InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation

Breuer, Karin; Foroushani, Amir; Laird, Matthew R.; Chen, Carol; Sribnaia, Anastasia; Lo, Raymond; Winsor, Geoffrey L.; Hancock, Robert E. W.; Brinkman, Fiona S. L.; Lynn, David J.

doi:10.1093/nar/gks1147

Cited by 1,102 publications

(980 citation statements)

References 24 publications

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“…4) and that it consists of a much larger number of genes than previously reported. In fact, comparison of the differentially regulated genes to the genes listed to be involved in functions related to human innate immune responses in the InnateDB (15) provides us with 17.4% overlap for the seroconverted children (19 genes; P = 0.0000003) and 9.5% overlap with the progressors (45 genes; P = 0.0000013), respectively (Supplementary Tables 1 and 3, InnateDB columns). IFN-a and -b were not differentially regulated in our data, indicating that the blood cells are showing an indirect response to cytokines produced elsewhere.…”

Section: Discussionmentioning

confidence: 99%

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Innate Immune Activity Is Detected Prior to Seroconversion in Children With HLA-Conferred Type 1 Diabetes Susceptibility

et al. 2014

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The insult leading to autoantibody development in children who will progress to develop type 1 diabetes (T1D) has remained elusive. To investigate the genes and molecular pathways in the pathogenesis of this disease, we performed genome-wide transcriptomics analysis on a unique series of prospective whole-blood RNA samples from at-risk children collected in the Finnish Type 1 Diabetes Prediction and Prevention study. We studied 28 autoantibody-positive children, out of which 22 progressed to clinical disease. Collectively, the samples covered the time span from before the development of autoantibodies (seroconversion) through the diagnosis of diabetes. Healthy control subjects matched for date and place of birth, sex, and HLA-DQB1 susceptibility were selected for each case. Additionally, we genotyped the study subjects with Immunochip to identify potential genetic variants associated with the observed transcriptional signatures. Genes and pathways related to innate immunity functions, such as the type 1 interferon (IFN) response, were active, and IFN response factors were identified as central mediators of the IFN-related transcriptional changes. Importantly, this signature was detected already before the T1D-associated autoantibodies were detected. Together, these data provide a unique resource for new hypotheses explaining T1D biology.

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Section: Discussionmentioning

confidence: 99%

“…The human genes with literature-annotated function in the innate immunity were downloaded from http://www .innatedb.org (15). The enrichment of these genes among our differentially expressed genes was calculated using Fisher exact test.…”

Section: Differentially Expressed Genes With Functions Related To Thementioning

confidence: 99%

Innate Immune Activity Is Detected Prior to Seroconversion in Children With HLA-Conferred Type 1 Diabetes Susceptibility

et al. 2014

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“…Interactions from 5 databases were imported and merged for constructing the PPI network-iRefIndex (Razick, Magklaras and Donaldson, 2008), APID Interactomes (Alonso- López et al, 2016), menthe (Calderone, Castagnoli and Cesareni, 2013), IntAct (Kerrien et al, 2012), InnateDB-all (Breuer et al, 2013). From the initial network, connected components were identified and the largest individual connected component was extracted to generate a separate second network.…”

Section: Protein-protein Interaction Network (Ppi Network) Analysis Fmentioning

confidence: 99%

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

Ahammad

2018

Preprint

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Myocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and downregulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

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“…These PPIs data come from various resources, including MatrixDB [31], InnateDB [32], IntAct [33], BioGRID [34] and DIP [35]. In this experiments, we only extract protein sequences in which two interaction partners are exactly the same and interactive type is the 'direct interaction' in relevant databases.…”

Section: Datasetmentioning

confidence: 99%

SIPEC: Systematic identification of self-interacting proteins with ensemble classifiers using evolutionary information

Wang¹,

You²,

Zhang³

et al. 2018

Oncotarget

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As the center of most biological processes, Protein-Protein Interactions (PPIs) constitute the basis of the formation of biological mechanisms. Deregulation of PPIs results in many diseases including cancer and pernicious anemia. As a special type of PPIs, the Self-interacting Proteins (SIPs) occupy an important position in them. Although a large number of SIPs data have been generated by experimental methods, currently-detected self-interacting proteins cover only a small part of the complete network. Therefore, there is a great need for computational methods to efficiently and accurately predict SIPs. In the present study, we introduce a novel computational method based on protein sequence information to predict SIPs. More specifically, each protein sequence is converted to Position-Specific Scoring Matrix (PSSM) containing the evolutionary information. And then an effective feature extraction approach, namely, Auto Covariance (AC) is employed to construct a feature set. Finally, the improved Rotation Forest (RF) model is used to remove the noise of the feature set and give prediction results. When performed on yeast and human SIPs data sets, the proposed method can achieve high accuracies of 80.50% and 93.70%, respectively. Our method also shows a good performance when compared with the SVM classifier and other existing methods. Consequently, the proposed method can be considered to be a promising model to predict SIPs. In addition, for the purpose of further research in the future, the user-friendly web server is freely available to academic use at http://www.proteininteraction.cn/sip/.

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InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation

Cited by 1,102 publications

References 24 publications

Innate Immune Activity Is Detected Prior to Seroconversion in Children With HLA-Conferred Type 1 Diabetes Susceptibility

Innate Immune Activity Is Detected Prior to Seroconversion in Children With HLA-Conferred Type 1 Diabetes Susceptibility

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

SIPEC: Systematic identification of self-interacting proteins with ensemble classifiers using evolutionary information

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