Laboratory of Organic Mass Spectrometry, Department of Chemical Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy.
Published: December 2022
AI Article Synopsis
Article Abstract
Mass-spectrometry-based wheat proteomics is challenging because the current interpretation of mass spectrometry data relies on public databases that are not exhaustive (UniProtKB/Swiss-Prot) or contain many redundant and poor or un-annotated entries (UniProtKB/TrEMBL). Here, we report the development of a manually curated database of the metabolic proteins of (hexaploid wheat), named TriMet_DB (Triticum aestivum Metabolic Proteins DataBase). The manually curated TriMet_DB was generated in FASTA format so that it can be read directly by programs used to interpret the mass spectrometry data. Furthermore, the complete list of entries included in the TriMet_DB is reported in a freely available resource, which includes for each protein the description, the gene code, the protein family, and the allergen name (if any). To evaluate its performance, the TriMet_DB was used to interpret the MS data acquired on the metabolic protein fraction extracted from the cultivar MEC of . Data are available via ProteomeXchange with identifier PXD037709.
Background And Purpose: Segmentation imperfections (noise) in radiotherapy organ-at-risk segmentation naturally arise from specialist experience and image quality. Using clinical contours can result in sub-optimal convolutional neural network (CNN) training and performance, but manual curation is costly. We address the impact of simulated and clinical segmentation noise on CNN parotid gland (PG) segmentation performance and provide proof-of-concept for an easily implemented auto-curation countermeasure.
Accurate variant classification is critical for genetic diagnosis. Variants without clear classification, known as "variants of uncertain significance" (VUS), pose a significant diagnostic challenge. This study examines AlphaMissense performance in variant classification, specifically for VUS.
Motivation: Thousands of genomes are publicly available, however, most genes in those genomes have poorly defined functions. This is partly due to a gap between previously published, experimentally-characterized protein activities and activities deposited in databases. This activity deposition is bottlenecked by the time-consuming biocuration process.
Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA. Electronic address:
Background: Adenocarcinoma of the esophagus and stomach demands a deeper molecular understanding to advance treatment strategies and improve patient outcomes. Here, we profiled the genome and transcriptome landscape of these cancers, explored molecular characteristics that are undetectable by other sequencing platforms, and analyzed their potential clinical ramifications.
Methods: Our study employed state-of-the-art integrative analyses of whole genome and transcriptome sequencing on 51 matched tumor and germline samples from 46 patients.
Cancer remains a global health challenge, with significant morbidity and mortality rates. In 2020, cancer caused nearly 10 million deaths, making it the second leading cause of death worldwide. The emergence of chemoresistance has become a major hurdle in successfully treating cancer patients.
