Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
Published: November 2024
AI Article Synopsis
Article Abstract
Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering - removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (F), nucleotide diversity (π) and effective population size (N).
Growing evidence suggests inflammatory bowel disease (IBD) is linked to ischemic stroke (IS); however, the results are inconclusive. Therefore, it remains uncertain whether the association between IBD and IS is causal. Herein, we performed a bidirectional Mendelian randomization (MR) study to examine the causal association of IBD with IS.
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.
Background: is a transmitted respiratory pathogen that causes high morbidity and mortality in children, especially those under 5 years of age. During the implementation of population control measures for COVID-19 in mainland China, the detection rate in pediatric patients decreased. However, with the second wave of the COVID-19 pandemic (2022), the incidence of pneumococcal disease (PD) and even invasive pneumococcal disease (IPD) began to rise again.
Botswana, like the rest of the world, has been significantly impacted by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In December 2022, we detected a monophyletic cluster of genomes comprising a sublineage of the Omicron variant of concern (VOC) designated as B.1.
This paper develops a method for cancer classification from microRNA data using a convolutional neural network (CNN)-based model optimized by genetic algorithm. The convolutional neural network has performed well in various recognition and perception tasks. This paper contributes to the cancer classification using a union of two CNNs.
