Dataset Information

Predicted Molecular Effects of Sequence Variants Link to System Level of Disease.

ABSTRACT: Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease.

SUBMITTER: Reeb J

PROVIDER: S-EPMC4990455 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicted Molecular Effects of Sequence Variants Link to System Level of Disease.

Reeb Jonas J Hecht Maximilian M Mahlich Yannick Y Bromberg Yana Y Rost Burkhard B

PLoS computational biology 20160818 8

Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid ...[more]

PMID: 27536940

Similar Datasets

Project description:Underreporting of infectious diseases is a pervasive challenge in public health that has emerged as a central issue in characterizing the dynamics of the COVID-19 pandemic. Infectious diseases are underreported for a range of reasons, including mild or asymptomatic infections, weak public health infrastructure, and government censorship. In this study, we investigated factors associated with cross-country and cross-pathogen variation in reporting. We performed a literature search to collect estimates of empirical reporting rates, calculated as the number of cases reported divided by the estimated number of true cases. This literature search yielded a dataset of reporting rates for 32 pathogens, representing 52 countries. We combined epidemiological and social science theory to identify factors specific to pathogens, country health systems, and politics that could influence empirical reporting rates. We performed generalized linear regression to test the relationship between the pathogen- and country-specific factors that we hypothesized could influence reporting rates, and the reporting rate estimates that we collected in our literature search. Pathogen- and country-specific factors were predictive of reporting rates. Deadlier pathogens and sexually transmitted diseases were more likely to be reported. Country epidemic preparedness was positively associated with reporting completeness, while countries with high levels of media bias in favor of incumbent governments were less likely to report infectious disease cases. Underreporting is a complex phenomenon that is driven by factors specific to pathogens, country health systems, and politics. In this study, we identified specific and measurable components of these broader factors that influence pathogen- and country-specific reporting rates and used model selection techniques to build a model that can guide efforts to diagnose, characterize, and reduce underreporting. Furthermore, this model can characterize uncertainty and correct for bias in reported infectious disease statistics, particularly when outbreak-specific empirical estimates of underreporting are unavailable. More precise estimates can inform control policies and improve the accuracy of infectious disease models.

Dataset Information

Predicted Molecular Effects of Sequence Variants Link to System Level of Disease.

Publications

Predicted Molecular Effects of Sequence Variants Link to System Level of Disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets