```{r setup, echo=FALSE}
library(LearnBioconductor)
stopifnot(BiocInstaller::biocVersion() == "3.1")
```
```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
knitr::opts_chunk$set(tidy=FALSE)
```
# Bioconductor for Sequence Analysis
Martin Morgan
February 2, 2015
## Sequence analysis work flows
1. Experimental design
- Keep it simple, e.g., 'control' and 'treatment' groups
- Replicate within treatments!
2. Wet-lab sequence preparation
- Record covariates, including processing day -- likely 'batch effects'
3. (Illumina) Sequencing (Bentley et al., 2008,
[doi:10.1038/nature07517](doi:10.1038/nature07517))
- 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
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
Data movement
## Sequence data representations
### DNA / amino acid sequences: FASTA files
Input & manipulation: [Biostrings][]
>NM_078863_up_2000_chr2L_16764737_f chr2L:16764737-16766736
gttggtggcccaccagtgccaaaatacacaagaagaagaaacagcatctt
gacactaaaatgcaaaaattgctttgcgtcaatgactcaaaacgaaaatg
...
atgggtatcaagttgccccgtataaaaggcaagtttaccggttgcacggt
>NM_001201794_up_2000_chr2L_8382455_f chr2L:8382455-8384454
ttatttatgtaggcgcccgttcccgcagccaaagcactcagaattccggg
cgtgtagcgcaacgaccatctacaaggcaatattttgatcgcttgttagg
...
Whole genomes: `2bit` and `.fa` formats: [rtracklayer][],
[Rsamtools][]; [BSgenome][]
### Reads: FASTQ files
Input & manipulation: [ShortRead][] `readFastq()`, `FastqStreamer()`,
`FastqSampler()`
@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?########################
- Quality scores: 'phred-like', encoded. See
[wikipedia](http://en.wikipedia.org/wiki/FASTQ_format#Encoding)
### Aligned reads: BAM files (e.g., ERR127306_chr14.bam)
Input & manipulation: 'low-level' [Rsamtools][], `scanBam()`,
`BamFile()`; 'high-level' [GenomicAlignments][]
- 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 ...
ERR127306.11052450 83 chr14 19653707 3 66M120N6M = 19652348 -1551 ...
ERR127306.24611331 147 chr14 19653708 1 65M120N7M = 19653675 -225 ...
ERR127306.2698854 419 chr14 19653717 0 56M120N16M = 19653935 290 ...
ERR127306.2698854 163 chr14 19653717 0 56M120N16M = 19653935 2019 ...
- Alignments: sequence and quality
... GAATTGATCAGTCTCATCTGAGAGTAACTTTGTACCCATCACTGATTCCTTCTGAGACTGCCTCCACTTCCC *'%%%%%#&&%''#'&%%%)&&%%$%%'%%'&*****$))$)'')'%)))&)%%%%$'%%%%&"))'')%))
... TTGATCAGTCTCATCTGAGAGTAACTTTGTACCCATCACTGATTCCTTCTGAGACTGCCTCCACTTCCCCAG '**)****)*'*&*********('&)****&***(**')))())%)))&)))*')&***********)****
... TGAGAGTAACTTTGTACCCATCACTGATTCCTTCTGAGACTGCCTCCACTTCCCCAGCAGCCTCTGGTTTCT '******&%)&)))&")')'')'*((******&)&'')'))$))'')&))$)**&&****************
... TGAGAGTAACTTTGTACCCATCACTGATTCCTTCTGAGACTGCCTCCACTTCCCCAGCAGCCTCTGGTTTCT ##&&(#')$')'%&&#)%$#$%"%###&!%))'%%''%'))&))#)&%((%())))%)%)))%*********
... GAGAGTAACTTTGTACCCATCACTGATTCCTTCTGAGACTGCCTCCACTTCCCCAGCAGCCTCTGGTTTCTT )&$'$'$%!&&%&&#!'%'))%''&%'&))))''$""'%'%&%'#'%'"!'')#&)))))%$)%)&'"')))
... TTTGTACCCATCACTGATTCCTTCTGAGACTGCCTCCACTTCCCCAGCAGCCTCTGGTTTCTTCATGTGGCT ++++++++++++++++++++++++++++++++++++++*++++++**++++**+**''**+*+*'*)))*)#
... TTTGTACCCATCACTGATTCCTTCTGAGACTGCCTCCACTTCCCCAGCAGCCTCTGGTTTCTTCATGTGGCT ++++++++++++++++++++++++++++++++++++++*++++++**++++**+**''**+*+*'*)))*)#
- 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
... AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:5 MD:Z:72 YT:Z:UU XS:A:+ NH:i:3 CC:Z:= CP:i:19921464 HI:i:0
... AS:i:0 XM:i:0 XO:i:0 XG:i:0 MD:Z:72 NM:i:0 XS:A:+ NH:i:5 CC:Z:= CP:i:19653717 HI:i:0
... AS:i:0 XM:i:0 XO:i:0 XG:i:0 MD:Z:72 NM:i:0 XS:A:+ NH:i:5 CC:Z:= CP:i:19921455 HI:i:1
### Called variants: VCF files
Input and manipulation: [VariantAnnotation][] `readVcf()`,
`readInfo()`, `readGeno()` selectively with `ScanVcfParam()`.
- 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 ...
20 1230237 . T . 47 PASS ...
20 1234567 microsat1 GTC G,GTCT 50 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 ...
20 1230237 ... NS=3;DP=13;AA=T ...
20 1234567 ... NS=3;DP=9;AA=G ...
- 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
... 1230237 ... GT:GQ:DP:HQ 0|0:54:7:56,60 0|0:48:4:51,51 0/0:61:2
... 1234567 ... GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3
### Genome annotations: BED, WIG, GTF, etc. files
Input: [rtracklayer][] `import()`
- BED: range-based annotation (see
http://genome.ucsc.edu/FAQ/FAQformat.html for definition of this and
related formats)
- WIG / bigWig: dense, continuous-valued data
- GTF: gene model
- 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";
...
## Sequence data in _R_ / _Bioconductor_
### Role for Bioconductor
- Pre-processing and alignment
- Data reduction
- Statistical analysis
- Comprehension -- integrative and 'down stream' analysis
### Sequences
_Biostrings_ classes for DNA or amino acid sequences
- XString, XStringSet, e.g., DNAString (genomes),
DNAStringSet (reads)
Methods
- [Cheat sheat](http://bioconductor.org/packages/release/bioc/vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf)
- Manipulation, e.g., `reverseComplement()`
- Summary, e.g., `letterFrequency()`
- Matching, e.g., `matchPDict()`, `matchPWM()`
Related packages
- [BSgenome][]
- Whole-genome representations
- Model organism or custom
- 'Masks' to exclude regions (pre-computed or arbitrary) from
calculations
- [BSgenome][]: Whole-genome sequence representation & manipulation)
- [Rsamtools][]: `FaFile` class for indexed on-disk representation
- [rtracklayer][]: UCSC '2bit' (`TwoBitFile`) class for indexed
on-disk representation
Example
- Whole-genome sequences are distrubuted by ENSEMBL, NCBI, and others
as FASTA files; model organism whole genome sequences are packaged
into more user-friendly `BSgenome` packages. The following
calculates GC content across chr14.
```{r BSgenome-require, message=FALSE}
suppressPackageStartupMessages({
library(BSgenome.Hsapiens.UCSC.hg19)
})
chr14_range = GRanges("chr14", IRanges(1, seqlengths(Hsapiens)["chr14"]))
chr14_dna <- getSeq(Hsapiens, chr14_range)
letterFrequency(chr14_dna, "GC", as.prob=TRUE)
```
### Ranges
Ranges represent:
- Data, e.g., aligned reads, ChIP peaks, SNPs, CpG islands, ...
- Annotations, e.g., gene models, regulatory elements, methylated
regions
- Ranges are defined by chromosome, start, end, and strand
- Often, metadata is associated with each range, e.g., quality of
alignment, strength of ChIP peak
Many common biological questions are range-based
- What reads overlap genes?
- What genes are ChIP peaks nearest?
- ...
The [GenomicRanges][] package defines essential classes and methods
- `GRanges`
- `GRangesList`
#### Range operations
Ranges
- IRanges
- `start()` / `end()` / `width()`
- List-like -- `length()`, subset, etc.
- 'metadata', `mcols()`
- GRanges
- 'seqnames' (chromosome), 'strand'
- `Seqinfo`, including `seqlevels` and `seqlengths`
Intra-range methods
- Independent of other ranges in the same object
- GRanges variants strand-aware
- `shift()`, `narrow()`, `flank()`, `promoters()`, `resize()`,
`restrict()`, `trim()`
- See `?"intra-range-methods"`
Inter-range methods
- Depends on other ranges in the same object
- `range()`, `reduce()`, `gaps()`, `disjoin()`
- `coverage()` (!)
- see `?"inter-range-methods"`
Between-range methods
- Functions of two (or more) range objects
- `findOverlaps()`, `countOverlaps()`, ..., `%over%`, `%within%`,
`%outside%`; `union()`, `intersect()`, `setdiff()`, `punion()`,
`pintersect()`, `psetdiff()`
Example
```{r ranges, message=FALSE}
suppressPackageStartupMessages({
library(GenomicRanges)
})
gr <- GRanges("A", IRanges(c(10, 20, 22), width=5), "+")
shift(gr, 1) # 1-based coordinates!
range(gr) # intra-range
reduce(gr) # inter-range
coverage(gr)
setdiff(range(gr), gr) # 'introns'
```
IRangesList, GRangesList
- List: all elements of the same type
- Many *List-aware methods, but a common 'trick': apply a vectorized
function to the unlisted representaion, then re-list
grl <- GRangesList(...)
orig_gr <- unlist(grl)
transformed_gr <- FUN(orig)
transformed_grl <- relist(, grl)
Reference
- Lawrence M, Huber W, Pages H, Aboyoun P, Carlson M, et al. (2013)
Software for Computing and Annotating Genomic Ranges. PLoS Comput
Biol 9(8): e1003118. doi:10.1371/journal.pcbi.1003118
### [GenomicAlignments][] (Aligned reads)
Classes -- GenomicRanges-like behaivor
- GAlignments, GAlignmentPairs, GAlignmentsList
- SummarizedExperiment
- Matrix where rows are indexed by genomic ranges, columns by a
DataFrame.
Methods
- `readGAlignments()`, `readGAlignmentsList()`
- Easy to restrict input, iterate in chunks
- `summarizeOverlaps()`
Example
- Find reads supporting the junction identified above, at position
19653707 + 66M = 19653773 of chromosome 14
```{r bam-require}
suppressPackageStartupMessages({
library(GenomicRanges)
library(GenomicAlignments)
library(Rsamtools)
})
## our 'region of interest'
roi <- GRanges("chr14", IRanges(19653773, width=1))
## sample data
suppressPackageStartupMessages({
library('RNAseqData.HNRNPC.bam.chr14')
})
bf <- BamFile(RNAseqData.HNRNPC.bam.chr14_BAMFILES[[1]], asMates=TRUE)
## alignments, junctions, overlapping our roi
paln <- readGAlignmentsList(bf)
j <- summarizeJunctions(paln, with.revmap=TRUE)
j_overlap <- j[j %over% roi]
## supporting reads
paln[j_overlap$revmap[[1]]]
```
### [VariantAnnotation][] (called variants)
Classes -- GenomicRanges-like behavior
- VCF -- 'wide'
- VRanges -- 'tall'
Functions and methods
- I/O and filtering: `readVcf()`, `readGeno()`, `readInfo()`,
`readGT()`, `writeVcf()`, `filterVcf()`
- Annotation: `locateVariants()` (variants overlapping ranges),
`predictCoding()`, `summarizeVariants()`
- SNPs: `genotypeToSnpMatrix()`, `snpSummary()`
Example
- Read variants from a VCF file, and annotate with respect to a known
gene model
```{r vcf, message=FALSE}
## input variants
suppressPackageStartupMessages({
library(VariantAnnotation)
})
fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
vcf <- readVcf(fl, "hg19")
seqlevels(vcf) <- "chr22"
## known gene model
suppressPackageStartupMessages({
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
})
coding <- locateVariants(rowData(vcf),
TxDb.Hsapiens.UCSC.hg19.knownGene,
CodingVariants())
head(coding)
```
Related packages
- [ensemblVEP][]
- Forward variants to Ensembl Variant Effect Predictor
- [VariantTools][], [h5vc][]
- Call variants
- [VariantFiltering][]
- Filter variants using criteria such as coding consequence, MAF,
..., inheritance model
Reference
- Obenchain, V, Lawrence, M, Carey, V, Gogarten, S, Shannon, P, and
Morgan, M. VariantAnnotation: a Bioconductor package for exploration
and annotation of genetic variants. Bioinformatics, first published
online March 28, 2014
[doi:10.1093/bioinformatics/btu168](http://bioinformatics.oxfordjournals.org/content/early/2014/04/21/bioinformatics.btu168)
### [rtracklayer][] (Genome annotations)
- Import BED, GTF, WIG, etc
- Export GRanges to BED, GTF, WIG, ...
- Access UCSC genome browser
## A sequence analysis package tour
This very open-ended topic points to some of the most prominent
Bioconductor packages for sequence analysis. Use the opportunity in
this lab to explore the package vignettes and help pages highlighted
below; many of the material will be covered in greater detail in
subsequent labs and lectures.
Basics
- Bioconductor packages are listed on the [biocViews][] page. Each
package has 'biocViews' (tags from a controlled vocabulary)
associated with it; these can be searched to identify appropriately
tagged packages, as can the package title and author.
- Each package has a 'landing page', e.g., for
[GenomicRanges][]. Visit this landing page, and note the
description, authors, and installation instructions. Packages are
often written up in the scientific literature, and if available the
corresponding citation is present on the landing page. Also on the
landing page are links to the vignettes and reference manual and, at
the bottom, an indication of cross-platform availability and
download statistics.
- A package needs to be installed once, using the instructions on the
landing page. Once installed, the package can be loaded into an R
session
```{r require}
suppressPackageStartupMessages({
library(GenomicRanges)
})
```
and the help system queried interactively, as outlined above:
```{r help, eval=FALSE}
help(package="GenomicRanges")
vignette(package="GenomicRanges")
vignette(package="GenomicRanges", "GenomicRangesHOWTOs")
?GRanges
```
Domain-specific analysis -- explore the landing pages, vignettes, and
reference manuals of two or three of the following packages.
- Important packages for analysis of differential expression include
[edgeR][] and [DESeq2][]; both have excellent vignettes for
exploration. Additional research methods embodied in Bioconductor
packages can be discovered by visiting the [biocViews][] web page,
searching for the 'DifferentialExpression' view term, and narrowing
the selection by searching for 'RNA seq' and similar.
- Popular ChIP-seq packages include [DiffBind][] for comparison of
peaks across samples, [ChIPQC][] for quality assessment, and
[ChIPpeakAnno][] for annotating results (e.g., discovering nearby
genes). What other ChIP-seq packages are listed on the [biocViews][]
page?
- Working with called variants (VCF files) is facilitated by packages
such as [VariantAnnotation][], [VariantFiltering][], [ensemblVEP][],
and [SomaticSignatures][]; packages for calling variants include,
e.g., [h5vc][] and [VariantTools][].
- Several packages identify copy number variants from sequence data,
including [cn.mops][]; from the [biocViews][] page, what other copy
number packages are available? The [CNTools][] package provides some
useful facilities for comparison of segments across samples.
- Microbiome and metagenomic analysis is facilitated by packages such
as [phyloseq][] and [metagenomeSeq][].
- Metabolomics, chemoinformatics, image analysis, and many other
high-throughput analysis domains are also represented in
Bioconductor; explore these via biocViews and title searches.
Working with sequences, alignments, common web file formats, and raw
data; these packages rely very heavily on the [IRanges][] /
[GenomicRanges][] infrastructure that we will encounter later in the
course.
- The [Biostrings][] package is used to represent DNA and other
sequences, with many convenient sequence-related functions. Check
out the functions documented on the help page `?consensusMatrix`,
for instance. Also check out the [BSgenome][] package for working
with whole genome sequences, e.g., `?"getSeq,BSgenome-method"`
- The [GenomicAlignments][] package is used to input reads aligned to
a reference genome. See for instance the `?readGAlignments` help
page and `vigentte(package="GenomicAlignments",
"summarizeOverlaps")`
- [rtracklayer][]'s `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. Check out the `?import` page
for basic usage.
- The [ShortRead][] and [Rsamtools][] packages can be used for
lower-level access to FASTQ and BAM files, respectively. Explore the
[ShortRead vignette](http://bioconductor.org/packages/release/bioc/vignettes/ShortRead/inst/doc/Overview.pdf)
and Scalable Genomics labs to see approaches to effectively
processing the large files.
Visualization
- The [Gviz][] package provides great tools for visualizing local
genomic coordinates and associated data.
- [epivizr][] drives the [epiviz](http://epiviz.cbcb.umd.edu/) genome
browser from within R; [rtracklayer][] provides easy ways to
transfer data to and manipulate UCSC browser sessions.
- Additionl packages include [ggbio][], [OmicCircos][], ...
## Lab
### Short read quality assessment
`fastqc` is a Java program commonly used for summarizing quality of
fastq files. It has a straight-forward graphical user interface. Here
we will use the command-line version.
1. From within _Rstudio_, choose 'Tools --> Shell...', or log on to
your Amazon machine instance using a Mac / linux terminal or on
Windows the PuTTY program.
2. Run fastqc on sample fastq files, sending the output to the
`~/fastqc_report` directory.
fastqc fastq/*fastq --threads 8 --outdir=fastqc_reports
3. Study the quality report and resulting on-line [documentation](FIXME):
In the Files tab, click on `fastqc_reports`. Click on the HTML file
there and then click on "View in Web Browser".
`r Biocpkg("ShortRead")` provides similar functionality, but from
within _R_. The following shows that _R_ can handle large data, and
illustrates some of the basic ways in which one might interact with
functionality provided by a _Bioconductor_ package.
```{r ShortRead, messages=FALSE}
## 1. attach ShortRead and BiocParallel
suppressPackageStartupMessages({
library(ShortRead)
library(BiocParallel)
})
## 2. create a vector of file paths
fls <- dir("~/fastq", pattern="*fastq", full=TRUE)
```
```{r fakestats, eval=FALSE}
## 3. collect statistics
stats0 <- qa(fls)
```
```{r realstats, echo=FALSE, results="hide"}
data(stats0)
```
```{r browseStats}
## 4. generate and browse the report
if (interactive())
browseURL(report(stats0))
```
Check out the qa report from all lanes
```{r ShortRead-qa-all}
data(qa_all)
if (interactive())
browseURL(report(qa_all))
```
### Alignments (and genomic annotations)
This data is from the `r Biocannopkg("airway")` Bioconductor
annotation package; see the
[vignette](http://bioconductor.org/packages/release/data/experiment/vignettes/airway/inst/doc/airway.html)
for details
_Bioconductor_: we'll explore how to map between different types of
identifiers, how to navigate genomic coordinates, and how to query BAM
files for aligned reads.
1. Attach 'Annotation' packages containing information about gene
symbols `r Biocannopkg("org.Hs.eg.db")` and genomic coordinates
(e.g., genes, exons, cds, transcripts)
`r Biocannopkg("TxDb.Hsapiens.UCSC.hg19.knownGene")`. Arrange for the
'seqlevels' (chromosome names) in the TxDb package to match those
in the BAM files.
2. Use an appropriate `org.*` package to map from gene symbol to
Entrez gene id, and the appropriate `TxDb.*` package to retrieve
gene coordinates of the SPARCL1 gene. N.B. -- The following uses a
single gene symbol, but we could have used 1, 2, or all gene
symbols in a _vectorized_ fashion.
3. Attach the `r Biocpkg("GenomicAlignments")` package for working
with aligned reads. Use `range()` to get the genomic coordinates
spanning the first and last exon of SPARCL1. Input paired reads
overlapping SPARCL1.
4. What questions can you easily answer about these alignments? E.g.,
how many reads overlap this region of interest?
```{r setup-view, message=FALSE, warning=FALSE}
## 1.a 'Annotation' packages
suppressPackageStartupMessages({
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
})
## 1.b -- map 'seqlevels' as recorded in the TxDb file to those in the
## BAM file
fl <- "~/igv/genomes/hg19_alias.tab"
map <- with(read.delim(fl, header=FALSE, stringsAsFactors=FALSE),
setNames(V1, V2))
seqlevels(TxDb.Hsapiens.UCSC.hg19.knownGene, force=TRUE) <- map
## 2. Symbol -> Entrez ID -> Gene coordinates
sym2eg <- select(org.Hs.eg.db, "SPARCL1", "ENTREZID", "SYMBOL")
exByGn <- exonsBy(TxDb.Hsapiens.UCSC.hg19.knownGene, "gene")
sparcl1exons <- exByGn[[sym2eg$ENTREZID]]
## 3. Aligned reads
suppressPackageStartupMessages({
library(GenomicAlignments)
})
fl <- "~/bam/SRR1039508_sorted.bam"
sparcl1gene <- range(sparcl1exons)
param <- ScanBamParam(which=sparcl1gene)
aln <- readGAlignmentPairs(fl, param=param)
```
5. As another exercise we ask how many of the reads we've input are
compatible with the known gene model. We have to find the
transcripts that belong to our gene, and then exons grouped by
transcript
```{r compatibleAlignments, warning=FALSE}
## 5.a. exons-by-transcript for our gene of interest
txids <- select(TxDb.Hsapiens.UCSC.hg19.knownGene, sym2eg$ENTREZID,
"TXID", "GENEID")$TXID
exByTx <- exonsBy(TxDb.Hsapiens.UCSC.hg19.knownGene, "tx")[txids]
## 5.b compatible alignments
hits <- findCompatibleOverlaps(query=aln, subject=exByTx)
good <- seq_along(aln) %in% queryHits(hits)
table(good)
```
6. Finally, let's go from gene model to protein coding
sequence. (a) Extract CDS regions grouped by transcript, select just
transcripts we're interested in, (b) attach and then extract the coding
sequence from the appropriate reference genome. Translating the
coding sequences to proteins.
```{r coding-sequence, warning=FALSE}
## reset seqlevels
restoreSeqlevels(TxDb.Hsapiens.UCSC.hg19.knownGene)
## a. cds coordinates, grouped by transcript
txids <- select(TxDb.Hsapiens.UCSC.hg19.knownGene, sym2eg$ENTREZID,
"TXID", "GENEID")$TXID
cdsByTx <- cdsBy(TxDb.Hsapiens.UCSC.hg19.knownGene, "tx")[txids]
## b. coding sequence from relevant reference genome
suppressPackageStartupMessages({
library(BSgenome.Hsapiens.UCSC.hg19)
})
dna <- extractTranscriptSeqs(BSgenome.Hsapiens.UCSC.hg19, cdsByTx)
protein <- translate(dna)
```
### Working with genomic ranges
Visit the "GenomicRanges HOWTOs" vignette.
```{r GenomicRanges-howtos, eval=FALSE}
browseVignettes("GenomicRanges")
```
Read section 1, and
do exercises 2.2, 2.4, 2.5, 2.8, 2.12, and 2.13. Perhaps select
additional topics of particular interest to you.
## Resources
_R_ / _Bioconductor_
- [Web site][Bioconductor] -- install, learn, use, develop _R_ /
_Bioconductor_ packages
- [Support](http://support.bioconductor.org) -- seek help and
guidance; also
[StackOverflow](http://stackoverflow.com/questions/tagged/r) for _R_
programming questions
- [biocViews](http://bioconductor.org/packages/release/BiocViews.html)
-- discover packages
- Package landing pages, e.g.,
[GenomicRanges](http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html),
including title, description, authors, installation instructions,
vignettes (e.g., GenomicRanges '[How
To](http://bioconductor.org/packages/release/bioc/vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf)'),
etc.
- [Course](http://bioconductor.org/help/course-materials/) and other
[help](http://bioconductor.org/help/) material (e.g., videos, EdX
course, community blogs, ...)
Publications and presentations
- Lawrence M, Huber W, Pages H, Aboyoun P, Carlson M, et al. (2013)
Software for Computing and Annotating Genomic Ranges. PLoS Comput
Biol 9(8): e1003118. doi:
[10.1371/journal.pcbi.1003118][GRanges.bib]
- Lawrence, M. 2014. Software for Enabling Genomic Data
Analysis. Bioc2014 conference [slides](http://bioconductor.org/help/course-materials/2014/BioC2014/Lawrence_Talk.pdf).
[R]: http://r-project.org
[Bioconductor]: http://bioconductor.org
[GRanges.bib]: http://dx.doi.org/10.1371/journal.pcbi.1003118
[Scalable.bib]: http://arxiv.org/abs/1409.2864
[Lawrence.bioc2014.bib]:
http://bioconductor.org/help/course-materials/2014/BioC2014/Lawrence_Talk.pdf
[AnnotationData]: http://bioconductor.org/packages/release/BiocViews.html#___AnnotationData
[AnnotationDbi]: http://bioconductor.org/packages/release/bioc/html/AnnotationDbi.html
[AnnotationHub]: http://bioconductor.org/packages/release/bioc/html/AnnotationHub.html
[BSgenome.Hsapiens.UCSC.hg19]: http://bioconductor.org/packages/release/data/annotation/html/BSgenome.Hsapiens.UCSC.hg19.html
[BSgenome]: http://bioconductor.org/packages/release/bioc/html/BSgenome.html
[Biostrings]: http://bioconductor.org/packages/release/bioc/html/Biostrings.html
[Bsgenome.Hsapiens.UCSC.hg19]: http://bioconductor.org/packages/release/data/annotation/html/Bsgenome.Hsapiens.UCSC.hg19.html
[CNTools]: http://bioconductor.org/packages/release/bioc/html/CNTools.html
[ChIPQC]: http://bioconductor.org/packages/release/bioc/html/ChIPQC.html
[ChIPpeakAnno]: http://bioconductor.org/packages/release/bioc/html/ChIPpeakAnno.html
[DESeq2]: http://bioconductor.org/packages/release/bioc/html/DESeq2.html
[DiffBind]: http://bioconductor.org/packages/release/bioc/html/DiffBind.html
[GenomicAlignments]: http://bioconductor.org/packages/release/bioc/html/GenomicAlignments.html
[GenomicRanges]: http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html
[Homo.sapiens]: http://bioconductor.org/packages/release/data/annotation/html/Homo.sapiens.html
[IRanges]: http://bioconductor.org/packages/release/bioc/html/IRanges.html
[KEGGREST]: http://bioconductor.org/packages/release/bioc/html/KEGGREST.html
[PSICQUIC]: http://bioconductor.org/packages/release/bioc/html/PSICQUIC.html
[Rsamtools]: http://bioconductor.org/packages/release/bioc/html/Rsamtools.html
[Rsubread]: http://bioconductor.org/packages/release/bioc/html/Rsubread.html
[ShortRead]: http://bioconductor.org/packages/release/bioc/html/ShortRead.html
[SomaticSignatures]: http://bioconductor.org/packages/release/bioc/html/SomaticSignatures.html
[TxDb.Hsapiens.UCSC.hg19.knownGene]: http://bioconductor.org/packages/release/data/annotation/html/TxDb.Hsapiens.UCSC.hg19.knownGene.html
[VariantAnnotation]: http://bioconductor.org/packages/release/bioc/html/VariantAnnotation.html
[VariantFiltering]: http://bioconductor.org/packages/release/bioc/html/VariantFiltering.html
[VariantTools]: http://bioconductor.org/packages/release/bioc/html/VariantTools.html
[biocViews]: http://bioconductor.org/packages/release/BiocViews.html#___Software
[biomaRt]: http://bioconductor.org/packages/release/bioc/html/biomaRt.html
[cn.mops]: http://bioconductor.org/packages/release/bioc/html/cn.mops.html
[edgeR]: http://bioconductor.org/packages/release/bioc/html/edgeR.html
[ensemblVEP]: http://bioconductor.org/packages/release/bioc/html/ensemblVEP.html
[h5vc]: http://bioconductor.org/packages/release/bioc/html/h5vc.html
[limma]: http://bioconductor.org/packages/release/bioc/html/limma.html
[metagenomeSeq]: http://bioconductor.org/packages/release/bioc/html/metagenomeSeq.html
[org.Hs.eg.db]: http://bioconductor.org/packages/release/data/annotation/html/org.Hs.eg.db.html
[org.Sc.sgd.db]: http://bioconductor.org/packages/release/data/annotation/html/org.Sc.sgd.db.html
[phyloseq]: http://bioconductor.org/packages/release/bioc/html/phyloseq.html
[rtracklayer]: http://bioconductor.org/packages/release/bioc/html/rtracklayer.html
[snpStats]: http://bioconductor.org/packages/release/bioc/html/snpStats.html
[Gviz]: http://bioconductor.org/packages/release/bioc/html/Gviz.html
[epivizr]: http://bioconductor.org/packages/release/bioc/html/epivizr.html
[ggbio]: http://bioconductor.org/packages/release/bioc/html/ggbio.html
[OmicCircos]: http://bioconductor.org/packages/release/bioc/html/OmicCircos.html