DecoyPyrat: Fast Non-redundant Hybrid Decoy Sequence Generation for Large Scale Proteomics

Evidence for widespread translation of 5' untranslated regions

Ribosome profiling experiments support the translation of a range of novel human open reading frames. By contrast, most peptides from large-scale proteomics experiments derive from just one source, 5' untranslated regions. Across the human genome we find evidence for 192 translated upstream regions, most of which would produce protein isoforms with extended N-terminal ends. Almost all of these N-terminal extensions are from highly abundant genes, which suggests that the novel regions we detect are just the tip of the iceberg. These upstream regions have characteristics that are not typical of coding exons. Their GC-content is remarkably high, even higher than 5' regions in other genes, and a large majority have non-canonical start codons. Although some novel upstream regions have cross-species conservation - five have orthologues in invertebrates for example - the reading frames of two thirds are not conserved beyond simians. These non-conserved regions also have no evidence of purifying selection, which suggests that much of this translation is not functional. In addition, non-conserved upstream regions have significantly more peptides in cancer cell lines than would be expected, a strong indication that an aberrant or noisy translation initiation process may play an important role in translation from upstream regions.

Exploring crop genomes: assembly features, gene prediction accuracy, and implications for proteomics studies

Plant genomics plays a pivotal role in enhancing global food security and sustainability by offering innovative solutions for improving crop yield, disease resistance, and stress tolerance. As the number of sequenced genomes grows and the accuracy and contiguity of genome assemblies improve, structural annotation of plant genomes continues to be a significant challenge due to their large size, polyploidy, and rich repeat content. In this paper, we present an overview of the current landscape in crop genomics research, highlighting the diversity of genomic characteristics across various crop species. We also assessed the accuracy of popular gene prediction tools in identifying genes within crop genomes and examined the factors that impact their performance. Our findings highlight the strengths and limitations of BRAKER2 and Helixer as leading structural genome annotation tools and underscore the impact of genome complexity, fragmentation, and repeat content on their performance. Furthermore, we evaluated the suitability of the predicted proteins as a reliable search space in proteomics studies using mass spectrometry data. Our results provide valuable insights for future efforts to refine and advance the field of structural genome annotation.

WAS IT A MATch I SAW? Approximate palindromes lead to overstated false match rates in benchmarks using reversed sequences

Background

Software for labeling biological sequences typically produces a theory-based statistic for each match (the E-value) that indicates the likelihood of seeing that match's score by chance. E-values accurately predict false match rate for comparisons of random (shuffled) sequences, and thus provide a reasoned mechanism for setting score thresholds that enable high sensitivity with low expected false match rate. This threshold-setting strategy is challenged by real biological sequences, which contain regions of local repetition and low sequence complexity that cause excess matches between non-homologous sequences. Knowing this, tool developers often develop benchmarks that use realistic-seeming decoy sequences to explore empirical tradeoffs between sensitivity and false match rate. A recent trend has been to employ reversed biological sequences as realistic decoys, because these preserve the distribution of letters and the existence of local repeats, while disrupting the original sequence's functional properties. However, we and others have observed that sequences appear to produce high scoring alignments to their reversals with surprising frequency, leading to overstatement of false match risk that may negatively affect downstream analysis.

Results

We demonstrate that an alignment between a sequence S and its (possibly mutated) reversal tends to produce higher scores than alignment between truly unrelated sequences, even when S is a shuffled string with no notable repetitive or low-complexity regions. This phenomenon is due to the unintuitive fact that (even randomly shuffled) sequences contain palindromes that are on average longer than the longest common substrings (LCS) shared between permuted variants of the same sequence. Though the expected palindrome length is only slightly larger than the expected LCS, the distribution of alignment scores involving reversed sequences is strongly right-shifted, leading to greatly increased frequency of high-scoring alignments to reversed sequences.

Impact

Overestimates of false match risk can motivate unnecessarily high score thresholds, leading to potentially reduced true match sensitivity. Also, when tool sensitivity is only reported up to the score of the first matched decoy sequence, a large decoy set consisting of reversed sequences can obscure sensitivity differences between tools. As a result of these observations, we advise that reversed biological sequences be used as decoys only when care is taken to remove positive matches in the original (un-reversed) sequences, or when overstatement of false labeling is not a concern. Though the primary focus of the analysis is on sequence annotation, we also demonstrate that the prevalence of internal palindromes may lead to an overstatement of the rate of false labels in protein identification with mass spectrometry.

Mixture effects of pharmaceuticals carbamazepine, diclofenac and venlafaxine on Mytilus galloprovincialis mussel probed by metabolomics and proteogenomics combined approach

Exposure to single molecules under laboratory conditions has led to a better understanding of the mechanisms of action (MeOAs) and effects of pharmaceutical active compounds (PhACs) on non-target organisms. However, not taking the co-occurrence of contaminants in the environment and their possible interactions into account may lead to underestimation of their impacts. In this study, we combined untargeted metabolomics and proteogenomics approaches to assess the mixture effects of diclofenac, carbamazepine and venlafaxine on marine mussels (Mytilus galloprovincialis). Our multi-omics approach and data fusion strategy highlighted how such xenobiotic cocktails induce important cellular changes that can be harmful to marine bivalves. This response is mainly characterized by energy metabolism disruption, fatty acid degradation, protein synthesis and degradation, and the induction of endoplasmic reticulum stress and oxidative stress. The known MeOAs and molecular signatures of PhACs were taken into consideration to gain insight into the mixture effects, thereby revealing a potential additive effect. Multi-omics approaches on mussels as sentinels offer a comprehensive overview of molecular and cellular responses triggered by exposure to contaminant mixtures, even at environmental concentrations.

Altered quorum sensing and physiology of Staphylococcus aureus during spaceflight detected by multi-omics data analysis

Staphylococcus aureus colonizes the nares of approximately 30% of humans, a risk factor for opportunistic infections. To gain insight into S. aureus virulence potential in the spaceflight environment, we analyzed RNA-Seq, cellular proteomics, and metabolomics data from the "Biological Research in Canisters-23" (BRIC-23) GeneLab spaceflight experiment, a mission designed to measure the response of S. aureus to growth in low earth orbit on the international space station. This experiment used Biological Research in Canisters-Petri Dish Fixation Units (BRIC-PDFUs) to grow asynchronous ground control and spaceflight cultures of S. aureus for 48 h. RNAIII, the effector of the Accessory Gene Regulator (Agr) quorum sensing system, was the most highly upregulated gene transcript in spaceflight relative to ground controls. The agr operon gene transcripts were also highly upregulated during spaceflight, followed by genes encoding phenol-soluble modulins and secreted proteases, which are positively regulated by Agr. Upregulated spaceflight genes/proteins also had functions related to urease activity, type VII-like Ess secretion, and copper transport. We also performed secretome analysis of BRIC-23 culture supernatants, which revealed that spaceflight samples had increased abundance of secreted virulence factors, including Agr-regulated proteases (SspA, SspB), staphylococcal nuclease (Nuc), and EsxA (secreted by the Ess system). These data also indicated that S. aureus metabolism is altered in spaceflight conditions relative to the ground controls. Collectively, these data suggest that S. aureus experiences increased quorum sensing and altered expression of virulence factors in response to the spaceflight environment that may impact its pathogenic potential.

Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides

Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.

Mistle: bringing spectral library predictions to metaproteomics with an efficient search index

MOTIVATION

Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics.

RESULTS

In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes.

AVAILABILITY AND IMPLEMENTATION

Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.

Current progress and critical challenges to overcome in the bioinformatics of mass spectrometry-based metaproteomics

Metaproteomics is a relatively young field that has only been studied for approximately 15 years. Nevertheless, it has the potential to play a key role in disease research by elucidating the mechanisms of communication between the human host and the microbiome. Although it has been useful in developing an understanding of various diseases, its analytical strategies remain limited to the extended application of proteomics. The sequence databases in metaproteomics must be large because of the presence of thousands of species in a typical sample, which causes problems unique to large databases. In this review, we demonstrate the usefulness of metaproteomics in disease research through examples from several studies. Additionally, we discuss the challenges of applying metaproteomics to conventional proteomics analysis methods and introduce studies that may provide clues to the solutions. We also discuss the need for a standard false discovery rate control method for metaproteomics to replace common target-decoy search approaches in proteomics and a method to ensure the reliability of peptide spectrum match.

Finding haplotypic signatures in proteins

BACKGROUND

The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown.

FINDINGS

Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches.

CONCLUSIONS

As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.

Aortic disease in Marfan syndrome is caused by overactivation of sGC-PRKG signaling by NO

Thoracic aortic aneurysm, as occurs in Marfan syndrome, is generally asymptomatic until dissection or rupture, requiring surgical intervention as the only available treatment. Here, we show that nitric oxide (NO) signaling dysregulates actin cytoskeleton dynamics in Marfan Syndrome smooth muscle cells and that NO-donors induce Marfan-like aortopathy in wild-type mice, indicating that a marked increase in NO suffices to induce aortopathy. Levels of nitrated proteins are higher in plasma from Marfan patients and mice and in aortic tissue from Marfan mice than in control samples, indicating elevated circulating and tissue NO. Soluble guanylate cyclase and cGMP-dependent protein kinase are both activated in Marfan patients and mice and in wild-type mice treated with NO-donors, as shown by increased plasma cGMP and pVASP-S239 staining in aortic tissue. Marfan aortopathy in mice is reverted by pharmacological inhibition of soluble guanylate cyclase and cGMP-dependent protein kinase and lentiviral-mediated Prkg1 silencing. These findings identify potential biomarkers for monitoring Marfan Syndrome in patients and urge evaluation of cGMP-dependent protein kinase and soluble guanylate cyclase as therapeutic targets.

Aortic aneurysm and dissection, the major problem linked to Marfan syndrome (MFS), lacks effective pharmacological treatment. Here, the authors show that the NO pathway is overactivated in MFS and that inhibition of guanylate cyclase and cGMP-dependent protein kinase reverts MFS aortopathy in mice.

An analysis of tissue-specific alternative splicing at the protein level

The role of alternative splicing is one of the great unanswered questions in cellular biology. There is strong evidence for alternative splicing at the transcript level, and transcriptomics experiments show that many splice events are tissue specific. It has been suggested that alternative splicing evolved in order to remodel tissue-specific protein-protein networks. Here we investigated the evidence for tissue-specific splicing among splice isoforms detected in a large-scale proteomics analysis. Although the data supporting alternative splicing is limited at the protein level, clear patterns emerged among the small numbers of alternative splice events that we could detect in the proteomics data. More than a third of these splice events were tissue-specific and most were ancient: over 95% of splice events that were tissue-specific in both proteomics and RNAseq analyses evolved prior to the ancestors of lobe-finned fish, at least 400 million years ago. By way of contrast, three in four alternative exons in the human gene set arose in the primate lineage, so our results cannot be extrapolated to the whole genome. Tissue-specific alternative protein forms in the proteomics analysis were particularly abundant in nervous and muscle tissues and their genes had roles related to the cytoskeleton and either the structure of muscle fibres or cell-cell connections. Our results suggest that this conserved tissue-specific alternative splicing may have played a role in the development of the vertebrate brain and heart.

We manually curated a set of 255 splice events detected in a large-scale tissue-based proteomics experiment and found that more than a third had evidence of significant tissue-specific differences. Events that were significantly tissue-specific at the protein level were highly conserved; almost 75% evolved over 400 million years ago. The tissues in which we found most evidence for tissue-specific splicing were nervous tissues and cardiac tissues. Genes with tissue-specific events in these two tissues had functions related to important cellular structures in brain and heart tissues. These splice events may have been essential for the development of vertebrate heart and muscle. However, our data set may not be representative of alternative exons as a whole. We found that most tissue specific splicing was strongly conserved, but just 5% of annotated alternative exons in the human gene set are ancient. More than three quarters of alternative exons are primate-derived. Although the analysis does not provide a definitive answer to the question of the functional role of alternative splicing, our results do indicate that alternative splice variants may have played a significant part in the evolution of brain and heart tissues in vertebrates.

Proteogenomics-Guided Evaluation of RNA-Seq Assembly and Protein Database Construction for Emergent Model Organisms

Proteogenomics is gaining momentum as, today, genomics, transcriptomics, and proteomics can be readily performed on any new species. This approach allows key alterations to molecular pathways to be identified when comparing conditions. For animals and plants, RNA-seq-informed proteomics is the most popular means of interpreting tandem mass spectrometry spectra acquired for species for which the genome has not yet been sequenced. It relies on high-performance de novo RNA-seq assembly and optimized translation strategies. Here, several pre-treatments for Illumina RNA-seq reads before assembly are explored to translate the resulting contigs into useful polypeptide sequences. Experimental transcriptomics and proteomics datasets acquired for individual Gammarus fossarum freshwater crustaceans are used, the most relevant procedure is defined by the ratio of MS/MS spectra assigned to peptide sequences. Removing reads with a mean quality score of less than 17-which represents a single probable nucleotide error on 150-bp reads-prior to assembly, increases the proteomics outcome. The best translation using Transdecoder is achieved with a minimal open reading frame length of 50 amino acids and systematic selection of ORFs longer than 900 nucleotides. Using these parameters, transcriptome assembly and translation informed by proteomics pave the way to further improvements in proteogenomics.

TASL is the SLC15A4-associated adaptor for IRF5 activation by TLR7-9

Toll-like receptors (TLRs) have a crucial role in the recognition of pathogens and initiation of immune responses^1–3. Here we show that a previously uncharacterized protein encoded by CXorf21—a gene that is associated with systemic lupus erythematosus^4,5—interacts with the endolysosomal transporter SLC15A4, an essential but poorly understood component of the endolysosomal TLR machinery also linked to autoimmune disease^4,6–9. Loss of this type-I-interferon-inducible protein, which we refer to as ‘TLR adaptor interacting with SLC15A4 on the lysosome’ (TASL), abrogated responses to endolysosomal TLR agonists in both primary and transformed human immune cells. Deletion of SLC15A4 or TASL specifically impaired the activation of the IRF pathway without affecting NF-κB and MAPK signalling, which indicates that ligand recognition and TLR engagement in the endolysosome occurred normally. Extensive mutagenesis of TASL demonstrated that its localization and function relies on the interaction with SLC15A4. TASL contains a conserved pLxIS motif (in which p denotes a hydrophilic residue and x denotes any residue) that mediates the recruitment and activation of IRF5. This finding shows that TASL is an innate immune adaptor for TLR7, TLR8 and TLR9 signalling, revealing a clear mechanistic analogy with the IRF3 adaptors STING, MAVS and TRIF^10,11. The identification of TASL as the component that links endolysosomal TLRs to the IRF5 transcription factor via SLC15A4 provides a mechanistic explanation for the involvement of these proteins in systemic lupus erythematosus^12–14.

The secreted inhibitor of invasive cell growth CREG1 is negatively regulated by cathepsin proteases

Previous clinical and experimental evidence strongly supports a breast cancer-promoting function of the lysosomal protease cathepsin B. However, the cathepsin B-dependent molecular pathways are not completely understood. Here, we studied the cathepsin-mediated secretome changes in the context of the MMTV-PyMT breast cancer mouse model. Employing the cell-conditioned media from tumor-macrophage co-cultures, as well as tumor interstitial fluid obtained by a novel strategy from PyMT mice with differential cathepsin B expression, we identified an important proteolytic and lysosomal signature, highlighting the importance of this organelle and these enzymes in the tumor micro-environment. The Cellular Repressor of E1A Stimulated Genes 1 (CREG1), a secreted endolysosomal glycoprotein, displayed reduced abundance upon over-expression of cathepsin B as well as increased abundance upon cathepsin B deletion or inhibition. Moreover, it was cleaved by cathepsin B in vitro. CREG1 reportedly could act as tumor suppressor. We show that treatment of PyMT tumor cells with recombinant CREG1 reduced proliferation, migration, and invasion; whereas, the opposite was observed with reduced CREG1 expression. This was further validated in vivo by orthotopic transplantation. Our study highlights CREG1 as a key player in tumor–stroma interaction and suggests that cathepsin B sustains malignant cell behavior by reducing the levels of the growth suppressor CREG1 in the tumor microenvironment.

Evidence for a novel overlapping coding sequence in POLG initiated at a CUG start codon

BACKGROUND

POLG, located on nuclear chromosome 15, encodes the DNA polymerase γ(Pol γ). Pol γ is responsible for the replication and repair of mitochondrial DNA (mtDNA). Pol γ is the only DNA polymerase found in mitochondria for most animal cells. Mutations in POLG are the most common single-gene cause of diseases of mitochondria and have been mapped over the coding region of the POLG ORF.

RESULTS

Using PhyloCSF to survey alternative reading frames, we found a conserved coding signature in an alternative frame in exons 2 and 3 of POLG, herein referred to as ORF-Y that arose de novo in placental mammals. Using the synplot2 program, synonymous site conservation was found among mammals in the region of the POLG ORF that is overlapped by ORF-Y. Ribosome profiling data revealed that ORF-Y is translated and that initiation likely occurs at a CUG codon. Inspection of an alignment of mammalian sequences containing ORF-Y revealed that the CUG codon has a strong initiation context and that a well-conserved predicted RNA stem-loop begins 14 nucleotides downstream. Such features are associated with enhanced initiation at near-cognate non-AUG codons. Reanalysis of the Kim et al. (2014) draft human proteome dataset yielded two unique peptides that map unambiguously to ORF-Y. An additional conserved uORF, herein referred to as ORF-Z, was also found in exon 2 of POLG. Lastly, we surveyed Clinvar variants that are synonymous with respect to the POLG ORF and found that most of these variants cause amino acid changes in ORF-Y or ORF-Z.

CONCLUSIONS

We provide evidence for a novel coding sequence, ORF-Y, that overlaps the POLG ORF. Ribosome profiling and mass spectrometry data show that ORF-Y is expressed. PhyloCSF and synplot2 analysis show that ORF-Y is subject to strong purifying selection. An abundance of disease-correlated mutations that map to exons 2 and 3 of POLG but also affect ORF-Y provides potential clinical significance to this finding.

Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics

Shotgun proteomics is the method of choice for high-throughput protein identification; however, robust statistical methods are essential to automatize this task while minimizing the number of false identifications. The standard method for estimating the false discovery rate (FDR) of individual identifications and keeping it below a threshold (typically 1%) is the target-decoy approach. However, numerous works have shown that FDR at the protein level may become much larger than FDR at the peptide level. The development of an appropriate scoring model to identify proteins from their peptides using high-throughput shotgun proteomics is highly needed. In this study, we present a novel protein-level scoring algorithm that uses the scores of the identified peptides and maintains all of the properties expected for a true protein probability. We also present a refinement of the picked method to calculate FDR at the protein level. These algorithms can be used together as a robust identification workflow suitable for large-scale proteomics, and we show that the identification performance of this workflow is superior to that of other widely used methods in several samples and using different search engines. Our protein probability model offers the scientific community an algorithm that is easy to integrate into protein identification workflows for the automated analysis of shotgun proteomics data.

Calculation of False Discovery Rate for Peptide and Protein Identification

Shotgun proteomics is the method of choice for large-scale protein identification. However, the use of a robust statistical workflow to validate such identification is mandatory to minimize false matches, ambiguities, and amplification of error rates from spectra to proteins. In this chapter we emphasize the key concepts to take into account when processing the output of a search engine to obtain reliable peptide or protein identifications. We assume that the reader is already familiar with tandem mass spectrometry so we can focus on the use of statistical confidence methods. After introducing the key concepts we present different software tools and how to use them with an example dataset.

Shotgun proteomics datasets acquired on Gammarus pulex animals sampled from the wild

This data article associated with the manuscript “Comparative proteomics in the wild: accounting for intrapopulation variability improves describing proteome response in a Gammarus pulex field population exposed to cadmium” refers to the shotgun proteomics analysis performed on 40 Gammarus pulex animals sampled from the wild. Proteins were extracted, digested with trypsin, and the resulting peptides were identified by tandem mass spectrometry. Here, we present the list of proteins from males and the list of proteins from females that are differentially detected between the Brameloup and the Pollon populations. Data are available via ProteomeXchange with identifiers PXD013656 and PXD013712, respectively.

Comparative proteomics in the wild: Accounting for intrapopulation variability improves describing proteome response in a Gammarus pulex field population exposed to cadmium

High-throughput proteomics can be performed on animal sentinels for discovering key molecular biomarkers signing the physiological response and adaptation of organisms. Ecotoxicoproteomics is today amenable by means of proteogenomics to small arthropods such as Gammarids which are well known sentinels of aquatic environments. Here, we analysed two regional Gammarus pulex populations to characterize the potential proteome divergence induced in one site by natural bioavailable mono-metallic contamination (cadmium) compared to a non-contaminated site. Two RNAseq-derived protein sequence databases were established previously on male and female individuals sampled from the reference site. Here, individual proteomes were acquired on 10 male and 10 female paired organisms sampled from each site. Proteins involved in protein lipidation, carbohydrate metabolism, proteolysis, innate immunity, oxidative stress response and lipid transport were found more abundant in animals exposed to cadmium, while hemocyanins were found in lower abundance. The intrapopulation proteome variability of long-term exposed G. pulex was inflated relatively to the non-contaminated population. These results show that, while remaining a challenge for such organisms with not yet sequenced genomes, taking into account intrapopulation variability is important to better define the molecular players induced by toxic stress in a comparative field proteomics approach.

GlycopeptideGraphMS: Improved Glycopeptide Detection and Identification by Exploiting Graph Theoretical Patterns in Mass and Retention Time

The leading proteomic method for identifying N-glycosylated peptides is liquid chromatography coupled with tandem fragmentation mass spectrometry (LCMS/MS) followed by spectral matching of MS/MS fragment masses to a database of possible glycan and peptide combinations. Such database-dependent approaches come with challenges such as needing high-quality informative MS/MS spectra, ignoring unexpected glycan or peptide sequences, and making incorrect assignments because some glycan combinations are equivalent in mass to amino acids. To address these challenges, we present GlycopeptideGraphMS, a graph theoretical bioinformatic approach complementary to the database-dependent method. Using the AXL receptor tyrosine kinase (AXL) as a model glycoprotein with multiple N-glycosylation sites, we show that those LCMS features that could be grouped into graph networks on the basis of glycan mass and retention time differences were actually N-glycopeptides with the same peptide backbone but different N-glycan compositions. Conversely, unglycosylated peptides did not exhibit this grouping behavior. Furthermore, MS/MS sequencing of the glycan and peptide composition of just one N-glycopeptide in the graph was sufficient to identify the rest of the N-glycopeptides in the graph. By validating the identifications with exoglycosidase cocktails and MS/MS fragmentation, we determined the experimental false discovery rate of identifications to be 2.21%. GlycopeptideGraphMS detected more than 500 unique N-glycopeptides from AXL, triple the number found by a database search with Byonic software, and detected incorrect assignments due to a nonspecific protease cleavage. This method overcomes some limitations of the database approach and is a step closer to comprehensive automated glycoproteomics.

GENCODE reference annotation for the human and mouse genomes

The accurate identification and description of the genes in the human and mouse genomes is a fundamental requirement for high quality analysis of data informing both genome biology and clinical genomics. Over the last 15 years, the GENCODE consortium has been producing reference quality gene annotations to provide this foundational resource. The GENCODE consortium includes both experimental and computational biology groups who work together to improve and extend the GENCODE gene annotation. Specifically, we generate primary data, create bioinformatics tools and provide analysis to support the work of expert manual gene annotators and automated gene annotation pipelines. In addition, manual and computational annotation workflows use any and all publicly available data and analysis, along with the research literature to identify and characterise gene loci to the highest standard. GENCODE gene annotations are accessible via the Ensembl and UCSC Genome Browsers, the Ensembl FTP site, Ensembl Biomart, Ensembl Perl and REST APIs as well as https://www.gencodegenes.org.

Updates on resources, software tools, and databases for plant proteomics in 2016-2017

Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.

Reannotation of Genomes by Means of Proteomics Data

Omics approaches have become popular in biology as powerful discovery tools, and currently gain in interest for diagnostic applications. Establishing the accurate genome sequence of any organism is easy, but the outcome of its annotation by means of automatic pipelines remains imprecise. Some protein-encoding genes may be missed as soon as they are specific and poorly conserved in a given taxon, while important to explain the specific traits of the organism. Translational starts are also poorly predicted in a relatively important number of cases, thus impacting the protein sequence database used in proteomics, comparative genomics, and systems biology. The use of high-throughput proteomics data to improve genome annotation is an attractive option to obtain a more comprehensive molecular picture of a given organism. Here, protocols for reannotating prokaryote genomes are described based on shotgun proteomics and derivatization of protein N-termini with a positively charged reagent coupled to high-resolution tandem mass spectrometry.