--- title: "S.3 -- Introduction to _Bioconductor_" author: Martin Morgan date: "21 November, 2016" output: BiocStyle::html_document: toc: true toc_depth: 2 vignette: > % \VignetteIndexEntry{S.3 -- Introduction to Bioconductor} % \VignetteEngine{knitr::rmarkdown} --- ```{r style, echo = FALSE, results = 'asis'} options(width=100) knitr::opts_chunk$set( eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")), cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE"))) ``` ```{r setup, echo=FALSE} suppressPackageStartupMessages({ library(Biostrings) library(GenomicRanges) library(SummarizedExperiment) library(airway) library(rtracklayer) library(ShortRead) library(GenomicAlignments) library(RNAseqData.HNRNPC.bam.chr14) library(VariantAnnotation) }) ``` # Project Overview ## About [Bioconductor][]: Analysis and comprehension of high-throughput genomic data - Statistical analysis: large data, technological artifacts, designed experiments; rigorous - Comprehension: biological context, visualization, reproducibility - High-throughput - Sequencing: RNASeq, ChIPSeq, variants, copy number, ... - Microarrays: expression, SNP, ... - Flow cytometry, proteomics, images, ... Packages, vignettes, work flows - 1296 software packages; also... - 'Annotation' packages -- static data bases of identifier maps, gene models, pathways, etc; e.g., [TxDb.Hsapiens.UCSC.hg19.knownGene][] - 'Experiment packages -- data sets used to illustrate software functionality, e.g., [airway][] - Discover and navigate via [biocViews][] - Package 'landing page' - Title, author / maintainer, short description, citation, installation instructions, ..., download statistics - All user-visible functions have help pages, most with runnable examples - 'Vignettes' an important feature in Bioconductor -- narrative documents illustrating how to use the package, with integrated code - 'Release' (every six months) and 'devel' branches - [Support site](https://support.bioconductor.org); [videos](https://www.youtube.com/user/bioconductor), [recent courses](https://bioconductor.org/help/course-materials/) Package installation and use - A package needs to be installed once, using the instructions on the package landing page (e.g., [DESeq2][]). ```{r install, eval=FALSE} source("https://bioconductor.org/biocLite.R") biocLite(c("DESeq2", "org.Hs.eg.db")) ``` - `biocLite()` installs _Bioconductor_, [CRAN][], and github packages. - Once installed, the package can be loaded into an R session ```{r require} library(GenomicRanges) ``` and the help system queried interactively, as outlined above: ```{r help-bioc, eval=FALSE} help(package="GenomicRanges") vignette(package="GenomicRanges") vignette(package="GenomicRanges", "GenomicRangesHOWTOs") ?GRanges ``` ## Key concepts Goals - Reproducibility - Interoperability - Use What a few lines of _R_ has to say ```{r five-lines} x <- rnorm(1000) y <- x + rnorm(1000) df <- data.frame(X=x, Y=y) plot(Y ~ X, df) fit <- lm(Y ~ X, df) anova(fit) abline(fit) ``` Classes and methods -- "S3" - `data.frame()` - Defines _class_ to coordinate data - Creates an _instance_ or _object_ - `plot()`, `lm()`, `anova()`, `abline()`: _methods_ defined on _generics_ to transform instances - Discovery and help ```{r help-r, eval=FALSE} class(fit) methods(class=class(fit)) methods(plot) ?"plot" ?"plot.formula" ``` - tab completion! _Bioconductor_ classes and methods -- "S4" - Example: working with DNA sequences ```{r classes-and-methods} library(Biostrings) dna <- DNAStringSet(c("AACAT", "GGCGCCT")) reverseComplement(dna) ``` - Discovery and help ```{r classes-and-methods-discovery, eval=FALSE} class(dna) ?"DNAStringSet-class" ?"reverseComplement,DNAStringSet-method" ``` ## High-throughput sequence analysis work flows 1. Experimental design 2. Wet-lab sequence preparation (figure from http://rnaseq.uoregon.edu/) ![](our_figures/fig-rna-seq.png) 3. (Illumina) Sequencing (Bentley et al., 2008, doi:10.1038/nature07517) ![](http://www.nature.com/nature/journal/v456/n7218/images/nature07517-f1.2.jpg) - Primary output: FASTQ files of short reads and their [quality scores](http://en.wikipedia.org/wiki/FASTQ_format#Encoding) 4. Alignment - Choose to match task, e.g., [Rsubread][], Bowtie2 good for ChIPseq, some forms of RNAseq; BWA, GMAP better for variant calling - Primary output: BAM files of aligned reads - More recently: [kallisto][] and similar programs that produce tables of reads aligned to transcripts 5. Reduction - e.g., RNASeq 'count table' (simple spreadsheets), DNASeq called variants (VCF files), ChIPSeq peaks (BED, WIG files) 6. Analysis - Differential expression, peak identification, ... 7. Comprehension - Biological context ## _Bioconductor_ sequencing ecosystem ![Alt Sequencing Ecosystem](our_figures/SequencingEcosystem.png) # High-Throughput Sequence Data ## DNA (and other) sequences [Biostrings][] ```{r} library(Biostrings) data(phiX174Phage) phiX174Phage letterFrequency(phiX174Phage, c("A", "C", "G", "T")) letterFrequency(phiX174Phage, "GC", as.prob=TRUE) ``` ## Genomic ranges [GenomicRanges][] - `GRanges()`: genomic coordinates to represent annotations (exons, genes, regulatory marks, ...) and data (called peaks, variants, aligned reads) ![Alt GRanges](our_figures/GRanges.png) - `GRangesList()`: genomic coordinates grouped into list elements (e.g., paired-end reads; exons grouped by transcript) ![Alt GRangesList](our_figures/GRangesList.png) Operations ![Alt GRanges operations](our_figures/RangeOperations.png) - intra-range: act on each range independently - e.g., `shift()` - inter-range: act on all ranges in a `GRanges` object or `GRangesList` element - e.g., `reduce()`; `disjoin()` - between-range: act on two separate `GRanges` or `GRangesList` objects - e.g., `findOverlaps()`, `nearest()` ```{r ranges, message=FALSE} library(GenomicRanges) gr <- GRanges("A", IRanges(c(10, 20, 22), width=5), "+") shift(gr, 1) # intra-range range(gr) # inter-range reduce(gr) # inter-range snps <- GRanges("A", IRanges(c(11, 17, 24), width=1)) findOverlaps(snps, gr) # between-range setdiff(range(gr), gr) # 'introns' ``` ## Summarized experiments [SummarizedExperiment][] ![Alt SummarizedExperiment](our_figures/SummarizedExperiment.png) - Coordinate feature x sample 'assays' with row (feature) and column (sample) descriptions. - 'assays' can be any matrix-like object, including very large on-disk representations such as [HDF5Array][] ### _SummarizedExperiment_ exercise The [airway][] experiment data package summarizes an RNA-seq experiment investigating human smooth-muscle airway cell lines treated with dexamethasone. Load the library and data set. ```{r} library(airway) data(airway) airway ``` `airway` is an example of the _SummarizedExperiment_ class. Explore its `assay()` (the matrix of counts of reads overlapping genomic regions of interest in each sample), `colData()` (a description of each sample), and `rowRanges()` (a description of each region of interest; here each region is an ENSEMBL gene). ```{r} x <- assay(airway) class(x) dim(x) head(x) colData(airway) rowRanges(airway) ``` It's easy to subset a _SummarizedExperiment_ on rows, columns and assays, e.g., retaining just those samples in the `trt` level of the `dex` factor. Accessing elements of the column data is common, so there is a short-cut. ```{r} cidx <- colData(airway)$dex %in% "trt" airway[, cidx] ## shortcut airway[, airway$dex %in% "trt"] ``` It's also easy to perform range-based operations on `SummarizedExperiment` objects, e.g., querying for range of chromosome 14 and then subsetting to contain only genes on this chromosome. Range operations on rows are very common, so there are shortcuts here, too. ```{r} chr14 <- as(seqinfo(rowRanges(airway)), "GRanges")["14"] ridx <- rowRanges(airway) %over% chr14 airway[ridx,] ## shortcut chr14 <- as(seqinfo(airway), "GRanges")["14"] airway[airway %over% chr14,] ``` Use the `assay()` and `rowSums()` function to remove all rows from the `airway` object that have 0 reads overlapping all samples. Summarize the library size (column sums of `assay()`) and plot a histogram of the distribution of reads per feature of interest. ## BED, GFF, GTF, WIG import and export Genome annotations: BED, WIG, GTF, etc. files. E.g., GTF: - Component coordinates 7 protein_coding gene 27221129 27224842 . - . ... ... 7 protein_coding transcript 27221134 27224835 . - . ... 7 protein_coding exon 27224055 27224835 . - . ... 7 protein_coding CDS 27224055 27224763 . - 0 ... 7 protein_coding start_codon 27224761 27224763 . - 0 ... 7 protein_coding exon 27221134 27222647 . - . ... 7 protein_coding CDS 27222418 27222647 . - 2 ... 7 protein_coding stop_codon 27222415 27222417 . - 0 ... 7 protein_coding UTR 27224764 27224835 . - . ... 7 protein_coding UTR 27221134 27222414 . - . ... - Annotations gene_id "ENSG00000005073"; gene_name "HOXA11"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; ... ... transcript_id "ENST00000006015"; transcript_name "HOXA11-001"; transcript_source "ensembl_havana"; tag "CCDS"; ccds_id "CCDS5411"; ... exon_number "1"; exon_id "ENSE00001147062"; ... exon_number "1"; protein_id "ENSP00000006015"; ... exon_number "1"; ... exon_number "2"; exon_id "ENSE00002099557"; ... exon_number "2"; protein_id "ENSP00000006015"; ... exon_number "2"; ... [rtracklayer][] - `import()`: import various formats to `GRanges` and similar instances - `export()`: transform from `GRanges` and similar types to BED, GTF, ... - Also, functions to interactively drive UCSC genome browser with data from _R_ / _Bioconductor_ ## FASTQ files Sequenced reads: FASTQ files @ERR127302.1703 HWI-EAS350_0441:1:1:1460:19184#0/1 CCTGAGTGAAGCTGATCTTGATCTACGAAGAGAGATAGATCTTGATCGTCGAGGAGATGCTGACCTTGACCT + HHGHHGHHHHHHHHDGG>CE?=896=: @ERR127302.1704 HWI-EAS350_0441:1:1:1460:16861#0/1 GCGGTATGCTGGAAGGTGCTCGAATGGAGAGCGCCAGCGCCCCGGCGCTGAGCCGCAGCCTCAGGTCCGCCC + DE?DD>ED4>EEE>DE8EEEDE8B?EB<@3;BA79?,881B?@73;1?######################## [ShortRead][] - `readFastq()`: input - `FastqStreamer()`: iterate through FASTQ files - `FastqSampler()`: sample from FASTQ files, e.g., for quality assessment - Functions for trimming and filters FASTQ files, QA assessment ## Aligned reads Aligned reads: BAM files - Header @HD VN:1.0 SO:coordinate @SQ SN:chr1 LN:249250621 @SQ SN:chr10 LN:135534747 @SQ SN:chr11 LN:135006516 ... @SQ SN:chrY LN:59373566 @PG ID:TopHat VN:2.0.8b CL:/home/hpages/tophat-2.0.8b.Linux_x86_64/tophat --mate-inner-dist 150 --solexa-quals --max-multihits 5 --no-discordant --no-mixed --coverage-search --microexon-search --library-type fr-unstranded --num-threads 2 --output-dir tophat2_out/ERR127306 /home/hpages/bowtie2-2.1.0/indexes/hg19 fastq/ERR127306_1.fastq fastq/ERR127306_2.fastq - Alignments: ID, flag, alignment and mate ERR127306.7941162 403 chr14 19653689 3 72M = 19652348 -1413 ... ERR127306.22648137 145 chr14 19653692 1 72M = 19650044 -3720 ... ERR127306.933914 339 chr14 19653707 1 66M120N6M = 19653686 -213 ... - Alignments: sequence and quality ... GAATTGATCAGTCTCATCTGAGAGTAACTTTGTACCCATCACTGATTCCTTCTGAGACTGCCTCCACTTCCC *'%%%%%#&&%''#'&%%%)&&%%$%%'%%'&*****$))$)'')'%)))&)%%%%$'%%%%&"))'')%)) ... TTGATCAGTCTCATCTGAGAGTAACTTTGTACCCATCACTGATTCCTTCTGAGACTGCCTCCACTTCCCCAG '**)****)*'*&*********('&)****&***(**')))())%)))&)))*')&***********)**** ... TGAGAGTAACTTTGTACCCATCACTGATTCCTTCTGAGACTGCCTCCACTTCCCCAGCAGCCTCTGGTTTCT '******&%)&)))&")')'')'*((******&)&'')'))$))'')&))$)**&&**************** - Alignments: Tags ... AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:72 YT:Z:UU NH:i:2 CC:Z:chr22 CP:i:16189276 HI:i:0 ... AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:72 YT:Z:UU NH:i:3 CC:Z:= CP:i:19921600 HI:i:0 ... AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:4 MD:Z:72 YT:Z:UU XS:A:+ NH:i:3 CC:Z:= CP:i:19921465 HI:i:0 ... AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:4 MD:Z:72 YT:Z:UU XS:A:+ NH:i:2 CC:Z:chr22 CP:i:16189138 HI:i:0 [GenomicAligments][] - `readGAlignments()`: Single-end reads - `readGAlignmentPairs()`, `readGAlignmentsList()`: paired end reads Working with large files - `ScanBamParam()`: restrict input - `BamFile(, yieldSize=)`: iteration - [GenomicFiles][] provides useful helpers, e.g., `reduceByYield()` ### _GenomicAlignments_ exercise The [RNAseqData.HNRNPC.bam.chr14][] package is an example of an experiment data package. It contains a subset of BAM files used in a gene knock-down experiment, as described in `?RNAseqData.HNRNPC.bam.chr14`. Load the package and get the path to the BAM files. ```{r} library(RNAseqData.HNRNPC.bam.chr14) fls = RNAseqData.HNRNPC.bam.chr14_BAMFILES basename(fls) ``` Create `BamFileList()`, basically telling R that these are paths to BAM files rather than, say, text files from a spreadsheet. ```{r} library(GenomicAlignments) bfls = BamFileList(fls) bfl = bfls[[1]] ``` Input and explore the aligments. See `?readGAlignments` and `?GAlignments` for details on how to manipulate these objects. ```{r} ga = readGAlignments(bfl) ga table(strand(ga)) ``` Many of the reads have cigar "72M". What does this mean? Can you create a subset of reads that do not have this cigar? Interpret some of the non-72M cigars. Any hint about what these cigars represent? ```{r} tail(sort(table(cigar(ga)))) ga[cigar(ga) != "72M"] ``` Use the function `summarizeJunctions()` to identify genomic regions that are spanned by reads with complicated cigars. Can you use the argument `with.revmap=TRUE` to extract the reads supporting a particular (e.g., first) junction? ```{r} summarizeJunctions(ga) junctions <- summarizeJunctions(ga, with.revmap=TRUE) ga[ junctions$revmap[[1]] ] ``` It is possible to do other actions on BAM files, e.g., calculating the 'coverage' (reads overlapping each base). ```{r} coverage(bfl)$chr14 ``` ## Called variants: VCF files - Header ##fileformat=VCFv4.2 ##fileDate=20090805 ##source=myImputationProgramV3.1 ##reference=file:///seq/references/1000GenomesPilot-NCBI36.fasta ##contig= ##phasing=partial ##INFO= ##INFO= ... ##FILTER= ##FILTER= ... ##FORMAT= ##FORMAT= - Location #CHROM POS ID REF ALT QUAL FILTER ... 20 14370 rs6054257 G A 29 PASS ... 20 17330 . T A 3 q10 ... 20 1110696 rs6040355 A G,T 67 PASS ... - Variant INFO #CHROM POS ... INFO ... 20 14370 ... NS=3;DP=14;AF=0.5;DB;H2 ... 20 17330 ... NS=3;DP=11;AF=0.017 ... 20 1110696 ... NS=2;DP=10;AF=0.333,0.667;AA=T;DB ... - Genotype FORMAT and samples ... POS ... FORMAT NA00001 NA00002 NA00003 ... 14370 ... GT:GQ:DP:HQ 0|0:48:1:51,51 1|0:48:8:51,51 1/1:43:5:.,. ... 17330 ... GT:GQ:DP:HQ 0|0:49:3:58,50 0|1:3:5:65,3 0/0:41:3 ... 1110696 ... GT:GQ:DP:HQ 1|2:21:6:23,27 2|1:2:0:18,2 2/2:35:4 [VariantAnnotation][] - `readVcf()`: VCF input - `ScanVcfParam()`: restrict input to necessary fields / ranges - `VcfFile()`: indexing and iterating through large VCF files - `locateVariants()`: annotate in relation to genes, etc; see also [ensemblVEP][], [VariantFiltering][] - `filterVcf()`: flexible filtering [Bioconductor]: https://bioconductor.org [CRAN]: https://cran.r-project.org [biocViews]: https://bioconductor.org/packages/ [HDF5Array]: https://bioconductor.org/packages/HDF5Array [AnnotationDbi]: https://bioconductor.org/packages/AnnotationDbi [AnnotationHub]: https://bioconductor.org/packages/AnnotationHub [BSgenome.Hsapiens.UCSC.hg19]: https://bioconductor.org/packages/BSgenome.Hsapiens.UCSC.hg19 [BSgenome]: https://bioconductor.org/packages/BSgenome [BiocParallel]: https://bioconductor.org/packages/BiocParallel [Biostrings]: https://bioconductor.org/packages/Biostrings [CNTools]: https://bioconductor.org/packages/CNTools [ChIPQC]: https://bioconductor.org/packages/ChIPQC [ChIPseeker]: https://bioconductor.org/packages/ChIPseeker [DESeq2]: https://bioconductor.org/packages/DESeq2 [DiffBind]: https://bioconductor.org/packages/DiffBind [GenomicAlignments]: https://bioconductor.org/packages/GenomicAlignments [GenomicFeatures]: https://bioconductor.org/packages/GenomicFeatures [GenomicFiles]: https://bioconductor.org/packages/GenomicFiles [GenomicRanges]: https://bioconductor.org/packages/GenomicRanges [Gviz]: https://bioconductor.org/packages/Gviz [Homo.sapiens]: https://bioconductor.org/packages/Homo.sapiens [IRanges]: https://bioconductor.org/packages/IRanges [KEGGREST]: https://bioconductor.org/packages/KEGGREST [OmicCircos]: https://bioconductor.org/packages/OmicCircos [PSICQUIC]: https://bioconductor.org/packages/PSICQUIC [Rsamtools]: https://bioconductor.org/packages/Rsamtools [Rsubread]: https://bioconductor.org/packages/Rsubread [ShortRead]: https://bioconductor.org/packages/ShortRead [SomaticSignatures]: https://bioconductor.org/packages/SomaticSignatures [SummarizedExperiment]: https://bioconductor.org/packages/SummarizedExperiment [TxDb.Hsapiens.UCSC.hg19.knownGene]: https://bioconductor.org/packages/TxDb.Hsapiens.UCSC.hg19.knownGene [VariantAnnotation]: https://bioconductor.org/packages/VariantAnnotation [VariantFiltering]: https://bioconductor.org/packages/VariantFiltering [VariantTools]: https://bioconductor.org/packages/VariantTools [airway]: https://bioconductor.org/packages/airway [biomaRt]: https://bioconductor.org/packages/biomaRt [cn.mops]: https://bioconductor.org/packages/cn.mops [csaw]: https://bioconductor.org/packages/csaw [edgeR]: https://bioconductor.org/packages/edgeR [ensemblVEP]: https://bioconductor.org/packages/ensemblVEP [epivizr]: https://bioconductor.org/packages/epivizr [ggbio]: https://bioconductor.org/packages/ggbio [h5vc]: https://bioconductor.org/packages/h5vc [limma]: https://bioconductor.org/packages/limma [metagenomeSeq]: https://bioconductor.org/packages/metagenomeSeq [org.Hs.eg.db]: https://bioconductor.org/packages/org.Hs.eg.db [org.Sc.sgd.db]: https://bioconductor.org/packages/org.Sc.sgd.db [phyloseq]: https://bioconductor.org/packages/phyloseq [rtracklayer]: https://bioconductor.org/packages/rtracklayer [snpStats]: https://bioconductor.org/packages/snpStats [dplyr]: https://cran.r-project.org/package=dplyr [data.table]: https://cran.r-project.org/package=data.table [Rcpp]: https://cran.r-project.org/package=Rcpp [kallisto]: https://pachterlab.github.io/kallisto