PIT-DB: A Resource for Sharing, Annotating and Analysing Translated Genomic Elements

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The publication of the human genome in 2001 was rightly hailed as a major scientific achievement, but over a decade later we are still far from a complete understanding of the structure of the genome and the role of the various elements within it. While protein coding regions of the genome were identified and used to annotate the genome soon after it was sequenced, many more exotic genomic elements have subsequently attracted interest including pseudogenes, non-coding RNAs and short open reading frames (sORFs).In recent years, post-genomic bioanalytical techniques such as RNA-seq transcriptomics (which tells us which genomic elements are expressed) and mass spectrometry based proteomics (which tells us which of the expressed elements are translated into peptides or proteins) have helped refine our understanding of the human genome at a fundamental level. Just this year, two proteomics studies published in Nature caused a stir by showing that no experimental evidence could be found for the expression of several genomic elements widely accepted to code for protein, while other regions of the genome that were not previously thought to be protein coding were in fact found to produce proteins. If this is the situation for the intensively studied human genome, we must assume that the genome annotations for less studied species (so called non-model organisms) are even less accurate.We recently developed (and tested, and published) a methodology called proteomics informed by transcriptomics (PIT) that rapidly generates large numbers of genome annotations underpinned by multiple sources of experimental evidence. In PIT, every sample is analysed using both RNA-seq and proteomic mass spectrometry and the data from these two analyses integrated to provide a list of observed proteins and any other translated genomic elements (TGEs), together with the detailed transcriptomic and spectral evidence that underpins these observations. The beauty of PIT compared with traditional proteomics is that no prior sequence knowledge is needed, so novel TGEs (be they proteins or other more exotic features) can be detected. RNA-seq can be used by itself to rapidly generate genome annotations without prior knowledge, but without PIT's mass spectrometry step the confidence in these annotations is limited and there is no guarantee that transcribed elements actually get translated.In a recent BBSRC TRDF project we developed easy to use web-based software workflows, implement in the popular Galaxy platform, to process the data from PIT experiments in a repeatable way with uniformly formatted output files. This has proven very useful for answering individual biological questions, but there is currently no meaningful way to share the results of PIT experiments. In this project we propose to plug this gap by developing PIT-DB, a web-accessible database of results produced by PIT. This publicly available database will immediately be populated with data from experiments conducted on various species at the University of Bristol, but other groups will be actively encouraged to submission their own data.Having data from multiple PIT experiments in one database will deliver exciting new scientific insights. As well as simply allowing researchers to share their results from individual PIT experiments, PIT-DB will pool information about individual novel TGEs from multiple experiments so evidence can be accumulated for each individual TGE. Improving the quality of results by using data from replicate experiments is a fundamental concept in science and the utility of doing this on a community-wide basis has been repeatedly demonstrated by other bioinformatics databases such as Ensembl, UniProt and PRIDE. As well as being of interest individually, the well evidenced TGEs in PIT-DB will provide large numbers of experimentally derived (as opposed to computationally predicted) genome annotations for all of the species for which data is present in the database.