A variant by any name: quantifying annotation discordance across tools and clinical databases

ABSTRACTBackgroundClinical genomic testing is dependent on the robust identification and reporting of variant-level information in relation to disease. With the shift to high-throughput sequencing, a major challenge for clinical diagnostics is the cross-identification of variants called on their genomic position to resources that rely on transcript- or protein-based descriptions.MethodsWe evaluated the accuracy of three tools (SnpEff, Variant Effect Predictor and Variation Reporter) that generate transcript and protein-based variant nomenclature from genomic coordinates according to guidelines by the Human Genome Variation Society (HGVS). Our evaluation was based on comparisons to a manually curated list of 127 test variants of various types drawn from data sources, each with HGVS-compliant transcript and protein descriptors. We further evaluated the concordance between annotations generated by Snpeff and Variant Effect Predictor with those in major germline and cancer databases: ClinVar and COSMIC, respectively.ResultsWe find that there is substantial discordance between the annotation tools and databases in the description of insertion and/or deletions. Accuracy based on our ground truth set was between 80-90% for coding and 50-70% for protein variants, numbers that are not adequate for clinical reporting. Exact concordance for SNV syntax was over 99.5% between ClinVar and Variant Effect Predictor (VEP) and SnpEff, but less than 90% for non-SNV variants. For COSMIC, exact concordance for coding and protein SNVs were between 65 and 88%, and less than 15% for insertions. Across the tools and datasets, there was a wide range of equivalent expressions describing protein variants.ConclusionOur results reveal significant inconsistency in variant representation across tools and databases. These results highlight the urgent need for the adoption and adherence to uniform standards in variant annotation, with consistent reporting on the genomic reference, to enable accurate and efficient data-driven clinical care.

Download Full-text

Interventions to change clinician behaviour in relation to suicide prevention care in the emergency department: A scoping review protocol

10.31219/osf.io/f3gdr ◽

2020 ◽

Author(s):

Hwayeon Danielle Shin ◽

Christine Cassidy ◽

Janet Curran ◽

Lori Weeks ◽

Leslie Anne Campbell ◽

...

Keyword(s):

Emergency Department ◽

Suicide Prevention ◽

Behaviour Change ◽

Clinical Care ◽

Grey Literature ◽

Allied Health Professionals ◽

Wide Range ◽

Knowledge And Attitude ◽

Clinician Behaviour ◽

Google Search

Objective: This review aims to explore, characterize, and map the literature on interventions implemented to change emergency department (ED) clinicians’ behaviour related to suicide prevention using the Behaviour Change Wheel (BCW) as a guiding theoretical framework. Introduction: An ED is a critical place for suicide prevention. Yet, many patients who present with suicide-related thoughts and behaviours are discharged without proper assessment or appropriate treatment. Supporting clinicians (who provide direct clinical care, including nurses, physicians, allied health professionals) to make the desired behaviour change following evidence-based suicide prevention care is an essential step toward improving patient outcomes. However, reviews to date have yet to take a theoretical approach to investigate interventions implemented to change clinicians’ behaviour. Inclusion criteria: This review will consider literature that includes interventions that target ED clinicians’ behaviour change related to suicide prevention. Behaviour change refers to observable practice changes as well as proxy measures of behaviour change including knowledge and attitude. There are many ways in which an intervention can change clinicians’ behaviour (e.g., education, altering service delivery). This review will include a wide range of interventions that target behaviour change regardless of the type but exclude interventions that exclusively target patients.Methods: Multiple databases will be searched: PubMed, PsycInfo, CINAHL and Embase. We will also include grey literature, including Google search, ProQuest Dissertations and Theses Global, and Scopus conference papers. Full text of included studies will be reviewed, critically appraised and extracted. Extracted data will be coded to identify intervention functions using the BCW. Findings will be summarized in tables accompanied by narrative reports.

Download Full-text

INSPECTOR: free software for magnetic resonance spectroscopy data inspection, processing, simulation and analysis

Scientific Reports ◽

10.1038/s41598-021-81193-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Martin Gajdošík ◽

Karl Landheer ◽

Kelley M. Swanberg ◽

Christoph Juchem

Keyword(s):

Magnetic Resonance ◽

Quality Management ◽

Magnetic Resonance Spectroscopy ◽

Data Processing ◽

Biomedical Research ◽

Clinical Diagnostics ◽

Resonance Spectroscopy ◽

User Friendliness ◽

Wide Range

AbstractIn vivo magnetic resonance spectroscopy (MRS) is a powerful tool for biomedical research and clinical diagnostics, allowing for non-invasive measurement and analysis of small molecules from living tissues. However, currently available MRS processing and analytical software tools are limited in their potential for in-depth quality management, access to details of the processing stream, and user friendliness. Moreover, available MRS software focuses on selected aspects of MRS such as simulation, signal processing or analysis, necessitating the use of multiple packages and interfacing among them for biomedical applications. The freeware INSPECTOR comprises enhanced MRS data processing, simulation and analytical capabilities in a one-stop-shop solution for a wide range of biomedical research and diagnostic applications. Extensive data handling, quality management and visualization options are built in, enabling the assessment of every step of the processing chain with maximum transparency. The parameters of the processing can be flexibly chosen and tailored for the specific research problem, and extended confidence information is provided with the analysis. The INSPECTOR software stands out in its user-friendly workflow and potential for automation. In addition to convenience, the functionalities of INSPECTOR ensure rigorous and consistent data processing throughout multi-experiment and multi-center studies.

Download Full-text

322. Evaluation of the BioFire® Bone and Joint Infection (BJI) Panel for the Detection of Microorganisms and Antimicrobial Resistance Genes in Synovial Fluid Specimens

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.518 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S233-S234

Author(s):

Corrin Graue ◽

Bryan H Schmitt ◽

Amy Waggoner ◽

Frederic Laurent ◽

Lelia Abad ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Synovial Fluid ◽

Resistance Genes ◽

Clinical Care ◽

Joint Infection ◽

High Morbidity ◽

Bone And Joint Infection ◽

Microbiological Methods ◽

Antimicrobial Resistance Genes ◽

Wide Range

Abstract Background Bone and Joint Infections (BJIs) present with non-specific symptoms that may include pain, swelling, and fever and are associated with high morbidity and significant risk of mortality. BJIs can be caused by a variety of bacteria and fungi, including anaerobes and microorganisms that can be challenging to culture or identify by traditional microbiological methods. Clinicians primarily rely on culture to identify the pathogen(s) responsible for infection. The BioFire® Bone and Joint Infection (BJI) Panel (BioFire Diagnostics, Salt Lake City, UT) is designed to detect 15 gram-positive bacteria (including seven anaerobes), 14 gram-negative bacteria (including one anaerobe), two yeast, and eight antimicrobial resistance (AMR) genes from synovial fluid specimens in about an hour. The objective of this study was to evaluate the performance of an Investigational Use Only (IUO) version of the BioFire BJI Panel compared to various reference methods. Methods Remnant synovial fluid specimens, which were collected for routine clinical care at 13 study sites in the US and Europe, underwent testing using an IUO version of the BioFire BJI Panel. Performance of this test was determined by comparison to Standard of Care (SoC) consisting of bacterial culture performed at each study site according to their routine procedures. Results A total of 1544 synovial fluid specimens were collected and tested with the BioFire BJI Panel. The majority of specimens were from knee joints (77.9%) and arthrocentesis (79.4%) was the most common collection method. Compared to SoC culture, overall sensitivity was 90.2% and specificity was 99.8%. The BioFire BJI Panel yielded a total of 268 Detected results, whereas SoC yielded a total of 215 positive results for on-panel analytes. Conclusion The BioFire BJI Panel is a sensitive, specific, and robust test for rapid detection of a wide range of analytes in synovial fluid specimens. The number of microorganisms and resistance genes included in the BioFire BJI Panel, together with a reduced time-to-result and increased diagnostic yield compared to culture, is expected to aid in the timely diagnosis and appropriate management of BJIs. Disclosures Benjamin von Bredow, PhD, BioFire (Grant/Research Support) Jennifer Dien Bard, PhD, BioFire Diagnostic (Consultant, Scientific Research Study Investigator) Bart Kensinger, PhD, BioFire Diagnostics (Employee) Benedicte Pons, PhD, bioMerieux SA (Employee) Corinne Jay, PhD, bioMerieux SA (Employee)

Download Full-text

Validation and standardization of DNA extraction and library construction methods for metagenomics-based human fecal microbiome measurements

Microbiome ◽

10.1186/s40168-021-01048-3 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Dieter M. Tourlousse ◽

Koji Narita ◽

Takamasa Miura ◽

Mitsuo Sakamoto ◽

Akiko Ohashi ◽

...

Keyword(s):

Dna Extraction ◽

High Throughput Sequencing ◽

Performance Metrics ◽

Collaborative Study ◽

Human Microbiome ◽

Second Phase ◽

Intermediate Precision ◽

Fecal Microbiome ◽

Library Construction ◽

Wide Range

Abstract Background Validation and standardization of methodologies for microbial community measurements by high-throughput sequencing are needed to support human microbiome research and its industrialization. This study set out to establish standards-based solutions to improve the accuracy and reproducibility of metagenomics-based microbiome profiling of human fecal samples. Results In the first phase, we performed a head-to-head comparison of a wide range of protocols for DNA extraction and sequencing library construction using defined mock communities, to identify performant protocols and pinpoint sources of inaccuracy in quantification. In the second phase, we validated performant protocols with respect to their variability of measurement results within a single laboratory (that is, intermediate precision) as well as interlaboratory transferability and reproducibility through an industry-based collaborative study. We further ascertained the performance of our recommended protocols in the context of a community-wide interlaboratory study (that is, the MOSAIC Standards Challenge). Finally, we defined performance metrics to provide best practice guidance for improving measurement consistency across methods and laboratories. Conclusions The validated protocols and methodological guidance for DNA extraction and library construction provided in this study expand current best practices for metagenomic analyses of human fecal microbiota. Uptake of our protocols and guidelines will improve the accuracy and comparability of metagenomics-based studies of the human microbiome, thereby facilitating development and commercialization of human microbiome-based products.

Download Full-text

Classification of unlabeled online media

Scientific Reports ◽

10.1038/s41598-021-85608-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sakthi Kumar Arul Prakash ◽

Conrad Tucker

Keyword(s):

Social Media ◽

Real World ◽

Graphical Model ◽

Ground Truth ◽

Classification Problem ◽

Machine Learning Algorithms ◽

Social Media Networks ◽

Online Social Media ◽

Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

Download Full-text

A virtual test environment for validating spacecraft optical navigation

The Aeronautical Journal ◽

10.1017/s000192400000871x ◽

2013 ◽

Vol 117 (1197) ◽

pp. 1075-1101 ◽

Cited By ~ 1

Author(s):

S. M. Parkes ◽

I. Martin ◽

M. N. Dunstan ◽

N. Rowell ◽

O. Dubois-Matra ◽

...

Keyword(s):

Small Body ◽

European Space Agency ◽

Ground Truth ◽

Camera Motion ◽

Test Environment ◽

Space Applications ◽

Vision Guidance ◽

Space Agency ◽

Wide Range ◽

And Performance

Abstract The use of machine vision to guide robotic spacecraft is being considered for a wide range of missions, such as planetary approach and landing, asteroid and small body sampling operations and in-orbit rendezvous and docking. Numerical simulation plays an essential role in the development and testing of such systems, which in the context of vision-guidance means that realistic sequences of navigation images are required, together with knowledge of the ground-truth camera motion. Computer generated imagery (CGI) offers a variety of benefits over real images, such as availability, cost, flexibility and knowledge of the ground truth camera motion to high precision. However, standard CGI methods developed for terrestrial applications lack the realism, fidelity and performance required for engineering simulations. In this paper, we present the results of our ongoing work to develop a suitable CGI-based test environment for spacecraft vision guidance systems. We focus on the various issues involved with image simulation, including the selection of standard CGI techniques and the adaptations required for use in space applications. We also describe our approach to integration with high-fidelity end-to-end mission simulators, and summarise a variety of European Space Agency research and development projects that used our test environment.

Download Full-text

Virtual Body Ownership Illusions for Mental Health: A Narrative Review

Journal of Clinical Medicine ◽

10.3390/jcm10010139 ◽

2021 ◽

Vol 10 (1) ◽

pp. 139

Author(s):

Marta Matamala-Gomez ◽

Antonella Maselli ◽

Clelia Malighetti ◽

Olivia Realdon ◽

Fabrizia Mantovani ◽

...

Keyword(s):

Mental Health ◽

Clinical Care ◽

Well Being ◽

Narrative Review ◽

Future Research ◽

Body Ownership ◽

Surrounding Environment ◽

Wide Range ◽

Clinical Conditions ◽

Virtual Body

Over the last 20 years, virtual reality (VR) has been widely used to promote mental health in populations presenting different clinical conditions. Mental health does not refer only to the absence of psychiatric disorders but to the absence of a wide range of clinical conditions that influence people’s general and social well-being such as chronic pain, neurological disorders that lead to motor o perceptual impairments, psychological disorders that alter behaviour and social cognition, or physical conditions like eating disorders or present in amputees. It is known that an accurate perception of oneself and of the surrounding environment are both key elements to enjoy mental health and well-being, and that both can be distorted in patients suffering from the clinical conditions mentioned above. In the past few years, multiple studies have shown the effectiveness of VR to modulate such perceptual distortions of oneself and of the surrounding environment through virtual body ownership illusions. This narrative review aims to review clinical studies that have explored the manipulation of embodied virtual bodies in VR for improving mental health, and to discuss the current state of the art and the challenges for future research in the context of clinical care.

Download Full-text

Finding a Needle in a Haystack: Variant Effect Predictor (VEP) Prioritizes Disease Causative Variants from Millions of Neutral Ones

Essentials of Bioinformatics, Volume II ◽

10.1007/978-3-030-18375-2_6 ◽

2019 ◽

pp. 85-104

Author(s):

Yashvant Khimsuriya ◽

Salil Vaniyawala ◽

Babajan Banaganapalli ◽

Muhammadh Khan ◽

Ramu Elango ◽

...

Keyword(s):

Variant Effect Predictor ◽

Variant Effect

Download Full-text

A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity

Bioinformatics ◽

10.1093/bioinformatics/bty960 ◽

2018 ◽

Vol 35 (13) ◽

pp. 2315-2317 ◽

Cited By ~ 5

Author(s):

Jannah Shamsani ◽

Stephen H Kazakoff ◽

Irina M Armean ◽

Will McLaren ◽

Michael T Parsons ◽

...

Keyword(s):

Splice Site ◽

Disease Risk ◽

Cancer Susceptibility ◽

Mrna Splicing ◽

Supplementary Information ◽

Mendelian Disease ◽

Functional Protein ◽

Variant Effect Predictor ◽

Cancer Susceptibility Genes ◽

Variant Effect

Abstract Summary Assessing the pathogenicity of genetic variants can be a complex and challenging task. Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode non-functional protein products, an important predictor of Mendelian disease risk. However, most variant annotation tools do not adequately assess spliceogenicity outside the native splice site and thus the disease-causing potential of variants in other intronic and exonic regions is often overlooked. Here, we present a plugin for the Ensembl Variant Effect Predictor that packages MaxEntScan and extends its functionality to provide splice site predictions using a maximum entropy model. The plugin incorporates a sliding window algorithm to predict splice site loss or gain for any variant that overlaps a transcript feature. We also demonstrate the utility of the plugin by comparing our predictions to two mRNA splicing datasets containing several cancer-susceptibility genes. Availability and implementation Source code is freely available under the Apache License, Version 2.0: https://github.com/Ensembl/VEP_plugins. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

DeepMAsED: evaluating the quality of metagenomic assemblies

Bioinformatics ◽

10.1093/bioinformatics/btaa124 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3011-3017 ◽

Cited By ~ 5

Author(s):

Olga Mineeva ◽

Mateo Rojas-Carulla ◽

Ruth E Ley ◽

Bernhard Schölkopf ◽

Nicholas D Youngblut

Keyword(s):

Large Scale ◽

State Of The Art ◽

Ground Truth ◽

Supplementary Information ◽

Learning Approach ◽

Wide Range ◽

Metagenome Assembly ◽

Model Training ◽

Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text