# Detecting broad peaks in sRNA-seq data

What kind of tool would be appropriate do detect "broad peaks" in small RNA-seq sequencing data?

MACS2 appears to be developed for ChIP-seq data, but I see that there is a --nomodel option. Would that make this program usable in my case?

I suppose I should also set the --shift and --extsize options, but I'm not sure how:

  --nomodel             Whether or not to build the shifting model. If True,
MACS will not build model. by default it means
shifting size = 100, try to set extsize to change it.
DEFAULT: False
--shift SHIFT         (NOT the legacy --shiftsize option!) The arbitrary
shift in bp. Use discretion while setting it other
than default value. When NOMODEL is set, MACS will use
this value to move cutting ends (5') towards 5'->3'
direction then apply EXTSIZE to extend them to
fragments. When this value is negative, ends will be
moved toward 3'->5' direction. Recommended to keep it
as default 0 for ChIP-Seq datasets, or -1 * half of
EXTSIZE together with EXTSIZE option for detecting
enriched cutting loci such as certain DNAseI-Seq
datasets. Note, you can't set values other than 0 if
format is BAMPE for paired-end data. DEFAULT: 0.
--extsize EXTSIZE     The arbitrary extension size in bp. When nomodel is
true, MACS will use this value as fragment size to
extend each read towards 3' end, then pile them up.
It's exactly twice the number of obsolete SHIFTSIZE.
In previous language, each read is moved 5'->3'
direction to middle of fragment by 1/2 d, then
extended to both direction with 1/2 d. This is
equivalent to say each read is extended towards 5'->3'
into a d size fragment. DEFAULT: 200. EXTSIZE and
SHIFT can be combined when necessary. Check SHIFT
option


In my case the tags are the same things as the fragments, so this "extension" thing is probably not appropriate.

Basically, I would like to call peaks based on the plain real coverage of my small reads. Are there other tools that would be more appropriate?

• Do you actually need peaks or is your end goal instead to find novel sRNAs? I would think stringTie followed by a considerable amount of filtering would be better. Jun 8 '19 at 8:20
• I mapped sRNA-seq reads on the genome, and I want to automatically delineate genomic regions that produce them. By "broad peaks", I mean such regions. Not sure what you mean by "novel sRNAs".
– bli
Jun 10 '19 at 7:50
• sRNAs shouldn’t appear as broad peaks unless they’re coming from repeat clusters. See the answer from Sebastian Müller for good tool options. Jun 10 '19 at 8:34

The sure why any software should be confined to only broad peaks, but there is two packages that should do what I thing you have in mind. Just to clarify, for smallRNAs peak detection is sometimes also referred to as loci or cluster detection:

• ShortStack which is developed for alignment, annotation, and quantification of small RNAs. The de-novo cluster finding option as copied from the manual might do the trick:
DE-NOVO CLUSTER FINDING
Unless options --locus or --locifile are used (see below), ShortStack
will de-novo identify clusters of small RNA accumulation genome-wide.
Cluster definition is simple: First, all regions containing at least one
primary alignment are found where the maximum distance between the ends
of the alignments is <= option --pad (default: 75). Second, if the
number of alignments in the cluster is >= option --mincov (default:
0.5rpm), the cluster is kept. The mincov threshold can also be specified
in terms of reads per million by using a value such as 3.2rpm (which
specifies the threshold to be 3.2 reads per million). Using a rpm
threshold allows the sensitivity of cluster discovery to be normalized
between libraries of different sizes. Alternatively, reads per million
mapped (rpmm) can be specified: A --mincov of 1.2rpmm indicates 1.2
reads per million mapped is the threshold. rpm is a fraction of total
library size, while rpmm is a fraction of only the aligned & placed
fraction of the library.

• segmentSeq is an R-package useing a Bayesian approach for small RNA locus detection. It's however really slow and not developed any further by the main author.