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  • Tool for RIP-seq analysis

    I have a RIP seq data to study the binding of RNA protein. I googled and found only 4 publicaations and searching this forum I found two posts. My question is - Is there a tool or work flow out there for analyzing Illumina Hi seq RNAseq data for specifically understanding the RNA binding proteins.
    Alternatively, I am planning to run my regular RNA-seq workflow of running-TopHat> Cufflink and then work with RPKM/ FPKMs
    Any suggestion please?

    Thanks

  • #2
    It's different to RNA-seq. You only get part of the RNA that it bound by the protein. There is no reason to use Cufflinks. I suggest TopHat followed by a ChIP-seq peak calling algorithm. There was a group that planned to publish a RIP-seq pipeline late last year, but I have not seen it released yet.

    Comment


    • #3
      RIP-seq

      So you mean after running TopHat I should run MACS or another ChIP- peak caller but how that will be useful? I apologize if someone can through some more explanation that will be great.

      So far from this forum ChIP-seq is not good for RIP -seq

      Discussion of next-gen sequencing related bioinformatics: resources, algorithms, open source efforts, etc


      Used RNA -seq work flow RIPseq

      Discussion of next-gen sequencing related bioinformatics: resources, algorithms, open source efforts, etc


      This paper used RPKM:


      However it is not clear what bioinfirmatics is done here:
      Matrin 3 (MATR3) is a highly conserved, inner nuclear matrix protein with two zinc finger domains and two RNA recognition motifs (RRM), whose function is largely unknown. Recently we found MATR3 to be phosphorylated by the protein kinase ATM, which activates the cellular response to double strand br …


      Any further insight will be greatly helpful.

      Thanks

      Comment


      • #4
        You only get part of the RNA that it bound by the protein.
        From the (limited) understanding I have of RIP-seq and CLIP-HITS/CLIP-seq, this only applies to the latter. For RIP-seq, as far as I know, you will get longer, less specific RNA fragments. I think using Cufflinks or some other FPKM quantification method actually makes sense, as part of the workflow; some more localized ChIP-seq-type enrichment analysis should probably be performed as well.

        Comment


        • #5
          RIP-seq tools

          Thanks kopi-o,
          Of the 4-5 RIP-seq publications the follwoing paper:

          TAR DNA-binding protein 43 (TDP-43) is associated with a spectrum of neurodegenerative diseases. Although TDP-43 resembles heterogeneous nuclear ribonucleoproteins, its RNA targets and physiological protein partners remain unknown. Here we identify RNA targets of TDP-43 from cortical neurons by RNA …


          Has just aligned with Bowtie and used filteration for reads mapping to exon and introns to coclude the binding patners. I dont understand how you may conclude that? I would love to see response from senior memebers about how should I procede with RIP seq analysis. I apologize for coming back with so many posts, But I want to initate a good discussion on this topic.

          Comment


          • #6
            RIP-seq analysis

            I believe I am on wrong track, I dont think TopHat will be ideal as it will be more suitable for splice junctions not for enrichment of specific targets in IP. So I am working with mouse cells and have input, control with isogenic control ab and IP sample with test ab I believe here is my proposed strategy:
            Align with Bowtie/ BWA (mouse genome)
            Calculate RPKM values per gene and transcript
            Remove gene/transcripts which are enriched in isogenic ab.
            Calculate the differential expression over input (enrichment in test Ab after removing enriched genes/ transcripts with isogenic ab)

            Any suggestion or feed back? Should I be using BWA or Bowtie or adopt some other strategy.
            Little lost

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            • #7
              But your RNA binding protein could be binding to locations on the RNA where a splice junction was and the intron has been spliced out already. You will lose good reads. Could you map to a splice junction RNA reference with Bowtie to see how often this happens ?

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              • #8
                RIP-seq tool

                Agreed Dario that I will lose some information, But I think the main aim is to find in general the binding partners of my protein of interest not that just ones which are specific for junctions. However I am open to suggestions and maybe I am thinking it wrong. I believe using gap junction aligners will be more specific for identification of splicing. TopHat throws out reads which are not specific for junctions. Am I correct?

                Comment


                • #9
                  TopHat throws out reads which are not specific for junctions. Am I correct?
                  No, TopHat gives you both regular genomic alignments and splice-junction alignments.

                  Comment


                  • #10
                    RIP-seq tool

                    kopi-o Thanks. My bad.

                    In one of you previous message you mentioned that in this situation I may be using ChIp-seq peak calling tool in addition to RPKM based RNA-seq (differential enrichment) work flow. Would you please comment little further how best you think I can integrate ChIP-se peak caller.
                    I guess Top hat> Cufflink > RPKM differential enrichment after filtering noise from control Ab enriched regions. Do You think if in parallel I run ChIP-seq peak caller and then overlay those results with above RPKM based differential enrichment?
                    I understand lot of labs may not be interested in this approach. But any input will be helpful.
                    Thanks

                    Comment


                    • #11
                      I am not sure about how to approach it, that's why I was a bit vague. I am thinking that transcript-based differential enrichment as you describe can give some information; then there may be other transcripts that don't look differentially expressed overall but where there is some localized over-representation which may be caught by a peak-calling type approach. E g specific overrepresentation of RIP-seq tags in the 3' UTR. Perhaps after defining peaks with e g MACS, a DE-seq type program can be used to assess differential enrichment of those defined peak regions between IP and controls.
                      Please keep posting your experiences here ;-)

                      Comment


                      • #12
                        I was working on a similar problem several months ago. My understanding is that RIPseq pulls down entire transcripts, and there is no RNAase that eats away RNA sequence that is not directly bound. Peak-calling seems to be irrelevant in this setting---you get information regarding RBP->transcript association but not binding site locations---you should just get enhanced transcript expression (e.g., as measured by Cuffdiff) for RBP target transcripts.
                        Last edited by duffman; 05-15-2012, 02:27 PM. Reason: "RNAase" rather than "DNAase"

                        Comment


                        • #13
                          Hi everybody,

                          Since the last post is a little old, I try to get an uptade of what you learned theses last years.

                          I have done a RIP-seq experiment and I'm looking for a tool (if possible existing on galaxy) which would allow me to quantify reads on the whole genome, not specificaly in the regions definied in the annotation (gff).
                          I used the tool "Create a BedGraph of genome coverage" followed by "Merge BedGraph files" but I could only see the results for de 10,000 first bases.

                          So, how do you do these kind of analysis ?

                          Thank you for your help

                          Comment

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