Human Pain Genetics Database
The Human Pain Genes Database (HPGDB) is a comprehensive compilation of single nucleotide polymorphisms (SNPs) that have been reported to be associated with pain. Interpersonal variability in chronic pain etiology, acute pain sensitivity, and analgesic response has been suggestive of differences in genetic predisposition, a theory supported by the numerous genetic association studies published during the last twenty years. As common, generally low-effect markers of genetic variability, SNPs are instrumental in sampling this predisposition and in identification of risk-modifying genes. With an ever-growing number of studies, the HPGDB is a regularly-updated tabular summary of SNP-phenotype associations, populated via manually-curated literature review. It is intended to serve as a reference and to generate new hypotheses that may lead to a better understanding of underlying molecular mechanisms and consequently the development of personalized treatment.
Results are presented in the following six-column format: genetic locus (or loci where applicable), SNP rsID, allele or haplotype reported in association analysis, direction of effect, associated phenotype, and citation. Additional information is displayed upon hovering over each field, and fields provide links to the corresponding reference database in NCBI and PubMed. The data is downloadable in a variety of formats.
Human pain genetics database: a resource dedicated to human pain genetics research. . Pain 159(4):749-763. 2019.
Single nucleotide polymorphisms (SNPs) associated with the expression level of a gene or an exon are called expression quantitative trait loci (eQTLs). We have mapped eQTLs in human dorsal root ganglions (DRG), a tissue highly relevant for pain research. This resource provides processed eQTL data (SNP, gene, beta, p-value) and input files for matrix_eQTL (genotype, mRNA levels, covariates).
Effect of genetic variability on gene expression in dorsal root ganglia and association with pain phenotypes. . Cell Reports 19(9):1940-1952. 2017.
Transcriptomes (CEL format; samples section) available in GEO.
Improved mRMR is a re-implementation of the minimum redundancy maximum relevance (mRMR) feature selection algorithm with emphasis on greatly increased perfomance (1000x or greater on large data sets) and an improved user interface. There are no disadvantages to using this utility as opposed to the original release by Hanchuan Peng, but benefits include:
- results identical to original mRMR implementation by Hanchuan Peng, excluding statistically inconsequential preservation of rank corresponce in the case of metric ties
- incorporation of all improvements from the Fast-MRMR implementation by Sergio Ramírez
- additional performance improvements, such as avoiding computing mutual information for zero-entropy attributes, and careful selection of and usage of data structures
- output for each attribute includes its selection rank, entropy, mutual information with the class attribute, and mRMR score in an easily parsed format friendly to downstream manipulation
- operates directly on original textual data, requiring no transformation into a one-time binary representation
- robust data set parser fails gracefully with bad input and reports the location of the first error
- modular support in the code for arbitrary discretization routines, with several examples already provided and implemented
- support to output the result of parsing and discretization so that it can be verified and analyzed with external tools
- supports stream-based processing, and can operate equally well reading a data set from standard input, in a pipeline, from a named pipe, or with process substitution
- standard GNU getopt_long POSIX-compliant option processing, including full-featured -h/–help capability, informative error messages, graceful failure, and sensible defaults
- high-quality C++14 compliant code base