Answers.Body: <p>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:</p>
  
  <ul>
  <li><a href="https://sites.psu.edu/axtell/software/shortstack/" rel="nofollow noreferrer">ShortStack</a> which is developed for alignment, annotation, and quantification of small RNAs. The <code>de-novo cluster finding</code> option as copied from the manual might do the trick:</li>
  </ul>
  
  <pre><code>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 &lt;= option --pad (default: 75). Second, if the
      number of alignments in the cluster is &gt;= 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 &amp; placed
      fraction of the library.
  </code></pre>
  
  <ul>
  <li><a href="https://bioconductor.org/packages/release/bioc/html/segmentSeq.html" rel="nofollow noreferrer">segmentSeq</a>
  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.</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-06-09T14:01:30.563Z
Answers.LastActivityDate: 2019-06-09T14:01:30.563Z
Answers.UserDisplayName: Sebastian Müller
Body: <p>What kind of tool would be appropriate do detect "broad peaks" in small RNA-seq sequencing data?</p>
  
  <p>MACS2 appears to be  developed for ChIP-seq data, but I see that there is a <code>--nomodel</code> option. Would that make this program usable in my case?</p>
  
  <p>I suppose I should also set the <code>--shift</code> and <code>--extsize</code> options, but I'm not sure how:</p>
  
  <pre><code>  --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'-&gt;3'
                          direction then apply EXTSIZE to extend them to
                          fragments. When this value is negative, ends will be
                          moved toward 3'-&gt;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'-&gt;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'-&gt;3'
                          into a d size fragment. DEFAULT: 200. EXTSIZE and
                          SHIFT can be combined when necessary. Check SHIFT
                          option
  </code></pre>
  
  <p>In my case the tags are the same things as the fragments, so this "extension" thing is probably not appropriate.</p>
  
  <p>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?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-08T08:20:29.233Z
Comments.Text: 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.
Comments.UserDisplayName: Devon Ryan
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-10T07:50:05.327Z
Comments.Text: 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".
Comments.UserDisplayName: bli
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-10T08:34:33.793Z
Comments.Text: sRNAs shouldn’t appear as broad peaks unless they’re coming from repeat clusters. See the answer from Sebastian Müller for good tool options.
Comments.UserDisplayName: Devon Ryan
Tags: <software-recommendation><hts><peak-calling>
Title: Detecting broad peaks in sRNA-seq data
ThreadText: # Detecting broad peaks in sRNA-seq data
  
  (Asked by: username_0 on 2019-06-07)
  
  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?
  
  **Tags:** software-recommendation, hts, peak-calling
  
  > **Comments:**
  > **username_1**: 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.  
  > **username_0**: 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".  
  > **username_1**: sRNAs shouldn’t appear as broad peaks unless they’re coming from repeat clusters. See the answer from username_2 username_2 for good tool options.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2019-06-09. Score: 3
  
  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](https://sites.psu.edu/axtell/software/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](https://bioconductor.org/packages/release/bioc/html/segmentSeq.html)
    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.
  
  
  ---
  
  