Quantitative transcriptomics using designed primer-based amplification.

We developed a novel Designed Primer-based RNA-sequencing strategy (DP-seq) that uses a defined set of heptamer primers to amplify the majority of expressed transcripts from limiting amounts of mRNA, while preserving their relative abundance. Our strategy reproducibly yielded high levels of amplification from as low as 50 picograms of mRNA while offering a dynamic range of over five orders of magnitude in RNA concentrations. We also demonstrated the potential of DP-seq to selectively suppress the amplification of the highly expressing ribosomal transcripts by more than 70% in our sequencing library. Using lineage segregation in embryonic stem cell cultures as a model of early mammalian embryogenesis, DP-seq revealed novel sets of low abundant transcripts, some corresponding to the identity of cellular progeny before they arise, reflecting the specification of cell fate prior to actual germ layer segregation.

Space-conserving optimal DNA-protein alignment.

DNA-protein alignment algorithms can be used to discover coding sequences in a genomic sequence, if the corresponding protein derivatives are known. They can also be used to identify potential coding sequences of a newly sequenced genome, by using proteins from related species. Previously known algorithms either solve a simplified formulation, or sacrifice optimality to achieve practical implementation. In this paper, we present a comprehensive formulation of the DNA-protein alignment problem, and an algorithm to compute the optimal alignment in O(mn) time using only four tables of size (m + 1) x (n + 1), where m and n are the lengths of the DNA and protein sequences, respectively. We also developed a Protein and DNA Alignment program PanDA that implements the proposed solution. Experimental results indicate that our algorithm produces high quality alignments.