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MoPad_notes.html
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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<title>/11973$1</title>
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<body> <br
/>************ We can post notes and links about our project *******************<br
/><br
/>Link to presentation:<br
/><br
/>pdf: <a href="https://drive.google.com/file/d/0B84zZBsjht1UWGZMNTZhSHZac00/view?usp=sharing">https://drive.google.com/file/d/0B84zZBsjht1UWGZMNTZhSHZac00/view?usp=sharing</a><br
/>powerpoint: <a href="https://drive.google.com/file/d/0B84zZBsjht1US3lKQWJvdkNHcHc/view?usp=sharing">https://drive.google.com/file/d/0B84zZBsjht1US3lKQWJvdkNHcHc/view?usp=sharing</a><br
/><br
/><br
/><b>WORK IN PROGRESS AND GOALS</b><br
/><br
/>3. Assign mouse genes to human orthologs -- HomolGene, orthoDB<br
/>4. Test gene set overlaps for significance to score mice as a model for a given disease, and/or "map" mouse phenotypes to human diseases<br
/>5. Repeat step 4 at the protein sub-network level<br
/> a. need to think about how to aggregate protein sub-network info from gene-level data<br
/>6. Create protein(gene) sub network assignments<br
/><br
/><br
/><b>Table 1</b><br
/><b>M. Phenotype Data</b> | M. Gene | H. Gene <br
/><br
/>Table 2<br
/>Human Gene | Disease DB | Disease db2<br
/><br
/>Data Table:<br
/>Inner join of T1 and T2 on H. Gene<br
/><br
/><b>Edward is getting columns 1-2 (column 1 may be multiple columns)</b><br
/>joined on mouse gene to: <br
/><b>Frank Is getting Columns 2-3</b><br
/>joined on human gene to: human HGNC --> human Entrez --> mouse entrez --> MGI<br
/> Human entrez --> mouse entrez can be done with NCBI's homologene<br
/> MGI to Entrez can be done with NCBI's gene2Alias (bioconductor???)<br
/><b>Emily and Qing are getting columns 3-5</b><br
/>David: familiarize with biogrid protein-protein interaction (PPIs) from Biogrid and Mint<br
/><a href="https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=biogrid">https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=biogrid</a><br
/><a href="http://mint.bio.uniroma2.it/mint/Welcome.do">http://mint.bio.uniroma2.it/mint/Welcome.do</a><br
/><br
/>GitHub Repo:<br
/><a href="https://github.com/emilliman5/Alzheimers_Dash">https://github.com/emilliman5/Alzheimers_Dash</a><br
/><br
/><a href="http://www.genomebiology.com/content/pdf/gb-2010-11-10-404.pdf">http://www.genomebiology.com/content/pdf/gb-2010-11-10-404.pdf</a> (Title: Do it yourself Genetic Testing).<br
/><br
/>Short tut on Short read alignment<br
/><a href="https://blog.sbgenomics.com/short-read-alignment-algorithms/">https://blog.sbgenomics.com/short-read-alignment-algorithms/</a><br
/><br
/>NHGRI: GWAS catalogue, Later generation: Grasp, ref:<br
/><br
/><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4072913/">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4072913/</a><br
/>download link to the data: <br
/><a href="https://www.ebi.ac.uk/gwas/docs/downloads">https://www.ebi.ac.uk/gwas/docs/downloads</a><br
/><br
/>GRASP DB<br
/><a href="http://omictools.com/grasp-s5332.html">http://omictools.com/grasp-s5332.html</a><br
/><br
/>JAX memory data<br
/><a href="http://www.informatics.jax.org/mp/annotations/MP:0002801">http://www.informatics.jax.org/mp/annotations/MP:0002801</a><br
/><br
/>Genome GWAS<br
/><a href="https://www.genome.gov/26525384">https://www.genome.gov/26525384</a><br
/><br
/>Geneset enrichment analysis<br
/><a href="https://en.wikipedia.org/wiki/Gene_set_enrichment">https://en.wikipedia.org/wiki/Gene_set_enrichment</a><br
/><a href="http://www.pnas.org/content/102/43/15545.full">http://www.pnas.org/content/102/43/15545.full</a><br
/><br
/>In NCBI-Hackathons <a href="https://github.com/NCBI-Hackathons">https://github.com/NCBI-Hackathons</a><br
/><br
/>DC genomics Meetup Repos<br
/><a href="https://github.com/DCGenomics?tab=repositories">https://github.com/DCGenomics?tab=repositories</a><br
/><br
/>Also, shiny app:<br
/><a href="http://shiny.as.uky.edu:3838/mcfsa/">http://shiny.as.uky.edu:3838/mcfsa/</a><br
/><br
/>Phenotype expression browser from Jax<br
/><a href="http://www.informatics.jax.org/searches/Phat.cgi?id=MP:0002063">http://www.informatics.jax.org/searches/Phat.cgi?id=MP:0002063</a><br
/><br
/>Other data source: Alzheimer's challenge data, which is already in accessible data format.<br
/><a href="https://www.synapse.org/#">https://www.synapse.org/#</a>!Synapse:syn2290704/wiki/64710; clinical data available at: <a href="https://github.com/juanjold/cis519/find/master">https://github.com/juanjold/cis519/find/master</a>. Still waiting to get genetics data access from <a href="http://utilities.loni.usc.edu/map/downloads.jsp">http://utilities.loni.usc.edu/map/downloads.jsp</a>.<br
/><br
/>And other data challenges: <a href="http://dreamchallenges.org/">http://dreamchallenges.org/</a><br
/><br
/>OrthoDB: <a href="http://orthodb.org/">http://orthodb.org/</a><br
/>NCBI Homologene: <a href="http://www.ncbi.nlm.nih.gov/homologene">http://www.ncbi.nlm.nih.gov/homologene</a><br
/><br
/>Get orthologous genes between mouse and human:<br
/><a href="http://www.ensembl.info/blog/2009/01/21/how-to-get-all-the-orthologous-genes-between-two-species/">http://www.ensembl.info/blog/2009/01/21/how-to-get-all-the-orthologous-genes-between-two-species/</a><br
/><br
/>Using Bioconductor to get human-mouse orthologous genes:<br
/><a href="https://support.bioconductor.org/p/52812/">https://support.bioconductor.org/p/52812/</a><br
/><br
/><br
/>Code for mapping MGI Alzheimer disease allele gene to human orthologous gene:<br
/><br
/>library(readr)<br
/><br
/># read in the MGI Alzheimer disease allele data<br
/>mgi_allele <- read_csv("MGI_alzheimer.csv")<br
/><br
/># change column name to replace spaces in name<br
/>names(mgi_allele) <- make.names(names(mgi_allele), unique=TRUE)<br
/><br
/># get only targeted mutation<br
/>mgi_target <- subset(mgi_allele, Allele.Type == "Targeted")<br
/># get allele information<br
/>mgi_target[,2]<br
/><br
/># get unique gene name from allele name<br
/>mgi_gene <- unique(sapply(strsplit(mgi_target[,2], "<"), "[", 1))<br
/><br
/><br
/><br
/>library(biomaRt)<br
/><br
/># set Biomart human gene set<br
/>human = useMart("ensembl", dataset = "hsapiens_gene_ensembl")<br
/># set Biomart mouse gene set<br
/>mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl")<br
/><br
/># map mouse gene symbol to human gene symbol<br
/>hs_gene <- getLDS(attributes = c("mgi_symbol"), filters = "mgi_symbol", values = mgi_gene ,mart = mouse, attributesL = c("hgnc_symbol","chromosome_name", "start_position"), martL = human, uniqueRows=T)<br
/><br
/>A<-biogrid2[,c(1,2)] colnames(A)<-c("Gene1","Gene2") T<-table(A) Td<-dist(T)<br
/><br
/><br
/>JOINing data in R using data.table<br
/><a href="https://rstudio-pubs-static.s3.amazonaws.com/52230_5ae0d25125b544caab32f75f0360e775.html">https://rstudio-pubs-static.s3.amazonaws.com/52230_5ae0d25125b544caab32f75f0360e775.html</a><br
/><br
/>reshape[is.na(reshape)]<-1<br
/>reshape.transform<--log10(reshape)<br
/>reshape.transform[reshape.transform==Inf]<-300<br
/><br
/><br
/>Heatmap:<br
/><a href="http://blog.rstudio.org/2015/06/24/d3heatmap/">http://blog.rstudio.org/2015/06/24/d3heatmap/</a><br
/><br
/>Example:<br
/><br
/>library(d3heatmap)<br
/>url <- "<a href="http://datasets.flowingdata.com/ppg2008.csv">http://datasets.flowingdata.com/ppg2008.csv</a>"<br
/>nba_players <- read.csv(url, row.names = 1)<br
/>d3heatmap(nba_players, scale = "column")<br
/><br
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