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  • RNA-Seq data analysis pipeline

    Hello from US. I am going to start working on RNA seq data analysis in soybean. I will appreciate if someone can suggest me a good pipeline to hit count data from raw sequence data (Fastq files).....
    Thanks

  • #2
    This might help.


    Background Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data. Results We conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data. Conclusions Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.


    A comparison of methods for differential expression analysis of RNA-seq data
    Charlotte Soneson1* and Mauro Delorenzi1,2

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    • #3
      If you are looking for a practical guidebook then: http://en.wikibooks.org/wiki/Next_Ge..._%28NGS%29/RNA or http://www.nature.com/nprot/journal/....2012.016.html

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      • #4
        Thanks for suggesting the references............

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        • #5
          Tophat followed by HTSeq-count will also work fine. The outcome of HTSeq-count is a list of genes with the number of reads mapped to each of them

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          • #6
            Thanks, what about using bowtie instead of top hat as I am only concerned about hit count data for particular gene not the splice variants.
            your suggestions are much appreciated
            Thanks

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            • #7
              With RNA-seq you expect to find your reads on the exons of a gene. So you need to deal with the different splice variants anyway. Tophat splits the read in segments to check if for example the first half of the read maps to exon A and the second half to exon B.
              Bowtie does just do the mapping to the reference genome.
              After the alignment you can use HTSeq-count to count the reads that are located on particular genes.

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              • #8
                Thanks........

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                • #9
                  Hello all guys,

                  I just start making RNA-seq data analysis about borrelia. Now I want to find specific gene transcription between different conditions. How to value it? use reads count or RPKM? Which is better? which kind of software is simple ans easy to use? Can I use artemis to do with it?
                  Thank you so much!

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                  • #10
                    Originally posted by geyihe View Post
                    Hello all guys,

                    I just start making RNA-seq data analysis about borrelia. Now I want to find specific gene transcription between different conditions. How to value it? use reads count or RPKM? Which is better? which kind of software is simple ans easy to use? Can I use artemis to do with it?
                    Thank you so much!
                    DESeq (or DESeq2), edgeR, limma (look at the voom function), etc. I would recommend raw counts rather than rpkm.

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                    • #11
                      Originally posted by preet View Post
                      Hello from US. I am going to start working on RNA seq data analysis in soybean. I will appreciate if someone can suggest me a good pipeline to hit count data from raw sequence data (Fastq files).....
                      Thanks
                      Hi Preet, you may consider using the Rsubread+limma/edgeR pipeline. You may use the Rsubread package to align the reads (align function) and summarize mapped reads to genes (featureCounts function), and then use limma to perform differential expression analysis.

                      Cheers,
                      Wei

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                      • #12
                        geyihe, I'm associated with a lab that's working on Borrelia -- please get in touch if you get a chance.

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                        • #13
                          If you are looking for differential expression between two or more groups, don't forget you NEED biological replicates.

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                          • #14
                            rna-seq pipeline

                            Hi I am a rookie bioinformatics who will process the rna-seq from Ion-proton(Single-ended).
                            The pipeline for processing which I know until now is
                            Bowtie, Tophat, Cufflinks, and cummeRbund for RNA-seq alignment, assembly, DE analysis, and .visualization.
                            Any other options?
                            Or What is this advantage or disadvantage of this pipeline?
                            Thank you!
                            Last edited by super0925; 02-14-2014, 07:39 AM.

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                            • #15
                              I found this guide to be very useful in understanding RNA-Seq experiment design and analysis: http://rnaseq.uoregon.edu/index.html

                              I hope you the best.

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