<i>NeuroTransDB</i>: highly curated and structured transcriptomic metadata for neurodegenerative diseases

Bagewadi, Shweta; Adhikari, Subash; Dhrangadhariya, Anjani; Irin, Afroza Khanam; Ebeling, Christian; Namasivayam, A; Page, Matthew J.; Hofmann‐Apitius, Martin; Senger, Philipp

doi:10.1093/database/bav099

Cited by 8 publications

(6 citation statements)

References 67 publications

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“…For querying AD-specific gene expression data, we used previously developed database, NeuroTransDB [ 78 ], which contains highly curated meta-data information for eligible AD studies. It assembles studies from public resources namely, GEO and ArrayExpress, using a keyword based search approach.…”

Section: Methodsmentioning

confidence: 99%

“…Similarly, in our recent publication [ 78 ], we have highlighted the key issues related to retrieval and reusability of the datasets from public transcriptomics archives, such as GEO and ArrayExpress. We showed that a simple keyword based search not necessarily asserts the specificity of the retrieved datasets to the queried disease or organism.…”

Section: Methodsmentioning

confidence: 99%

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NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease

et al. 2016

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BackgroundNeurodegenerative diseases are incurable and debilitating indications with huge social and economic impact, where much is still to be learnt about the underlying molecular events. Mechanistic disease models could offer a knowledge framework to help decipher the complex interactions that occur at molecular and cellular levels. This motivates the need for the development of an approach integrating highly curated and heterogeneous data into a disease model of different regulatory data layers. Although several disease models exist, they often do not consider the quality of underlying data. Moreover, even with the current advancements in semantic web technology, we still do not have cure for complex diseases like Alzheimer’s disease. One of the key reasons accountable for this could be the increasing gap between generated data and the derived knowledge.ResultsIn this paper, we describe an approach, called as NeuroRDF, to develop an integrative framework for modeling curated knowledge in the area of complex neurodegenerative diseases. The core of this strategy lies in the usage of well curated and context specific data for integration into one single semantic web-based framework, RDF. This increases the probability of the derived knowledge to be novel and reliable in a specific disease context. This infrastructure integrates highly curated data from databases (Bind, IntAct, etc.), literature (PubMed), and gene expression resources (such as GEO and ArrayExpress). We illustrate the effectiveness of our approach by asking real-world biomedical questions that link these resources to prioritize the plausible biomarker candidates. Among the 13 prioritized candidate genes, we identified MIF to be a potential emerging candidate due to its role as a pro-inflammatory cytokine. We additionally report on the effort and challenges faced during generation of such an indication-specific knowledge base comprising of curated and quality-controlled data.ConclusionAlthough many alternative approaches have been proposed and practiced for modeling diseases, the semantic web technology is a flexible and well established solution for harmonized aggregation. The benefit of this work, to use high quality and context specific data, becomes apparent in speculating previously unattended biomarker candidates around a well-known mechanism, further leveraged for experimental investigations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0079-8) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease

et al. 2016

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“…To faithfully identify the correct studies to include in such a meta-analysis strategy requires detailed, domain-specific meta-data for each study, which is only partially addressed by the MIAME standard. To enrich the available meta-data for Alzheimer’s disease gene expression studies, Bagewadi et al [ 87 ] followed a semi-automated, manually curated approach to harvest detailed and standardised annotations from the accompanying publications. The annotations are modeled using a custom RDF schema that allows in-depth assessment of the respective phenotype, the type of tissue used in an experiment, and accessory information for the donor of the tissue such as; gender, age, possible comorbidities, etc.…”

Section: Bioinformatics Methods For the Identification Of Disease mentioning

confidence: 99%

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders

Hofmann-Apitius

Ball

Gebel

et al. 2015

IJMS

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Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies—data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

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“…One study retrieved corresponding records from two biological databases, Gene Expression Omnibus and ArrayExpress, but surprisingly found the number of records to be significantly different: the former has 72 whereas only 36 in latter. Some of the records were identical, but in some cases records were in one but not the other (23). Indeed, duplication commonly interacts with inconsistency (5).…”

Section: Impact Of Duplicatesmentioning

confidence: 99%

Benchmarks for Measurement of Duplicate Detection Methods in Nucleotide Databases

Chen

Zobel

Verspoor

2016

Preprint

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Duplication of information in databases is a major data quality challenge. The presence of duplicates, implying either redundancy or inconsistency, can have a range of impacts on the quality of analyses that use the data. To provide a sound basis for research on this issue in databases of nucleotide sequences, we have developed new, large-scale validated collections of duplicates, which can be used to test the effectiveness of duplicate detection methods. Previous collections were either designed primarily to test efficiency, or contained only a limited number of duplicates of limited kinds. To date, duplicate detection methods have been evaluated on separate, inconsistent benchmarks, leading to results that cannot be compared and, due to limitations of the benchmarks, of questionable generality.In this study we present three nucleotide sequence database benchmarks, based on information drawn from a range of resources, including information derived from mapping to Swiss-Prot and TrEMBL. Each benchmark has distinct characteristics. We quantify these characteristics and argue for their complementary value in evaluation. The benchmarks collectively contain a vast number of validated biological duplicates; the largest has nearly half a billion duplicate pairs (although this is probably only a tiny fraction of the total that is present). They are also the first benchmarks targeting the primary nucleotide databases. The records include the 21 most heavily studied organisms in molecular biology research. Our quantitative analysis shows that duplicates in the different benchmarks, and in different organisms, have different characteristics. It is thus unreliable to evaluate duplicate detection methods against any single benchmark. For example, the benchmark derived from Swiss-Prot mappings identifies more diverse types of duplicates, showing the importance of expert curation, but is limited to coding sequences. Overall, these benchmarks form a resource that we believe will be of great value for development and evaluation of the duplicate detection methods that are required to help maintain these essential resources.Availability: The benchmark data sets are available at https://bitbucket.org/biodbqual/benchmarks.

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NeuroTransDB: highly curated and structured transcriptomic metadata for neurodegenerative diseases

Cited by 8 publications

References 67 publications

NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease

NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders

Benchmarks for Measurement of Duplicate Detection Methods in Nucleotide Databases

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