Biomedical subjects

Michael N Edmonson

Publications and source records attributed to Michael N Edmonson.

4 recordsLinked to original sources

PubMed2019

Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): a cloud-based platform for curating and classifying germline variants.

Variant interpretation in the era of massively parallel sequencing is challenging. Although many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here, we present the Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE), a web- and cloud-based platform for annotation, identification, and classification of variations in known or putative disease genes. Starting from a set of variants in variant call format (VCF), variants are annotated, ranked by putative pathogenicity, and presented for formal classification using a decision-support interface based on published guidelines from the American College of Medical Genetics and Genomics (ACMG). The system can accept files containing millions of variants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nucleotide variants (MNVs), and complex substitutions. PeCanPIE has been applied to classify variant pathogenicity in cancer predisposition genes in two large-scale investigations involving >4000 pediatric cancer patients and serves as a repository for the expert-reviewed results. PeCanPIE was originally developed for pediatric cancer but can be easily extended for use for nonpediatric cancers and noncancer genetic diseases. Although PeCanPIE's web-based interface was designed to be accessible to non-bioinformaticians, its back-end pipelines may also be run independently on the cloud, facilitating direct integration and broader adoption. PeCanPIE is publicly available and free for research use.

Child↗

PubMed2004

Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms.

MOTIVATION: Single nucleotide polymorphisms (SNPs) are the most common form of genetic variant in humans. SNPs causing amino acid substitutions are of particular interest as candidates for loci affecting susceptibility to complex diseases, such as diabetes and hypertension. To efficiently screen SNPs for disease association, it is important to distinguish neutral variants from deleterious ones. RESULTS: We describe the use of Pfam protein motif models and the HMMER program to predict whether amino acid changes in conserved domains are likely to affect protein function. We find that the magnitude of the change in the HMMER E-value caused by an amino acid substitution is a good predictor of whether it is deleterious. We provide internet-accessible display tools for a genomewide collection of SNPs, including 7391 distinct non-synonymous coding region SNPs in 2683 genes. AVAILABILITY: http://lpgws.nci.nih.gov/cgi-bin/GeneViewer.cgi

Amino Acid Motifs↗

PubMed2004

Bioinformatics tools for single nucleotide polymorphism discovery and analysis.

Single nucleotide polymorphisms (SNPs) are a valuable resource for investigating the genetic basis of disease. These variants can serve as markers for fine-scale genetic mapping experiments and genome-wide association studies. Certain of these nucleotide polymorphisms may predispose individuals to illnesses such as diabetes, hypertension, or cancer, or affect disease progression. Bioinformatics techniques can play an important role in SNP discovery and analysis. We use computational methods to identify SNPs and to predict whether they are likely to be neutral or deleterious. We also use informatics to annotate genes that contain SNPs. To make this information available to the research community, we provide a variety of Internet-accessible tools for data access and display. These tools allow researchers to retrieve data about SNPs based on gene of interest, genetic or physical map location, or expression pattern.

Chromosome Mapping↗

PubMed2002

A phylogenetic analysis identifies heterogeneity among hepatocellular carcinomas.

Primary hepatocellular carcinoma (HCC) is a significant cause of cancer morbidity and mortality on the global scale. Although epidemiologic studies have identified major risk factors for HCC, the sequence of oncogenic events at the molecular level remains poorly understood. While genetic allele loss appears to be a common event, the significance of the loss is not clear. In order to determine whether allele loss appears to be a random event among HCCs or whether patterns of loss cluster in groups of tumors, a phylogenetic approach was used to examine 32 tumors for genome-wide loss of heterozygosity employing 391 markers. Clusters identified by the phylogenetic analysis were then contrasted to compare candidate locus variation among individuals and to determine whether certain clusters exhibited higher loss rates than other clusters. The analysis found that 3 major and 1 minor cluster of loss could be identified and, further, these clusters were distinguished by variable rates of loss (cluster 1, 29%; cluster 2, 21%; cluster 3, 16%). The analyses also indicated that the allele loss rates in HCC were not insignificant and that the patterns of allele loss were complex. In addition, the results indicated that an individual's constitutional genotype at the EPHX1 locus may be a critical factor in determining the path of tumor evolution. In conclusion, it appears that in HCC, allele loss is not random, but clusters into definable groups that are characterized by distinctive rates of loss.

Adult↗