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useful_tools_for_functional_and_pathway_analysis [2020/04/22 18:09] – [Useful tools for functional and pathway analysis] ishauseful_tools_for_functional_and_pathway_analysis [2024/03/28 15:32] (current) – [Useful tools for functional and pathway analysis and target identification] admin
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-===== Useful tools for functional and pathway analysis =====+===== Useful tools for functional and pathway analysis and target identification =====
  
 +If you have a list of genes, and you want to know their functions, the diseases and pathways they might be associated with, and some known or candidate drug targets for them, you can use the following tools and databases. Your gene list might have been obtained from differential expression or other omics analyses, gene network analysis, a genome-wide association study ([[https://en.wikipedia.org/wiki/Genome-wide_association_study|GWAS]]), and alike.
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 +  * [[https://agora.adknowledgeportal.org/|Agora]] hosts evidence for association of genes with Alzheimer's disease (AD) and includes over 600 potential drug [[https://agora.adknowledgeportal.org/genes/nominated-targets|targets]] for AD.
 +  * Functional genomics repository ([[https://tf.lisanwanglab.org/FILER/|FILER]]) and Alzheimer’s Disease Variants Portal ([[https://advp.niagads.org/|ADVP]]) are functional genomics database with harmonized, extensible, indexed, searchable human functional genomics data collected from 20 data sources and 80 cohorts, respectively.
   * [[http://www.humanmine.org/%20|Humanmine ]] : given a list of genes as input (Entrez/Ensembl/Symbol or a mix), it returns interaction networks, pathways, Gene Ontology categories, relevant literature, protein domains, chromosome distributions and much more on a single page. There are also nice summaries that pop-up by hovering the mouse over a gene name.   * [[http://www.humanmine.org/%20|Humanmine ]] : given a list of genes as input (Entrez/Ensembl/Symbol or a mix), it returns interaction networks, pathways, Gene Ontology categories, relevant literature, protein domains, chromosome distributions and much more on a single page. There are also nice summaries that pop-up by hovering the mouse over a gene name.
  
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   * [[http://www.broadinstitute.org/gsea/msigdb/index.jsp%20|MSIGDB ]]has around 10,000 sets (collections) of interesting genes, including curated sets from publications (e.g. genes affected by specific genetic and chemical perturbations), canonical pathways, GO, microRNA and transcription factor (TF) targets, as well as Hallmarks of particular processes, oncogenic signatures and more. We have some custom made scripts for mining these sets. The BROAD institute has also their own software with sleek GUIs (GSEA).   * [[http://www.broadinstitute.org/gsea/msigdb/index.jsp%20|MSIGDB ]]has around 10,000 sets (collections) of interesting genes, including curated sets from publications (e.g. genes affected by specific genetic and chemical perturbations), canonical pathways, GO, microRNA and transcription factor (TF) targets, as well as Hallmarks of particular processes, oncogenic signatures and more. We have some custom made scripts for mining these sets. The BROAD institute has also their own software with sleek GUIs (GSEA).
  
-  * In addition to TFT collection from MSigDB, other resources for identifying transcription factor targets include: [[http://www.biobase-international.com/gene-regulation|TRANSFAC]], [[https://en.wikipedia.org/wiki/Open_Regulatory_Annotation_Database|ORegAnno]] and [[https://github.com/slowkow/tftargets|tftargets]]. Alternatives computational tools, such as [[http://meme-suite.org/|MEME]], [[http://pazar.info/|PAZAR]], and [[http://itfp.biosino.org/itfp/|ITFP]], should be used with care because their predictions are not as reliable as targets validated by real experiments.+  * In addition to TFT collection from MSigDB, other resources for identifying transcription factor targets include: [[http://www.biobase-international.com/gene-regulation|TRANSFAC]], [[https://en.wikipedia.org/wiki/Open_Regulatory_Annotation_Database|ORegAnno]] and [[https://github.com/slowkow/tftargets|tftargets]]. Alternatives computational tools, such as [[http://meme-suite.org/|MEME]], [[http://homer.ucsd.edu/homer/motif/|HOMER]], [[http://pazar.info/|PAZAR]], and [[http://itfp.biosino.org/itfp/|ITFP]], should be used with care because their predictions are not as reliable as targets validated by real experiments.
  
   * [[https://cran.r-project.org/web/packages/gProfileR/index.html|gProfileR]] does Pathway,GO,TF,microRNA,OMIM, and Phenotype over-representation analysis, with quite up to date annotations.   * [[https://cran.r-project.org/web/packages/gProfileR/index.html|gProfileR]] does Pathway,GO,TF,microRNA,OMIM, and Phenotype over-representation analysis, with quite up to date annotations.
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   * Functional Enrichment Analysis [[https://yulab-smu.github.io/clusterProfiler-book/chapter1.html|overview]]. Isha's list of [[https://docs.google.com/presentation/d/1c4HAcImyyU1as5qEwzmH8QIAxD25N4Z5__sUZE3n6kk/edit#slide=id.g6f90f06c9f_0_0|functional enrichment]] analysis tools in R.   * Functional Enrichment Analysis [[https://yulab-smu.github.io/clusterProfiler-book/chapter1.html|overview]]. Isha's list of [[https://docs.google.com/presentation/d/1c4HAcImyyU1as5qEwzmH8QIAxD25N4Z5__sUZE3n6kk/edit#slide=id.g6f90f06c9f_0_0|functional enrichment]] analysis tools in R.
   * [[http://gepia.cancer-pku.cn/|GEPIA]] and [[http://gepia2.cancer-pku.cn/#index|GEPIA2]] are web based resources for interactive gene expression profiling and analysis. The data sources used are TCGA and GTEx.   * [[http://gepia.cancer-pku.cn/|GEPIA]] and [[http://gepia2.cancer-pku.cn/#index|GEPIA2]] are web based resources for interactive gene expression profiling and analysis. The data sources used are TCGA and GTEx.
 +  * [[https://academic.oup.com/nar/article/49/15/8471/6329115|VarSAn]]: associating pathways with a set of **genomic variants**  using network analysis. Results are similar to a simple overlap analysis using a hypergeometric test.