text: user1375: Given an experiment consisting of an input (baseline RNA) and IP (pulldown to find RNAs bound to certain protein of interest)... Is a DE analysis performed over the RNA-seq data from the samples (lets say with EdgeR or DESEQ2) suitable to reveal the preferentially bound RNAs? What other software tools would you recommend?
  
  user235: You need to be careful of terminology. To me, a RIP-seq experiment involves a pull down, followed by a RNAseq library prep. Thus, the whole transcript is captured, not just the binding site of the protein (as in CLIP-Seq, HITS-CLIP, PAR-CLIP, iCLIP or eCLIP). Thus "peak-callers" whether they be designed for calling protein-DNA or protein-RNA binding sites are not suitable as the signal will not be punctuate or peaky. Of the methods mentioned by @Devon Ryan, only RIPSeeker seems setup to deal with this sort of data. 
  In some of the papers that talk about calling RNA-protein interactions they lump RIP-seq in with the CLIP techniques (see piranha and ASPeak papers referenced by @Devon Ryan). They appear to be talking about a technique where the RNA is fragmented before it is pulled down. Thus you would capture peaky binding sites (actually, the protocol would be remarkably similar to CLIP). 
  You should work out which of these you have. 
  As for using DESeq/EdgeR etc, in the RIPSeeker paper they say:
  
  Furthermore, programs for de novo transcript assembly followed by
    differential expression (DE) analysis, such as the Cufflinks/Cuffdiff
    suite (15,16), and for DE on a set of known transcripts, such as DESeq
    (17), may appear applicable to RIP-seq analysis. Unlike peak-calling
    strategy, however, the transcript-based methods assume the full
    transcriptome being sequenced at a fairly deep coverage (as usually
    the case in RNA-seq) and thus may be sensitive to background noise
    typical to the IP-based protocols, which is due to both the
    non-specific RNA interactions with a protein of interest and the
    non-specific RNA input from the pull-down of the (mutant) control
    (Supplementary Figures S3 and S4).
  
  I don't know to what extent RIPSeeker performs better than the naive DESeq approach as its not included as one of the comparators in the paper. 
  
  user77: You can try doing standard differential expression, but I worry that the between-sample normalization will work poorly. Personally, I would do peak calling instead, followed by diffBind. You have a few tools to choose from when it comes to this. In the past, I've rolled my own methods for this using MACS2 and genomic alignments (I then converted those to bedGraph files where entries are transcripts, so peak calling is then in transcript-coordinates).
  It's probably easier, though, to use premade software. There are a number of packages out there for RIP-seq. These include piranha, RIPSeeker and ASPeak. Particularly if you're just starting out, you'd be well served with using one of those tools.
  
original_text: 1375: <p>Given an experiment consisting of an input (baseline RNA) and IP (pulldown to find RNAs bound to certain protein of interest)... Is a DE analysis performed over the RNA-seq data from the samples (lets say with EdgeR or DESEQ2) suitable to reveal the preferentially bound RNAs? What other software tools would you recommend?</p>
   235: <p>You need to be careful of terminology. To me, a RIP-seq experiment involves a pull down, followed by a RNAseq library prep. Thus, the whole transcript is captured, not just the binding site of the protein (as in CLIP-Seq, HITS-CLIP, PAR-CLIP, iCLIP or eCLIP). Thus "peak-callers" whether they be designed for calling protein-DNA or protein-RNA binding sites are not suitable as the signal will not be punctuate or peaky. Of the methods mentioned by @Devon Ryan, only <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3632129/" rel="nofollow noreferrer">RIPSeeker</a> seems setup to deal with this sort of data. </p>
  
  <p>In some of the papers that talk about calling RNA-protein interactions they lump RIP-seq in with the CLIP techniques (see piranha and ASPeak papers referenced by @Devon Ryan). They appear to be talking about a technique where the RNA is fragmented before it is pulled down. Thus you would capture peaky binding sites (actually, the protocol would be remarkably similar to CLIP). </p>
  
  <p>You should work out which of these you have. </p>
  
  <p>As for using DESeq/EdgeR etc, in the RIPSeeker paper they say:</p>
  
  <blockquote>
    <p>Furthermore, programs for de novo transcript assembly followed by
    differential expression (DE) analysis, such as the Cufflinks/Cuffdiff
    suite (15,16), and for DE on a set of known transcripts, such as DESeq
    (17), may appear applicable to RIP-seq analysis. Unlike peak-calling
    strategy, however, the transcript-based methods assume the full
    transcriptome being sequenced at a fairly deep coverage (as usually
    the case in RNA-seq) and thus may be sensitive to background noise
    typical to the IP-based protocols, which is due to both the
    non-specific RNA interactions with a protein of interest and the
    non-specific RNA input from the pull-down of the (mutant) control
    (Supplementary Figures S3 and S4).</p>
  </blockquote>
  
  <p>I don't know to what extent RIPSeeker performs better than the naive DESeq approach as its not included as one of the comparators in the paper. </p>
   77: <p>You can try doing standard differential expression, but I worry that the between-sample normalization will work poorly. Personally, I would do peak calling instead, followed by diffBind. You have a few tools to choose from when it comes to this. In the past, I've rolled my own methods for this using MACS2 and genomic alignments (I then converted those to bedGraph files where entries are transcripts, so peak calling is then in transcript-coordinates).</p>
  
  <p>It's probably easier, though, to use premade software. There are a number of packages out there for RIP-seq. These include <a href="https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/bts569" rel="noreferrer">piranha</a>, <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3632129/" rel="noreferrer">RIPSeeker</a> and <a href="https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btt428" rel="noreferrer">ASPeak</a>. Particularly if you're just starting out, you'd be well served with using one of those tools.</p>
  