In jmacdon/BiocAnno2016: Package for Bioconductor 2016 annotation workshop

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

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

load(system.file("data/eset.Rdata", package = "BiocAnno2016"))
eset

ExpressionSet (continued)

head(exprs(eset))
head(pData(phenoData(eset)))

ExpressionSet (continued)

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

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.

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?

keytypes(hugene20sttranscriptcluster.db)
columns(hugene20sttranscriptcluster.db)

Another example

There is one issue with select however.

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

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.

mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID", multiVals = "list")

Choices for multiVals (continued)

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

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:

gns <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)
gns

GRangesList

Or the genomic position of all transcripts by gene:

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:

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.

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

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

library(BSgenome)
head(available.genomes())

BSgenome packages

We can load and inspect a BSgenome package

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.

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.

library(AnnotationHub)
hub <- AnnotationHub()

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

names(mcols(hub))

AnnotationHub Data providers

unique(hub$dataprovider)

AnnotationHub Data classes

unique(hub$rdataclass)

AnnotationHub Species

head(unique(hub$species))
length(unique(hub$species))

AnnotationHub Data sources

unique(hub$sourcetype)

AnnotationHub query

qry <- query(hub, c("granges","homo sapiens","ensembl"))
qry

AnnotationHub query

qry$sourceurl

Selecting AnnotationHub resource

whatIwant <- qry[["AH50377"]]

We can use these data as they are, or convert to a TxDb format:

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:

library(biomaRt)
listMarts()

biomaRt data sets

And we can then check for the available data sets on a particular server.

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.

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

atrib <- listAttributes(mart)
filts <- listFilters(mart)
head(atrib)
head(filts)

biomaRt query

A simple example query

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?

jmacdon/BiocAnno2016 documentation built on May 19, 2019, 12:49 p.m.