Recent advances in computational algorithms and software for large-scale glycoproteomics

Glycoproteomics, or characterizing glycosylation events at a proteome scale, has seen rapid advances in methods for analyzing glycopeptides by tandem mass spectrometry in recent years. These advances have enabled acquisition of far more comprehensive and large-scale datasets, precipitating an urgent need for improved informatics methods to analyze the resulting data. A new generation of glycoproteomics search methods has recently emerged, using glycan fragmentation to split the identification of a glycopeptide into peptide and glycan components and solve each component separately. In this review, we discuss these new methods and their implications for large-scale glycoproteomics, as well as several outstanding challenges in glycoproteomics data analysis, including validation of glycan assignments and quantitation. Finally, we provide an outlook on the future of glycoproteomics from an informatics perspective, noting the key challenges to achieving widespread and reproducible glycopeptide annotation and quantitation.     

Glycoproteomics Landscape of Asymptomatic and Symptomatic Human Alzheimer’s Disease Brain

Molecular changes in the brain of individuals afflicted with Alzheimer's disease (AD) are an intense area of study. Little is known about the role of protein abundance and posttranslational modifications in AD progression and treatment, in particular large-scale intact N-linked glycoproteomics analysis. To elucidate the N-glycoproteome landscape, we developed an approach based on multi-lectin affinity enrichment, hydrophilic interaction chromatography, and LC-MS–based glycoproteomics. We analyzed brain tissue from 10 persons with no cognitive impairment or AD, 10 with asymptomatic AD, and 10 with symptomatic AD, detecting over 300 glycoproteins and 1900 glycoforms across the samples. The majority of glycoproteins have N-glycans that are high-mannosidic or complex chains that are fucosylated and bisected. The Man5 N-glycan was found to occur most frequently at >20% of the total glycoforms. Unlike the glycoproteomes of other tissues, sialylation is a minor feature of the brain N-glycoproteome, occurring at <9% among the glycoforms. We observed AD-associated differences in the number of antennae, frequency of fucosylation, bisection, and other monosaccharides at individual glycosylation sites among samples from our three groups. Further analysis revealed glycosylation differences in subcellular compartments across disease stage, including glycoproteins in the lysosome frequently modified with paucimannosidic glycans. These results illustrate the N-glycoproteomics landscape across the spectrum of AD clinical and pathologic severity and will facilitate a deeper understanding of progression and treatment development.     

Mass Spectrometric and Glycan Microarray–Based Characterization of the Filarial Nematode Brugia malayi Glycome Reveals Anionic and Zwitterionic Glycan Antigens

Millions of people worldwide are infected with filarial nematodes, responsible for lymphatic filariasis (LF) and other diseases causing chronic disablement. Elimination programs have resulted in a substantial reduction of the rate of infection in certain areas creating a need for improved diagnostic tools to establish robust population surveillance and avoid LF resurgence. Glycans from parasitic helminths are emerging as potential antigens for use in diagnostic assays. However, despite its crucial role in host–parasite interactions, filarial glycosylation is still largely, structurally, and functionally uncharacterized. Therefore, we investigated the glycan repertoire of the filarial nematode Brugia malayi. Glycosphingolipid and N-linked glycans were extracted from several life-stages using enzymatic release and characterized using a combination of MALDI-TOF-MS and glycan sequencing techniques. Next, glycans were purified by HPLC and printed onto microarrays to assess the host anti-glycan antibody response. Comprehensive glycomic analysis of B. malayi revealed the presence of several putative antigenic motifs such as phosphorylcholine and terminal glucuronic acid. Glycan microarray screening showed a recognition of most B. malayi glycans by immunoglobulins from rhesus macaques at different time points after infection, which permitted the characterization of the dynamics of anti-glycan immunoglobulin G and M during the establishment of brugian filariasis. A significant level of IgG binding to the parasite glycans was also detected in infected human plasma, while IgG binding to glycans decreased after anthelmintic treatment. Altogether, our work identifies B. malayi glycan antigens and reveals antibody responses from the host that could be exploited as potential markers for LF.     

SIMSI-Transfer: Software-Assisted Reduction of Missing Values in Phosphoproteomic and Proteomic Isobaric Labeling Data Using Tandem Mass Spectrum Clustering

Isobaric stable isotope labeling techniques such as tandem mass tags (TMTs) have become popular in proteomics because they enable the relative quantification of proteins with high precision from up to 18 samples in a single experiment. While missing values in peptide quantification are rare in a single TMT experiment, they rapidly increase when combining multiple TMT experiments. As the field moves toward analyzing ever higher numbers of samples, tools that reduce missing values also become more important for analyzing TMT datasets. To this end, we developed SIMSI-Transfer (Similarity-based Isobaric Mass Spectra 2 [MS2] Identification Transfer), a software tool that extends our previously developed software MaRaCluster (© Matthew The) by clustering similar tandem MS2 from multiple TMT experiments. SIMSI-Transfer is based on the assumption that similarity-clustered MS2 spectra represent the same peptide. Therefore, peptide identifications made by database searching in one TMT batch can be transferred to another TMT batch in which the same peptide was fragmented but not identified. To assess the validity of this approach, we tested SIMSI-Transfer on masked search engine identification results and recovered >80% of the masked identifications while controlling errors in the transfer procedure to below 1% false discovery rate. Applying SIMSI-Transfer to six published full proteome and phosphoproteome datasets from the Clinical Proteomic Tumor Analysis Consortium led to an increase of 26 to 45% of identified MS2 spectra with TMT quantifications. This significantly decreased the number of missing values across batches and, in turn, increased the number of peptides and proteins identified in all TMT batches by 43 to 56% and 13 to 16%, respectively.     

Application of bioanalytical and computational methods in decoding the roles of glycans in host-pathogen interactions

Host-pathogen interactions (HPIs) are complex processes that require tight regulation. A common regulatory mechanism of HPIs is through glycans of either host cells or pathogens. Due to their diverse sequences, complex structures, and conformations, studies of glycans require highly sensitive and powerful tools. Recent improvements in technology have enabled the application of many bioanalytical techniques and modeling methods to investigate glycans and their mechanisms in HPIs. This mini-review highlights how these advances have been used to understand the role glycans play in HPIs in the past 2 years.     

High-Throughput Human Complement C3 N-Glycoprofiling Identifies Markers of Early Onset Type 1 Diabetes Mellitus in Children

Recently, it was shown that children at the onset of type 1 diabetes (T1D) have a higher proportion of oligomannose glycans in their total plasma protein N-glycome compared to their healthy siblings. The most abundant complement component, glycoprotein C3, contains two N-glycosylation sites occupied exclusively by this type of glycans. Furthermore, complement system, as well as C3, was previously associated with T1D. It is also known that changes in glycosylation can modulate inflammatory responses, so our aim was to characterize the glycosylation profile of C3 in T1D. For this purpose, we developed a novel high-throughput workflow for human C3 concanavalin A lectin affinity enrichment and subsequent LC-MS glycopeptide analysis which enables protein-specific N-glycosylation profiling. From the Danish Childhood Diabetes Register, plasma samples of 61 children/adolescents newly diagnosed with T1D and 84 of their unaffected siblings were C3 N-glycoprofiled. Significant changes of C3 N-glycan profiles were found. T1D was associated with an increase in the proportion of unprocessed glycan structures with more mannose units. A regression model including C3 N-glycans showed notable discriminative power between children with early onset T1D and their healthy siblings with area under curve of 0.879. This study confirmed our previous findings of plasma high-mannose glycan changes in a cohort of recent onset T1D cases, suggesting the involvement of C3 N-glycome in T1D development. Our C3 glycan-based discriminative model could be valuable in assessment of T1D risk in children.     

Structures and functions of algal glycans shape their capacity to sequester carbon in the ocean

Algae synthesise structurally complex glycans to build a protective barrier, the extracellular matrix. One function of matrix glycans is to slow down microorganisms that try to enzymatically enter living algae and degrade and convert their organic carbon back to carbon dioxide. We propose that matrix glycans lock up carbon in the ocean by controlling degradation of organic carbon by bacteria and other microbes not only while algae are alive, but also after death. Data revised in this review shows accumulation of algal glycans in the ocean underscoring the challenge bacteria and other microbes face to breach the glycan barrier with carbohydrate active enzymes. Briefly we also update on methods required to certify the uncertain magnitude and unknown molecular causes of glycan-controlled carbon sequestration in a changing ocean.     

A Complex Connection Between the Diversity of Human Gastric Mucin O-Glycans,Helicobacter pylori Binding,Helicobacter Infection and Fucosylation

Helicobacter pylori colonizes the stomach of half of the human population. Most H. pylori are located in the mucus layer, which is mainly comprised by glycosylated mucins. Using mass spectrometry, we identified 631 glycans (whereof 145 were fully characterized and the remainder assigned as compositions) on mucins isolated from 14 Helicobacter spp.-infected and 14 Helicobacter spp.-noninfected stomachs. Only six identified glycans were common to all individuals, from a total of 60 to 189 glycans in each individual. An increased number of unique glycan structures together with an increased intraindividual diversity and larger interindividual variation were identified among O-glycans from Helicobacter spp.-infected stomachs compared with noninfected stomachs. H. pylori strain J99, which carries the blood group antigen–binding adhesin (BabA), the sialic acid–binding adhesin (SabA), and the LacdiNAc-binding adhesin, bound both to Lewis b (Leb)-positive and Leb-negative mucins. Among Leb-positive mucins, H. pylori J99 binding was higher to mucins from Helicobacter spp.-infected individuals than noninfected individuals. Statistical correlation analysis, binding experiments with J99 wt, and J99ΔbabAΔsabA and inhibition experiments using synthetic glycoconjugates demonstrated that the differences in H. pylori-binding ability among these four groups were governed by BabA-dependent binding to fucosylated structures. LacdiNAc levels were lower in mucins that bound to J99 lacking BabA and SabA than in mucins that did not, suggesting that LacdiNAc did not significantly contribute to the binding. We identified 24 O-glycans from Leb-negative mucins that correlated well with H. pylori binding whereof 23 contained α1,2-linked fucosylation. The large and diverse gastric glycan library identified, including structures that correlated with H. pylori binding, could be used to select glycodeterminants to experimentally investigate further for their importance in host–pathogen interactions and as candidates to develop glycan-based therapies.     

A Novel Spectral Annotation Strategy Streamlines Reporting of Mono-ADP-ribosylated Peptides Derived from Mouse Liver and Spleen in Response to IFN-γ

Mass-spectrometry-enabled ADP-ribosylation workflows are developing rapidly, providing researchers a variety of ADP-ribosylome enrichment strategies and mass spectrometric acquisition options. Despite the growth spurt in upstream technologies, systematic ADP-ribosyl (ADPr) peptide mass spectral annotation methods are lacking. HCD-dependent ADP-ribosylome studies are common, but the resulting MS2 spectra are complex, owing to a mixture of b/y-ions and the m/p-ion peaks representing one or more dissociation events of the ADPr moiety (m-ion) and peptide (p-ion). In particular, p-ions that dissociate further into one or more fragment ions can dominate HCD spectra but are not recognized by standard spectral annotation workflows. As a result, annotation strategies that are solely reliant upon the b/y-ions result in lower spectral scores that in turn reduce the number of reportable ADPr peptides. To improve the confidence of spectral assignments, we implemented an ADPr peptide annotation and scoring strategy. All MS2 spectra are scored for the ADPr m-ions, but once spectra are assigned as an ADPr peptide, they are further annotated and scored for the p-ions. We implemented this novel workflow to ADPr peptides enriched from the liver and spleen isolated from mice post 4 h exposure to systemic IFN-γ. HCD collision energy experiments were first performed on the Orbitrap Fusion Lumos and the Q Exactive, with notable ADPr peptide dissociation properties verified with CID (Lumos). The m-ion and p-ion series score distributions revealed that ADPr peptide dissociation properties vary markedly between instruments and within instrument collision energy settings, with consequences on ADPr peptide reporting and amino acid localization. Consequentially, we increased the number of reportable ADPr peptides by 25% (liver) and 17% (spleen) by validation and the inclusion of lower confidence ADPr peptide spectra. This systematic annotation strategy will streamline future reporting of ADPr peptides that have been sequenced using any HCD/CID-based method.     

Regio-Specific N-Glycome and N-Glycoproteome Map of the Elderly Human Brain With and Without Alzheimer’s Disease

The proteins in the cell membrane of the brain are modified by glycans in highly interactive regions. The glycans and glycoproteins are involved in cell–cell interactions that are of fundamental importance to the brain. In this study, the comprehensive N-glycome and N-glycoproteome of the brain were determined in 11 functional brain regions, some of them known to be affected with the progression of Alzheimer’s disease. N-glycans throughout the regions were generally highly branched and highly sialofucosylated. Regional variations were also found with regard to the glycan types including high mannose and complex-type structures. Glycoproteomic analysis identified the proteins that differed in glycosylation in the various regions. To obtain the broader representation of glycan compositions, four subjects with two in their 70s and two in their 90s representing two Alzheimer's disease subjects, one hippocampal sclerosis subject, and one subject with no cognitive impairment were analyzed. The four subjects were all glycomically mapped across 11 brain regions. Marked differences in the glycomic and glycoproteomic profiles were observed between the samples.     

SP3-Enabled Rapid and High Coverage Chemoproteomic Identification of Cell-State–Dependent Redox-Sensitive Cysteines

Proteinaceous cysteine residues act as privileged sensors of oxidative stress. As reactive oxygen and nitrogen species have been implicated in numerous pathophysiological processes, deciphering which cysteines are sensitive to oxidative modification and the specific nature of these modifications is essential to understanding protein and cellular function in health and disease. While established mass spectrometry-based proteomic platforms have improved our understanding of the redox proteome, the widespread adoption of these methods is often hindered by complex sample preparation workflows, prohibitive cost of isotopic labeling reagents, and requirements for custom data analysis workflows. Here, we present the SP3-Rox redox proteomics method that combines tailored low cost isotopically labeled capture reagents with SP3 sample cleanup to achieve high throughput and high coverage proteome-wide identification of redox-sensitive cysteines. By implementing a customized workflow in the free FragPipe computational pipeline, we achieve accurate MS1-based quantitation, including for peptides containing multiple cysteine residues. Application of the SP3-Rox method to cellular proteomes identified cysteines sensitive to the oxidative stressor GSNO and cysteine oxidation state changes that occur during T cell activation.     

Improving Statistical Certainty of Glycosylation Similarity between Influenza A Virus Variants Using Data-Independent Acquisition Mass Spectrometry

Amino acid sequences of immunodominant domains of hemagglutinin (HA) on the surface of influenza A virus (IAV) evolve rapidly, producing viral variants. HA mediates receptor recognition, binding and cell entry, and serves as the target for IAV vaccines. Glycosylation, a post-translational modification that places large branched polysaccharide molecules on proteins, can modulate the function of HA and shield antigenic regions allowing for viral evasion from immune responses. Our previous work showed that subtle changes in the HA protein sequence can have a measurable change in glycosylation. Thus, being able to quantitatively measure glycosylation changes in variants is critical for understanding how HA function may change throughout viral evolution. Moreover, understanding quantitatively how the choice of viral expression systems affects glycosylation can help in the process of vaccine design and manufacture. Although IAV vaccines are most commonly expressed in chicken eggs, cell-based vaccines have many advantages, and the adoption of more cell-based vaccines would be an important step in mitigating seasonal influenza and protecting against future pandemics. Here, we have investigated the use of data-independent acquisition (DIA) mass spectrometry for quantitative glycoproteomics. We found that DIA improved the sensitivity of glycopeptide detection for four variants of A/Switzerland/9715293/2013 (H3N2): WT and mutant, each expressed in embryonated chicken eggs and Madin–Darby canine kidney cells. We used the Tanimoto similarity metric to quantify changes in glycosylation between WT and mutant and between egg-expressed and cell-expressed virus. Our DIA site-specific glycosylation similarity comparison of WT and mutant expressed in eggs confirmed our previous analysis while achieving greater depth of coverage. We found that sequence variations and changing viral expression systems affected distinct glycosylation sites of HA. Our methods can be applied to track glycosylation changes in circulating IAV variants to bolster genomic surveillance already being done, for a more complete understanding of IAV evolution.     

Tuning DO:DM Ratios Modulates MHC Class II Immunopeptidomes

Major histocompatibility complex class II (MHC-II) antigen presentation underlies a wide range of immune responses in health and disease. However, how MHC-II antigen presentation is regulated by the peptide-loading catalyst HLA-DM (DM), its associated modulator, HLA-DO (DO), is incompletely understood. This is due largely to technical limitations: model antigen-presenting cell (APC) systems that express these MHC-II peptidome regulators at physiologically variable levels have not been described. Likewise, computational prediction tools that account for DO and DM activities are not presently available. To address these gaps, we created a panel of single MHC-II allele, HLA-DR4-expressing APC lines that cover a wide range of DO:DM ratio states. Using a combined immunopeptidomic and proteomic discovery strategy, we measured the effects DO:DM ratios have on peptide presentation by surveying over 10,000 unique DR4-presented peptides. The resulting data provide insight into peptide characteristics that influence their presentation with increasing DO:DM ratios. These include DM sensitivity, peptide abundance, binding affinity and motif, peptide length, and choice of binding register along the source protein. These findings have implications for designing improved HLA-II prediction algorithms and research strategies for dissecting the variety of functions that different APCs serve in the body.     

inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction

Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spectrum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.     

Multiple Layers of Complexity in O-Glycosylation Illustrated With the Urinary Glycoproteome

While N-glycopeptides are relatively easy to characterize, O-glycosylation analysis is more complex. In this article, we illustrate the multiple layers of O-glycopeptide characterization that make this task so challenging. We believe our carefully curated dataset represents perhaps the largest intact human glycopeptide mixture derived from individuals, not from cell lines. The samples were collected from healthy individuals, patients with superficial or advanced bladder cancer (three of each group), and a single bladder inflammation patient. The data were scrutinized manually and interpreted using three different search engines: Byonic, Protein Prospector, and O-Pair, and the tool MS-Filter. Despite all the recent advances, reliable automatic O-glycopeptide assignment has not been solved yet. Our data reveal such diversity of site-specific O-glycosylation that has not been presented before. In addition to the potential biological implications, this dataset should be a valuable resource for software developers in the same way as some of our previously released data has been used in the development of O-Pair and O-Glycoproteome Analyzer. Based on the manual evaluation of the performance of the existing tools with our data, we lined up a series of recommendations that if implemented could significantly improve the reliability of glycopeptide assignments.     

Imaging Mass Spectrometry Reveals Alterations in N-Linked Glycosylation That Are Associated With Histopathological Changes in Nonalcoholic Steatohepatitis in Mouse and Human

Nonalcoholic steatohepatitis (NASH) is the progressive form of nonalcoholic fatty liver disease (NAFLD) and is characterized by inflammation, hepatocyte injury, and fibrosis. Further, NASH is a risk factor for cirrhosis and hepatocellular carcinoma. Previous research demonstrated that serum N-glycan profiles can be altered in NASH patients. Here, we hypothesized that these N-glycan modifications may be associated with specific liver damage in NAFLD and NASH. To investigate the N-glycome profile in tissue, imaging mass spectrometry was used for a qualitative and quantitative in situ N-linked glycan analysis of mouse and human NAFLD/NASH tissue. A murine model was used to induce NAFLD and NASH through ad libitum feeding with either a high-fat diet or a Western diet, respectively. Mice fed a high-fat diet or Western diet developed inflammation, steatosis, and fibrosis, consistent with NAFLD/NASH phenotypes. Induction of NAFLD/NASH for 18 months using high caloric diets resulted in increased expression of mannose, complex/fucosylated, and hybrid N-glycan structures compared to control mouse livers. To validate the animal results, liver biopsy specimens from 51 human NAFLD/NASH patients representing the full range of NASH Clinical Research Network fibrosis stages were analyzed. Importantly, the same glycan alterations observed in mouse models were observed in human NASH biopsies and correlated with the degree of fibrosis. In addition, spatial glycan alterations were localized specifically to histopathological changes in tissue like fibrotic and fatty areas. We demonstrate that the use of standard staining’s combined with imaging mass spectrometry provide a full profile of the origin of N-glycan modifications within the tissue. These results indicate that the spatial distribution of abundances of released N-glycans correlate with regions of tissue steatosis associated with NAFLD/NASH.     

Deep-lipidotyping by mass spectrometry: recent technical advances and applications

In-depth structural characterization of lipids is an essential component of lipidomics. There has been a rapid expansion of mass spectrometry methods that are capable of resolving lipid isomers at various structural levels over the past decade. These developments finally make deep-lipidotyping possible, which provides new means to study lipid metabolism and discover new lipid biomarkers. In this review, we discuss recent advancements in tandem mass spectrometry (MS/MS) methods for identification of complex lipids beyond the species (known headgroup information) and molecular species (known chain composition) levels. These include identification at the levels of carbon-carbon double bond (C=C) location and sn-position, as well as characterization of acyl chain modifications. We also discuss the integration of isomer-resolving MS/MS methods with different lipid analysis workflows and their applications in lipidomics. The results showcase the distinct capabilities of deep-lipidotyping in untangling the metabolism of individual isomers and sensitive phenotyping by using relative fractional quantitation of the isomers.     

Reanalysis of ProteomicsDB Using an Accurate,Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups

Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry–based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam’s razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.     

An Optimized Proteomics Approach Reveals Novel Alternative Proteins in Mouse Liver Development

Alternative ORFs (AltORFs) are unannotated sequences in genome that encode novel peptides or proteins named alternative proteins (AltProts). Although ribosome profiling and bioinformatics predict a large number of AltProts, mass spectrometry as the only direct way of identification is hampered by the short lengths and relative low abundance of AltProts. There is an urgent need for improvement of mass spectrometry methodologies for AltProt identification. Here, we report an approach based on size-exclusion chromatography for simultaneous enrichment and fractionation of AltProts from complex proteome. This method greatly simplifies the variance of AltProts discovery by enriching small proteins smaller than 40 kDa. In a systematic comparison between 10 methods, the approach we reported enabled the discovery of more AltProts with overall higher intensities, with less cost of time and effort compared to other workflows. We applied this approach to identify 89 novel AltProts from mouse liver, 39 of which were differentially expressed between embryonic and adult mice. During embryonic development, the upregulated AltProts were mainly involved in biological pathways on RNA splicing and processing, whereas the AltProts involved in metabolisms were more active in adult livers. Our study not only provides an effective approach for identifying AltProts but also novel AltProts that are potentially important in developmental biology.