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Filename: controllers/Detail.php
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The specificity of knowledge that Gene Ontology (GO) annotations currently can represent is still restricted by the legacy format of the GO annotation file, a format intentionally designed for simplicity to keep the barriers to entry low and thus encourage initial adoption. Historically, the information that could be captured in a GO annotation was simply the role or location of a gene product, although genetically interacting or binding partners could be specified. While there was no mechanism within the original GO annotation format for capturing additional information about the context of a GO term, such as the target gene of an activity or the location of a molecular function, the long-term vision for the GO Consortium was to provide greater expressivity in its annotations to capture physiologically relevant information.Thus, as a step forwards, the GO Consortium has introduced a new field into the annotation format, annotation extensions, which can be used to capture valuable contextual detail. This provides experimentally verified links between gene products and other physiological information that is crucial for accurate analysis of pathway and network data. This chapter will provide a simple overview of annotation extensions, illustrated with examples of their usage, and explain why they are useful for scientists and bioinformaticians alike.
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http://dx.doi.org/10.1007/978-1-4939-3743-1_17 | DOI Listing |
Sci Data
December 2024
Paris-Lodron-University of Salzburg, Department of Psychology, Centre for Cognitive Neuroscience, Salzburg, Austria.
Experimental design in language cognition research often involves presenting language material while measuring associated behavior and/or neural activity. To make the collected data easily and fully analyzable by both the original data authors and others, it is important to have detailed information about the stimulus presentation events, including the nature and properties of the presented stimuli, using a common vocabulary and syntax. We present HED LANG, a library extension of the Hierarchical Event Descriptors (HED) event annotation schema for time series behavioral and neuroimaging data.
View Article and Find Full Text PDFSci Data
December 2024
School of Marine Sciences, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, Guangdong, China.
Ochetobius elongatus, a critically endangered species found in the Yangtze River was the subject of our study in which we leveraged PacBio and Hi-C data to assemble a chromosome-scale genome. This assembly comprises 24 pseudo-chromosomes, yielding a genome size of 883.1 Mb with a scaffold N50 length of 35.
View Article and Find Full Text PDFCommun Med (Lond)
December 2024
Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark.
Background: Neonatal herpes simplex virus (HSV) infection is life-threatening, with a mortality of up to 70-80% when disseminated, often due to vague symptoms and delayed treatment. Neonatal screening using dried blood spot (DBS) samples is among the most impactful preventative health measures ever implemented, but screening for HSV has not been investigated.
Methods: We investigated high throughput multiplexed proteomics on DBS samples collected on days 2-3 of life from a nationwide cohort of neonates with HSV infection (n = 53) and matched controls.
Conf Comput Vis Pattern Recognit Workshops
June 2024
Osteoclast cell image analysis plays a key role in osteoporosis research, but it typically involves extensive manual image processing and hand annotations by a trained expert. In the last few years, a handful of machine learning approaches for osteoclast image analysis have been developed, but none have addressed the full instance segmentation task required to produce the same output as that of the human expert led process. Furthermore, none of the prior, fully automated algorithms have publicly available code, pretrained models, or annotated datasets, inhibiting reproduction and extension of their work.
View Article and Find Full Text PDFPLoS Comput Biol
December 2024
Department of Computer Science, University of Milano-Bicocca, Milan, Italy.
Pangenomes are becoming a powerful framework to perform many bioinformatics analyses taking into account the genetic variability of a population, thus reducing the bias introduced by a single reference genome. With the wider diffusion of pangenomes, integrating genetic variability with transcriptome diversity is becoming a natural extension that demands specific methods for its exploration. In this work, we extend the notion of spliced pangenomes to that of annotated spliced pangenomes; this allows us to introduce a formal definition of Alternative Splicing (AS) events on a graph structure.
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