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
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
NCLists constructor function on it.
The Ranges or RangesList object to preprocess.
Use only if the space (or spaces if
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
for a RangesList object (replacement for
GNCList for a
GenomicRanges object (replacement for
However, for a one-time use, it is NOT advised to explicitely
preprocess the input. This is because
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,
Ranges or GenomicRanges objects doesn't
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.
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.
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
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
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
An NCList object for the
NCList constructor and an NCLists object
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
GNCList constructor and class
defined in the GenomicRanges package.
findOverlaps for finding/counting interval overlaps
between two range-based objects.
Ranges and RangesList objects.
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## 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)))