Nested Containment List objects

Description

The NCList class is a container for storing the Nested Containment List representation of a Ranges object. Preprocessing a Ranges object as a Nested Containment List allows efficient overlap-based operations like findOverlaps.

The NCLists class is a container for storing a collection of NCList objects. An NCLists object is typically the result of preprocessing each list element of a RangesList object as a Nested Containment List. Like with NCList, the NCLists object can then be used for efficient overlap-based operations.

To preprocess a Ranges or RangesList object, simply call the NCList or NCLists constructor function on it.

Usage

NCList(x, circle.length=NA_integer_)
NCLists(x, circle.length=NA_integer_)

Arguments

Details

The GenomicRanges package also defines the GNCList constructor and class for preprocessing and representing a vector of genomic ranges as a data structure based on Nested Containment Lists.

Some important differences between the new findOverlaps/countOverlaps implementation based on Nested Containment Lists (BioC >= 3.1) and the old implementation based on Interval Trees (BioC < 3.1):

• With the new implementation, the hits returned by findOverlaps are not fully ordered (i.e. ordered by queryHits and subject Hits) anymore, but only partially ordered (i.e. ordered by queryHits only). Other than that, and except for the 2 particular situations mentioned below, the 2 implementations produce the same output. However, the new implementation is faster and more memory efficient.

• With the new implementation, either the query or the subject can be preprocessed with NCList for a Ranges object (replacement for IntervalTree), NCLists for a RangesList object (replacement for IntervalForest), and GNCList for a GenomicRanges object (replacement for GIntervalTree). However, for a one-time use, it is NOT advised to explicitely preprocess the input. This is because findOverlaps or countOverlaps will take care of it and do a better job at it (by preprocessing only what's needed when it's needed, and releasing memory as they go).

• With the new implementation, countOverlaps on Ranges or GenomicRanges objects doesn't call findOverlaps in order to collect all the hits in a growing Hits object and count them only at the end. Instead, the counting happens at the C level and the hits are not kept. This reduces memory usage considerably when there is a lot of hits.

• When minoverlap=0, zero-width ranges are now interpreted as insertion points and considered to overlap with ranges that contain them. With the old alogrithm, zero-width ranges were always ignored. This is the 1st situation where the new and old implementations produce different outputs.

• When using select="arbitrary", the new implementation will generally not select the same hits as the old implementation. This is the 2nd situation where the new and old implementations produce different outputs.

• The new implementation supports preprocessing of a GenomicRanges object with ranges defined on circular sequences (e.g. on the mitochnodrial chromosome). See GNCList in the GenomicRanges package for some examples.

• Objects preprocessed with NCList, NCLists, and GNCList are serializable (with save) for later use. Not a typical thing to do though, because preprocessing is very cheap (i.e. very fast and memory efficient).

Value

An NCList object for the NCList constructor and an NCLists object for the NCLists constructor.

Author(s)

Herv<c3><83><c2><a9> Pag<c3><83><c2><a8>s

References

Alexander V. Alekseyenko and Christopher J. Lee – Nested Containment List (NCList): a new algorithm for accelerating interval query of genome alignment and interval databases. Bioinformatics (2007) 23 (11): 1386-1393. doi: 10.1093/bioinformatics/btl647

See Also

• The GNCList constructor and class defined in the GenomicRanges package.

• findOverlaps for finding/counting interval overlaps between two range-based objects.

• Ranges and RangesList objects.

Examples

## The example below is for illustration purpose only and does NOT
## reflect typical usage. This is because, for a one-time use, it is
## NOT advised to explicitely preprocess the input for findOverlaps()
## or countOverlaps(). These functions will take care of it and do a
## better job at it (by preprocessing only what's needed when it's
## needed, and release memory as they go).

query <- IRanges(c(1, 4, 9), c(5, 7, 10))
subject <- IRanges(c(2, 2, 10), c(2, 3, 12))

## Either the query or the subject of findOverlaps() can be preprocessed:

ppsubject <- NCList(subject)
hits1 <- findOverlaps(query, ppsubject)
hits1

ppquery <- NCList(query)
hits2 <- findOverlaps(ppquery, subject)
hits2

## Note that 'hits1' and 'hits2' contain the same hits but not in the
## same order.
stopifnot(identical(sort(hits1), sort(hits2)))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(IRanges)
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/IRanges/NCList-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: NCList-class
> ### Title: Nested Containment List objects
> ### Aliases: class:NCList NCList-class NCList ranges,NCList-method
> ###   length,NCList-method names,NCList-method start,NCList-method
> ###   end,NCList-method width,NCList-method coerce,NCList,IRanges-method
> ###   coerce,Ranges,NCList-method class:NCLists NCLists-class NCLists
> ###   ranges,NCLists-method length,NCLists-method names,NCLists-method
> ###   start,NCLists-method end,NCLists-method width,NCLists-method
> ###   elementNROWS,NCLists-method
> ###   coerce,NCLists,CompressedIRangesList-method
> ###   coerce,NCLists,IRangesList-method coerce,RangesList,NCLists-method
> ### Keywords: classes methods
> 
> ### ** Examples
> 
> ## The example below is for illustration purpose only and does NOT
> ## reflect typical usage. This is because, for a one-time use, it is
> ## NOT advised to explicitely preprocess the input for findOverlaps()
> ## or countOverlaps(). These functions will take care of it and do a
> ## better job at it (by preprocessing only what's needed when it's
> ## needed, and release memory as they go).
> 
> query <- IRanges(c(1, 4, 9), c(5, 7, 10))
> subject <- IRanges(c(2, 2, 10), c(2, 3, 12))
> 
> ## Either the query or the subject of findOverlaps() can be preprocessed:
> 
> ppsubject <- NCList(subject)
> hits1 <- findOverlaps(query, ppsubject)
> hits1
Hits object with 3 hits and 0 metadata columns:
      queryHits subjectHits
      <integer>   <integer>
  [1]         1           2
  [2]         1           1
  [3]         3           3
  -------
  queryLength: 3 / subjectLength: 3
> 
> ppquery <- NCList(query)
> hits2 <- findOverlaps(ppquery, subject)
> hits2
Hits object with 3 hits and 0 metadata columns:
      queryHits subjectHits
      <integer>   <integer>
  [1]         1           1
  [2]         1           2
  [3]         3           3
  -------
  queryLength: 3 / subjectLength: 3
> 
> ## Note that 'hits1' and 'hits2' contain the same hits but not in the
> ## same order.
> stopifnot(identical(sort(hits1), sort(hits2)))
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>