---
title: "Lab 1.3: Data Representations"
output:
  BiocStyle::html_document:
    toc: true
vignette: >
  % \VignetteIndexEntry{Lab 1.3: Data Represenations}
  % \VignetteEngine{knitr::rmarkdown}
---

```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
```

```{r setup, echo=FALSE}
knitr::opts_chunk$set(cache=TRUE)
suppressPackageStartupMessages({
    library(GenomicRanges)
})
```

Authors: Valerie Obenchain (<a
  href="mailto:valerie.obenchain@roswellpark.org">valerie.obenchain@roswellpark.org.org</a>),
  Lori Shepherd (<a
  href="mailto:lori.shepherd@roswellpark.org">lori.shepherd@roswellpark.org</a>),
  Martin Morgan (<a
  href="mailto:martin.morgan@roswellpark.org">martin.morgan@roswellpark.org</a>)
  <br />
Date: 25 June, 2016<br />

# Classes, methods, and packages

This section focuses on classes, methods, and packages, with the goal
being to learn to navigate the help system and interactive discovery
facilities.

## Motivation

Sequence analysis is specialized

- Large data needs to be processed in a memory- and time-efficient manner
- Specific algorithms have been developed for the unique
  characteristics of sequence data

Additional considerations

- Re-use of existing, tested code is easier to do and less error-prone
  than re-inventing the wheel.
- Interoperability between packages is easier when the packages share
  similar data structures.

Solution: use well-defined _classes_ to represent complex data;
_methods_ operate on the classes to perform useful functions.  Classes
and methods are placed together and distributed as _packages_ so that
we can all benefit from the hard work and tested code of others.

# Case study: _IRanges_ and _GRanges_

The [IRanges][] package defines an important class for specifying
integer ranges, e.g.,
```{r iranges}
library(IRanges)
ir <- IRanges(start=c(10, 20, 30), width=5)
ir
```

There are many interesting operations to be performed on ranges, e.g,
`flank()` identifies adjacent ranges
```{r iranges-flank}
flank(ir, 3)
```

The `IRanges` class is part of a class hierarchy. To see this, ask R for
the class of `ir`, and for the class definition of the `IRanges` class
```{r iranges-class}
class(ir)
getClass(class(ir))
```

Notice that `IRanges` extends the `Ranges` class. Now try entering
`?flank` (`?"flank,<tab>"` if not using _RStudio, where `<tab>` means
to press the tab key to ask for tab completion). You can see that
there are help pages for `flank` operating on several different
classes. Select the completion

```{r iranges-flank-method, eval=FALSE}
?"flank,Ranges-method" 
```

and verify that you're at the page that describes the method relevant
to an `IRanges` instance.  Explore other range-based operations.

The [GenomicRanges][] package extends the notion of ranges to include
features relevant to application of ranges in sequence analysis,
particularly the ability to associate a range with a sequence name
(e.g., chromosome) and a strand. Create a `GRanges` instance based on
our `IRanges` instance, as follows
```{r granges}
library(GenomicRanges)
gr <- GRanges(c("chr1", "chr1", "chr2"), ir, strand=c("+", "-", "+"))
gr
```

The notion of flanking sequence has a more nuanced meaning in
biology. In particular we might expect that flanking sequence on the
`+` strand would precede the range, but on the minus strand would
follow it. Verify that `flank` applied to a `GRanges` object has this
behavior.
```{r granges-flank}
flank(gr, 3)
```

Discover what classes `GRanges` extends, find the help page
documenting the behavior of `flank` when applied to a `GRanges` object,
and verify that the help page documents the behavior we just observed.
```{r granges-class}
class(gr)
getClass(class(gr))
```
```{r granges-flank-method, eval=FALSE}
?"flank,GenomicRanges-method"
```
Notice that the available `flank()` methods have been augmented by the
methods defined in the _GenomicRanges_ package.

It seems like there might be a number of helpful methods available for
working with genomic ranges; we can discover some of these from the
command line, indicating that the methods should be on the current
`search()` path

```{r granges-methods, eval=FALSE}
showMethods(class="GRanges", where=search())
```

Use `help()` to list the help pages in the `GenomicRanges` package,
and `vignettes()` to view and access available vignettes; these are
also available in the Rstudio 'Help' tab.
```{r granges-man-and-vignettes, eval=FALSE}
help(package="GenomicRanges")
vignette(package="GenomicRanges")
vignette(package="GenomicRanges", "GenomicRangesHOWTOs")
```

# High-throughput sequence data

The following sections briefly summarize some of the most important
file types in high-throughput sequence analysis. _Briefly_ review
these, or those that are most relevant to your research, before
starting on the section [Data Representation in _R_ /
_Bioconductor_](#data-representation-in-r-bioconductor)

![Alt Files and the Bioconductor packages that input them](our_figures/FilesToPackages.png)

## 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
    ...


## Reads: FASTQ files

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?########################
        
- 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=<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
            
## 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";
              ...
              ...
              
# Data representation in _R_ / _Bioconductor_

This section briefly illustrates how different high-throughput
sequence data types are represented in _R_ / _Bioconductor_. Select
relevant data types for your area of interest, and work through the
examples. Take time to consult help pages, understand the output of
function calls, and the relationship between standard data formats
(summarized in the previous section) and the corresponding _R_ /
_Bioconductor_ representation. 

## Background: Ranges

![Alt Ranges Algebra](our_figures/RangeOperations.png)

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}
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(transformed_gr, grl)
        
Reference

- Lawrence M, Huber W, Pag&egrave;s 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


## _Biostrings_ (DNA or amino acid sequences)

Classes

- 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 and custom
- [ShortRead][]
  - FASTQ files

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}
    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)
    ```
    
## _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}
    library(GenomicRanges)
    library(GenomicAlignments)
    library(Rsamtools)
    
    ## our 'region of interest'
    roi <- GRanges("chr14", IRanges(19653773, width=1)) 
    ## sample data
    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
    library(VariantAnnotation)
    fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
    vcf <- readVcf(fl, "hg19")
    seqlevels(vcf) <- "chr22"
    ## known gene model
    library(TxDb.Hsapiens.UCSC.hg19.knownGene)
    coding <- locateVariants(rowRanges(vcf),
        TxDb.Hsapiens.UCSC.hg19.knownGene,
        CodingVariants())
    head(coding)
    ```
  
Related packages

- [ensemblVEP][] 
  - Forward variants to Ensembl Variant Effect Predictor
- [VariantTools][], [h5vc][]
  - Call variants

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

# Big data

Much Bioinformatic data is very large. The discussion so far has
assumed that the data can be read into memory. Here we mention two
important general strategies for working with large data; we will
explore these in greater detail in a later lab, but feel free to ask
questions and explore this material now.

Restriction
- e.g., `ScanBamParam()` limits input to desired data at specific
  genomic ranges

Iteration
- e.g., `yieldSize` argument of `BamFile()`, or `FastqStreamer()`
  allows iteration through large files.

Compression
- Genomic vectors represented as `Rle` (run-length encoding) class
- Lists e.g., `GRangesList` are efficiently maintain the illusion that
  vector elements are grouped.

Parallel processing
- e.g., via [BiocParallel][] package

Reference

- Lawrence, M and Morgan, M. Scalable Genomic Computing and
  Visualization with _R_ and _Bioconductor_. Statistical Science 29
  (2) (2014), [214-226](http://arxiv.org/abs/1409.2864).

# Exercises

## Summarize overlaps

The goal is to count the number of reads overlapping exons grouped
into genes. This type of count data is the basic input for RNASeq
differential expression analysis, e.g., through [DESeq2][] and
[edgeR][]. 

1. Identify the regions of interest. We use a 'TxDb' package with gene
   models alreaddy defined
   
    ```{r summarizeOverlaps-roi, message=FALSE}
    library(TxDb.Hsapiens.UCSC.hg19.knownGene)
    exByGn <- exonsBy(TxDb.Hsapiens.UCSC.hg19.knownGene, "gene")
    ## only chromosome 14
    seqlevels(exByGn, force=TRUE) = "chr14"
    ```
     
2. Identify the sample BAM files.

    ```{r summarizeOverlaps-bam, message=FALSE}
    library(RNAseqData.HNRNPC.bam.chr14)
    length(RNAseqData.HNRNPC.bam.chr14_BAMFILES)
    ```
     
3. Summarize overlaps, optionally in parallel

    ```{r summarizeOverlaps}
    ## next 2 lines optional; non-Windows
    library(BiocParallel)
    register(MulticoreParam(workers=2))
    olaps <- summarizeOverlaps(exByGn, RNAseqData.HNRNPC.bam.chr14_BAMFILES[1:2])
    ```
     
4. Explore our handiwork, e.g., library sizes (column sums),
   relationship between gene length and number of mapped reads, etc.

    ```{r summarizeOverlaps-explore}
    olaps
    head(assay(olaps))
    colSums(assay(olaps))                # library sizes
    plot(sum(width(olaps)), rowMeans(assay(olaps)), log="xy")
    ```
     
5. As an advanced exercise, investigate the relationship between GC
   content and read count
   
    ```{r summarizeOverlaps-gc}
    library(BSgenome.Hsapiens.UCSC.hg19)
    sequences <- getSeq(BSgenome.Hsapiens.UCSC.hg19, rowRanges(olaps))
    gcPerExon <- letterFrequency(unlist(sequences), "GC")
    gc <- relist(as.vector(gcPerExon), sequences)
    gc_percent <- sum(gc) / sum(width(olaps))
    plot(gc_percent, rowMeans(assay(olaps)), log="y")
    ```

# Resources

Acknowledgements

The material for this lab was taken from a presentation given by Martin
Morgan at CSAMA 2015. 
   
[biocViews]: http://bioconductor.org/packages/BiocViews.html#___Software
[AnnotationData]: http://bioconductor.org/packages/BiocViews.html#___AnnotationData

[aprof]: http://cran.r-project.org/web/packages/aprof/index.html
[hexbin]: http://cran.r-project.org/web/packages/hexbin/index.html
[lineprof]: https://github.com/hadley/lineprof
[microbenchmark]: http://cran.r-project.org/web/packages/microbenchmark/index.html

[AnnotationDbi]: http://bioconductor.org/packages/AnnotationDbi
[BSgenome]: http://bioconductor.org/packages/BSgenome
[BiocParallel]: http://bioconductor.org/packages/BiocParallel
[Biostrings]: http://bioconductor.org/packages/Biostrings
[CNTools]: http://bioconductor.org/packages/CNTools
[ChIPQC]: http://bioconductor.org/packages/ChIPQC
[ChIPpeakAnno]: http://bioconductor.org/packages/ChIPpeakAnno
[DESeq2]: http://bioconductor.org/packages/DESeq2
[DiffBind]: http://bioconductor.org/packages/DiffBind
[GenomicAlignments]: http://bioconductor.org/packages/GenomicAlignments
[GenomicRanges]: http://bioconductor.org/packages/GenomicRanges
[IRanges]: http://bioconductor.org/packages/IRanges
[KEGGREST]: http://bioconductor.org/packages/KEGGREST
[PSICQUIC]: http://bioconductor.org/packages/PSICQUIC
[rtracklayer]: http://bioconductor.org/packages/rtracklayer
[Rsamtools]: http://bioconductor.org/packages/Rsamtools
[ShortRead]: http://bioconductor.org/packages/ShortRead
[VariantAnnotation]: http://bioconductor.org/packages/VariantAnnotation
[VariantFiltering]: http://bioconductor.org/packages/VariantFiltering
[VariantTools]: http://bioconductor.org/packages/VariantTools
[biomaRt]: http://bioconductor.org/packages/biomaRt
[cn.mops]: http://bioconductor.org/packages/cn.mops
[h5vc]: http://bioconductor.org/packages/h5vc
[edgeR]: http://bioconductor.org/packages/edgeR
[ensemblVEP]: http://bioconductor.org/packages/ensemblVEP
[limma]: http://bioconductor.org/packages/limma
[metagenomeSeq]: http://bioconductor.org/packages/metagenomeSeq
[phyloseq]: http://bioconductor.org/packages/phyloseq
[snpStats]: http://bioconductor.org/packages/snpStats

[org.Hs.eg.db]: http://bioconductor.org/packages/org.Hs.eg.db
[TxDb.Hsapiens.UCSC.hg19.knownGene]: http://bioconductor.org/packages/TxDb.Hsapiens.UCSC.hg19.knownGene
[BSgenome.Hsapiens.UCSC.hg19]: http://bioconductor.org/packages/BSgenome.Hsapiens.UCSC.hg19
