Bioinformatics for Human Genetics: Promises and Challenges

Lindblom, Annika; Robinson, Peter N.

doi:10.1002/humu.21468

Cited by 22 publications

(17 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Novel, easyto-use, bioinformatic tools provide a method to interrogate and understand data generated from ''-omics'' experiments (Lindblom and Robinson 2011). Ontological analysis has become a commonly used bioinformatics technique to organize and characterize large amounts of information.…”

Section: Introductionmentioning

confidence: 99%

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

et al. 2014

View full text Add to dashboard Cite

Advances in the development of bioinformatic tools continue to improve investigators' ability to interrogate, organize, and derive knowledge from large amounts of heterogeneous information. These tools often require advanced technical skills not possessed by life scientists. User-friendly, low-barrier-to-entry methods of visualizing nutrigenomics information are yet to be developed. We utilized concept mapping software from the Institute for Human and Machine Cognition to create a conceptual model of diet and health-related data that provides a foundation for future nutrigenomics ontologies describing published nutrient-gene/polymorphism-phenotype data. In this model, maps containing phenotype, nutrient, gene product, and genetic polymorphism interactions are visualized as triples of two concepts linked together by a linking phrase. These triples, or ''knowledge propositions,'' contextualize aggregated data and information into easy-to-read knowledge maps. Maps of these triples enable visualization of genes spanning the One-Carbon Metabolism (OCM) pathway, their sequence variants, and multiple literature-mined associations including concepts relevant to nutrition, phenotypes, and health. The concept map development process documents the incongruity of information derived from pathway databases versus literature resources. This conceptual model highlights the importance of incorporating information about genes in upstream pathways that provide substrates, as well as downstream pathways that utilize products of the pathway under investigation, in this case OCM. Other genes and their polymorphisms, such as TCN2 and FUT2, although not directly involved in OCM, potentially alter OCM pathway functionality. These upstream gene products regulate substrates such as B12. Constellations of polymorphisms affecting the functionality of genes along OCM, together with substrate and cofactor availability, may impact resultant phenotypes. These conceptual maps provide a foundational framework for development of nutrient-gene/polymorphism-phenotype ontologies and systems visualization.

show abstract

Section: Introductionmentioning

confidence: 99%

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Each of these knowledge bases, as well as the commercial diagnosis support programs of the London Dysmorphology Database [Fryns and de Ravel, 2002], and the Pictures of Standard Syndromes and Undiagnosed Malformations database (http://www.possum.net.au) have used their own vocabularies to describe phenotypic abnormalities. The multiplicity of mutually incompatible vocabularies in databases and publications on human genetics has hindered integrative research in clinical genetics [Lindblom and Robinson, 2011].…”

Section: The Human Phenotype Ontologymentioning

confidence: 99%

Ontological phenotype standards for neurogenetics

Köhler¹,

Doelken²,

Rath³

et al. 2012

Hum. Mutat.

Self Cite

View full text Add to dashboard Cite

Neurological disorders comprise one of the largest groups of human diseases. Due to the myriad symptoms and the extreme degree of clinical variability characteristic of many neurological diseases, the differential diagnosis process is extremely challenging. Even though most neurogenetic diseases are individually rare, collectively, the subgroup of neurogenetic disorders is large, comprising more than 2,400 different disorders. Recently, increasing efforts have been undertaken to unravel the molecular basis of neurogenetic diseases and to correlate pathogenetic mechanisms with clinical signs and symptoms. In order to enable computer-based analyses, the systematic representation of the neurological phenotype is of major importance. We demonstrate how the Human Phenotype Ontology (HPO) can be incorporated into these efforts by providing a systematic semantic representation of phenotypic abnormalities encountered in human ge- KEY WORDS: phenotype; ontology; neurogenetics; data integration; orphan diseases Representation of Phenotypic Data in Clinical NeurologyThe differential diagnostic process for neurological disorders is one of the most challenging in medical practice, which is especially true for rare neurological diseases, defined as those that affect less than 1 in 2,000 persons. Unlike many other clinical disciplines, the day-to-day practice of neurology involves a pantheon of symptoms and long lists of differential diagnoses; the challenge is often to identify rare neurological diseases, especially those that require an individualized approach to clinical management [Chinnery, 2010]. Many rare neurological diseases are genetic in origin, and are associated with one or more functional and structural abnormalities of the nervous system. They originate from a wide spectrum of molecular and cellular defects and include skeletal muscle channelopathies, neuropathies, epilepsies, neurovascular disorders, neurocutaneous syndromes, mitochondrial diseases, nondegenerative movement disorders, inherited ataxias, neurodegenerative disorders, dementias, myopathies, and muscular atrophies. Currently, the classification of rare neurological diseases in the Orphanet database lists more than 2,400 different diseases. The differential diagnosis can be a daunting challenge for physicians, and affected patients may wait years before receiving a correct and precise diagnosis. For many patients, the underlying cause of the clinical manifestations may remain unknown.Several major international initiatives in neuroscience have emerged with the goal of developing computational models of the brain including the Neuroscience Information Framework and the Human Brain Project [Gardner et al., 2008]. Ontologies are playing a major role in these efforts to ensure interoperability across neuroscience databases, to integrate neuroscience information from disparate sources to improve our understanding of the brain, and to enable computer reasoning. In computer science, the word "ontology" is used to describe a structured, automated...

show abstract

“…In addition, microarray-identified mutations require validation to eliminate false positive or false negative results (Johnson et al, 2010). On that point, NGS is a prospective approach in F8 mutation studies (Lindblom & Robinson, 2011). NGS is an alternative sequencing strategy that redefines "high-throughput sequencing".…”

Section: New Approach Of the Mutation Profilingmentioning

confidence: 99%

Chapter 1 Profiling of Mutations in the F8 and F9, Causative Genes of Hemophilia A and Hemophilia B"

Hwang¹,

Kim²,

Kim³

2012

Hemophilia

View full text Add to dashboard Cite

Bioinformatics for Human Genetics: Promises and Challenges

Cited by 22 publications

References 33 publications

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

Ontological phenotype standards for neurogenetics

Chapter 1 Profiling of Mutations in the F8 and F9, Causative Genes of Hemophilia A and Hemophilia B"

Contact Info

Product

Resources

About

Bioinformatics for Human Genetics: Promises and Challenges

Cited by 22 publications

References 33 publications

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

Ontological phenotype standards for neurogenetics

﻿Chapter 1 Profiling of Mutations in the F8 and F9, Causative Genes of Hemophilia A and Hemophilia B"

Contact Info

Product

Resources

About

Chapter 1 Profiling of Mutations in the F8 and F9, Causative Genes of Hemophilia A and Hemophilia B"