Switch branches/tags
Nothing to show
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
349 lines (273 sloc) 12.3 KB
author title date output
Sonali Arora, Martin Morgan
Introduction to Bioconductor for Sequence Data
June 3, 2015
toc_depth number_sections
knitr::opts_chunk$set(message = FALSE, error = FALSE, warning = FALSE, fig.width=6, fig.height=4)

setAnnotationHubOption("CACHE", "/liftrroot/")
ah = AnnotationHub()

Bioconductor enables the analysis and comprehension of high- throughput genomic data. We have a vast number of packages that allow rigorous statistical analysis of large data while keeping technological artifacts in mind. Bioconductor helps users place their analytic results into biological context, with rich opportunities for visualization. Reproducibility is an important goal in Bioconductor analyses. Different types of analysis can be carried out using Bioconductor, for example

  • Sequencing : RNASeq, ChIPSeq, variants, copy number..
  • Microarrays: expression, SNP, ...
  • Domain specific analysis : Flow cytometry, Proteomics ..

For these analyses, one typically imports and works with diverse sequence-related file types, including fasta, fastq, BAM, gtf, bed, and wig files, among others. Bioconductor packages support import, common and advanced sequence manipulation operations such as trimming, transformation, and alignment including quality assessment.

Sequencing Resources

Here is a illustrative description elaborating the different file types at various stages in a typical analysis, with the package names (in pink boxes) that one will use for each stage.

The following packages illustrate the diversity of functionality available; all are in the release version of Bioconductor.

  • r Biocpkg("IRanges") and r Biocpkg("GenomicRanges") for range-based (e.g., chromosomal regions) calculation, data manipulation, and general-purpose data representation. r Biocpkg("Biostrings") for DNA and amino acid sequence representation, alignment, pattern matching (e.g., primer removal), and data manipulation of large biological sequences or sets of sequences. r Biocpkg("ShortRead") for working with FASTQ files of short reads and their quality scores.

  • r Biocpkg("Rsamtools") and r Biocpkg("GenomicAlignments") for aligned read (BAM file) I/O and data manipulation. r Biocpkg("rtracklayer") for import and export of diverse data formats (e.g., BED, WIG, bigWig, GTF, GFF) and manipualtion of tracks on the UCSC genome browser.

  • r Biocpkg("BSgenome") for accessing and manipulating curated whole-genome representations. r Biocpkg("GenomicFeatures") for annotation of sequence features across common genomes, r Biocpkg("biomaRt") for access to Biomart databases.

  • r Biocpkg("SRAdb") for querying and retrieving data from the Sequence Read Archive.

Bioconductor packages are organized by biocViews. Some of the entries under Sequencing and other terms, and representative packages, include:

  • RNASeq, e.g., r Biocpkg("edgeR"), r Biocpkg("DESeq2"), r Biocpkg("edgeR"), r Biocpkg("derfinder"), and r Biocpkg("QuasR").

  • ChIPSeq, e.g.,r Biocpkg("DiffBind"), r Biocpkg("csaw"), r Biocpkg("ChIPseeker"), r Biocpkg("ChIPQC").

  • SNPs and other variants, e.g., r Biocpkg("VariantAnnotation"), r Biocpkg("VariantFiltering"), r Biocpkg("h5vc").

  • CopyNumberVariation e.g., r Biocpkg("DNAcopy"), r Biocpkg("crlmm"), r Biocpkg("fastseg").

  • Microbiome and metagenome sequencing, e.g., r Biocpkg("metagenomeSeq"), r Biocpkg("phyloseq"), r Biocpkg("DirichletMultinomial").

Ranges Infrastructure

Many Bioconductor packages rely heavily on the IRanges / GenomicRanges infrastructure. Thus we will begin with a quick introduction to these and then cover different file types.

The r Biocpkg("GenomicRanges") package allows us to associate a range of chromosome coordinates with a sequence name (e.g., chromosome) and a strand. Such genomic ranges are very useful for describing both data (e.g., the coordinates of aligned reads, called ChIP peaks, SNPs, or copy number variants) and annotations (e.g., gene models, Roadmap Epigenomics regulatory elements, known clinically relevant variants from dbSNP). GRanges is an object representing a vector of genomic locations and associated annotations. Each element in the vector is comprised of a sequence name, a range, a strand, and optional metadata (e.g. score, GC content, etc.).

GRanges(seqnames=Rle(c('chr1', 'chr2', 'chr3'), c(3, 3, 4)),
      IRanges(1:10, width=5), strand='-',
      score=101:110, GC = runif(10))

Genomic ranges can be created 'by hand', as above, but are often the result of importing data (e.g., via GenomicAlignments::readGAlignments()) or annotation (e.g., via GenomicFeatures::select() or rtracklayer::import() of BED, WIG, GTF, and other common file formats). Use help() to list the help pages in the r Biocpkg("GenomicRanges") package, and vignettes() to view and access available vignettes.


Some of the common operations on GRanges include findOverlaps(query, subject) and nearest(query, subject), which identify the ranges in query that overlap ranges in subject, or the range in subject nearest to `query. These operations are useful both in data analysis (e.g., counting overlaps between aligned reads and gene models in RNAseq) and comprehension (e.g., annotating genes near ChIP binding sites).

DNA /amino acid sequence from FASTA files

r Biocpkg("Biostrings") classes (e.g., DNAStringSet) are used to represent DNA or amino acid sequences. In the example below we will construct a DNAString and show some manipulations.

d <- DNAString("TTGAAAA-CTC-N")
length(d)  #no of letters in the DNAString

We will download all Homo sapiens cDNA sequences from the FASTA file 'Homo_sapiens.GRCh38.cdna.all.fa' from Ensembl using r Biocpkg("AnnotationHub").

ah <- AnnotationHub()

This file is downloaded as a FaFile which can be read in using readFasta() from the r Biocpkg("ShortRead") package

ah2 <- query(ah, c("fasta", "homo sapiens", "Ensembl"))
fa <- ah2[["AH18522"]]

We will open the file and get the sequences and widths of the records in the the fasta file using r Biocpkg("Rsamtools").

idx <- scanFaIndex(fa)

The information is returned as a GRanges object. getSeq() returns the sequences indicated by param as a DNAStringSet instance.

long <- idx[width(idx) > 82000]
getSeq(fa, param=long)

r Biocpkg("BSgenome") packages inside Bioconductor contain whole genome sequences as distributed by ENSEMBL, NCBI and others. In this next example we will load the whole genome sequence for Homo sapiens from UCSC's hg19 build, and calculate the GC content across chromosome 14.


chr14_range = GRanges("chr14", IRanges(1, seqlengths(Hsapiens)["chr14"]))
chr14_dna <- getSeq(Hsapiens, chr14_range)
letterFrequency(chr14_dna, "GC", as.prob=TRUE)

Reads from FASTQ files

r Biocpkg("ShortRead") package from Bioconductor can be used for working with fastq files. Here we illustrate a quick example where one can read in multiple fasta files, collect some statistics and generate a report about the same.

r Biocpkg("BiocParallel") is another package from Bioconductor which parallelizes this task and speeds up the process.

## 1. attach ShortRead and BiocParallel

## 2. create a vector of file paths
fls <- dir("~/fastq", pattern="*fastq", full=TRUE)

## 3. collect statistics
stats0 <- qa(fls)

## 4. generate and browse the report
if (interactive())

Two useful functions in r Biocpkg("ShortRead") are trimTails() for processing FASTQ files, and FastqStreamer() for iterating through FASTQ files in manageable chunks (e.g., 1,000,000 records at a time).

Aligned Reads from BAM files

The r Biocpkg("GenomicAlignments") package is used to input reads aligned to a reference genome.

In this next example, we will read in a BAM file and specifically read in reads supporting an apparent exon splice junction spanning position 19653773 of chromosome 14.

The package r Biocexptpkg("RNAseqData.HNRNPC.bam.chr14_BAMFILES") contains 8 BAM files. We will use only the first BAM file. We will load the software packages and the data package, construct a GRanges with our region of interest, and use summarizeJunctions() to find reads in our region of interest.

## 1. load software packages

## 2. load sample data
bf <- BamFile(RNAseqData.HNRNPC.bam.chr14_BAMFILES[[1]], asMates=TRUE)

## 3. define our 'region of interest'
roi <- GRanges("chr14", IRanges(19653773, width=1)) 

## 4. alignments, junctions, overlapping our roi
paln <- readGAlignmentsList(bf)
j <- summarizeJunctions(paln, with.revmap=TRUE)
j_overlap <- j[j %over% roi]

## 5. supporting reads

For a detailed tutorial on working with BAM files do check out this detailed Overlap Encodings vignette of GenomicAlignments.

Called Variants from VCF files

VCF (Variant Call Files) describe SNP and other variants. The files contain meta-information lines, a header line with column names, and then (many!) data lines, each with information about a position in the genome, and optional genotype information on samples for each position.

Data are parsed into a VCF object with readVcf() from r Biocpkg("VariantAnnoation")

fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
vcf <- readVcf(fl, "hg19")

An excellent workflow on working with Variants can be found here. In particular it is possible to read in specific components of the VCF file (e.g., readInfo(), readGeno()) and parts of the VCF at specific genomic locations (using GRanges and the param = ScanVcfParam() argument to input functions).

Genome Annotations from BED, WIG, GTF etc files

r Biocpkg("rtracklayer") import and export functions can read in many common file types, e.g., BED, WIG, GTF, …, in addition to querying and navigating the UCSC genome browser.

r Biocpkg("rtracklayer") contains a 'test' BED file which we will read in here

test_path <- system.file("tests", package = "rtracklayer")
test_bed <- file.path(test_path, "test.bed")
test <- import(test_bed, format = "bed")

The file is returned to the user as a GRanges instance. A more detailed tutorial can be found here

r Biocpkg("AnnotationHub") also contains a variety of genomic annotation files (eg BED, GTF, BigWig) which use import() from rtracklayer behind the scenes. For a detailed tutorial the user is referred to Annotation workflow and AnnotationHub HOW TO vignette