Three different data sets with clinical data and markers from genome-wide screens were submitted for analysis at Genetic Analysis Workshop 12. In each study, participants were carefully characterized for asthma and related phenotypes. Testing for bronchial hyper-responsiveness using methacholine and standardized protocols was performed. Total serum IgE levels were measured using standardized techniques. In addition, similar questionnaire data on symptoms and relevant environmental exposures were obtained. Relevant clinical data and genotypes for the polymorphic markers used for each genome-wide screen were submitted. The data set from the United States Collaborative Study on the Genetics of Asthma represents a heterogeneous population consisting of both Caucasian and African American families ascertained through two siblings with clinical asthma from multiple centers. Likewise, the families from the German Asthma Genetics Group were also ascertained through two siblings with asthma at multiple centers. In a contrast to these data sets, Dr. Carole Ober and her collaborators submitted data from the inbred Hutterite population in South Dakota.
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http://dx.doi.org/10.1002/gepi.2001.21.s1.s4 | DOI Listing |
Brief Bioinform
November 2024
Department of Biology, University of Padova, Via U.Bassi 58/ B, 35131, Italy.
Shallow whole-genome sequencing (sWGS) offers a cost-effective approach to detect copy number alterations (CNAs). However, there remains a gap for a standardized workflow specifically designed for sWGS analysis. To address this need, in this work we present SAMURAI, a bioinformatics pipeline specifically designed for analyzing CNAs from sWGS data in a standardized and reproducible manner.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Psychiatry, University of California San Diego, La Jolla, CA, United States of America.
Background: Bipolar Disorder (BD) is a complex disease. It is heterogeneous, both at the phenotypic and genetic level, although the extent and impact of this heterogeneity is not fully understood. One way to assess this heterogeneity is to look for patterns in the subphenotype data.
View Article and Find Full Text PDFmSystems
January 2025
Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland.
Average nucleotide identity (ANI) is a widely used metric to estimate genetic relatedness, especially in microbial species delineation. While ANI calculation has been well optimized for bacteria and closely related viral genomes, accurate estimation of ANI below 80%, particularly in large reference data sets, has been challenging due to a lack of accurate and scalable methods. To bridge this gap, we introduce MANIAC, an efficient computational pipeline optimized for estimating ANI and alignment fraction (AF) in viral genomes with divergence around ANI of 70%.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas 66506, United States.
Cell-penetrating peptides (CPPs) are short peptides capable of penetrating cell membranes, making them valuable for drug delivery and intracellular targeting. Accurate prediction of CPPs can streamline experimental validation in the lab. This study aims to assess pretrained protein language models (pLMs) for their effectiveness in representing CPPs and develop a reliable model for CPP classification.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China.
Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing methods often encounter several common issues: first, the data representations lack sufficient information; second, the extracted features are not comprehensive; and third, most methods lack interpretability when modeling drug-target binding.
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