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...

Proteogenomics has emerged as a field at the junction of genomics and proteomics. It is a loose collection of technologies that allow the search of tandem mass spectra against genomic databases to identify and characterize protein-coding genes. Proteogenomic peptides provide invaluable information for gene annotation, which is difficult or impossible to ascertain using standard annotation methods. Examples include confirmation of translation, reading-frame determination, identification of gene and exon boundaries, evidence for post-translational processing, identification of splice-forms including alternative splicing, and also, prediction of completely novel genes. For proteogenomics to deliver on its promise, however, it must overcome a number of technological hurdles, including speed and accuracy of peptide identification, construction and search of specialized databases, correction of sampling bias, and others. This article reviews the state of the art of the field, focusing on the current successes, and the role of computation in overcoming these challenges. We describe how technological and algorithmic advances have already enabled large-scale proteogenomic studies in many model organisms, including arabidopsis, yeast, fly, and human. We also provide a preview of the field going forward, describing early efforts in tackling the problems of complex gene structures, searching against genomes of related species, and immunoglobulin gene reconstruction.

The advent of inexpensive RNA-Seq technologies and other deep sequencing technologies for RNA has the promise to radically improve genomic annotation, providing information on transcribed regions and splicing events in a variety of cellular conditions. Using MS based proteogenomics, many of these events can be confirmed directly at the protein level. However, the integration of large amounts of redundant RNA-seq data and mass spectrometry data poses a challenging problem. Our manuscript addresses this by construction of a compact database that contains all useful information expressed in RNA-seq reads. Applying our method to cumulative C. elegans data reduced 496.2GB of aligned RNA-seq SAM files to 410MB of splice graph database written in FASTA format. This corresponds to 1000× compression of data size, without loss of sensitivity. We performed a proteogenomics study using the custom dataset, using a completely automated pipeline and identified a total of 4044 novel events, including 215 novel genes, 808 novel exons, 12 alternative splicings, 618 gene-boundary corrections, 245 exon-boundary changes, 938 frame-shifts, 1166 reverse-strands, and 42 translated UTR. Our results highlight the usefulness of transcript+proteomic integration for improved genome annotations.

New technologies in genomics and proteomics have influenced the emergence of proteogenomics, a field at the confluence of genomics, transcriptomics, and proteomics. First generation proteogenomic toolkits employ peptide mass spectrometry to identify novel protein coding regions. We extend first generation proteogenomic tools to achieve greater accuracy and enable the analysis of large, complex genomes. We apply our pipeline to Zea mays, which has a genome comparable in size to human. Our pipeline begins with the comparison of mass spectra to a putative translation of the genome. We select novel peptides, those that match a region of the genome that was not previously known to be protein coding, for grouping into refinement events. We present a novel, probabilistic framework for evaluating the accuracy of each event. Our calculated event probability, or eventProb, considers the number of supporting peptides and spectra, and the quality of each supporting peptide-spectrum match. Our pipeline predicts 165 novel protein-coding genes and proposes updated models for 741 additional genes. Molecular & Cellular Proteomics 13: 10.1074/ mcp.M113.031260, 157-167, 2014.Accurate genome annotation, wherein the location and structure of all protein coding genes are identified, is critically important and yet it remains elusive for even the most extensively studied organisms. The wide availability of inexpensive next-generation sequencing technologies ensures that model organisms from all branches of the tree of life will continue to be sequenced at an ever increasing pace. However, the annotation pipelines are not able to keep up.Much recent focus on computational gene finding is on incorporating transcript evidence. As with genomic sequencing, availability of high-throughput technologies for transcript sequencing such as RNA-Seq (1) has dramatically changed the genome annotation landscape. Although RNA-Seq provides valuable evidence for genome annotation (2-5) it does not provide a comprehensive solution either. Increasing evidence suggests that a discrepancy exists between protein isoforms that are transcribed versus translated (6). Indeed in our own observation, we find evidence for genes in sampling proteins that are not visible at the transcript level. Moreover, the transcript evidence is confounded by prespliced messages, nontargeted expression noise, ncRNA, and lack of strand and frame information. All of these pose challenges for gene finding.Tandem mass spectrometry is a key technology for assaying the expressed proteome. In typical bottom-up workflows, enzymatically digested peptides are isolated via chromatography and then fragmented in the mass spectrometer. The collection of masses of peptide fragments (tandem mass spectrum) is used as a fingerprint for identification of expressed peptides.Historically, the genomics community has provided the annotations (aa sequences) and the proteomics community has focused on identifying peptides and proteins from this annotated list to assay for expression of proteins in specif...

Database search algorithms are the primary workhorses for the identification of tandem mass spectra. However, these methods are limited to the identification of spectra for which peptides are present in the database, preventing the identification of peptides from mutated or alternatively spliced sequences. A variety of methods has been developed to search a spectrum against a sequence allowing for variations. Some tools determine the sequence of the homologous protein in the related species but do not report the peptide in the target organism. Other tools consider variations, including modifications and mutations, in reconstructing the target sequence. However, these tools will not work if the template (homologous peptide) is missing in the database, and they do not attempt to reconstruct the entire protein target sequence. De novo identification of peptide sequences is another possibility, because it does not require a protein database. However, the lack of database reduces the accuracy. We present a novel proteogenomic approach, GenoMS, that draws on the strengths of database and de novo peptide identification methods. Protein sequence templates (i.e. proteins or genomic sequences that are similar to the target protein) are identified using the database search tool InsPecT. The templates are then used to recruit, align, and de novo sequence regions of the target protein that have diverged from the database or are missing. We used GenoMS to reconstruct the full sequence of an antibody by using spectra acquired from multiple digests using different proteases. Antibodies are a prime example of proteins that confound standard database identification techniques. The mature antibody genes result from large-scale genome rearrangements with flexible fusion boundaries and somatic hypermutation. Using GenoMS we automatically reconstruct the complete sequences of two immunoglobulin chains with accuracy greater than 98% using a diverged protein database. Using the genome as the template, we achieve accuracy exceeding 97%. Molecular & Cellular Proteomics 9: 1260 -1270, 2010.Database search algorithms, such as Sequest (1), Mascot (2), and InsPecT (3), are the primary workhorses for the identification of tandem mass spectra. However, these methods are limited to the identification of spectra for which peptides are present in the database. It is well recognized that curated protein databases are, at best, an imperfect template for the extant peptides. For example, peptides arising from novel splice forms or fusion proteins would be difficult to identify using most protein databases.Recent developments have extended the identifications to peptides that have diverged from the database entry. By allowing divergence, the methods enable the identification of small-scale mutations, and post-translational modifications, albeit with some loss of sensitivity (4 -7). Among these tools, MS-Blast is able to determine a homologous protein in the related species but does not report the (diverged) protein in the target organism. The other t...

A mouse hybridoma antibody directed against a member of the TNF-superfamily, lymphotoxin alpha (LT-α), was isolated from stored mouse ascites and purified to homogeneity. After more than a decade of storage the genetic material was not available for cloning, however biochemical assays with the ascites showed this antibody against LT-α (LT-3F12) to be a pre-clinical candidate for the treatment of several inflammatory pathologies. We have successfully rescued the LT-3F12 antibody by performing mass spectrometric analysis, primary amino acid sequence determination by template proteogenomics, and synthesis of the corresponding recombinant DNA by reverse engineering. The resurrected antibody was expressed, purified and shown to demonstrate the desired specificity and binding properties in a panel of immuno-biochemical tests. The work described herein demonstrates the powerful combination of high throughput informatic proteomic de novo sequencing with reverse engineering to re-establish monoclonal antibody expressing cells from archived protein sample, exemplifying the development of novel therapeutics from cryptic protein sources.

Nanobodies, a subclass of antibodies found in camelids, are versatile molecular binding scaffolds composed of a single polypeptide chain. The small size of nanobodies bestows multiple therapeutic advantages (stability, tumor penetration) with the first therapeutic approval in 2018 cementing the clinical viability of this format. Structured data and sequence information of nanobodies will enable the accelerated clinical development of nanobody-based therapeutics. Though the nanobody sequence and structure data are deposited in the public domain at an accelerating pace, the heterogeneity of sources and lack of standardization hampers reliable harvesting of nanobody information. We address this issue by creating the Integrated Database of Nanobodies for Immunoinformatics (INDI, http://naturalantibody.com/nanobodies). INDI collates nanobodies from all the major public outlets of biological sequences: patents, GenBank, next-generation sequencing repositories, structures and scientific publications. We equip INDI with powerful nanobody-specific sequence and text search facilitating access to >11 million nanobody sequences. INDI should facilitate development of novel nanobody-specific computational protocols helping to deliver on the therapeutic promise of this drug format.

Historically many genome annotation strategies have lacked experimental evidence at the protein level, which and have instead relied heavily on ab initio gene prediction tools, which consequently resulted in many incorrectly annotated genomic sequences. Proteogenomics aims to address these issues using mass spectrometry (MS)-based proteomics, genomic mapping, and providing statistical significance measures such as false discovery rates (FDRs) to validate the mapped peptides. Presented here is a tool capable of meeting this goal, the UCSD proteogenomic pipeline, which maps peptide-spectrum matches (PSMs) to the genome using the Inspect MS/MS database search tool and assigns a statistical significance to the match using a target-decoy search approach to assign estimated FDRs. This pipeline also provides the option of using a more reliable approach to proteogenomics by determining the precise false-positive rates (FPRs) and p-values of each PSM by calculating their spectral probabilities and rescoring each PSM accordingly. In addition to the protein prediction challenges in the rapidly growing number of sequenced plant genomes, it is difficult to extract high-quality protein samples from many plant species. For that reason, this chapter contains methods for protein extraction and trypsin digestion that reliably produce samples suitable for proteogenomic analysis.