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Genetic association is when one or more genotypes within a population co-occur with a phenotypic trait more often than would be expected by chance occurrence. Studies of genetic association aim to test whether single-locus alleles or genotype frequencies (or more generally, multilocus haplotype frequencies) differ between two groups of individuals (usually diseased subjects and healthy controls). [Source: Wikipedia]

DiseaseConnect

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms.

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DMPred

Submitted by ChenLiang on Tue, 01/09/2018 - 16:55

Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results.

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ncDR

Submitted by ChenLiang on Tue, 01/09/2018 - 16:58

As a promising field of individualized therapy, non-coding RNA pharmacogenomics promotes the understanding of different individual responses to certain drugs and acts as a reasonable reference for clinical treatment. However, relevant information is scattered across the published literature, which is inconvenient for researchers to explore non-coding RNAs that are involved in drug resistance. To address this, we systemically identified validated and predicted drug resistance-associated microRNAs and long non-coding RNAs through manual curation and computational analysis.

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GRNMF

Submitted by ChenLiang on Tue, 01/09/2018 - 17:03

MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information.

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PACdb

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

We have developed Pharmacogenomics And Cell database (PACdb), a results database that makes available relationships between single nucleotide polymorphisms, gene expression, and cellular sensitivity to various drugs in cell-based models to help determine genetic variants associated with drug response. The current version also supports summary analysis on differentially expressed genes between the HapMap samples of European and African ancestry, as well as queries for summary information of correlations between gene expression and pharmacological phenotypes.

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Functional interpretation of microRNA-mRNA association

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The prediction of microRNA targets is a challenging task that has given rise to several prediction algorithms. Databases of predicted targets can be used in a microRNA target enrichment analysis, enhancing our capacity to extract functional information from gene lists. However, the available tools in this field analyze gene sets one by one limiting their use in a meta-analysis. Here, we present an R system for miRNA enrichment analysis that is suitable for systems biology.

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DMTHNDM

Submitted by ChenLiang on Tue, 01/09/2018 - 17:18

MicroRNAs (miRNAs), as a kind of important small endogenous single-stranded non-coding RNA, play critical roles in a large number of human diseases. However, the currently known experimental verifications of the disease-miRNA associations are still rare and experimental identification is time-consuming and labor-intensive. Accordingly, identifying potential disease-related miRNAs to help people understand the pathogenesis of complex diseases has become a hot topic.

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OncomiR

Submitted by ChenLiang on Tue, 01/09/2018 - 17:26

Dysregulation of microRNAs (miRNAs) is extensively associated with cancer development and progression. miRNAs have been shown to be biomarkers for predicting tumor formation and outcome. However, identification of the relationships between miRNA expression and tumor characteristics can be difficult and time-consuming without appropriate bioinformatics expertise. To address this issue, we present OncomiR, an online resource for exploring miRNA dysregulation in cancer.

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GAMDB

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNAs) are endogenous ~23 nucleotides (nt) RNAs, regulating gene expression by pairing to the mRNAs of protein-coding genes to direct their post-transcriptional repression. Both in normal and aberrant activities, miRNAs contribute to a recurring paradigm of cellular behaviors in pathological settings, especially in gerontology. Autophagy, a multi-step lysosomal degradation process with function to degrade long-lived proteins and damaged organelles, has significant impact on gerontology.

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LncEnvironmentDB

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The complex traits of an organism are associated with a complex interplay between genetic factors (GFs) and environmental factors (EFs). However, compared with protein-coding genes and microRNAs, there is a paucity of computational methods and bioinformatic resource platform for understanding the associations between lncRNA and EF.

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