included targets for the characteristic -Ig translocation, mutations in intron 1 of , mutations in exon 2 of , and three EBV genes: EBV-encoded RNA (EBER)1, EBER2, and EBV nuclear antigen 2.
Cell Rep
January 2025
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA; Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address:
Understanding how corticostriatal circuits mediate behavioral selection and initiation in a naturalistic setting is critical to understanding behavior choice and execution in unconstrained situations. The central striatum (CS) is well poised to play an important role in these spontaneous processes. Using fiber photometry and optogenetics, we identify a role for CS in grooming initiation.
View Article and Find Full Text PDFJ Physiol
January 2025
Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Motor cortical high-gamma oscillations (60-90 Hz) occur at movement onset and are spatially focused over the contralateral primary motor cortex. Although high-gamma oscillations are widely recognized for their significance in human motor control, their precise function on a cortical level remains elusive. Importantly, their relevance in human stroke pathophysiology is unknown.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Division of Physics & Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore.
With remarkable stability and exceptional optoelectronic properties, two-dimensional (2D) halide layered perovskites hold immense promise for revolutionizing photovoltaic technology. Effective data representations are key to the success of all learning models. Currently, the lack of comprehensive and accurate material representations has hindered AI-based design and discovery of 2D perovskites, limiting their potential for advanced photovoltaic applications.
View Article and Find Full Text PDFInt J Cardiovasc Imaging
January 2025
Shanxi Cardiovascular Hospital, 18 Yifen Street, Taiyuan, 030024, Shanxi, China.
Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessment and better patient outcomes. The evolution of artificial intelligence (AI) presents new avenues for predicting heart failure mortality.
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