Gappy Total Recaller: Efficient Algorithms and Data Structures for Accurate Transcriptomics

Abstract

Understanding complex mammalian biology depends crucially on our ability to define a precise map of all the transcripts encoded in a genome, and to measure their relative abundances. A promising assay depends on RNASeq approaches, which builds on next generation sequencing pipelines capable of interrogating cDNAs extracted from a cell. The underlying pipeline starts with base-calling, collect the sequence reads and interpret the raw-read in terms of transcripts that are grouped with respect to different splice-variant isoforms of a messenger RNA. We address a very basic problem involved in all of these pipelines, namely accurate Bayesian base-calling, which could combine the analog intensity data with suitable underlying priors on base-composition in the transcripts. In the context of sequencing genomic DNA, a powerful approach for base-calling has been developed in the TotalReCaller pipeline. For these purposes, it uses a suitable reference whole-genome sequence in a compressed self-indexed format to derive its priors. However, TotalReCaller faces many new challenges in the transcriptomic domain, especially since we still lack a fully annotated library of all possible transcripts, and hence a sufficiently good prior. There are many possible solutions, similar to the ones developed for TotalReCaller, in applications addressing de novo sequencing and assembly, where partial contigs or string-graphs could be used to boot-strap the Bayesian priors on base-composition. A similar approach would be applicable here too, partial assembly of transcripts can be used to characterize the splicing junctions or organize them in incompatibility graphs and then provided as priors for TotalReCaller. The key algorithmic techniques for this purpose have been addressed in a forthcoming paper on Stringomics. Here, we address a related but fundamental problem, by assuming that we only have a reference genome, with certain intervals marked as candidate regions for ORF (Open Reading Frames), but not necessarily complete annotations regarding the 5’ or 3’ termini of a gene or its exon-intron structure. The algorithms we describe find the most accurate base-calls of a cDNA with the best possible segmentation, all mapped to the genome appropriately.