--- title: "Introduction to Bioconductor Annotation Resources" author: "James W. MacDonald" date: 26 June | Bioc 2016 output: ioslides_presentation: fig_retina: null css: style.css widescreen: false vignette: > %\VignetteIndexEntry{Introduction to Bioconductor Annotation Resources} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ## Goals for this workshop - Learn about various annotation package types - Learn the basics of querying these resources - Discuss annotations in regard to Bioc data structures - Get in some practice ```{r setup, include = FALSE} library(BiocAnno2016) library(hugene20sttranscriptcluster.db) library(EnsDb.Mmusculus.v79) library(org.Hs.eg.db) library(TxDb.Hsapiens.UCSC.hg19.knownGene) library(Homo.sapiens) library(BSgenome) library(BSgenome.Hsapiens.UCSC.hg19) library(AnnotationHub) ``` ## What do we mean by annotation? Map a known ID to other functional or positional information

## Specific goal We have data and statistics, and we want to add other useful information

The end result might be as simple as a data.frame or HTML table, or as complex as a `RangedSummarizedExperiment` ## Data containers

## ExpressionSet ```{r} load(system.file("data/eset.Rdata", package = "BiocAnno2016")) eset ``` ## ExpressionSet (continued) ```{r} head(exprs(eset)) head(pData(phenoData(eset))) ``` ## ExpressionSet (continued) ```{r} head(pData(featureData(eset))) ``` ## BioC containers vs basic structures ### Pros * Validity checking * Subsetting * Function dispatch * Automatic behaviors ### Cons * Difficult to create * Cumbersome to extract data by hand * Useful only within R ## Annotation sources ```{r, results = "asis", echo = FALSE} df <- data.frame("Package type" = c("ChipDb","OrgDb","TxDb/EnsDb","OrganismDb","BSgenome","Others","AnnotationHub","biomaRt"), Example = c("hugene20sttranscriptcluster.db","org.Hs.eg.db","TxDb.Hsapiens.UCSC.hg19.knownGene; EnsDb.Hsapiens.v75", "Homo.sapiens","BSgenome.Hsapiens.UCSC.hg19","GO.db; KEGG.db", "Online resource","Online resource"), check.names = FALSE) knitr::kable(df) ``` ## Interacting with AnnoDb packages The main function is `select`: select(*annopkg*, *keys*, *columns*, *keytype*) Where * annopkg is the annotation package * keys are the IDs that we **know** * columns are the values we **want** * keytype is the type of key used + if the keytype is the **central** key, it can remain unspecified ## Simple example Say we have analyzed data from an Affymetrix Human Gene ST 2.0 array and want to know what the genes are. For purposes of this lab, we just select some IDs at random. ```{r} library(hugene20sttranscriptcluster.db) set.seed(12345) ids <- featureNames(eset)[sample(1:25000, 5)] ids select(hugene20sttranscriptcluster.db, ids, "SYMBOL") ``` ## Questions! How do you know what the central keys are? * If it's a ChipDb, the central key are the manufacturer's probe IDs * It's sometimes in the name - org.Hs.eg.db, where 'eg' means Entrez Gene ID * You can see examples using e.g., head(keys(*annopkg*)), and infer from that * But note that it's never necessary to know the central key, as long as you specify the keytype ## More questions! What keytypes or columns are available for a given annotation package? ```{r} keytypes(hugene20sttranscriptcluster.db) columns(hugene20sttranscriptcluster.db) ``` ## Another example There is one issue with `select` however. ```{r} ids <- c('16737401','16657436' ,'16678303') select(hugene20sttranscriptcluster.db, ids, c("SYMBOL","MAP")) ``` ## The `mapIds` function An alternative to `select` is `mapIds`, which gives control of duplicates * Same arguments as `select` with slight differences - The columns argument can only specify one column - The keytype argument **must** be specified - An additional argument, multiVals used to control duplicates ```{r} mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID") ``` ## Choices for multiVals Default is `first`, where we just choose the first of the duplicates. Other choices are `list`, `CharacterList`, `filter`, `asNA` or a user-specified function. ```{r} mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID", multiVals = "list") ``` ## Choices for multiVals (continued) ```{r} mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID", multiVals = "CharacterList") mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID", multiVals = "filter") mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID", multiVals = "asNA") ``` ## ChipDb/OrgDb questions Using either the hugene20sttranscriptcluster.db or org.Hs.eg.db package, * What gene symbol corresponds to Entrez Gene ID 1000? * What is the Ensembl Gene ID for PPARG? * What is the UniProt ID for GAPDH? * How many of the probesets from the ExpressionSet (eset) we loaded map to a single gene? How many don't map to a gene at all? ## TxDb packages TxDb packages contain positional information; the contents can be inferred by the package name **TxDb.Species.Source.Build.Table** * TxDb.Hsapiens.UCSC.hg19.knownGene - *Homo sapiens* - UCSC genome browser - hg19 (their version of GRCh37) - knownGene table TxDb.Dmelanogaster.UCSC.dm3.ensGene TxDb.Athaliana.BioMart.plantsmart22 ## EnsDb packages EnsDb packages are similar to TxDb packages, but based on Ensembl mappings EnsDb.Hsapiens.v79 EnsDb.Mmusculus.v79 EnsDb.Rnorvegicus.v79 ## Transcript packages As with ChipDb and OrgDb packages, `select` and `mapIds` can be used to make queries ```{r} select(TxDb.Hsapiens.UCSC.hg19.knownGene, c("1","10"), c("TXNAME","TXCHROM","TXSTART","TXEND"), "GENEID") select(EnsDb.Hsapiens.v79, c("1", "10"), c("GENEID","GENENAME","SEQNAME","GENESEQSTART","GENESEQEND"), "ENTREZID") ``` But this is not how one normally uses them... ## GRanges The normal use case for transcript packages is to extract positional information into a `GRanges` or `GRangesList` object. An example is the genomic position of all genes: ```{r} gns <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene) gns ``` ## GRangesList Or the genomic position of all transcripts **by** gene: ```{r} txs <- transcriptsBy(TxDb.Hsapiens.UCSC.hg19.knownGene) txs ``` ## Other accessors * Positional information can be extracted for `transcripts`, `genes`, coding sequences (`cds`), `promoters` and `exons`. * Positional information can be extracted for most of the above, grouped by a second element. For example, our `transcriptsBy` call was all transcripts, grouped by gene. * More detail on these *Ranges objects is beyond the scope of this workshop, but why we want them is not. ## Why *Ranges objects The main rationale for *Ranges objects is to allow us to easily select and subset data based on genomic position information. This is really powerful! `GRanges` and `GRangesLists` act like data.frames and lists, and can be subsetted using the `[` function. As a really artificial example: ```{r} txs[txs %over% gns[1:2,]] ``` ## *Ranges use cases * Gene expression changes near differentially methylated CpG islands * Closest genes to a set of interesting SNPs * Genes near DNAseI hypersensitivity clusters * Number of CpGs measured over Gene X by Chip Y ## SummarizedExperiment objects SummarizedExperiment objects are like ExpressionSets, but the row-wise annotations are GRanges, so you can subset by genomic locations:

## TxDb exercises * How many transcripts does PPARG have, according to UCSC? * Does Ensembl agree? * How many genes are between 2858473 and 3271812 on chr2 in the hg19 genome? - Hint: you make a `GRanges` like this - `GRanges("chr2", IRanges(2858473,3271812))` ## OrganismDb packages OrganismDb packages are meta-packages that contain an OrgDb, a TxDb, and a GO.db package and allow cross-queries between those packages. All previous accessors work; `select`, `mapIds`, `transcripts`, etc. ```{r} library(Homo.sapiens) Homo.sapiens ``` ## OrganismDb packages * Updateable - can change TxDb object * columns and keytypes span all underlying objects * Calls to TxDb accessors include a 'columns' argument ```{r } head(genes(Homo.sapiens, columns = c("ENTREZID","ALIAS","UNIPROT")),4) ``` ## OrganismDb exercises * Get all the GO terms for BRCA1 * What gene does the UCSC transcript ID uc002fai.3 map to? * How many other transcripts does that gene have? * Get all the transcripts from the hg19 genome build, along with their Ensembl gene ID, UCSC transcript ID and gene symbol ## BSgenome packages BSgenome packages contain sequence information for a given species/build. There are many such packages - you can get a listing using `available.genomes` ```{r} library(BSgenome) head(available.genomes()) ``` ## BSgenome packages We can load and inspect a BSgenome package ```{r} library(BSgenome.Hsapiens.UCSC.hg19) Hsapiens ``` ## BSgenome packages The main accessor is `getSeq`, and you can get data by sequence (e.g., entire chromosome or unplaced scaffold), or by passing in a GRanges object, to get just a region. ```{r} getSeq(Hsapiens, "chr1") getSeq(Hsapiens, gns["5467",]) ``` The Biostrings package contains most of the code for dealing with these `*StringSet` objects - please see the Biostrings vignettes and help pages for more information. ## BSgenome exercises * Get the sequences for all transcripts of the TP53 gene ## AnnotationHub AnnotationHub is a package that allows us to query and download many different annotation objects, without having to explicitly install them. ```{r, include = FALSE} library(AnnotationHub) hub <- AnnotationHub() ``` ```{r} library(AnnotationHub) hub <- AnnotationHub() hub ``` ## Querying AnnotationHub Finding the 'right' resource on AnnotationHub is like using Google - a well posed query is necessary to find what you are after. Useful queries are based on * Data provider * Data class * Species * Data source ```{r} names(mcols(hub)) ``` ## AnnotationHub Data providers ```{r} unique(hub$dataprovider) ``` ## AnnotationHub Data classes ```{r} unique(hub$rdataclass) ``` ## AnnotationHub Species ```{r} head(unique(hub$species)) length(unique(hub$species)) ``` ## AnnotationHub Data sources ```{r} unique(hub$sourcetype) ``` ## AnnotationHub query ```{r} qry <- query(hub, c("granges","homo sapiens","ensembl")) qry ``` ## AnnotationHub query ```{r} qry$sourceurl ``` ## Selecting AnnotationHub resource ```{r, message = FALSE} whatIwant <- qry[["AH50377"]] ``` We can use these data as they are, or convert to a TxDb format: ```{r} GRCh38TxDb <- makeTxDbFromGRanges(whatIwant) GRCh38TxDb ``` ## AnnotationHub exercises * How many resources are on AnnotationHub for Atlantic salmon (Salmo salar)? * Get the most recent Ensembl build for domesticated dog (Canis familiaris) and make a TxDb ## biomaRt The biomaRt package allows queries to an Ensembl Biomart server. We can see the choices of servers that we can use: ```{r} library(biomaRt) listMarts() ``` ## biomaRt data sets And we can then check for the available data sets on a particular server. ```{r} mart <- useMart("ENSEMBL_MART_ENSEMBL") head(listDatasets(mart)) ``` ## biomaRt queries After setting up a `mart` object pointing to the server and data set that we care about, we can make queries. We first set up the `mart` object. ```{r} mart <- useMart("ENSEMBL_MART_ENSEMBL","hsapiens_gene_ensembl") ``` Queries are of the form getBM(*attributes*, *filters*, *values*, *mart*) where * attributes are the things we **want** * filters are the *types of* IDs we **have** * values are the IDs we **have** * mart is the `mart` object we set up ## biomaRt attributes and filters Both attributes and filters have rather inscrutable names, but a listing can be accessed using ```{r} atrib <- listAttributes(mart) filts <- listFilters(mart) head(atrib) head(filts) ``` ## biomaRt query A simple example query ```{r} afyids <- c("1000_at","1001_at","1002_f_at","1007_s_at") getBM(c("affy_hg_u95av2", "hgnc_symbol"), c("affy_hg_u95av2"), afyids, mart) ``` ## biomaRt exercises * Get the Ensembl gene IDs and HUGO symbol for Entrez Gene IDs 672, 5468 and 7157 * What do you get if you query for the 'gene_exon' for GAPDH?