Proteogenomics: concepts, applications and computational strategies

GenomewidePDB 2.0: A Newly Upgraded Versatile Proteogenomic Database for the Chromosome-Centric Human Proteome Project

Since the launch of the Chromosome-centric Human Proteome Project (C-HPP) in 2012, the number of "missing" proteins has fallen to 2932, down from ∼5932 since the number was first counted in 2011. We compared the characteristics of missing proteins with those of already annotated proteins with respect to transcriptional expression pattern and the time periods in which newly identified proteins were annotated. We learned that missing proteins commonly exhibit lower levels of transcriptional expression and less tissue-specific expression compared with already annotated proteins. This makes it more difficult to identify missing proteins as time goes on. One of the C-HPP goals is to identify alternative spliced product of proteins (ASPs), which are usually difficult to find by shot-gun proteomic methods due to their sequence similarities with the representative proteins. To resolve this problem, it may be necessary to use a targeted proteomics approach (e.g., selected and multiple reaction monitoring [S/MRM] assays) and an innovative bioinformatics platform that enables the selection of target peptides for rarely expressed missing proteins or ASPs. Given that the success of efforts to identify missing proteins may rely on more informative public databases, it was necessary to upgrade the available integrative databases. To this end, we attempted to improve the features and utility of GenomewidePDB by integrating transcriptomic information (e.g., alternatively spliced transcripts), annotated peptide information, and an advanced search interface that can find proteins of interest when applying a targeted proteomics strategy. This upgraded version of the database, GenomewidePDB 2.0, may not only expedite identification of the remaining missing proteins but also enhance the exchange of information among the proteome community. GenomewidePDB 2.0 is available publicly at http://genomewidepdb.proteomix.org/.

Mining Missing Membrane Proteins by High-pH Reverse-Phase StageTip Fractionation and Multiple Reaction Monitoring Mass Spectrometry

Despite significant efforts in the past decade toward complete mapping of the human proteome, 3564 proteins (neXtProt, 09-2014) are still "missing proteins". Over one-third of these missing proteins are annotated as membrane proteins, owing to their relatively challenging accessibility with standard shotgun proteomics. Using nonsmall cell lung cancer (NSCLC) as a model study, we aim to mine missing proteins from disease-associated membrane proteome, which may be still largely under-represented. To increase identification coverage, we employed Hp-RP StageTip prefractionation of membrane-enriched samples from 11 NSCLC cell lines. Analysis of membrane samples from 20 pairs of tumor and adjacent normal lung tissue was incorporated to include physiologically expressed membrane proteins. Using multiple search engines (X!Tandem, Comet, and Mascot) and stringent evaluation of FDR (MAYU and PeptideShaker), we identified 7702 proteins (66% membrane proteins) and 178 missing proteins (74 membrane proteins) with PSM-, peptide-, and protein-level FDR of 1%. Through multiple reaction monitoring using synthetic peptides, we provided additional evidence of eight missing proteins including seven with transmembrane helix domains. This study demonstrates that mining missing proteins focused on cancer membrane subproteome can greatly contribute to map the whole human proteome. All data were deposited into ProteomeXchange with the identifier PXD002224.

Integration of Proteomics and Transcriptomics Data Sets for the Analysis of a Lymphoma B-Cell Line in the Context of the Chromosome-Centric Human Proteome Project

A comprehensive study of the molecular active landscape of human cells can be undertaken to integrate two different but complementary perspectives: transcriptomics, and proteomics. After the genome era, proteomics has emerged as a powerful tool to simultaneously identify and characterize the compendium of thousands of different proteins active in a cell. Thus, the Chromosome-centric Human Proteome Project (C-HPP) is promoting a full characterization of the human proteome combining high-throughput proteomics with the data derived from genome-wide expression profiling of protein-coding genes. Here we present a full proteomic profiling of a human lymphoma B-cell line (Ramos) performed using a nanoUPLC-LTQ-Orbitrap Velos proteomic platform, combined to an in-depth transcriptomic profiling of the same cell type. Data are available via ProteomeXchange with identifier PXD001933. Integration of the proteomic and transcriptomic data sets revealed a 94% overlap in the proteins identified by both -omics approaches. Moreover, functional enrichment analysis of the proteomic profiles showed an enrichment of several functions directly related to the biological and morphological characteristics of B-cells. In turn, about 30% of all protein-coding genes present in the whole human genome were identified as being expressed by the Ramos cells (stable average of 30% genes along all the chromosomes), revealing the size of the protein expression-set present in one specific human cell type. Additionally, the identification of missing proteins in our data sets has been reported, highlighting the power of the approach. Also, a comparison between neXtProt and UniProt database searches has been performed. In summary, our transcriptomic and proteomic experimental profiling provided a high coverage report of the expressed proteome from a human lymphoma B-cell type with a clear insight into the biological processes that characterized these cells. In this way, we demonstrated the usefulness of combining -omics for a comprehensive characterization of specific biological systems.

Metrics for the Human Proteome Project 2015: Progress on the Human Proteome and Guidelines for High-Confidence Protein Identification

Remarkable progress continues on the annotation of the proteins identified in the Human Proteome and on finding credible proteomic evidence for the expression of "missing proteins". Missing proteins are those with no previous protein-level evidence or insufficient evidence to make a confident identification upon reanalysis in PeptideAtlas and curation in neXtProt. Enhanced with several major new data sets published in 2014, the human proteome presented as neXtProt, version 2014-09-19, has 16,491 unique confident proteins (PE level 1), up from 13,664 at 2012-12 and 15,646 at 2013-09. That leaves 2948 missing proteins from genes classified having protein existence level PE 2, 3, or 4, as well as 616 dubious proteins at PE 5. Here, we document the progress of the HPP and discuss the importance of assessing the quality of evidence, confirming automated findings and considering alternative protein matches for spectra and peptides. We provide guidelines for proteomics investigators to apply in reporting newly identified proteins.

State of the Human Proteome in 2014/2015 As Viewed through PeptideAtlas: Enhancing Accuracy and Coverage through the AtlasProphet

The Human PeptideAtlas is a compendium of the highest quality peptide identifications from over 1000 shotgun mass spectrometry proteomics experiments collected from many different laboratories, all reanalyzed through a uniform processing pipeline. The latest 2015-03 build contains substantially more input data than past releases, is mapped to a recent version of our merged reference proteome, and uses improved informatics processing and the development of the AtlasProphet to provide the highest quality results. Within the set of ∼20,000 neXtProt primary entries, 14,070 (70%) are confidently detected in the latest build, 5% are ambiguous, 9% are redundant, leaving the total percentage of proteins for which there are no mapping detections at just 16% (3166), all derived from over 133 million peptide-spectrum matches identifying more than 1 million distinct peptides using AtlasProphet to characterize and classify the protein matches. Improved handling for detection and presentation of single amino-acid variants (SAAVs) reveals the detection of 5326 uniquely mapping SAAVs across 2794 proteins. With such a large amount of data, the control of false positives is a challenge. We present the methodology and results for maintaining rigorous quality along with a discussion of the implications of the remaining sources of errors in the build.

Advanced Proteogenomic Analysis Reveals Multiple Peptide Mutations and Complex Immunoglobulin Peptides in Colon Cancer

Aiming toward an improved understanding of the regulation of proteins in cancer, recent studies from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have focused on analyzing cancer tissue using proteomic technologies and workflows. Although many proteogenomics approaches for the study of cancer samples have been proposed, serious methodological challenges remain, especially in the identification of multiple mutational variants or structural variations such as fusion gene events. In addition, although immune system genes play an important role in cancer, identification of IgG peptides remains challenging in proteomic data sets. Here, we describe an integrative proteogenomic method that extends the limit of proteogenomic searches to identify multiple variant peptides as well as immunoglobulin gene variations/rearrangements using customized mining of RNA-seq data. Our results also provide the first extensive characterization of tumor immune response and demonstrate the potential of this method to improve the molecular characterization of tumor subtypes.

The role of proteomics in progressing insights into plant secondary metabolism

The development of omics has enabled the genome-wide exploration of all kinds of biological processes at the molecular level. Almost every field of plant biology has been analyzed at the genomic, transcriptomic and proteomic level. Here we focus on the particular contribution that proteomic technologies have made in progressing knowledge and characterising plant secondary metabolism (SM) pathways since early expectations were created 15 years ago. We analyzed how three major issues in the proteomic analysis of plant SM have been implemented in various research studies. These issues are: (i) the selection of a suitable plant material rich in secondary metabolites of interest, such as specialized tissues and organs, and in vitro cell cultures; (ii) the proteomic strategy to access target proteins, either a comprehensive or a differential analysis; (iii) the proteomic approach, represented by the hypothesis-free discovery proteomics and the hypothesis-driven targeted proteomics. We also examine to what extent the most-advanced technologies have been incorporated into proteomic research in plant SM and highlight some cutting edge techniques that would strongly benefit the progress made in this field.

Proteomic analysis of colon and rectal carcinoma using standard and customized databases

Understanding proteomic differences underlying the different phenotypic classes of colon and rectal carcinoma is important and may eventually lead to a better assessment of clinical behavior of these cancers. We here present a comprehensive description of the proteomic data obtained from 90 colon and rectal carcinomas previously subjected to genomic analysis by The Cancer Genome Atlas (TCGA). Here, the primary instrument files and derived secondary data files are compiled and presented in forms that will allow further analyses of the biology of colon and rectal carcinoma. We also discuss new challenges in processing these large proteomic datasets for relevant proteins and protein variants.

Transcriptome-wide measurement of ribosomal occupancy by ribosome profiling

Gene expression profiling provides a tool to analyze the internal states of cells or organisms, and their responses to perturbations. While global measurements of mRNA levels have thus been widely used for many years, it is only through the recent development of the ribosome profiling technique that an analogous examination of global mRNA translation programs has become possible. Ribosome profiling reveals which RNAs are being translated to what extent and where the translated open reading frames are located. In addition, different modes of translation regulation can be distinguished and characterized. Here, we present an optimized, step-by-step protocol for ribosome profiling. Although established in Caenorhabditis elegans, our protocol and optimization approaches should be equally usable for other model organisms or cell culture with little adaptation. Next to providing a protocol, we compare two different methods for isolation of single ribosomes and two different library preparations, and describe strategies to optimize the RNase digest and to reduce ribosomal RNA contamination in the libraries. Moreover, we discuss bioinformatic strategies to evaluate the quality of the data and explain how the data can be analyzed for different applications. In sum, this article seeks to facilitate the understanding, execution, and optimization of ribosome profiling experiments.

A note on the false discovery rate of novel peptides in proteogenomics

Motivation: Proteogenomics has been well accepted as a tool to discover novel genes. In most conventional proteogenomic studies, a global false discovery rate is used to filter out false positives for identifying credible novel peptides. However, it has been found that the actual level of false positives in novel peptides is often out of control and behaves differently for different genomes.

Results: To quantitatively model this problem, we theoretically analyze the subgroup false discovery rates of annotated and novel peptides. Our analysis shows that the annotation completeness ratio of a genome is the dominant factor influencing the subgroup FDR of novel peptides. Experimental results on two real datasets of Escherichia coli and Mycobacterium tuberculosis support our conjecture.

Contact:yfu@amss.ac.cn or xupingghy@gmail.com or smhe@ict.ac.cn

Supplementary information: Supplementary data are available at Bioinformatics online.

NextSearch: A Search Engine for Mass Spectrometry Data against a Compact Nucleotide Exon Graph

Proteogenomics research has been using six-frame translation of the whole genome or amino acid exon graphs to overcome the limitations of reference protein sequence database; however, six-frame translation is not suitable for annotating genes that span over multiple exons, and amino acid exon graphs are not convenient to represent novel splice variants and exon skipping events between exons of incompatible reading frames. We propose a proteogenomic pipeline NextSearch (Nucleotide EXon-graph Transcriptome Search) that is based on a nucleotide exon graph. This pipeline consists of constructing a compact nucleotide exon graph that systematically incorporates novel splice variations and a search tool that identifies peptides by directly searching the nucleotide exon graph against tandem mass spectra. Because our exon graph stores nucleotide sequences, it can easily represent novel splice variations and exon skipping events between incompatible reading frame exons. Searching for peptide identification is performed against this nucleotide exon graph, without converting it into a protein sequence in FASTA format, achieving an order of magnitude reduction in the size of the sequence database storage. NextSearch outputs the proteome-genome/transcriptome mapping results in a general feature format (GFF) file, which can be visualized by public tools such as the UCSC Genome Browser.

Proteogenomic Analysis of Trichophyton rubrum Aided by RNA Sequencing

Infections caused by dermatophytes, Trichophyton rubrum in particular, are among the most common diseases in humans. In this study, we present a proteogenomic analysis of T. rubrum based on whole-genome proteomics and RNA-Seq studies. We confirmed 4291 expressed proteins in T. rubrum and validated their annotated gene structures based on 35 874 supporting peptides. In addition, we identified 323 novel peptides (not present in the current annotated protein database of T. rubrum) that can be used to enhance current T. rubrum annotations. A total of 104 predicted genes supported by novel peptides were identified, and 127 gene models suggested by the novel peptides that conflicted with existing annotations were manually assigned based on transcriptomic evidence. RNA-Seq confirmed the validity of 95% of the total peptides. Our study provides evidence that confirms and improves the genome annotation of T. rubrum and represents the first survey of T. rubrum genome annotations based on experimental evidence. Additionally, our integrated proteomics and multisourced transcriptomics approach provides stronger evidence for annotation refinement than proteomic data alone, which helps to address the dilemma of one-hit wonders (uncertainties supported by only one peptide).

Found in translation: functions and evolution of a recently discovered alternative proteome

A major goal in biology is to map entire proteomes to better understand the biology and evolution of cells. However, our current views of proteomes are conservative and biased against small proteins. Besides serendipitous discoveries of small proteins, it has been largely assumed that eukaryotic mature mRNAs contain a single ORF and that non-coding RNAs are not translated because their ORFs are too short to play a functional role. A flurry of recent studies brought to light an unexplored proteome that is mainly translated from short ORFs in non-coding regions and from alternative ORFs (AltORFs) in reference genes. The detection of these small proteins and the elucidation of their functions remain challenging and open a new dimension of eukaryotic proteomes, including the birth of novel genes and proteins.

Proteogenomics: emergence and promise

Proteogenomics, or the integration of proteomics with genomics and transcriptomics, is emerging as the next step towards a unified understanding of cellular functions. Looking globally and simultaneously at gene structure, RNA expression, protein synthesis and post-translational modifications have become technically feasible and offer a new perspective to molecular processes. Recent publications have highlighted the value of proteogenomics in oncology for defining the molecular signature of human tumors, and translation to other areas of biomedicine and life sciences is anticipated. This mini-review will discuss recent developments, challenges and perspectives in proteogenomics.

Onco-proteogenomics: cancer proteomics joins forces with genomics

The complexities of tumor genomes are rapidly being uncovered, but how they are regulated into functional proteomes remains poorly understood. Standard proteomics workflows use databases of known proteins, but these databases do not capture the uniqueness of the cancer transcriptome, with its point mutations, unusual splice variants and gene fusions. Onco-proteogenomics integrates mass spectrometry-generated data with genomic information to identify tumor-specific peptides. Linking tumor-derived DNA, RNA and protein measurements into a central-dogma perspective has the potential to improve our understanding of cancer biology.