Version: 0.1.1
Compiled: Sat Oct 24 10:12:10 2015
Objectives
Time | Topic |
---|---|
09:15 - 10:15 | Sequencing work flows and file types |
10:15 | Tea/Coffee break |
10:30 - 12:30 | Introduction to R and Bioconductor |
12:30 | Lunch |
13:30 -14:00 | Scalable computing |
Wet-lab sequence preparation (figure from http://rnaseq.uoregon.edu/)
(Illumina) Sequencing (Bentley et al., 2008, doi:10.1038/nature07517
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
Input & manipulation: ShortRead readFastq()
, FastqStreamer()
, FastqSampler()
@ERR127302.1703 HWI-EAS350_0441:1:1:1460:19184#0/1
CCTGAGTGAAGCTGATCTTGATCTACGAAGAGAGATAGATCTTGATCGTCGAGGAGATGCTGACCTTGACCT
+
HHGHHGHHHHHHHHDGG<GDGGE@GDGGD<?B8??ADAD<BE@EE8EGDGA3CB85*,77@>>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?########################
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
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=<ID=20,length=62435964,assembly=B36,md5=f126cdf8a6e0c7f379d618ff66beb2da,species="Homo sapiens",taxonomy=x>
##phasing=partial
##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">
##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">
...
##FILTER=<ID=q10,Description="Quality below 10">
##FILTER=<ID=s50,Description="Less than 50% of samples have data">
...
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
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
Input: rtracklayer import()
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";
...
Language and environment for statistical computing and graphics
factor()
, NA
Vector, class, object
logical
, integer
, numeric
, complex
, character
, byte
matrix
– atomic vector with ‘dim’ attributedata.frame
– list of equal length atomic vectorslm()
, belowFunction, generic, method
rnorm(1000)
print()
.print.factor
; methods are invoked indirectly, via the generic.Introspection
class()
, str()
dim()
Help
?print
: help on the generic print?print.data.frame
: help on print method for objects of class data.frame.Example
x <- rnorm(1000) # atomic vectors
y <- x + rnorm(1000, sd=.5)
df <- data.frame(x=x, y=y) # object of class 'data.frame'
plot(y ~ x, df) # generic plot, method plot.formula
fit <- lm(y ~x, df) # object of class 'lm'
methods(class=class(fit)) # introspection
## [1] add1 alias anova case.names coerce confint
## [7] cooks.distance deviance dfbeta dfbetas drop1 dummy.coef
## [13] effects extractAIC family formula hatvalues influence
## [19] initialize kappa labels logLik model.frame model.matrix
## [25] nobs plot predict print proj qr
## [31] residuals rstandard rstudent show simulate slotsFromS3
## [37] summary variable.names vcov
## see '?methods' for accessing help and source code
Analysis and comprehension of high-throughput genomic data
Packages, vignettes, work flows
Objects
methods()
, getClass()
, selectMethod()
method?"substr,<tab>"
to select help on methods, class?D<tab>
for help on classesExample
require(Biostrings) # Biological sequences
data(phiX174Phage) # sample data, see ?phiX174Phage
phiX174Phage
## A DNAStringSet instance of length 6
## width seq names
## [1] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA Genbank
## [2] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA RF70s
## [3] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA SS78
## [4] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA Bull
## [5] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA G97
## [6] 5386 GAGTTTTATCGCTTCCATGACGCAGAAGTTAAC...TTCGATAAAAATGATTGGCGTATCCAACCTGCA NEB03
m <- consensusMatrix(phiX174Phage)[1:4,] # nucl. x position counts
polymorphic <- which(colSums(m != 0) > 1)
m[, polymorphic]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## A 4 5 4 3 0 0 5 2 0
## C 0 0 0 0 5 1 0 0 5
## G 2 1 2 3 0 0 1 4 0
## T 0 0 0 0 1 5 0 0 1
methods(class=class(phiX174Phage))
## [1] ! !=
## [3] [ [[
## [5] [[<- [<-
## [7] %in% <
## [9] <= ==
## [11] > >=
## [13] $ $<-
## [15] aggregate alphabetFrequency
## [17] anyNA append
## [19] as.character as.complex
## [21] as.data.frame as.env
## [23] as.integer as.list
## [25] as.logical as.matrix
## [27] as.numeric as.raw
## [29] as.vector c
## [31] chartr coerce
## [33] compact compare
## [35] compareStrings complement
## [37] consensusMatrix consensusString
## [39] countOverlaps countPattern
## [41] countPDict dinucleotideFrequencyTest
## [43] do.call droplevels
## [45] duplicated elementLengths
## [47] elementMetadata elementMetadata<-
## [49] elementType endoapply
## [51] eval expand
## [53] extractAt extractROWS
## [55] Filter Find
## [57] findOverlaps hasOnlyBaseLetters
## [59] head high2low
## [61] ifelse intersect
## [63] is.na is.unsorted
## [65] isEmpty isMatchingEndingAt
## [67] isMatchingStartingAt lapply
## [69] length lengths
## [71] letterFrequency Map
## [73] match matchPattern
## [75] matchPDict mcols
## [77] mcols<- mendoapply
## [79] metadata metadata<-
## [81] mstack names
## [83] names<- narrow
## [85] nchar neditEndingAt
## [87] neditStartingAt NROW
## [89] nucleotideFrequencyAt oligonucleotideFrequency
## [91] order overlapsAny
## [93] PairwiseAlignments PairwiseAlignmentsSingleSubject
## [95] parallelSlotNames PDict
## [97] Position PWM
## [99] rank Reduce
## [101] relist relistToClass
## [103] rename rep
## [105] rep.int replaceAt
## [107] replaceLetterAt replaceROWS
## [109] rev revElements
## [111] reverse reverseComplement
## [113] ROWNAMES sapply
## [115] seqinfo seqinfo<-
## [117] seqlevelsInUse seqtype
## [119] seqtype<- setdiff
## [121] setequal shiftApply
## [123] show showAsCell
## [125] sort split
## [127] split<- splitAsList
## [129] stack stringDist
## [131] subseq subseq<-
## [133] subset subsetByOverlaps
## [135] table tail
## [137] tapply threebands
## [139] toString translate
## [141] trimLRPatterns twoWayAlphabetFrequency
## [143] union unique
## [145] uniqueLetters unlist
## [147] unsplit unstrsplit
## [149] updateObject values
## [151] values<- vcountPattern
## [153] vcountPDict vmatchPattern
## [155] vwhichPDict which.isMatchingEndingAt
## [157] which.isMatchingStartingAt whichPDict
## [159] width window
## [161] window<- with
## [163] within xtfrm
## [165] xvcopy
## see '?methods' for accessing help and source code
selectMethod(reverseComplement, class(phiX174Phage))
## Method Definition:
##
## function (x, ...)
## xvcopy(x, lkup = getDNAComplementLookup(), reverse = TRUE)
## <environment: namespace:Biostrings>
##
## Signatures:
## x
## target "DNAStringSet"
## defined "DNAStringSet"
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
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
library(GenomicRanges)
and the help system queried interactively, as outlined above:
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.
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.
?consensusMatrix
, for instance. Also check out the BSgenome package for working with whole genome sequences, e.g., ?"getSeq,BSgenome-method"
?readGAlignments
help page and vigentte(package="GenomicAlignments", "summarizeOverlaps")
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.Visualization
Classes
Methods –
reverseComplement()
letterFrequency()
matchPDict()
, matchPWM()
Related packages
Example
BSgenome
packages. The following calculates GC content across chr14. require(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)
## G|C
## [1,] 0.336276
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
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
require(GenomicRanges)
gr <- GRanges("A", IRanges(c(10, 20, 22), width=5), "+")
shift(gr, 1) # 1-based coordinates!
## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] A [11, 15] +
## [2] A [21, 25] +
## [3] A [23, 27] +
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
range(gr) # intra-range
## GRanges object with 1 range and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] A [10, 26] +
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
reduce(gr) # inter-range
## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] A [10, 14] +
## [2] A [20, 26] +
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
coverage(gr)
## RleList of length 1
## $A
## integer-Rle of length 26 with 6 runs
## Lengths: 9 5 5 2 3 2
## Values : 0 1 0 1 2 1
setdiff(range(gr), gr) # 'introns'
## GRanges object with 1 range and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] A [15, 19] +
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
IRangesList, GRangesList - List: all elements of the same type - Many *List-aware methods, but a common ‘trick’: apply a vectorized function to the unlisted representaion, then re-list
grl <- GRangesList(...)
orig_gr <- unlist(grl)
transformed_gr <- FUN(orig)
transformed_grl <- relist(, grl)
Reference
Classes – GenomicRanges-like behaivor
Methods
readGAlignments()
, readGAlignmentsList()
summarizeOverlaps()
Example
require(GenomicRanges)
require(GenomicAlignments)
## Loading required package: GenomicAlignments
## Loading required package: Rsamtools
require(Rsamtools)
## our 'region of interest'
roi <- GRanges("chr14", IRanges(19653773, width=1))
## sample data
require('RNAseqData.HNRNPC.bam.chr14')
## Loading required package: RNAseqData.HNRNPC.bam.chr14
bf <- BamFile(RNAseqData.HNRNPC.bam.chr14_BAMFILES[[1]], asMates=TRUE)
## alignments, junctions, overlapping our roi
paln <- readGAlignmentsList(bf)
j <- summarizeJunctions(paln, with.revmap=TRUE)
j_overlap <- j[j %over% roi]
## supporting reads
paln[j_overlap$revmap[[1]]]
## GAlignmentsList object of length 8:
## [[1]]
## GAlignments object with 2 alignments and 0 metadata columns:
## seqnames strand cigar qwidth start end width njunc
## [1] chr14 - 66M120N6M 72 19653707 19653898 192 1
## [2] chr14 + 7M1270N65M 72 19652348 19653689 1342 1
##
## [[2]]
## GAlignments object with 2 alignments and 0 metadata columns:
## seqnames strand cigar qwidth start end width njunc
## [1] chr14 - 66M120N6M 72 19653707 19653898 192 1
## [2] chr14 + 72M 72 19653686 19653757 72 0
##
## [[3]]
## GAlignments object with 2 alignments and 0 metadata columns:
## seqnames strand cigar qwidth start end width njunc
## [1] chr14 + 72M 72 19653675 19653746 72 0
## [2] chr14 - 65M120N7M 72 19653708 19653899 192 1
##
## ...
## <5 more elements>
## -------
## seqinfo: 93 sequences from an unspecified genome
Classes – GenomicRanges-like behavior
Functions and methods
readVcf()
, readGeno()
, readInfo()
, readGT()
, writeVcf()
, filterVcf()
locateVariants()
(variants overlapping ranges), predictCoding()
, summarizeVariants()
genotypeToSnpMatrix()
, snpSummary()
Example
## input variants
require(VariantAnnotation)
fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
vcf <- readVcf(fl, "hg19")
seqlevels(vcf) <- "chr22"
## known gene model
require(TxDb.Hsapiens.UCSC.hg19.knownGene)
coding <- locateVariants(rowRanges(vcf),
TxDb.Hsapiens.UCSC.hg19.knownGene,
CodingVariants())
head(coding)
## GRanges object with 6 ranges and 9 metadata columns:
## seqnames ranges strand | LOCATION LOCSTART LOCEND QUERYID TXID
## <Rle> <IRanges> <Rle> | <factor> <integer> <integer> <integer> <character>
## 1 chr22 [50301422, 50301422] - | coding 939 939 24 75253
## 2 chr22 [50301476, 50301476] - | coding 885 885 25 75253
## 3 chr22 [50301488, 50301488] - | coding 873 873 26 75253
## 4 chr22 [50301494, 50301494] - | coding 867 867 27 75253
## 5 chr22 [50301584, 50301584] - | coding 777 777 28 75253
## 6 chr22 [50302962, 50302962] - | coding 698 698 57 75253
## CDSID GENEID PRECEDEID FOLLOWID
## <IntegerList> <character> <CharacterList> <CharacterList>
## 1 218562 79087
## 2 218562 79087
## 3 218562 79087
## 4 218562 79087
## 5 218562 79087
## 6 218563 79087
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
Related packages
Reference
assays()
colData()
data frame for desciption of samplesrowRanges()
GRanges / GRangeList or data frame for description of featuresexptData()
to describe the entire object
library(SummarizedExperiment)
library(airway)
data(airway)
airway
## class: RangedSummarizedExperiment
## dim: 64102 8
## metadata(1): ''
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowRanges metadata column names(0):
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample
colData(airway)
## DataFrame with 8 rows and 9 columns
## SampleName cell dex albut Run avgLength Experiment Sample
## <factor> <factor> <factor> <factor> <factor> <integer> <factor> <factor>
## SRR1039508 GSM1275862 N61311 untrt untrt SRR1039508 126 SRX384345 SRS508568
## SRR1039509 GSM1275863 N61311 trt untrt SRR1039509 126 SRX384346 SRS508567
## SRR1039512 GSM1275866 N052611 untrt untrt SRR1039512 126 SRX384349 SRS508571
## SRR1039513 GSM1275867 N052611 trt untrt SRR1039513 87 SRX384350 SRS508572
## SRR1039516 GSM1275870 N080611 untrt untrt SRR1039516 120 SRX384353 SRS508575
## SRR1039517 GSM1275871 N080611 trt untrt SRR1039517 126 SRX384354 SRS508576
## SRR1039520 GSM1275874 N061011 untrt untrt SRR1039520 101 SRX384357 SRS508579
## SRR1039521 GSM1275875 N061011 trt untrt SRR1039521 98 SRX384358 SRS508580
## BioSample
## <factor>
## SRR1039508 SAMN02422669
## SRR1039509 SAMN02422675
## SRR1039512 SAMN02422678
## SRR1039513 SAMN02422670
## SRR1039516 SAMN02422682
## SRR1039517 SAMN02422673
## SRR1039520 SAMN02422683
## SRR1039521 SAMN02422677
airway[, airway$dex %in% "trt"]
## class: RangedSummarizedExperiment
## dim: 64102 4
## metadata(1): ''
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowRanges metadata column names(0):
## colnames(4): SRR1039509 SRR1039513 SRR1039517 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample
?select
?exonsBy
page to retrieve all exons grouped by gene or transcript.open()
, read chunk(s), close()
.yieldSize
argument to Rsamtools::BamFile()
Rsamtools::ScanBamParam()
ShortRead::FastqSampler()
lapply()
-like operationsParallel evaluation in Bioconductor
bplapply()
for lapply()
-like functions, increasingly used by package developers to provide easy, standard way of gaining parallel evaluation.R / Bioconductor
Publications (General Bioconductor)
Other