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Bioinformatics Data Skills Vince Buffalo 1 image (Working with Range…

- - - - NCBI Genome Remaping Service
      - Lift Over
      - CrossMap
  - - - format/library: BED, BAM, BCF
  - - - format/library: GTF, GFF, SAM, VCF, Wiggle, GenomicRanges, BLAST, GenBank/EMBL Feature Table
  - - - to manipulate genomic ranges
        
        start(), width(), end()
        
        IRanges
        
        restrict() cuts a set of ranges
        such that they fall inside of a certain bound
        
        flank()
        
        .> x
        IRanges of length 2
        start end width
        [1] 40 67 28
        [2] 80 114 35
        .> flank(x, width=7)
        IRanges of length 2
        start end width
        [1] 33 39 7
        [2] 73 79 7
        
        is useful increating ranges upstream and downstream of protein codding genes that could contian promoter sequences
        
        upstrema rages are created by default
        
        downstream r.can be created by adding start=FALSE
        
        zoom in. zoom out by adding and substacting
        
        reduce()
        
        to choose nonoverlapping ranges
        
        gaps()
        
        similar to reduce()
        
        useful to find gaps in coverage, defining intragenic regions, creating intron ranges etc.
        
        intersect(a,b)
        
        setdiff(a,b) or (b,a)
        
        union(a,b)
    - - used to work with different features of a genome(genes, exones, UTRs, transcripts, etc.)
    - - to manipulate genomic sequence data
    - - for reading common bioinformatics formats (e.g. BED, WIG, GTF/GFF)
  - - - can be created through IntervalTree class (IRanges)
  - - - upstream
    - - downstream
    - - regardles stream
  - - - Coverage
        
        the depth of overlap of ranges across the length of a sequence
      - Conservation tracks
        
        base-by-base evolutionary conservation scores between species (pair of species?)
        
        can be calculated by phastCons
      - per-base pair estimates of population genomics summary statistics (nucleotide diversity)
    - - to compress runs
      - run (consecutive stretches of the same value)
      - 4 4 4 3 3 2 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 4 4 4 4 4 4 4
        
        3 fours, 2 trees, 1 two...
      - x <- as.integer(c(4, 4, 4, 3, 3, 2, 1, 1, 1, 1, 1, 0, 0, 0,
        0, 0, 0, 0, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4))
        xrle <- Rle(x)
        xrle
        integer-Rle of length 28 with 7 runs
        Lengths: 3 2 1 5 7 3 7
        Values : 4 3 2 1 0 1 4
    - - slice()
        
        takes a run-length encoded numeric vector ast
        its argument and slices it, creating a set of ranges where the run-length encoded vector has some minimal value
        
        min_cov2 <- slice(rngs_cov, lower=2)
        min_cov2
        Views on a 63-length Rle subject
        views:
        start end width
        [1] 16 18 3 [2 2 2]
        [2] 22 22 1 [2]
        [3] 35 39 5 [2 2 2 2 2]
        [4] 51 62 12 [2 2 2 3 3 3 4 3 3 3 2 2]
        
        sliced coverage Rle object whcih corresponds to re
        region with more than 2x coverage.
  - - - gr1 <- GRanges(c("chr1", "chr2"), IRanges(start=c(32, 95), width=c(24, 123)))
        gr2 <- GRanges(c("chr8", "chr2"), IRanges(start=c(27, 12), width=c(42, 34)))
        grl <- GRangesList(gr1, gr2)
        grl
        GRangesList of length 2:
        [[1]]
        GRanges with 2 ranges and 0 metadata columns:
        seqnames ranges strand<Rle> <IRanges> <Rle>
        [1] chr1 [32, 55]
        [2] chr2 [95, 217]
        [[2]]
        GRanges with 2 ranges and 0 metadata columns:
        etc
        
        behave as R's list
    - - library(GenomicRanges)
        gr <- GRanges(seqname=c("chr1", "chr1", "chr2", "chr3"),
        ranges=IRanges(start=5:8, width=10),
        strand=c("+", "-", "-", "+"))
        gr
        GRanges with 4 ranges and 0 metadata columns:
        seqnames ranges strand<Rle> <IRanges> <Rle>
        [1] chr1 [5, 14] +
        [1] chr1 [5, 14] +
        [2] chr1 [6, 15] -
        [3] chr2 [7, 16] -
        [4] chr3 [8, 17] +
        -
        seqlengths:
        chr1 chr2 chr3
        NA NA NA
      - all metadata attached to GRanges object are stored in a DataFrame
      - sequence name, strandeand metadata coumns