Biology subjects

Edwards, E.

Publications and source records attributed to Edwards, E..

3 recordsLinked to original sources

bioRxiv2017

CardioClassifier: demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation

PurposeInternationally-adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (www.cardioclassifier.org), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).\n\nMethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.\n\nResultsWe benchmarked CardioClassifier on 57 expertly-curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically-actionable variants (64/219 vs 156/219, Fishers P=1.1x10-18), with important false positives; illustrating the critical importance of disease and gene-specific annotations.\n\nCardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually-curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.\n\nConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible and interactive variant pathogenicity reports, according to best practice guidelines.

genomics↗

bioRxiv2017

Persistent homology demarcates a leaf morphospace

Current morphometric methods that comprehensively measure shape cannot compare the disparate leaf shapes found in seed plants and are sensitive to processing artifacts. We explore the use of persistent homology, a topological method applied across the scales of a function, to overcome these limitations. The described method isolates subsets of shape features and measures the spatial relationship of neighboring pixel densities in a shape. We apply the method to the analysis of 182,707 leaves, both published and unpublished, representing 141 plant families collected from 75 sites throughout the world. By measuring leaves from throughout the seed plants using persistent homology, a defined morphospace comparing all leaves is demarcated. Clear differences in shape between major phylogenetic groups are detected and estimates of leaf shape diversity within plant families are made. This approach does not only predict plant family, but also the collection site, confirming phylogenetically invariant morphological features that characterize leaves from specific locations. The application of a persistent homology method to measure leaf shape allows for a unified morphometric framework to measure plant form, including shape and branching architectures.

plant biology↗

bioRxiv2016

Parallel streams define the temporal dynamics of speech processing across human auditory cortex

To derive meaning from speech, we must extract multiple dimensions of concurrent information from incoming speech signals, including phonetic and prosodic cues. Equally important is the detection of acoustic cues that give structure and context to the information we hear, such as sentence boundaries. How the brain organizes this information processing is unknown. Here, using data-driven computational methods on an extensive set of high-density intracranial recordings, we reveal a large-scale partitioning of the entire human speech cortex into two spatially distinct regions that detect important cues for parsing natural speech. These caudal (Zone 1) and rostral (Zone 2) regions work in parallel to detect onsets and prosodic information, respectively, within naturally spoken sentences. In contrast, local processing within each region supports phonetic feature encoding. These findings demonstrate a fundamental organizational property of the human auditory cortex that has been previously unrecognized.

neuroscience↗