Background Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. Typhimurium 14028 using “shotgun” proteomics to accurately uncover the translational scenery and post-translational features. The data provide protein-level experimental validation for approximately half of the predicted protein-coding genes in Salmonella and suggest revisions to several genes that appear to have incorrectly assigned translational start sites including a potential novel alternate start codon. Additionally we uncovered 12 non-annotated genes missed by gene prediction programs as well as evidence suggesting a role for one of these novel ORFs in Salmonella pathogenesis. We also characterized post-translational features in the Salmonella genome including chemical modifications and proteolytic cleavages. We find that bacteria possess a much larger and more complex repertoire of chemical modifications than previously thought including several novel modifications. Our in vivo proteolysis data recognized more than 130 transmission peptide and N-terminal methionine cleavage events critical for protein function. Summary This work shows several ways in which software of proteomics data can improve the quality of genome annotations to help novel biological insights and provides a comprehensive proteome map of Salmonella as a source for systems ARRY-334543 evaluation. Keywords: gene annotation proteomics post-translational adjustments Background Many areas of contemporary biological study are reliant on accurate recognition from the protein-coding genes in each genome aswell as the type of the adult functional proteins products an activity commonly known as genome annotation. Using the exponential upsurge in the amount of sequenced prokaryotic genomes afforded by advancements in genome sequencing systems during the last decade current prokaryotic genome annotation is actually an computerized high-throughput procedure that relies seriously on de novo gene prediction applications [1-3]. While LAMP3 de novo gene prediction applications have considerably improved for prokaryotic genomes substantial challenges stay [4] such as for example determining the complete start and ARRY-334543 ARRY-334543 prevent site of the gene accurately predicting brief genes and identifying an end codon that represents an alternative solution amino acid rather than true prevent site. As attempts to sequence even more branches from the tree of existence expand the amount of precision for current gene prediction applications qualified on proteobacteria datasets will markedly reduce leading to a rise in wrong predictions of protein-coding genes [4]. Compounding the problem is having less experimental evidence to get expected protein-coding areas for the overpowering most annotated genomes. Where obtainable experimental proof is normally predicated on indicated RNA sequences such as for example from microarray or RNA Seq experiments. However these genome-centric analyses do not independently and unequivocally determine whether a predicted protein-coding gene is translated into a protein or importantly provide any reliable information on post-translational processing. Bottom-up proteomics offers the ability to directly measure peptides arising from expressed proteins representing the current best option for independently and unambiguously identifying at least an important subset of the protein-coding genes in a ARRY-334543 genome and can be used to experimentally validate gene annotations [4-9]. In a bottom up approach proteins within a complex mixture are typically digested with a protease after which the resulting peptides are separated by chromatographic methods and then analyzed using tandem mass spectrometry ARRY-334543 (MS/MS) [10 11 Each MS/MS spectrum is a measure of fragment masses ideally from a single peptide sequence of ~ 6-50 amino acids. This set of mass values is analogous to a ‘fingerprint’ that identifies the peptide. Interpretation of MS/MS ARRY-334543 peptide spectra is accomplished 1) by using algorithms such as X!Tandem [12] SEQUEST [13] or Mascot [14] to compare measured masses against a set of theoretical masses of possible protein sequences or 2) less commonly by de novo analysis which does not depend on any prior knowledge of the possible sequences [15 16 Similar to searching MS/MS spectra against a couple of predicted proteins sequences additionally it is possible (and simple for basic genomes) to recognize the protein-coding genes within a genome by searching MS/MS spectra against a six-frame.