Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK.
Published: November 2019
AI Article Synopsis
Article Abstract
Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder's high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder's comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.
Low-frequency non-syndromic hearing loss (LFNSHL) is a rare auditory disorder affecting frequencies ≤ 2000 Hz. To elucidate its genetic basis, we conducted whole-exome sequencing on nine Chinese families (31 affected individuals) with LFNSHL. Four heterozygous pathogenic variants, including two novel variants, were identified in common LFNSHL-related genes (WFS1, DIAPH1) and less common genes (TNC, EYA4), achieving a 44% genetic diagnosis rate.
Decades of basic and translational research have led to a momentum shift in dissecting the relationship between immune cells and cancer. This culminated in the emergence of breakthrough immunotherapies that paved the way for oncologists to manage certain hard-to-treat cancers. The application of high-throughput techniques of genomics, transcriptomics, and proteomics was conclusive in making and expediting the manufacturing process of cancer vaccines.
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Breast cancer is one of the most common causes of death in women in the modern world. Cancerous tissue detection in histopathological images relies on complex features related to tissue structure and staining properties. Convolutional neural network (CNN) models like ResNet50, Inception-V1, and VGG-16, while useful in many applications, cannot capture the patterns of cell layers and staining properties.
