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  • #1

    Optimal Analysis Software?

    I am carrying out an RNA-seq project in which I hope to identify DE genes between two conditions in a non-model organism. I have two biological and two technical replicates for each condition, with 75bp Illumina reads. My reference sequence is a low-coverage (1-2X), unannotated genomic sequence obtained by 454 GS FLX titanium sequencing. This reference sequence has been assembled, and consists of ~140,000 contigs/singletons that range from a few hundred base pairs to a few kb in length.

    My questions are about the best software to use for my analysis. From my own literature search, it seems like most mapping software will work, but in terms of DE analysis, I'm not very sure. For example, it appears that DEGseq requires an annotated genome. Can the parameters be adjusted so that annotation is not required? Does any other DE software require annotations? Would it somehow be possible to specify the contigs/singletons as "genes" and simply examine DE among them (and subsequently identify which genes/gene fragments are contained in the sequence)?

    It is also not clear to me which software can deal with biological and technical replicates most effectively. Which programs can and cannot account for these?

    I am new to NGS and relatively new to bioinformatics, so I apologize if my questions are somewhat naive. Any help would be appreciated!
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  • #2
    personally I like Vencio's method, described in
    Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE) - BMC Bioinformatics
    http://www.biomedcentral.com/1471-2105/5/119/
    Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.


    It was sevelopped for SAGE but it works fine with RNAseq.

    The R code can be downloaded from the journal.

    Alternative, you can use edgeR.

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