Dataset Information

Prioritization and functional assessment of noncoding variants associated with complex diseases.

ABSTRACT: Unraveling functional noncoding variants associated with complex diseases is still a great challenge. We present a novel algorithm, Prioritization And Functional Assessment (PAFA), that prioritizes and assesses the functionality of genetic variants by introducing population differentiation measures and recalibrating training variants. Comprehensive evaluations demonstrate that PAFA exhibits much higher sensitivity and specificity in prioritizing noncoding risk variants than existing methods. PAFA achieves improved performance in distinguishing both common and rare recurrent variants from non-recurrent variants by integrating multiple annotations and metrics. An integrated platform was developed, providing comprehensive functional annotations for noncoding variants by integrating functional genomic data, which can be accessed at http://159.226.67.237:8080/pafa .

SUBMITTER: Zhou L

PROVIDER: S-EPMC6042373 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prioritization and functional assessment of noncoding variants associated with complex diseases.

Zhou Lin L Zhao Fangqing F

Genome medicine 20180711 1

Unraveling functional noncoding variants associated with complex diseases is still a great challenge. We present a novel algorithm, Prioritization And Functional Assessment (PAFA), that prioritizes and assesses the functionality of genetic variants by introducing population differentiation measures and recalibrating training variants. Comprehensive evaluations demonstrate that PAFA exhibits much higher sensitivity and specificity in prioritizing noncoding risk variants than existing methods. PAF ...[more]

PMID: 29996888

Similar Datasets

Project description:With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings.We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence.Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

Project description:BACKGROUND:Despite the known burden of neglected tropical diseases (NTDs) on child health, there is limited information on current efforts to increase pediatric therapeutic options. Our objective was to quantify and characterize research activity and treatment availability for NTDs in children in order to inform the prioritization of future research efforts. METHODOLOGY/PRINCIPAL FINDINGS:We conducted a review of the World Health Organization's (WHO) International Clinical Trials Registry Platform to assess research activity for NTDs. The burden of disease of each NTD was measured in terms of disability adjusted life years (DALYs), which was extracted from the Global Health Data Exchange. First- and second-line medications for each NTD were identified from WHO guidelines. We reviewed FDA drug labels for each medication to determine whether they were adequately labeled for use in children. Descriptive statistics, binomial tests, and Spearman's rank order correlations were calculated to assess research activity compared to burden of disease. Children comprised 34% of the 20 million DALYs resulting from NTDs, but pediatric trials contributed just 17% (63/369) of trials studying these conditions (p<0.001 for binomial test). Conditions that were particularly under-represented in pediatric populations compared to adults included rabies, leishmaniasis, scabies, and dengue. Pediatric drug trial activity was poorly correlated with pediatric burden of disease across NTDs (Spearman's rho = 0.41, p = 0.12). There were 47 medications recommended by the WHO for the treatment of NTDs, of which only 47% (n = 22) were adequately labeled for use in children. Of the 25 medications lacking adequate pediatric labeling, three were under study in pediatric trials. CONCLUSIONS/SIGNIFICANCE:There is a substantial gap between the burden of disease for NTDs in children and research devoted to this population. Most medications lack adequate pediatric prescribing information, highlighting the urgency to increase pediatric research activity for NTDs with high burden of disease and limited treatment options.

Dataset Information

Prioritization and functional assessment of noncoding variants associated with complex diseases.

Publications

Prioritization and functional assessment of noncoding variants associated with complex diseases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets