Lynx: a database and knowledge extraction engine for integrative medicine

Sulakhe, Dinanath; Balasubramanian, Sandhya; Xie, Bingqing; Feng, Bo; Taylor, Andrew; Wang, Sheng; Berrocal, Eduardo; Dave, Utpal J.; Xu, Jinbo; Börnigen, Daniela; Gilliam, T. Conrad; Maltsev, Natalia

doi:10.1093/nar/gkt1166

Cited by 39 publications

(35 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been increasingly popular for biological database providers to provide data access via web services, for example NCBI eUtils (8-11), BioMart (12) and UniProt web services (3). There are also efforts to make aggregated annotation databases across multiple data sources with web service access (13,14). Compared with those existing resources, MyGene.info distinguishes itself in these respects: a) broad annotation and species coverage; b) superior query performance with the support of high concurrency; c) Simple API (just two service .…”

Section: Discussionmentioning

confidence: 99%

MyGene.info: gene annotation query as a service

Mark

2014

Preprint

View full text Add to dashboard Cite

Biomedical knowledge is often represented as annotations of biological entities such as genes, genetic variants, diseases, and drugs. For gene annotations, they are fragmented across data repositories like NCBI Entrez, Ensembl, UniProt, and hundreds (or more) of other specialized databases. While the volume and breadth of annotations is valuable, their fragmentation across many data silos is often frustrating and inefficient. Bioinformaticians everywhere must continuously and repetitively engage in data wrangling in an effort to comprehensively integrate knowledge from all these resources, and these uncoordinated efforts represent an enormous duplication of work. We previously released MyGene.info (http://mygene.info) to enable bioinformatics developers to gain programmatic access to gene annotation data through our high-performance web services. This article focuses on the updates to MyGene.info since our last paper (2013 database issue). With the completely re-factored system, MyGene.info now expands the support from the original nine species to over 14K species, covering >17M genes with >50 gene-specific annotation types. Two simple web service endpoints provides high-performance query access to all these aggregated gene annotations. The infrastructure underlying MyGene.info is highly scalable, which offers both high-performance and high-concurrency, and makes MyGene.info particularly suitable for the use cases of real-time applications and analysis pipelines.

show abstract

Section: Discussionmentioning

confidence: 99%

MyGene.info: gene annotation query as a service

Mark

2014

Preprint

View full text Add to dashboard Cite

show abstract

“…First, a training set is acquired from the user input (i.e., a set of genes known to be associated with a certain disease or phenotype, which is referred to as user input or training/seed genes). Second, these training genes are annotated using various classes of information (e.g., GO terms) from LYNX knowledge base (Sulakhe et al, 2014) to obtain a union of annotations for the user input genes. Here, the union of annotations per source is the feature set for the individual source.…”

Section: Approach Overviewmentioning

confidence: 99%

“…An enrichment analysis was used to characterize sets of target genes with different functional and structural properties (e.g., participation in the same pathway, or biological process, or association with the similar phenotype) (Huang et al, 2009). Here, multiple classes of annotations (e.g., GO terms, associations with particular molecular pathways or phenotypes) from the LYNX knowledge base (Sulakhe et al, 2014) were used for gene annotation and enrichment, while statistical significance scores were assigned to each annotation by estimating differences between the input genes and background genes using the Bayes factor (Chang and Nevins, 2006). Thus, for each factor f a , a weight w a was assigned by Bayes factor B = b 1 ‚ .…”

Section: Xie Et Almentioning

confidence: 99%

“…Multidimensional biological information was acquired from our in-house Lynx knowledge base (Sulakhe et al, 2014), which integrates various classes of information from over 35 public databases and private collections (NCBI databases, EMBL, UniProt, TIGR); molecular pathways (e.g., Reactome, BioCarta, KEGG, NCI pathways); phenotypic databases (OMIM, disease ontology, phenotype ontology databases); and ontologies [Gene Ontology (GO) (Ashburner et al, 2000), BioPAX, phenotype ontology, disease ontology, MI-PSI, etc.]. Here, a novel way to prioritize candidate genes is introduced by using both gene annotations and reliable information that describe gene-gene interactions based on natural fusion of an underlying gene interaction network (Szklarczyk et al, 2011), as well as various classes of biological information (Sulakhe et al, 2014).…”

mentioning

confidence: 99%

“…Here, a novel way to prioritize candidate genes is introduced by using both gene annotations and reliable information that describe gene-gene interactions based on natural fusion of an underlying gene interaction network (Szklarczyk et al, 2011), as well as various classes of biological information (Sulakhe et al, 2014). Network information and annotations were retained in their original form without manually converting them into each other.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Disease Gene Prioritization Using Network and Feature

Xie

Agam

Balasubramanian

et al. 2015

Journal of Computational Biology

Self Cite

View full text Add to dashboard Cite

Identifying high-confidence candidate genes that are causative for disease phenotypes, from the large lists of variations produced by high-throughput genomics, can be both timeconsuming and costly. The development of novel computational approaches, utilizing existing biological knowledge for the prioritization of such candidate genes, can improve the efficiency and accuracy of the biomedical data analysis. It can also reduce the cost of such studies by avoiding experimental validations of irrelevant candidates. In this study, we address this challenge by proposing a novel gene prioritization approach that ranks promising candidate genes that are likely to be involved in a disease or phenotype under study. This algorithm is based on the modified conditional random field (CRF) model that simultaneously makes use of both gene annotations and gene interactions, while preserving their original representation. We validated our approach on two independent disease benchmark studies by ranking candidate genes using network and feature information. Our results showed both high area under the curve (AUC) value (0.86), and more importantly high partial AUC (pAUC) value (0.1296), and revealed higher accuracy and precision at the top predictions as compared with other well-performed gene prioritization tools, such as Endeavour (AUC-0.82, pAUC-0.083) and PINTA (AUC-0.76, pAUC-0.066). We were able to detect more target genes (9/18/19/27) on top positions (1/5/10/20) compared to Endeavour (3/11/14/23) and PINTA (6/10/13/18). To demonstrate its usability, we applied our method to a case study for the prediction of molecular mechanisms contributing to intellectual disability and autism. Our approach was able to correctly recover genes related to both disorders and provide suggestions for possible additional candidates based on their rankings and functional annotations.

show abstract

The Developmental Brain Disorders Database (DBDB): A curated neurogenetics knowledge base with clinical and research applications

Mirzaa

Millen

Barkovich

et al. 2014

American J of Med Genetics Pt A

View full text Add to dashboard Cite

The number of single genes associated with neurodevelopmental disorders has increased dramatically over the past decade. The identification of causative genes for these disorders is important to clinical outcome as it allows for accurate assessment of prognosis, genetic counseling, delineation of natural history, inclusion in clinical trials, and in some cases determines therapy. Clinicians face the challenge of correctly identifying neurodevelopmental phenotypes, recognizing syndromes, and prioritizing the best candidate genes for testing. However, there is no central repository of definitions for many phenotypes, leading to errors of diagnosis. Additionally, there is no system of levels of evidence linking genes to phenotypes, making it difficult for clinicians to know which genes are most strongly associated with a given condition. We have developed the Developmental Brain Disorders Database (DBDB: https://www.dbdb.urmc.rochester.edu/home), a publicly available, online-curated repository of genes, phenotypes, and syndromes associated with neurodevelopmental disorders. DBDB contains the first referenced ontology of developmental brain phenotypes, and uses a novel system of levels of evidence for gene-phenotype associations. It is intended to assist clinicians in arriving at the correct diagnosis, select the most appropriate genetic test for that phenotype, and improve the care of patients with developmental brain disorders. For researchers interested in the discovery of novel genes for developmental brain disorders, DBDB provides a well-curated source of important genes against which research sequencing results can be compared. Finally, DBDB allows novel observations about the landscape of the neurogenetics knowledge base.

show abstract

Lynx: a database and knowledge extraction engine for integrative medicine

Cited by 39 publications

References 43 publications

MyGene.info: gene annotation query as a service

MyGene.info: gene annotation query as a service

Disease Gene Prioritization Using Network and Feature

The Developmental Brain Disorders Database (DBDB): A curated neurogenetics knowledge base with clinical and research applications

Contact Info

Product

Resources

About