PgxSAVy: A tool for comprehensive evaluation of variant peptide quality in proteogenomics - catching the (un)usual suspects.

Variant peptides resulting from single nucleotide polymorphisms (SNPs) can lead to aberrant protein functions and have translational potential for disease diagnosis and personalized therapy. Variant peptides detected by proteogenomics are fraught with high number of false positives, but there is no uniform and comprehensive approach to assess variant quality across analysis pipelines. Despite class-specific FDR along with ad-hoc filters, the problem is far from solved. These protocols are typically manual and tedious, and thus not uniform across labs. We demonstrate that variant peptide rescoring, integrated with intensity, variant event information and search result features, allows better discrimination of correct variant peptides. Implemented into PgxSAVy - a tool for quality control of variant peptides, this method can tackle the high rate of false positives. PgxSAVy provides a rigorous framework for quality control and annotations of variant peptides on the basis of (i) variant quality, (ii) isobaric masses, and (iii) disease annotation. PgxSAVy demonstrated high accuracy by identifying true variants with 98.43% accuracy on simulated data. Large-scale proteogenomic reanalysis of ∼2.8 million spectra (PXD004010 and PXD001468) resulted in 12,705 variant peptide spectrum matches (PSMs), of which PgxSAVy evaluated 3028 (23.8%), 1409 (11.1%) and 8268 (65.1%) as confident, semi-confident and doubtful respectively. PgxSAVy also annotates the variants based on their pathogenicity and provides support for assisted manual validation. The analysis of proteins carrying variants can provide fine granularity in discovering important pathways. PgxSAVy will advance personalized medicine by providing a comprehensive framework for quality control and prioritization of proteogenomics variants. PgxSAVy is freely available at https://pgxsavy.igib.res.in/ as a webserver and https://github.com/anuragraj/PgxSAVy as a stand-alone tool.

Extracellular vesicles recovered from plasma of severe dengue patients induce CD4+ T cell suppression through PD-L1/PD-1 interaction.

IMPORTANCE: Severe dengue manifestations caused by the dengue virus are a global health problem. Studies suggest that severe dengue disease depends on uncontrolled immune cell activation, and excessive inflammation adds to the pathogenesis of severe dengue disease. Therefore, it is important to understand the process that triggers the uncontrolled activation of the immune cells. The change in immune response in mild to severe dengue may be due to direct virus-to-cell interaction or it could be a contact-independent process through the extracellular vesicles (EVs) released from infected cells. The importance of circulating EVs in the context of dengue virus infection and pathogenesis remains unexplored. Therefore, understanding the possible biological function of circulating EVs may help to delineate the role of EVs in the progression of disease. Our present study highlights that EVs from plasma of severe dengue patients can have immunosuppressive properties on CD4+ T cells which may contribute to T cell suppression and may contribute to dengue disease progression.

False discovery rate: the Achilles' heel of proteogenomics.

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

Golden Syrian hamster as a model to study cardiovascular complications associated with SARS-CoV-2 infection.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in the Golden Syrian hamster causes lung pathology that resembles human coronavirus disease (COVID-19). However, extrapulmonary pathologies associated with SARS-CoV-2 infection and post-COVID sequelae remain to be understood. Here, we show, using a hamster model, that the early phase of SARS-CoV-2 infection leads to an acute inflammatory response and lung pathologies, while the late phase of infection causes cardiovascular complications (CVCs) characterized by ventricular wall thickening associated with increased ventricular mass/body mass ratio and interstitial coronary fibrosis. Molecular profiling further substantiated our findings of CVC as SARS-CoV-2-infected hamsters showed elevated levels of serum cardiac troponin I, cholesterol, low-density lipoprotein, and long-chain fatty acid triglycerides. Serum metabolomics profiling of SARS-CoV-2-infected hamsters identified N-acetylneuraminate, a functional metabolite found to be associated with CVC, as a metabolic marker was found to be common between SARS-CoV-2-infected hamsters and COVID-19 patients. Together, we propose hamsters as a suitable animal model to study post-COVID sequelae associated with CVC, which could be extended to therapeutic interventions.

Studying Autophagy Using a TMT-Based Quantitative Proteomics Approach.

Maintenance of cellular homeostasis through regulated degradation of proteins and organelles is a defining feature of autophagy. This process itself is tightly regulated in a series of well-defined biochemical reactions governed largely by the highly conserved ATG protein family. Given its crucial role in regulating protein levels under both basal and stress conditions such as starvation and infection, genetic or pharmacological perturbation of autophagy results in massive changes in the cellular proteome and impacts nearly every biological process. Therefore, studying autophagy perturbations at a global scale assumes prime importance. In recent years, quantitative mass spectrometry (MS)-based proteomics has emerged as a powerful approach to explore biological processes through global proteome quantification analysis. Tandem mass tag (TMT)-based MS proteomics is one such robust quantitative technique that can examine relative protein abundances in multiple samples (parallel multiplexing). Investigating autophagy through TMT-based MS approach can give great insights into autophagy-regulated biological processes, protein-protein interaction networks, spatiotemporal protein dynamics, and identification of new autophagy substrates. This chapter provides a detailed protocol for studying the impact of a dysfunctional autophagy pathway on the cellular proteome and pathways in a healthy vs. disease (virus infection) condition using a 16-plex TMT-based quantitative proteomics approach. We also provide a pipeline on data processing and analysis using available web-based tools.

Proteomic landscape of Japanese encephalitis virus-infected fibroblasts.

Advances in proteomics have enabled a comprehensive understanding of host-pathogen interactions. Here we have characterized Japanese encephalitis virus (JEV) infection-driven changes in the mouse embryonic fibroblast (MEF) proteome. Through tandem mass tagging (TMT)-based mass spectrometry, we describe changes in 7.85 % of the identified proteome due to JEV infection. Pathway enrichment analysis showed that proteins involved in innate immune sensing, interferon responses and inflammation were the major upregulated group, along with the immunoproteasome and poly ADP-ribosylation proteins. Functional validation of several upregulated anti-viral innate immune proteins, including an active cGAS-STING axis, was performed. Through siRNA depletion, we describe a crucial role of the DNA sensor cGAS in restricting JEV replication. Further, many interferon-stimulated genes (ISGs) were observed to be induced in infected cells. We also observed activation of TLR2 and inhibition of TLR2 signalling using TLR1/2 inhibitor CU-CPT22-blocked production of inflammatory cytokines IL6 and TNF-α from virus-infected N9 microglial cells. The major proteins that were downregulated by infection were involved in cell adhesion (collagens), transport (solute carrier and ATP-binding cassette transporters), sterol and lipid biosynthesis. Several collagens were found to be transcriptionally downregulated in infected MEFs and mouse brain. Collectively, our data provide a bird's-eye view into how fibroblast protein composition is rewired following JEV infection.

Big data, integrative omics and network biology.

A cell integrates various signals through a network of biomolecules that crosstalk to synergistically regulate the replication, transcription, translation and other metabolic activities of a cell. These networks regulate signal perception and processing that drives biological functions. The biological complexity cannot be fully captured by a single -omics discipline. The holistic study of an organism-in health, perturbation, exposure to environment and disease, is studied under systems biology. The bottom-up molecular approaches (genes, mRNA, protein, metabolite, etc.) have laid the foundation of current biological knowledge covering the horizon from viruses, bacteria, fungi, plants and animals. Yet, these techniques provide a rather myopic view of biology at the molecular level. To understand how the interconnected molecular components are formed and rewired in disease or exposure to environmental stimuli is the holy grail of modern biology. The omics era was heralded by the genomics revolution but advanced sequencing techniques are now also ubiquitous in transcriptomics, proteomics, metabolomics and lipidomics. Multi-omics data analysis and integration techniques are driving the quest for deeper insights into how the different layers of biomolecules talk to each other in diverse contexts.

Posttranslational modifications in systems biology.

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.

Proteome analysis revealed the essential functions of protein phosphatase PP2A in the induction of Th9 cells.

Proteomic analysis identifies post-translational functions of proteins, which remains obscure in transcriptomics. Given the important functions of Th9 cells in anti-tumor immunity, we performed proteome analysis of Th9 cells to understand the involvement of proteins that might be crucial for the anti-tumor functions of Th9 cells. Here we performed a comprehensive proteomic analysis of murine Th0 and Th9 cells, and identified proteins that are enriched in Th9 cells. Pathway analysis identified an abundance of phosphoproteins in the proteome of Th9 cells as compared to Th0 cells. Among upregulated phosphoproteins, Ppp2ca (catalytic subunit of protein phosphatase, PP2A) was found to be highly enriched in Th9 cells. Although the role of PP2A has been shown to regulate the differentiation and functions of Th1, Th2, Th17 and Tregs, its role in the differentiation and functions of Th9 cells is not identified yet. Here we found that PP2A is required for the induction of Th9 cells, as PP2A inhibition leads to the suppression of IL-9 and expression of key transcription factors of Th9 cells. PP2A inhibition abrogates Th9 cell-mediated anti-tumor immune response in B16-OVA melanoma tumor model. Thus, we report that PP2A is essential for the differentiation and anti-tumor functions of Th9 cells.

Post-translational Modification Crosstalk and Hotspots in Sirtuin Interactors Implicated in Cardiovascular Diseases.

Sirtuins are protein deacetylases that play a protective role in cardiovascular diseases (CVDs), as well as many other diseases. Absence of sirtuins can lead to hyperacetylation of both nuclear and mitochondrial proteins leading to metabolic dysregulation. The protein post-translational modifications (PTMs) are known to crosstalk among each other to bring about complex phenotypic outcomes. Various PTM types such as acetylation, ubiquitination, and phosphorylation, and so on, drive transcriptional regulation and metabolism, but such crosstalks are poorly understood. We integrated protein-protein interactions (PPI) and PTMs from several databases to integrate information on 1,251 sirtuin-interacting proteins, of which 544 are associated with cardiac diseases. Based on the ∼100,000 PTM sites obtained for sirtuin interactors, we observed that the frequency of PTM sites (83 per protein), as well as PTM types (five per protein), is higher than the global average for human proteome. We found that ∼60-70% PTM sites fall into ordered regions. Approximately 83% of the sirtuin interactors contained at least one competitive crosstalk (in situ) site, with half of the sites occurring in CVD-associated proteins. A large proportion of identified crosstalk sites were observed for acetylation and ubiquitination competition. We identified 614 proteins containing PTM hotspots (≥5 PTM sites) and 133 proteins containing crosstalk hotspots (≥3 crosstalk sites). We observed that a large proportion of disease-associated sequence variants were found in PTM motifs of CVD proteins. We identified seven proteins (TP53, LMNA, MAPT, ATP2A2, NCL, APEX1, and HIST1H3A) containing disease-associated variants in PTM and crosstalk hotspots. This is the first comprehensive bioinformatics analysis on sirtuin interactors with respect to PTMs and their crosstalks. This study forms a platform for generating interesting hypotheses that can be tested for a deeper mechanistic understanding gained or derived from big-data analytics.

HyperQuant-A Computational Pipeline for Higher Order Multiplexed Quantitative Proteomics.

Quantitative proteomics has evolved considerably over the last decade with the advent of higher order multiplexing (HOM) techniques. With the development of methods such as-multitagging, cPILOT, hyperplexing, BONPlex, and MITNCAT, the HOM technique is rapidly taking the center stage in multiplexed quantitative proteomics. These studies combined MS1 and MS2 labels in a single experiment enabling higher sample throughput. While HOM is highly promising, the computational analysis is still a big challenge, as the available tools cannot harness its power completely. We have developed a new quantitative pipeline, HyperQuant to aid in accurately quantitating complex HOM data. The pipeline uses identification results from either MaxQuant or any other search engine and quantitation results from QuantWizIQ. The Mapper and Combiner modules of HyperQuant allow facile integration of the labeled data, along with peptide spectrum match (PSM) intensity/ratio integration for proteins, respectively, for each PSM label combination. This also includes appropriate combination of replicates/fractions before summarizing the protein intensity/ratio, leading to robust quantitation. To the best of our knowledge, this is the first tool for the quantitation of HOM data with flexibility for any combination of MS1 and MS2 labels. We demonstrate its utility in analyzing two 18-plex data sets from the hyperplexing and the BONplex studies. The tool is open source and freely available for noncommercial use. HyperQuant is a highly valuable tool that will help in advancing the field of multiplexed quantitative proteomics.

Quantitative Proteome Analysis of Atg5-Deficient Mouse Embryonic Fibroblasts Reveals the Range of the Autophagy-Modulated Basal Cellular Proteome.

Basal autophagy is crucial for maintenance of cellular homeostasis. ATG5 is an essential protein for autophagosome formation, and its depletion has been extensively used as a tool to disrupt autophagy. Here, we characterize the impact of Atg5 deficiency on the cellular proteome of mouse embryonic fibroblasts (MEFs). Using a tandem mass tagging (TMT)-based quantitative proteomics analysis, we observe that 14% of identified proteins show dysregulated levels in atg5-/- MEFs. These proteins were distributed across diverse biological processes, such as cell adhesion, development, differentiation, transport, metabolism, and immune responses. Several of the upregulated proteins were receptors involved in transforming growth factor ß (TGF-ß) signaling, JAK-STAT signaling, junction adhesion, and interferon/cytokine-receptor interactions and were validated as autophagy substrates. Nearly equal numbers of proteins, including several lysosomal proteins and enzymes, were downregulated, suggesting a complex role of autophagy/ATG5 in regulating their levels. The atg5-/- MEFs had lower levels of key immune sensors and effectors, including Toll-like receptor 2 (TLR2), interferon regulatory factor 3 (IRF3), IRF7, MLKL, and STAT1/3/5/6, which were restored by reexpression of ATG5. While these cells could efficiently mount a type I interferon response to the double-stranded RNA (dsRNA) mimic poly(I·C), they were compromised in their inflammatory response to the bacterial pathogen-associated molecular patterns (PAMPs) lipopolysaccharide (LPS) and Pam3CSK4. Transcriptional activation and secretion of interleukin-6 (IL-6) in these cells could be recovered by ATG5 expression, supporting the role of autophagy in the TLR2-induced inflammatory response. This study provides a key resource for understanding the effect of autophagy/ATG5 deficiency on the fibroblast proteome.IMPORTANCE Autophagy performs housekeeping functions for cells and maintains a functional mode by degrading damaged proteins and organelles and providing energy under starvation conditions. The process is tightly regulated by the evolutionarily conserved Atg genes, of which Atg5 is one such crucial mediator. Here, we have done a comprehensive quantitative proteome analysis of mouse embryonic fibroblasts that lack a functional autophagy pathway (Atg5 knockout). We observe that 14% of the identified cellular proteome is remodeled, and several proteins distributed across diverse cellular processes with functions in signaling, cell adhesion, development, and immunity show either higher or lower levels under autophagy-deficient conditions. These cells have lower levels of crucial immune proteins that are required to mount a protective inflammatory response. This study will serve as a valuable resource to determine the role of autophagy in modulating specific protein levels in cells.

Advances in Higher Order Multiplexing Techniques in Proteomics.

Proteomics by mass spectrometry (MS) allows the large-scale identification and quantitation of the cellular proteins in a given biological context. Systems biology studies from proteomics data are largely limited by the accuracy and coverage of quantitative proteomics along with missing values. Toward this end, statistically robust biological observations are required, comprising multiple replicates, preferably with little technical variations. Multiplexed labeling techniques in proteomics allow quantitative comparisons of several biological samples or conditions. In this focused Review, we discuss an emerging technique called higher order multiplexing or enhanced multiplexing, a unique combination of traditional MS1- and MS2-based quantitative proteomics methods that allows for expanding the multiplexing capability of MS methods to save valuable instrument time, achieve statistical robustness, enhance coverage and quantitation accuracy, and reduce the run-to-run variability. We discuss the various innovative studies and experimental designs that exploit the power of this technique and its variants to provide an overview of a rapidly growing area and also to highlight the advantages and challenges that lie ahead in the widespread adoption of this technique.

Comparative analysis of homologous aminopeptidase PepN from pathogenic and non-pathogenic mycobacteria reveals divergent traits.

Mycobacterium tuberculosis (Mtb) secretes proteases and peptidases to subjugate its host. Out of its sixty plus proteases, atleast three are reported to reach host macrophages. In this study, we show that Mtb also delivers a lysyl alanine aminopeptidase, PepN (Rv2467) into host macrophage cytosol. Our comparative in silico analysis shows PepNMtb highly conserved across all pathogenic mycobacteria. Non-pathogenic mycobacteria including M. smegmatis (Msm) also encode pepN. PepN protein levels in both Mtb (pathogenic) and Msm (non-pathogenic) remain uniform across all in vitro growth phases. Despite such tight maintenance of PepNs' steady state levels, upon supplementation, Mtb alone allows accumulation of any excessive PepN. In contrast, Msm does not. It not only proteolyzes, but also secretes out the excessive PepN, be it native or foreign. Interestingly, while PepNMtb is required for modulating virulence in vivo, PepNMsm is essential for Msm growth in vitro. Despite such essentiality difference, both PepNMtb and PepNMsm harbor almost identical N-terminal M1-type peptidase domains that significantly align in their amino acid sequences and overlap in their secondary structures. Their C-terminal ERAP1_C-like domains however align much more moderately. Our in vitro macrophage-based infection experiments with MtbΔpepN-expressing pepNMsm reveals PepNMsm also retaining the ability to reach host cytosol. Lastly, but notably, we determined the PepNMtb and PepNMsm interactomes and found them to barely coincide. While PepNMtb chiefly interacts with Mtb's secreted proteins, PepNMsm primarily coimmunoprecipitates with Msm's housekeeping proteins. Thus, despite high sequence homology and several common properties, our comparative analytical study reveals host-centric traits of pathogenic and bacterial-centric traits of non-pathogenic PepNs.

Serum protein signature of coronary artery disease in type 2 diabetes mellitus.

BACKGROUND: Coronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers. METHODS: Using sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD). Network analysis was performed to create protein-protein interaction networks by using significantly (p < 0.05) altered protein markers in each disease using STRING 10.5 database. We used two supervised analysis methods i.e., between class analysis (BCA) and principal component analysis (PCA) to reveals distinct biomarkers profiles. Further, random forest classification (RF) was used to classify the diseases by the panel of markers. RESULTS: Our two supervised analysis methods BCA and PCA revealed a distinct biomarker profiles and high degree of variability in the marker profiles for T2DM_CAD and CAD. Thereafter, the present study identified multiple potential biomarkers to differentiate T2DM, CAD, and T2DM_CAD patients based on their relative abundance in serum. RF classified T2DM based on the abundance patterns of nine markers i.e., IL-1ß, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP and Apo-B; CAD by 14 markers i.e., resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII and IP-10; and T2DM _CAD by 12 markers i.e., insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6 and ghrelin with respect to the control subjects. Using network analysis, we have identified several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis. CONCLUSION: We have identified three distinct sets of serum markers for diabetes, CAD and diabetes associated with CAD in Indian patients using nonparametric-based machine learning approach. These multiple marker classifiers may be useful for monitoring progression from a healthy person to T2DM and T2DM to T2DM_CAD. However, these findings need to be further confirmed in the future studies with large number of samples.

Ubiquitin Proteasome pathway proteins as potential drug targets in parasite Trypanosoma cruzi.

Trypanosomiasis infects more than 21 million people and claims approximately 2 million lives annually. Due to the development of resistance against currently available anti-trypanosomal drugs, there is a growing need for specific inhibitors and novel drug targets. Of late, the proteins from the Ubiquitin Proteasome Pathway (UPP): ubiquitin ligases and deubiquitinase have received attention as potential drug targets in other parasites from the apicomplexan family. The completion of Trypanosoma cruzi (Tc) genome sequencing in 2005 and subsequent availability of database resources like TriTrypDB has provided a platform for the systematic study of the proteome of this parasite. Here, we present the first comprehensive survey of the UPP enzymes, their homologs and other associated proteins in trypanosomes and the UPPs from T. cruzi were explored in detail. After extensive computational analyses using various bioinformatics tools, we have identified 269 putative UPP proteins in the T. cruzi proteome along with their homologs in other Trypanosoma species. Characterization of T. cruzi proteome was done based on their predicted subcellular localization, domain architecture and overall expression profiles. Specifically, unique domain architectures of the enzymes and the UPP players expressed exclusively in the amastigote stage provide a rationale for designing inhibitors against parasite UPP proteins.

Dataset generated using hyperplexing and click chemistry to monitor temporal dynamics of newly synthesized macrophage secretome post infection by mycobacterial strains.

Here we provide data for SILAC and iTRAQ based hyperplexing combined with BONCAT based click chemistry for selective enrichment of newly synthesized proteins secreted by THP1 macrophages at various time points after infection with four different strains of Mycobacterium tuberculosis. The macrophages were infected with H37Ra, H37Rv, BND433 and JAL2287 strains of M. tuberculosis. Newly-synthesized secreted host proteins were observed, starting from six hours post-infection till 26 h, at 4 h intervals. We have combined BONCAT with hyperplexing (18-plex), which blends SILAC and iTRAQ, for the first time. Two sets of triplex SILAC were used to encode the strains of M. tuberculosis - H37Ra & H37Rv in one and BND433 & JAL2287 in another with a control in each. BONCAT was used to enrich the secretome for newly synthesized proteins while 6-plex iTRAQ labeling was employed to quantify the temporal changes in the captured proteome. Each set of 18-plex was run in 4 MS replicates with two linear and two non-linear separation modes. This new variant of hyperplexing method, combining triplex SILAC with 6-plex iTRAQ, achieves 18-plex quantitation in a single MS run. Hyperplexing enables large scale spatio-temporal systems biology studies where large number of samples can be processed simultaneously and in quantitative manner. Data are available via ProteomeXchange with identifier ProteomeXchange: PXD004281.

False Discovery Rate Estimation in Proteomics.

With the advancement in proteomics separation techniques and improvements in mass analyzers, the data generated in a mass-spectrometry based proteomics experiment is rising exponentially. Such voluminous datasets necessitate automated computational tools for high-throughput data analysis and appropriate statistical control. The data is searched using one or more of the several popular database search algorithms. The matches assigned by these tools can have false positives and statistical validation of these false matches is necessary before making any biological interpretations. Without such procedures, the biological inferences do not hold true and may be outright misleading. There is a considerable overlap between true and false positives. To control the false positives amongst a set of accepted matches, there is a need for some statistical estimate that can reflect the amount of false positives present in the data processed. False discovery rate (FDR) is the metric for global confidence assessment of a large-scale proteomics dataset. This chapter covers the basics of FDR, its application in proteomics, and methods to estimate FDR.

Dissecting the iTRAQ Data Analysis.

In the era of large-scale quantitative biology, mass spectrometry-based quantitative proteomics is progressively becoming indispensable for gaining insights into the biological systems at molecular level. Various quantitative study designs rely on chemical tagging approaches to study disease, stress, or drug response and temporal studies aiming at disease/developmental progression in a biological system. Isobaric tags for relative and absolute quantitation (iTRAQ) is one of the most popular chemical labeling techniques which allows four, six, or eight samples to be multiplexed in a single run. As the iTRAQ tag has a balancer group to equalize all states of a labeled peptide to same mass, the differentially labeled iTRAQ peptides are mixed before chromatography and elute as a single combined peak in MS. This enhances the peptide signal and quantitation is performed during MS/MS along with sequencing, where reporter ions of different masses are released to give relative quantitation. Known amount of a spiked-in protein can also help in absolute quantitation of the proteins in a sample.