--- title: "Lab 1.4: Annotation Resources" output: BiocStyle::html_document: toc: true vignette: > % \VignetteIndexEntry{Lab 1.4: Annotation Resources} % \VignetteEngine{knitr::rmarkdown} --- ```{r style, echo = FALSE, results = 'asis'} BiocStyle::markdown() ``` ```{r setup, echo=FALSE, warning=FALSE} options(max.print=1000, width=100) knitr::opts_chunk$set(cache=TRUE) suppressPackageStartupMessages({ library(org.Hs.eg.db) library(TxDb.Hsapiens.UCSC.hg19.knownGene) library(BSgenome.Hsapiens.UCSC.hg19) library(GenomicRanges) library(biomaRt) library(rtracklayer) library(Gviz) library(AnnotationHub) }) ``` Authors: Valerie Obenchain (valerie.obenchain@roswellpark.org.org), Lori Shepherd (lori.shepherd@roswellpark.org), Martin Morgan (martin.morgan@roswellpark.org)
Date: 25 June, 2016
# Gene annotation ## Data packages Organism-level ('org') packages contain mappings between a central identifier (e.g., Entrez gene ids) and other identifiers (e.g. GenBank or Uniprot accession number, RefSeq id, etc.). The name of an org package is always of the form `org...db` (e.g. [org.Sc.sgd.db][]) where `` is a 2-letter abbreviation of the organism (e.g. `Sc` for *Saccharomyces cerevisiae*) and `` is an abbreviation (in lower-case) describing the type of central identifier (e.g. `sgd` for gene identifiers assigned by the *Saccharomyces* Genome Database, or `eg` for Entrez gene ids). The "How to use the '.db' annotation packages" vignette in the [AnnotationDbi][] package (org packages are only one type of ".db" annotation packages) is a key reference. The '.db' and most other Bioconductor annotation packages are updated every 6 months. Annotation packages usually contain an object named after the package itself. These objects are collectively called `AnnotationDb` objects, with more specific classes named `OrgDb`, `ChipDb` or `TranscriptDb` objects. Methods that can be applied to these objects include `cols()`, `keys()`, `keytypes()` and `select()`. Common operations for retrieving annotations are summarized in the table. | Category | Function | Description | |------------|---------------------------------------|------------------------------------------------------------------| | Discover | `columns()` | List the kinds of columns that can be returned | | | `keytypes()` | List columns that can be used as keys | | | `keys()` | List values that can be expected for a given keytype | | | `select()` | Retrieve annotations matching `keys`, `keytype` and `columns` | | Manipulate | `setdiff()`, `union()`, `intersect()` | Operations on sets | | | `duplicated()`, `unique()` | Mark or remove duplicates | | | `%in%`, `match()` | Find matches | | | `any()`, `all()` | Are any `TRUE`? Are all? | | | `merge()` | Combine two different \Robject{data.frames} based on shared keys | | `GRanges*` | `transcripts()`, `exons()`, `cds()` | Features (transcripts, exons, coding sequence) as `GRanges`. | | | `transcriptsBy()` , `exonsBy()` | Features group by gene, transcript, etc., as `GRangesList`. | | | `cdsBy()` | | ## Internet resources A short summary of select Bioconductor packages enabling web-based queries is in following Table. | Package | Description | |-----------------------------------------------------|-------------------------------------------| | [AnnotationHub][] | Ensembl, Encode, dbSNP, UCSC data objects | | [biomaRt](http://biomart.org) | Ensembl and other annotations | | [PSICQUIC](https://code.google.com/p/psicquic) | Protein interactions | | [uniprot.ws](http://uniprot.org) | Protein annotations | | [KEGGREST](http://www.genome.jp/kegg) | KEGG pathways | | [SRAdb](http://www.ncbi.nlm.nih.gov/sra) | Sequencing experiments. | | [rtracklayer](http://genome.ucsc.edu) | genome tracks. | | [GEOquery](http://www.ncbi.nlm.nih.gov/geo/) | Array and other data | | [ArrayExpress](http://www.ebi.ac.uk/arrayexpress/) | Array and other data | *Using biomaRt* The [biomaRt][] package offers access to the online [biomart](http://www.biomart.org) resource. this consists of several data base resources, referred to as 'marts'. Each mart allows access to multiple data sets; the [biomaRt][] package provides methods for mart and data set discovery, and a standard method `getBM()` to retrieve data. ## Exercises **Exercise**: This exercise illustrates basic use of the `select' interface to annotation packages. 1. What is the name of the org package for *Homo sapiens*? Load it. Display the `OrgDb` object for the [org.Hs.eg.db][] package. Use the `columns()` method to discover which sorts of annotations can be extracted from it. 2. Use the `keys()` method to extract ENSEMBL identifiers and then pass those keys in to the `select()` method in such a way that you extract the SYMBOL (gene symbol) and GENENAME information for each. Use the following ENSEMBL ids. ```{r select-setup} ensids <- c("ENSG00000130720", "ENSG00000103257", "ENSG00000156414", "ENSG00000144644", "ENSG00000159307", "ENSG00000144485") ``` **Solution** The `OrgDb` object is named `org.Hs.eg.db`. ```{r select} library(org.Hs.eg.db) keytypes(org.Hs.eg.db) columns(org.Hs.eg.db) cols <- c("SYMBOL", "GENENAME") select(org.Hs.eg.db, keys=ensids, columns=cols, keytype="ENSEMBL") ``` **Exercise** Internet access required for this exercise 1. Load the [biomaRt][] package and list the available marts. Choose the *ensembl* mart and list the datasets for that mart. Set up a mart to use the *ensembl* mart and the *hsapiens gene ensembl* dataset. 2. A [biomaRt][] dataset can be accessed via `getBM()`. In addition to the mart to be accessed, this function takes filters and attributes as arguments. Use `filterOptions()` and `listAttributes()` to discover values for these arguments. Call `getBM()` using filters and attributes of your choosing. **Solution** ```{r biomaRt1, eval=FALSE, results="hide"} ## NEEDS INTERNET ACCESS !! library(biomaRt) head(listMarts(), 3) ## list the marts head(listDatasets(useMart("ensembl")), 3) ## mart datasets ensembl <- ## fully specified mart useMart("ensembl", dataset = "hsapiens_gene_ensembl") head(listFilters(ensembl), 3) ## filters myFilter <- "chromosome_name" substr(filterOptions(myFilter, ensembl), 1, 50) ## return values myValues <- c("21", "22") head(listAttributes(ensembl), 3) ## attributes myAttributes <- c("ensembl_gene_id","chromosome_name") ## assemble and query the mart res <- getBM(attributes = myAttributes, filters = myFilter, values = myValues, mart = ensembl) ``` **Exercise** As an optional exercise to be completed after Tuesday's lab, annotate the genes that are differentially expressed in the DESeq2 laboratory, e.g., find the *GENENAME* associated with the five most differentially expressed genes. Do these make biological sense? Can you `merge()` the annotation results with the `top table' results to provide a statistically and biologically informative summary? # Genome annotation There are a diversity of packages and classes available for representing large genomes. Several include: - 'TxDb.*' For transcript and other genome / coordinate annotation. - [BSgenome][] For whole-genome representation. See `available.genomes()` for pre-packaged genomes, and the vignette 'How to forge a BSgenome data package' in the - [Homo.sapiens][] For integrating 'TxDb*' and 'org.*' packages. - 'SNPlocs.*' For model organism SNP locations derived from dbSNP. - `FaFile()` ([Rsamtools][]) for accessing indexed FASTA files. - [ensemblVEP][] Variant effect scores. ## Transcript annotation packages Genome-centric packages are very useful for annotations involving genomic coordinates. It is straight-forward, for instance, to discover the coordinates of coding sequences in regions of interest, and from these retrieve corresponding DNA or protein coding sequences. Other examples of the types of operations that are easy to perform with genome-centric annotations include defining regions of interest for counting aligned reads in RNA-seq experiments and retrieving DNA sequences underlying regions of interest in ChIP-seq analysis, e.g., for motif characterization. ## _rtracklayer_ The [rtracklayer][] package allows us to query the UCSC genome browser, as well as providing `import()` and `export()` functions for common annotation file formats like GFF, GTF, and BED. The exercise below illustrates some of the functionality of [rtracklayer][]. ## Exercises **Exercise** This exercise uses annotation resources to go from a gene symbol 'BRCA1' through to the genomic coordinates of each transcript associated with the gene, and finally to the DNA sequences of the transcripts. 1. Use the [org.Hs.eg.db][] package to map from the gene symbol 'BRCA1' to its Entrez identifier. Do this using the `select` command. 2. Use the [TxDb.Hsapiens.UCSC.hg19.knownGene][] package to retrieve the transcript names (`TXNAME`) corresponding to the BRCA1 Entrez identifier. (The 'org\*' packages are based on information from NCBI, where Entrez identifiers are labeled ENTREZID; the 'TxDb*' package we are using is from UCSC, where Entrez identifiers are labelled GENEID). 3. Use the `cdsBy()` function to retrieve the genomic coordinates of all coding sequences grouped by transcript, and select the transcripts corresponding to the identifiers we're interested in. The coding sequences are returned as an `GRangesList`, where each element of the list is a `GRanges` object representing the exons in the coding sequence. As a sanity check, ensure that the sum of the widths of the exons in each coding sequence is evenly divisble by 3 (the R 'modulus' operator `%%` returns the remainder of the division of one number by another, and might be helpful in this case). 4. Visualize the transcripts in genomic coordinates using the [Gviz][] package to construct a `GeneRegionTrack`, and plotting it using `plotTracks()`. 5. Use the [Bsgenome.Hsapiens.UCSC.hg19][] package and `extractTranscriptSeqs()` function to extract the DNA sequence of each transcript. **Solution** Retrieve the Entrez identifier corresponding to the BRCA1 gene symbol ```{r symbol-to-entrez} library(org.Hs.eg.db) eid <- select(org.Hs.eg.db, "BRCA1", "ENTREZID", "SYMBOL")[["ENTREZID"]] ``` Map from Entrez gene identifier to transcript name ```{r entrez-to-tx, message=FALSE} library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene txid <- select(txdb, eid, "TXNAME", "GENEID")[["TXNAME"]] ``` Retrieve all coding sequences grouped by transcript, and select those matching the transcript ids of interest, verifying that each coding sequence width is a multiple of 3 ```{r tx-to-cds-coords} cds <- cdsBy(txdb, by="tx", use.names=TRUE) brca1cds <- cds[names(cds) %in% txid] class(brca1cds) length(brca1cds) brca1cds[[1]] # exons in cds cdswidth <- width(brca1cds) # width of each exon all((sum(cdswidth) %% 3) == 0) # sum within cds, modulus 3 ``` Visualize the BRCA1 transcirpts using [Gviz][] (this package has an excellent vignette, `vignette("Gviz")`) ```{r Gviz, message=FALSE} library(Gviz) grt <- GeneRegionTrack(txdb) plotTracks(list(GenomeAxisTrack(), grt), chromosome="chr17", from=min(start(unlist(brca1cds))), to=max(end(unlist(brca1cds)))) ``` Extract the coding sequences of each transcript ```{r cds-to-seq} library(BSgenome.Hsapiens.UCSC.hg19) genome <- BSgenome.Hsapiens.UCSC.hg19 tx_seq <- extractTranscriptSeqs(genome, brca1cds) tx_seq ``` Intron coordinates can be identified by first calculating the range of the genome (from the start of the first exon to the end of the last exon) covered by each transcript, and then taking the (algebraic) set difference between this and the genomic coordinates covered by each exon ```{r introns} introns <- psetdiff(unlist(range(brca1cds)), brca1cds) ``` Retrieve the intronic sequences with `getSeq()` (these are *not* assembled, the way that `extractTranscriptSeqs()` assembles exon sequences into mature transcripts); note that introns start and end with the appropriate acceptor and donor site sequences. ```{r intron-seqs} seq <- getSeq(genome, introns) names(seq) seq[["uc010whl.2"]] # 21 introns ``` **Exercise** Internet access required for this exercise Here we use [rtracklayer][] to retrieve estrogen receptor binding sites identified across cell lines in the ENCODE project. We focus on binding sites in the vicinity of a particularly interesting region of interest. 1. Define our region of interest by creating a `GRanges` instance with appropriate genomic coordinates. Our region corresponds to 10Mb up- and down-stream of a particular gene. 2. Create a session for the UCSC genome browser 3. Query the UCSC genome browser for ENCODE estrogen receptor ERalpha_a transcription marks; identifying the appropriate track, table, and transcription factor requires biological knowledge and detective work. 4. Visualize the location of the binding sites and their scores; annotate the mid-point of the region of interest. **Solution** Define the region of interest ```{r rtracklayer-roi} library(GenomicRanges) roi <- GRanges("chr10", IRanges(92106877, 112106876, names="ENSG00000099194")) ``` Create a session ```{r rtracklayer-session, eval=FALSE} library(rtracklayer) session <- browserSession() ``` Query the UCSC for a particular track, table, and transcription factor, in our region of interest ```{r rtracklayer-marks, eval=FALSE} trackName <- "wgEncodeRegTfbsClusteredV2" tableName <- "wgEncodeRegTfbsClusteredV2" trFactor <- "ERalpha_a" ucscTable <- getTable(ucscTableQuery(session, track=trackName, range=roi, table=tableName, name=trFactor)) ``` Visualize the result ```{r rtracklayer-plot, fig.height=3, eval=FALSE} plot(score ~ chromStart, ucscTable, pch="+") abline(v=start(roi) + (end(roi) - start(roi) + 1) / 2, col="blue") ``` # AnnotationHub [AnnotationHub][] is a data base of large-scale whole-genome resources, e.g., regulatory elements from the Roadmap Epigenomics project, Ensembl GTF and FASTA files for model and other organisms, and the NHLBI [grasp2db][] data base of GWAS results. There are many interesting ways in which these resources can be used. Examples include - Easily access and import Roadmap Epigenomics files. - 'liftOver' genomic range-based annotations from one coordinate system (e.g, hg19) to another (e.g., GRCh 38); - Create TranscriptDb and BSgenome-style annotation resources 'on the fly' for a diverse set of organisms. - Programmatically access the genomic coordiantes of clinically relevant variants cataloged in dbSNP. Unfortunately, [AnnotationHub][] makes extensive use of internet resources and so we will not pursue it in this course; see the vignettes that come with the pacakge, for instance [_AnnotationHub_ HOW-TOs](http://bioconductor.org/packages/devel/bioc/vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html). # Resources Acknowledgements The material for this lab was taken from a presentation given by Martin Morgan at CSAMA 2015. [AnnotationDbi]: http://bioconductor.org/packages/AnnotationDbi [AnnotationHub]: http://bioconductor.org/packages/AnnotationHub [BSgenome]: http://bioconductor.org/packages/release/BSgenome [Bsgenome.Hsapiens.UCSC.hg19]: http://bioconductor.org/packages/Bsgenome.Hsapiens.UCSC.hg19 [grasp2db]: http://bioconductor.org/packages/release/grasp2db [Gviz]: http://bioconductor.org/packages/release/Gviz [Homo.sapiens]: http://bioconductor.org/packages/release/Homo.sapiens [Rsamtools]: http://bioconductor.org/packages/Rsamtools [TxDb.Hsapiens.UCSC.hg19.knownGene]: http://bioconductor.org/packages/TxDb.Hsapiens.UCSC.hg19.knownGene [biomaRt]: http://bioconductor.org/packages/biomaRt [org.Hs.eg.db]: http://bioconductor.org/packages/org.Hs.eg.db [org.Sc.sgd.db]: http://bioconductor.org/packages/org.Sc.sgd.db [rtracklayer]: http://bioconductor.org/packages/release/rtracklayer