Discovery and revision of Arabidopsis genes by proteogenomics

Abstract

Gene annotation underpins genome science. Most often protein coding sequence is inferred from the genome based on transcript evidence and computational predictions. While generally correct, gene models suffer from errors in reading frame, exon border definition, and exon identification. To ascertain the error rate of Arabidopsis thaliana gene models, we isolated proteins from a sample of Arabidopsis tissues and determined the amino acid sequences of 144,079 distinct peptides by tandem mass spectrometry. The peptides corresponded to 1 or more of 3 different translations of the genome: a 6-frame translation, an exon splicegraph, and the currently annotated proteome. The majority of the peptides (126,055) resided in existing gene models (12,769 confirmed proteins), comprising 40% of annotated genes. Surprisingly, 18,024 novel peptides were found that do not correspond to annotated genes. Using the gene finding program AUGUSTUS and 5,426 novel peptides that occurred in clusters, we discovered 778 new protein-coding genes and refined the annotation of an additional 695 gene models. The remaining 13,449 novel peptides provide high quality annotation (>99% correct) for thousands of additional genes. Our observation that 18,024 of 144,079 peptides did not match current gene models suggests that 13% of the Arabidopsis proteome was incomplete due to approximately equal numbers of missing and incorrect gene models.annotation ͉ genomics ͉ proteomics A fundamental goal of genome projects is to generate a protein-coding catalog. Much of modern biological research depends on a complete and accurate proteome. Extensive proteomic catalogs have been developed through the integration of gene prediction algorithms, cDNA sequences, and comparative genomics (1, 2). As emerging research is incorporated into annotation pipelines and manual curation efforts, gene models continue to improve. High throughput gene annotation pipelines use a variety of information sources, and benefit most significantly when new data contains information that is orthogonal to the kinds currently available (3).Recent advances in chemistry and algorithms for peptide mass spectrometry have enabled the production of large proteomics datasets with broad coverage of the proteome (4-6). Proteogenomics (using proteomic information to annotate the genome) complements nucleotide-based annotation in that it unambiguously determines reading frame, translation start and stop sites, splice boundaries, and the validity of short ORFs. By combining DNA-based annotation with proteogenomics, an accurate and more complete protein-coding catalog can be obtained (6-10). With its clear potential for improving genome annotation, proteogenomics could be integrated with genome projects.A recent publication by Baerenfaller et al. (4) demonstrated the ability of extensive resampling to provide good coverage of the Arabidopsis proteome. From 1,354 LC runs the authors identified 86,456 distinct peptides covering 13,029 proteins. In addition to providing an organ specific proteome catal...

Super-resolution ribosome profiling reveals unannotated translation events inArabidopsis

“…We also found that 37 annotated pseudogenes are actually expressed and translated (Table 1 and Dataset S1H). Mining publicly available proteomics data, four ORFs that we identified in either annotated ncRNAs or pseudogenes also have unique peptides detected by mass spectrometry (Dataset S1I) (45). The Western blots and the mass spectrometry data not only support the translation of these unannotated ORFs but also demonstrate that some of the ORFs produce stable proteins in plants.…”

Reconstruction of protein networks from an atlas of maize seed proteotypes

“…Sample preparation and MS are based on previously described methods (48)(49)(50) and are detailed in SI Materials and Methods. Briefly, the generated spectra were searched using the B73 RefGen_v2 5a WGS (14).…”

Augmented Annotation of the Schizosaccharomyces pombe Genome Reveals Additional Genes Required for Growth and Viability

“…A number of pipelines have successfully exploited high-throughput tandem mass-spectrometry (MS/MS) data to predict novel proteins in H. sapiens (Fermin et al 2006;Tanner et al 2007;Bitton et al 2010), Caenorhabditis elegans (Schrimpf et al 2009), Drosophila melanogaster (Schrimpf et al 2009), Arabidopsis thaliana (Castellana et al 2008), and bacteria (Gupta et al 2007). We have therefore applied a pipeline that integrates this proteogenomics approach (Bitton et al 2010) with comparative genomics and genomewide domain prediction to augment the current annotation of S. pombe.…”