Transcriptomics Breakout

Topics Discussed Transcriptomics Applications and Challenges For Each Systems Biology Project –Host and Pathogen Bacteria Viruses Mammalian Hosts Transcriptomics Platforms –Microarrays Tiling arrays DNA Glass Spotted Arrays –Genome-wide RT-PCR –RNA Seq Normalization of Transcriptomics Data Strategies for Removing Ribosomal and Host RNA

Normalizing Expression Data This Is An Unsolved Challenge: normalizing expression data of microbes between samples: infected tissues containing different numbers of the infectious agent –Normalization must be gene specific (individual genes exhibit unique expression behavior) –Normalization methods Based on microbial burden, but microbial load estimates are method-dependent Based on the identification of genes whose expression is stable under the tested conditions, e.g. by the GeneNorm algorithm (easier with host profiling than microbial profiling) Based on values derived from an empirical study of the relationship between microbial input (over a range) and the gene- specific expression values that result The significance of normalization to experimental interpretation was considered to be HUGE and inappropriate method could lead to completely misleading conclusions.

RNA Seq: An Emerging, Powerful Application of Next Gen Sequencing Hybridization-independent, genome-wide (including intergenic regions), with the capacity to generate data that reannote the genome sequence Multiplexing methods under development and will dramatically reduce cost per sample, potentially to the level of glass-spotted arrays Experience varies by project, but interest is wide-spread and intense Rapid technical developments must be matched by the development of appropriate bioinformatics tools and databases (a new, very cool, genome browser was demonstrated that had been adapted for the display of RNASeq data)

RNA Seq (but also some Tiling Arrays) Interrogate RNAs coming from Intergenic Regions Detection of small, medium and large non- coding RNAs with powerful roles in gene regulation. Their detection and functional assessment must be integrated with the roles of protein transcription factors in regulatory network analysis: thus very germane to systems biology Methods will need to be employed to prepare total RNA that retains or even size-selects for non-coding RNAs of various sizes, including small RNAs

RNASeq for Bacterial Expression Profiling Must Deal With The “Ribosome Challenge” and the Challenge of Host RNAs To Get Needed Sequencing Depth The efficiencies of commercial methods (based on a few capture oligonucleotides per ribosomal sequence) for the removal of ribosomal RNAs by capture are inconsistent and may be bacterial species dependent The use of non-random oligonucleotides (rather than random heximers) designed to exclude ribosomal sequences may work well for human RNA applications, but their effectiveness for bacterial systems has not been fully explored. A method to capturing all RNAs, except ribosomal RNAs, in a comprehensive and non-biased manner was described.