Biostrings. Martin Morgan Bioconductor / Fred Hutchinson Cancer Research Center Seattle, WA, USA June 2009

Transcription

1 Biostrings Martin Morgan Bioconductor / Fred Hutchinson Cancer Research Center Seattle, WA, USA June 2009

2 Biostrings Representation DNA, RNA, amino acid, and general biological strings Manipulation Sequence summary Pattern matching Views and masks Genomes, via BSgenome Example tasks 1. Remap microarray probes 2. Identify and remove short read contaminants 3. Align reads to a custom-masked genome

3 Representation Type One Several DNA DNAString DNAStringSet RNA RNAString RNAStringSet Amino acid AAString AAStringSet Biological BString BStringSet

4 Creating objects I > library(biostrings) > DNAString() 0-letter "DNAString" instance seq: > DNAString("ACACGACT") 8-letter "DNAString" instance seq: ACACGACT > alphabet(dnastring()) # IUPAC [1] "A" "C" "G" "T" "M" "R" "W" "S" "Y" "K" [11] "V" "H" "D" "B" "N" "-" "+" > try(dnastring("e")) # error

5 Creating objects II > data(phix174phage) > phix174phage A DNAStringSet instance of length 6 width seq names [1] 5386 GAGTTT...CTGCA Genbank [2] 5386 GAGTTT...CTGCA RF70s [3] 5386 GAGTTT...CTGCA SS78 [4] 5386 GAGTTT...CTGCA Bull [5] 5386 GAGTTT...CTGCA G97 [6] 5386 GAGTTT...CTGCA NEB03 > gb <- phix174phage[["genbank"]] # or [[1]] > gb 5386-letter "DNAString" instance seq: GAGTTTTATCGCTTCCATG...TGGCGTATCCAACCTGCA

6 Manipulation Input / Output read.dnastringset, write.xstringset Description alphabet valid letters alphabetfrequency, dinucleotidefrequency nucleotide and dinucleotide counts Transformation reverse, complement, reversecomplement chartr translate character, e.g., C to T

7 Manipulation: examples > gb 5386-letter "DNAString" instance seq: GAGTTTTATCGCTTCCATG...TGGCGTATCCAACCTGCA > reversecomplement(gb) 5386-letter "DNAString" instance seq: TGCAGGTTGGATACGCCAA...ATGGAAGCGATAAAACTC > alphabetfrequency(gb) A C G T M R W S Y K V H D B N > dinucleotidefrequency(gb) AA AC AG AT CA CC CG CT GA GC GG GT TA TC TG TT

8 Subsequences, views, and masks Subsequences: copying sequences Constructors (e.g., DNAString) subseq Views Motivation: wasteful to copy large read-only strings Views Masks Motivation: exclude well-defined regions from consideration for analysis maskmotif, mask

9 Subsequences and views I > subseq(phix174phage, start = 2800, + end = 2820) A DNAStringSet instance of length 6 width seq names [1] 21 CCGGGC...ATGTT Genbank [2] 21 CCGGGC...ATGTT RF70s [3] 21 CCGGGC...ATGTT SS78 [4] 21 CCGGGC...ATGTT Bull [5] 21 CCGGGC...ATGTT G97 [6] 21 CCGGGC...ATGTT NEB03 > Views(gb, start = 2800, end = 2820) Views on a 5386-letter DNAString subject subject: GAGTTTTATCGCTTCCA...GCGTATCCAACCTGCA views: start end width [1] [CCGGGCAATAACGTTTATGTT]

10 Subsequences and views II > width <- 25 > starts <- round(runif(1000, 1, nchar(gb) - + width)) > gbv <- Views(gb, start = starts, width = width) > length(gbv) [1] 1000 > head(gbv, 3) Views on a 5386-letter DNAString subject subject: GAGTTTTATCGCTTCCA...GCGTATCCAACCTGCA views: start end width [1] [ATTCTGTGCC...CTTTGTTCC] [2] [CTGAGACTGA...TCGCCAAAT] [3] [CCTACAGGTA...ACCCTAATT]

11 Masks > gb 5386-letter "DNAString" instance seq: GAGTTTTATCGCTTCCATG...TGGCGTATCCAACCTGCA > (gbm <- maskmotif(gb, "TTTT")) 5386-letter "MaskedDNAString" instance (# for masking) seq: GAG####ATCGCTTCCATG...TGGCGTATCCAACCTGCA masks: maskedwidth maskedratio active desc TRUE TTTT-blocks > alphabetfrequency(gb, baseonly = TRUE) A C G T other > alphabetfrequency(gbm, baseonly = TRUE) A C G T other

12 Pattern matching Terminology pattern: subsequence or pattern being looked for subject: string being searched Types of match Exact: pattern is identical to subsequence(s) of subject Mismatch: substitutions between pattern and subject Indel: gap opening and extension Alignemnts Global, local, ends-free,... Scoring scheme for mismatches, insertions and deletions edit distance

13 Types of alignment Example: align pattern succeed to subject supersede Global (Needleman-Wunch) Align entire sequences, e.g., pattern: succe--ed subject: sup-ersed Local (Smith-Waterman) Best alignment of portion of a sequence pattern: su subject: su Ends-free / overlap Restricted to right end of subject and left end of pattern, or vice versa

14 Edit distance Alignment scores: penalities for Mismatch Constant, e.g., PAM / BLOSUM Probability-based, e.g., resolving IUPAC ambiguities Gap opening and extension Linear: extension only Affine: opening and extension Edit distance Score summed over the alignment E.g., Levenshtein edit distance: Mismatch -1 Gap opening 0 Gap extension -1

15 Position weight matrix Matrix of weights for each nucleotide, at several positions Useful for, e.g., motif-finding > pwm <- rbind( + A=c( 1, 0, 19, 20, 18, 1, 20, 7), + C=c( 1, 0, 1, 0, 1, 18, 0, 2), + G=c(17, 0, 0, 0, 1, 0, 0, 3), + T=c( 1, 20, 0, 0, 0, 1, 0, 8))

16 Pattern matching in Biostrings All matches of pattern in subject Subject One Many Pattern One matchpattern vmatchpattern countpattern vcountpattern Many matchpdict??? vcountpdict pairwisealignment: global (Needleman-Wunch), local (Smith-Waternman), and overlap (ends-free) alignments matchpwm: position weight matrix-defined motifs Special-purpose: trimlrpatterns, matchprobepair, findpalindromes

17 Genome representations Motivation: mechanism for representing and manipulating large, unchanging representations Model organism BSgenome packages already exist Custom BSgenome packages can be created Often contain masks, e.g., RepeatMasker > library(bsgenome) > available.genomes() > library(bsgenome.hsapiens.ucsc.hg19) > Hsapiens > Hsapiens[["chr1"]]

18 Example: remapping probes > library(hgu95av2probe) > library(bsgenome.hsapiens.ucsc.hg19) > dict <- PDict(hgu95av2probe[["sequence"]]) > subj <- unmasked(hsapiens[["chr1"]]) > m <- matchpdict(dict, subj) > m[[700]] INCOMPLETE: match to all chromosomes; see vignette

19 Example: trimming adapters > pcrprimer <- "GGACTACCVGGGTATCTAAT" > trimmed <- trimlrpatterns(pcrprimer, + subject = sread(aln), max.lmismatch = 2, + Lfixed = FALSE) Trim PCR primer pcrprimer from left end of AlignedRead object aln, allowing up to two mismatches and IUPAC ambiguity in the primer

20 Summary Biostrings Representations for large or numerous biological sequences Convenient manipulations Extensive and flexible pattern matching Biostrings vignettes are extensive, e.g., alignments, genome manipulations