Identifying Disease Genes Based on Functional Annotation and Text Mining

2011 ◽  
Vol 3 (1) ◽  
pp. 45-54
Author(s):  
Fang Yuan ◽  
Mingliang Li ◽  
Jing Li
Keyword(s):  
Text Mining ◽  
Functional Annotation ◽  
Target Gene ◽  
Disease Genes ◽  
Web Tool ◽  
Related Function ◽  
Disease Phenotypes ◽  
Pathogenic Genes ◽  
Omim Database ◽  
Detailed Function

The identification of disease genes from candidated regions is one of the most important tasks in bioinformatics research. Most approaches based on function annotations cannot be used to identify genes for diseases without any known pathogenic genes or related function annotations. The authors have built a new web tool, DGHunter, to predict genes associated with these diseases which lack detailed function annotations. Its performance was tested with a set of 1506 genes involved in 1147 disease phenotypes derived from the morbid map table in the OMIM database. The results show that, on average, the target gene was in the top 13.60% of the ranked lists of candidates, and the target gene was in the top 5% with a 40.70% chance. DGHunter can identify disease genes effectively for those diseases lacking sufficient function annotations.

Identifying Disease Genes Based on Functional Annotation and Text Mining

2013 ◽  
pp. 48-57
Author(s):  
Fang Yuan ◽  
Mingliang Li ◽  
Jing Li
Keyword(s):  
Text Mining ◽  
Functional Annotation ◽  
Target Gene ◽  
Disease Genes ◽  
Web Tool ◽  
Related Function ◽  
Disease Phenotypes ◽  
Pathogenic Genes ◽  
Omim Database ◽  
Detailed Function

The identification of disease genes from candidated regions is one of the most important tasks in bioinformatics research. Most approaches based on function annotations cannot be used to identify genes for diseases without any known pathogenic genes or related function annotations. The authors have built a new web tool, DGHunter, to predict genes associated with these diseases which lack detailed function annotations. Its performance was tested with a set of 1506 genes involved in 1147 disease phenotypes derived from the morbid map table in the OMIM database. The results show that, on average, the target gene was in the top 13.60% of the ranked lists of candidates, and the target gene was in the top 5% with a 40.70% chance. DGHunter can identify disease genes effectively for those diseases lacking sufficient function annotations.

scholarly journals PLAN2L: a web tool for integrated text mining and literature-based bioentity relation extraction

2009 ◽  
Vol 37 (Web Server) ◽  
pp. W160-W165 ◽  
Author(s):  
M. Krallinger ◽  
C. Rodriguez-Penagos ◽  
A. Tendulkar ◽  
A. Valencia
Keyword(s):  
Text Mining ◽  
Relation Extraction ◽  
Web Tool

The Identification and Genomic Analysis of microRNAs in Human Erythrocytes in Sickle Cell Diseases.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 3400-3400
Author(s):  
Shao-Yin Chen ◽  
Yulei Wang ◽  
Marilyn I. Telen ◽  
Jen-Tsan A. Chi
Keyword(s):  
Microarray Analysis ◽  
Target Gene ◽  
Terminal Differentiation ◽  
Genomic Analysis ◽  
Microrna Expression ◽  
Rt Pcr ◽  
Northern Blots ◽  
Disease Phenotypes ◽  
Differentiated Cells ◽  
The Poor

Abstract Erythrocytes are circulating blood cells responsible for efficient gas exchange in human body. Since mature erythrocytes are terminally differentiated cells without nuclei and organelles, it is commonly thought that they do not contain nucleic acids. In this study, we re-examined this issue and found that human mature erythrocytes, while lacking ribosomal and large-sized RNAs, possess abundant small-sized RNAs. Using a combination of microarray analysis, real-time RT-PCR and Northern blots we found that mature erythrocytes contained abundant and diverse microRNAs which were distinct from microRNAs observed in reticulocytes/leukocytes and contributed to the majority of the microRNA expression in whole blood. When we used microarrays to analyze erythrocytes from normal (HbAA) and homozygous sickle (HbSS) individulas, we noted dramatic a difference in their microRNA expression pattern. To investigate how this difference is associated with erythrocyte disease phenotypes, we found that the poor expression of miR-320 was responsible for the defective downregulation of its target gene CD71 in HbSS cells during terminal differentiation. Collectively, we have discovered significant microRNA expression in human mature erythrocytes, which enables the microarray analysis of erythrocyte property to provide insights into the human erythrocyte diseases.

scholarly journals Functional Annotation and Identification of Candidate Disease Genes by Computational Analysis of Normal Tissue Gene Expression Data

PLoS ONE ◽  
2008 ◽  
Vol 3 (6) ◽  
pp. e2439 ◽  
Author(s):  
Laura Miozzi ◽  
Rosario Michael Piro ◽  
Fabio Rosa ◽  
Ugo Ala ◽  
Lorenzo Silengo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Functional Annotation ◽  
Normal Tissue ◽  
Computational Analysis ◽  
Disease Genes ◽  
Expression Data ◽  
Tissue Gene Expression

scholarly journals An integrative, translational approach to understanding rare and orphan genetically based diseases

2013 ◽  
Vol 3 (2) ◽  
pp. 20120055 ◽  
Author(s):  
Robert Hoehndorf ◽  
Paul N. Schofield ◽  
Georgios V. Gkoutos
Keyword(s):  
Animal Model ◽  
Candidate Genes ◽  
Molecular Basis ◽  
Genetic Diseases ◽  
Clinical Signs ◽  
Mendelian Inheritance ◽  
Disease Phenotypes ◽  
Guilt By Association ◽  
Translational Approach ◽  
Omim Database

PhenomeNet is an approach for integrating phenotypes across species and identifying candidate genes for genetic diseases based on the similarity between a disease and animal model phenotypes. In contrast to ‘guilt-by-association’ approaches, PhenomeNet relies exclusively on the comparison of phenotypes to suggest candidate genes, and can, therefore, be applied to study the molecular basis of rare and orphan diseases for which the molecular basis is unknown. In addition to disease phenotypes from the Online Mendelian Inheritance in Man (OMIM) database, we have now integrated the clinical signs from Orphanet into PhenomeNet. We demonstrate that our approach can efficiently identify known candidate genes for genetic diseases in Orphanet and OMIM. Furthermore, we find evidence that mutations in the HIP1 gene might cause Bassoe syndrome, a rare disorder with unknown genetic aetiology. Our results demonstrate that integration and computational analysis of human disease and animal model phenotypes using PhenomeNet has the potential to reveal novel insights into the pathobiology underlying genetic diseases.

scholarly journals KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 832
Author(s):  
Finn Kuusisto ◽  
Daniel Ng ◽  
John Steill ◽  
Ian Ross ◽  
Miron Livny ◽  
...  
Keyword(s):  
Text Mining ◽  
Web Application ◽  
Biomedical Applications ◽  
Analysis Tool ◽  
Trial And Error ◽  
Web Tool ◽  
Interactive Analysis ◽  
Term List ◽  
Public Resource ◽  
The Web

Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

Genetic basis of human left–right asymmetry disorders

2014 ◽  
Vol 16 ◽  
Author(s):  
Hao Deng ◽  
Hong Xia ◽  
Sheng Deng
Keyword(s):  
Situs Inversus ◽  
Genetic Basis ◽  
Chromosomal Abnormalities ◽  
The Body ◽  
Disease Genes ◽  
Internal Organs ◽  
Abdominal Organs ◽  
Pathogenic Genes ◽  
Left Right Asymmetry ◽  
Reduced Penetrance

Humans and other vertebrates exhibit left–right (LR) asymmetric arrangement of the internal organs, and failure to establish normal LR asymmetry leads to internal laterality disorders, includingsitus inversusandheterotaxy.Situs inversusis complete mirror-imaged arrangement of the internal organs along LR axis, whereasheterotaxyis abnormal arrangement of the internal thoraco-abdominal organs across LR axis of the body, most of which are associated with complex cardiovascular malformations. Both disorders are genetically heterogeneous with reduced penetrance, presumably because of monogenic, polygenic or multifactorial causes. Research in genetics of LR asymmetry disorders has been extremely prolific over the past 17 years, and a series of loci and disease genes involved insitus inversusandheterotaxyhave been described. The review highlights the classification, chromosomal abnormalities, pathogenic genes and the possible mechanism of human LR asymmetry disorders.

scholarly journals NFκB activation by Fas is mediated through FADD, caspase-8, and RIP and is inhibited by FLIP

2004 ◽  
Vol 166 (3) ◽  
pp. 369-380 ◽  
Author(s):  
Sebastian Kreuz ◽  
Daniela Siegmund ◽  
Jost-Julian Rumpf ◽  
Dierk Samel ◽  
Martin Leverkus ◽  
...  
Keyword(s):  
Target Gene ◽  
Molecular Mechanisms ◽  
Death Receptor ◽  
Caspase 8 ◽  
Independent Manner ◽  
Related Function ◽  
Fas Signaling ◽  
A Cell ◽  
Cell Type Specific ◽  
Induced Apoptosis

Fas (APO-1/CD95) is the prototypic death receptor, and the molecular mechanisms of Fas-induced apoptosis are comparably well understood. Here, we show that Fas activates NFκB via a pathway involving RIP, FADD, and caspase-8. Remarkably, the enzymatic activity of the latter was dispensable for Fas-induced NFκB signaling pointing to a scaffolding-related function of caspase-8 in nonapoptotic Fas signaling. NFκB was activated by overexpressed FLIPL and FLIPS in a cell type–specific manner. However, in the context of Fas signaling both isoforms blocked FasL-induced NFκB activation. Moreover, down-regulation of both endogenous FLIP isoforms or of endogenous FLIPL alone was sufficient to enhance FasL-induced expression of the NFκB target gene IL8. As NFκB signaling is inhibited during apoptosis, FasL-induced NFκB activation was most prominent in cells that were protected by Bcl2 expression or caspase inhibitors and expressed no or minute amounts of FLIP. Thus, protection against Fas-induced apoptosis in a FLIP-independent manner converted a proapoptotic Fas signal into an inflammatory NFκB-related response.

scholarly journals ScanBious: Survey for Obesity Genes Using PubMed Abstracts and DisGeNET

2021 ◽  
Vol 11 (4) ◽  
pp. 246
Author(s):  
Svetlana Tarbeeva ◽  
Ekaterina Lyamtseva ◽  
Andrey Lisitsa ◽  
Anna Kozlova ◽  
Elena Ponomarenko ◽  
...  
Keyword(s):  
Text Mining ◽  
Personalized Medicine ◽  
Intercellular Signaling ◽  
Web Tool ◽  
Medical Subject Heading ◽  
Physiological Conditions ◽  
Key Concepts ◽  
General Network ◽  
Obesity Genes ◽  
Automatic Text

We used automatic text-mining of PubMed abstracts of papers related to obesity, with the aim of revealing that the information used in abstracts reflects the current understanding and key concepts of this widely explored problem. We compared expert data from DisGeNET to the results of an automated MeSH (Medical Subject Heading) search, which was performed by the ScanBious web tool. The analysis provided an overview of the obesity field, highlighting major trends such as physiological conditions, age, and diet, as well as key well-studied genes, such as adiponectin and its receptor. By intersecting the DisGeNET knowledge with the ScanBious results, we deciphered four clusters of obesity-related genes. An initial set of 100+ thousand abstracts and 622 genes was reduced to 19 genes, distributed among just a few groups: heredity, inflammation, intercellular signaling, and cancer. Rapid profiling of articles could drive personalized medicine: if the disease signs of a particular person were superimposed on a general network, then it would be possible to understand which are non-specific (observed in cohorts and, therefore, most likely have known treatment solutions) and which are less investigated, and probably represent a personalized case.

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