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Motivation: BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters). [ABSTRACT FROM AUTHOR]

A well-functioning placenta is essential for fetal and maternal health throughout pregnancy. Using placental weight as a proxy for placental growth, we report genome-wide association analyses in the fetal (n = 65,405), maternal (n = 61,228) and paternal (n = 52,392) genomes, yielding 40 independent association signals. Twenty-six signals are classified as fetal, four maternal and three fetal and maternal. A maternal parent-of-origin effect is seen near KCNQ1. Genetic correlation and colocalization analyses reveal overlap with birth weight genetics, but 12 loci are classified as predominantly or only affecting placental weight, with connections to placental development and morphology, and transport of antibodies and amino acids. Mendelian randomization analyses indicate that fetal genetically mediated higher placental weight is causally associated with preeclampsia risk and shorter gestational duration. Moreover, these analyses support the role of fetal insulin in regulating placental weight, providing a key link between fetal and placental growth., (© 2023. The Author(s).)

Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, and the problem is thus rather the ordering and allocation of samples to batches such that comparisons between different treatments can be made with similar precision. In certain situations, this allocation can be done by hand, but this rapidly becomes impractical with more challenging cohort setups. Here, we present a fast and intuitive algorithm to generate balanced allocations of samples to batches for any single-variable model where the treatment variable is nominal. This greatly simplifies the grouping of samples into batches, makes the process reproducible, and provides a marked improvement over completely random allocations. The general challenges of allocation and why good solutions can be hard to find are also discussed, as well as potential extensions to multivariable settings., (© The Author 2022. Published by Oxford University Press.)

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.

Motivation: Biological network analysis for high-throughput biomedical data interpretation relies heavily on topological characteristics. Networks are commonly composed of nodes representing genes or proteins that are connected by edges when interacting. In this study, we use the rich information available in the Reactome pathway database to build biological networks accounting for small molecules and proteoforms modeled using protein isoforms and post-translational modifications to study the topological changes induced by this refinement of the network representation., Results: We find that improving the interactome modeling increases the number of nodes and interactions, but that isoform and post-translational modification annotation is still limited compared to what can be expected biologically. We also note that small molecule information can distort the topology of the network due to the high connectedness of these molecules, which does not necessarily represent the reality of biology. However, by restricting the connections of small molecules to the context of biochemical reactions, we find that these improve the overall connectedness of the network and reduce the prevalence of isolated components and nodes. Overall, changing the representation of the network alters the prevalence of articulation points and bridges globally but also within and across pathways. Hence, some molecules can gain or lose in biological importance depending on the level of detail of the representation of the biological system, which might in turn impact network-based studies of diseases or druggability., Availability and Implementation: Networks are constructed based on data publicly available in the Reactome Pathway knowledgebase: reactome.org., (© The Author(s) 2023. Published by Oxford University Press.)

The timing of parturition is crucial for neonatal survival and infant health. Yet, its genetic basis remains largely unresolved. We present a maternal genome-wide meta-analysis of gestational duration (n = 195,555), identifying 22 associated loci (24 independent variants) and an enrichment in genes differentially expressed during labor. A meta-analysis of preterm delivery (18,797 cases, 260,246 controls) revealed six associated loci and large genetic similarities with gestational duration. Analysis of the parental transmitted and nontransmitted alleles (n = 136,833) shows that 15 of the gestational duration genetic variants act through the maternal genome, whereas 7 act both through the maternal and fetal genomes and 2 act only via the fetal genome. Finally, the maternal effects on gestational duration show signs of antagonistic pleiotropy with the fetal effects on birth weight: maternal alleles that increase gestational duration have negative fetal effects on birth weight. The present study provides insights into the genetic effects on the timing of parturition and the complex maternal-fetal relationship between gestational duration and birth weight., (© 2023. The Author(s).)

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., (© The Author(s) 2023. Published by Oxford University Press GigaScience.)

Early childhood obesity is a growing global concern; however, the role of common genetic variation on infant and child weight development is unclear. Here, we identify 46 loci associated with early childhood body mass index at specific ages, matching different child growth phases, and representing four major trajectory patterns. We perform genome-wide association studies across 12 time points from birth to 8 years in 28,681 children and their parents (27,088 mothers and 26,239 fathers) in the Norwegian Mother, Father and Child Cohort Study. Monogenic obesity genes are overrepresented near identified loci, and several complex association signals near LEPR, GLP1R, PCSK1 and KLF14 point towards a major influence for common variation affecting the leptin-melanocortin system in early life, providing a link to putative treatment strategies. We also demonstrate how different polygenic risk scores transition from birth to adult profiles through early child growth. In conclusion, our results offer a fine-grained characterization of a changing genetic landscape sustaining early childhood growth., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets., (© 2021. The Author(s).)

Autoimmune Addison's disease (AAD) is characterized by the autoimmune destruction of the adrenal cortex. Low prevalence and complex inheritance have long hindered successful genetic studies. We here report the first genome-wide association study on AAD, which identifies nine independent risk loci (P < 5 × 10 -8 ). In addition to loci implicated in lymphocyte function and development shared with other autoimmune diseases such as HLA, BACH2, PTPN22 and CTLA4, we associate two protein-coding alterations in Autoimmune Regulator (AIRE) with AAD. The strongest, p.R471C (rs74203920, OR = 3.4 (2.7-4.3), P = 9.0 × 10 -25 ) introduces an additional cysteine residue in the zinc-finger motif of the second PHD domain of the AIRE protein. This unbiased elucidation of the genetic contribution to development of AAD points to the importance of central immunological tolerance, and explains 35-41% of heritability (h 2 ).

Parental genetic relatedness may lead to adverse health and fitness outcomes in the offspring. However, the degree to which it affects human delivery timing is unknown. We use genotype data from ≃25 000 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study to optimize runs of homozygosity (ROH) calling by maximizing the correlation between parental genetic relatedness and offspring ROHs. We then estimate the effect of maternal, paternal and fetal autozygosity and that of autozygosity mapping (common segments and gene burden test) on the timing of spontaneous onset of delivery. The correlation between offspring ROH using a variety of parameters and parental genetic relatedness ranged between -0.2 and 0.6, revealing the importance of the minimum number of genetic variants included in an ROH and the use of genetic distance. The optimized compared to predefined parameters showed a ≃45% higher correlation between parental genetic relatedness and offspring ROH. We found no evidence of an effect of maternal, paternal nor fetal overall autozygosity on spontaneous delivery timing. Yet, through autozygosity mapping, we identified three maternal loci TBC1D1, SIGLECs and EDN1 gene regions reducing the median time-to-spontaneous onset of delivery by ≃2-5% (P-value < 2.3 × 10-6). We also found suggestive evidence of a fetal locus at 3q22.2, near the RYK gene region (P-value = 2.0 × 10-6). Autozygosity mapping may provide new insights on the genetic determinants of delivery timing beyond traditional genome-wide association studies, but particular and rigorous attention should be given to ROH calling parameter selection., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Recurrent copy number variations (CNVs) are common causes of neurodevelopmental disorders (NDDs) and associated with a range of psychiatric traits. These CNVs occur at defined genomic regions that are particularly prone to recurrent deletions and duplications and often exhibit variable expressivity and incomplete penetrance. Robust estimates of the population prevalence and inheritance pattern of recurrent CNVs associated with neurodevelopmental disorders (NDD CNVs) are lacking. Here we perform array-based CNV calling in 12,252 mother-father-child trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa) and analyse the inheritance pattern of 26 recurrent NDD CNVs in 13 genomic regions. We estimate the total prevalence of recurrent NDD CNVs (duplications and deletions) in live-born children to 0.48% (95% C.I.: 0.37-0.62%), i.e., ~1 in 200 newborns has either a deletion or duplication in these NDDs associated regions. Approximately a third of the newborn recurrent NDD CNVs (34%, N = 20/59) are de novo variants. We provide prevalence estimates and inheritance information for each of the 26 NDD CNVs and find higher prevalence than previously reported for 1q21.1 deletions (~1:2000), 15q11.2 duplications (~1:4000), 15q13.3 microdeletions (~1:2500), 16p11.2 proximal microdeletions (~1:2000) and 17q12 deletions (~1:4000) and lower than previously reported prevalence for the 22q11.2 deletion (~1:12,000). In conclusion, our analysis of an unselected and representative population of newborns and their parents provides a clearer picture of the rate of recurrent microdeletions/duplications implicated in neurodevelopmental delay. These results will provide an important resource for genetic diagnostics and counseling.

The genetic background of childhood body mass index (BMI), and the extent to which the well-known associations of childhood BMI with adult diseases are explained by shared genetic factors, are largely unknown. We performed a genome-wide association study meta-analysis of BMI in 61,111 children aged between 2 and 10 years. Twenty-five independent loci reached genome-wide significance in the combined discovery and replication analyses. Two of these, located near NEDD4L and SLC45A3, have not previously been reported in relation to either childhood or adult BMI. Positive genetic correlations of childhood BMI with birth weight and adult BMI, waist-to-hip ratio, diastolic blood pressure and type 2 diabetes were detected (Rg ranging from 0.11 to 0.76, P-values <0.002). A negative genetic correlation of childhood BMI with age at menarche was observed. Our results suggest that the biological processes underlying childhood BMI largely, but not completely, overlap with those underlying adult BMI. The well-known observational associations of BMI in childhood with cardio-metabolic diseases in adulthood may reflect partial genetic overlap, but in light of previous evidence, it is also likely that they are explained through phenotypic continuity of BMI from childhood into adulthood., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: MMcC: The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. He serves on advisory panels for Pfizer, NovoNordisk, Zoe Global; has received honoraria from Merck, Pfizer, NovoNordisk and Eli Lilly; has stock options in Zoe Global; has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier & Takeda. MS receives funding from Pfizer Inc. for a project not related to this research. IB and spouse own stock in GlaxoSmithKline and Incyte Corp. ZE and CDB currently serve on the editorial board of PLOS Genetics. AC reports personal fees from Novartis, personal fees from Thermo Fisher Scientific, personal fees from Philips, personal fees from Sanofi, personal fees from Stallergenes Greer, outside the submitted work. KC in involved in consultancy for Danone Research, LNC-therapeutic and Confo-therapeutic but no personal funding is received and this activity not linked to the present research.

There is increasing interest within the genetics community in estimating the relative contribution of parental genetic effects on offspring phenotypes. Here we describe the user-friendly M-GCTA software package used to estimate the proportion of phenotypic variance explained by maternal (or alternatively paternal) and offspring genotypes on offspring phenotypes. The tool requires large studies where genome-wide genotype data are available on mother- (or alternatively father-) offspring pairs. The software includes several options for data cleaning and quality control, including the ability to detect and automatically remove cryptically related pairs of individuals. It also allows users to construct genetic relationship matrices indexing genetic similarity across the genome between parents and offspring, enabling the estimation of variance explained by maternal (or alternatively paternal) and offspring genetic effects. We evaluated the performance of the software using a range of data simulations and estimated the computing time and memory requirements. We demonstrate the use of M-GCTA on previously analyzed birth weight data from two large population based birth cohorts, the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Norwegian Mother and Child Cohort Study (MoBa). We show how genetic variation in birth weight is predominantly explained by fetal genetic rather than maternal genetic sources of variation.

Infant and childhood growth are dynamic processes with large changes in BMI during development. By performing genome-wide association studies of BMI at 12 time points from birth to eight years (9286 children, 74,105 measurements) in the Norwegian Mother, Father, and Child Cohort Study, replicated in 5235 children, we identify a transient effect in the leptin receptor (LEPR) locus: no effect at birth, increasing effect in infancy, peaking at 6-12 months (rs2767486, P 6m  = 2.0 × 10 -21 , β 6m  = 0.16 sd-BMI), and little effect after age five. We identify a similar transient effect near the leptin gene (LEP), peaking at 1.5 years (rs10487505, P 1.5y  = 1.3 × 10 -8 , β 1.5y  = 0.079 sd-BMI). Both signals are protein quantitative trait loci for soluble-LEPR and LEP in plasma in adults independent from adult traits mapped to the respective genes, suggesting key roles of common variation in the leptin signaling pathway for healthy infant growth.

Although hundreds of genome-wide association studies-implicated loci have been reported for adult obesity-related traits, less is known about the genetics specific for early-onset obesity and with only a few studies conducted in non-European populations to date. Searching for additional genetic variants associated with childhood obesity, we performed a trans-ancestral meta-analysis of 30 studies consisting of up to 13 005 cases (≥95th percentile of body mass index (BMI) achieved 2-18 years old) and 15 599 controls (consistently <50th percentile of BMI) of European, African, North/South American and East Asian ancestry. Suggestive loci were taken forward for replication in a sample of 1888 cases and 4689 controls from seven cohorts of European and North/South American ancestry. In addition to observing 18 previously implicated BMI or obesity loci, for both early and late onset, we uncovered one completely novel locus in this trans-ancestral analysis (nearest gene, METTL15). The variant was nominally associated with only the European subgroup analysis but had a consistent direction of effect in other ethnicities. We then utilized trans-ancestral Bayesian analysis to narrow down the location of the probable causal variant at each genome-wide significant signal. Of all the fine-mapped loci, we were able to narrow down the causative variant at four known loci to fewer than 10 single nucleotide polymorphisms (SNPs) (FAIM2, GNPDA2, MC4R and SEC16B loci). In conclusion, an ethnically diverse setting has enabled us to both identify an additional pediatric obesity locus and further fine-map existing loci., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.

Na + /H + Exchanger 9 (NHE9) is an endosomal membrane protein encoded by the Solute Carrier 9A, member 9 gene (SLC9A9). SLC9A9 has been implicated in attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASDs), epilepsy, multiple sclerosis and cancers. To better understand the function of NHE9 and the effects of disease-associated variants on protein-protein interactions, we conducted a quantitative analysis of the NHE9 interactome using co-immunoprecipitation and isobaric labeling-based quantitative mass spectrometry. We identified 100 proteins that interact with NHE9. These proteins were enriched in known functional pathways for NHE9: the endocytosis, protein ubiquitination and phagosome pathways, as well as some novel pathways including oxidative stress, mitochondrial dysfunction, mTOR signaling, cell death and RNA processing pathways. An ADHD-associated mutation (A409P) significantly altered NHE9's interactions with a subset of proteins involved in caveolae-mediated endocytosis and MAP2K2-mediated downstream signaling. An ASD nonsense mutation in SLC9A9, R423X, produced no-detectable amount of NHE9, suggesting the overall loss of NHE9 functional networks. In addition, seven of the NHE9 interactors are products of known autism candidate genes (Simons Foundation Autism Research Initiative, SFARI Gene) and 90% of the NHE9 interactome overlap with SFARI protein interaction network PIN (p < 0.0001), supporting the role of NHE9 interactome in ASDs molecular mechanisms. Our results provide a detailed understanding of the functions of protein NHE9 and its disrupted interactions, possibly underlying ADHD and ASDs. Furthermore, our methodological framework proved useful for functional characterization of disease-associated genetic variants and suggestion of druggable targets.

Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother-child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P < 5 × 10-8. In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights., (© The Author(s) 2018. Published by Oxford University Press.)

Aquaporin-4, encoded by AQP4 , is the major water channel in the central nervous system and plays an important role in the brain's water balance, including edema formation and clearance. Using genomic copy-number analysis and trio-exome sequencing, we investigated a male patient with intellectual disability, hearing loss, and progressive gait dysfunction and found a de novo missense change Ser111Thr in AQP4 as the only suspicious finding. Perinatally, signs of brain ischemia were detected in relation to acute collapse 2 h after birth that resolved a few days later. At the age of 3 mo, cardiac hypertrophy was detected that persisted through childhood but was completely resolved by age 16. In theory, this neurodevelopmental disorder with transient cardiomyopathy could be caused by a disturbance of cellular water balance. Ser111 is an extremely conserved residue in the short cytoplasmic loop between AQP4 transmembrane helix 2 and 3, present across all AQP isoforms from plants to mammals, and it does not appear to be a phosphorylation site. We found that the Ser111Thr change does not affect water permeability or protein stability, suggesting another and possibly regulatory role. Although causality remains unproven, this case study draws attention to AQP4 as a candidate gene for a unique developmental disorder and to a specific serine as a residue of possibly great functional importance in many AQPs., (© 2018 Berland et al.; Published by Cold Spring Harbor Laboratory Press.)

The first stable version of the Proteomics Standards Initiative mzIdentML open data standard (version 1.1) was published in 2012-capturing the outputs of peptide and protein identification software. In the intervening years, the standard has become well-supported in both commercial and open software, as well as a submission and download format for public repositories. Here we report a new release of mzIdentML (version 1.2) that is required to keep pace with emerging practice in proteome informatics. New features have been added to support: (1) scores associated with localization of modifications on peptides; (2) statistics performed at the level of peptides; (3) identification of cross-linked peptides; and (4) support for proteogenomics approaches. In addition, there is now improved support for the encoding of de novo sequencing of peptides, spectral library searches, and protein inference. As a key point, the underlying XML schema has only undergone very minor modifications to simplify as much as possible the transition from version 1.1 to version 1.2 for implementers, but there have been several notable updates to the format specification, implementation guidelines, controlled vocabularies and validation software. mzIdentML 1.2 can be described as backwards compatible, in that reading software designed for mzIdentML 1.1 should function in most cases without adaptation. We anticipate that these developments will provide a continued stable base for software teams working to implement the standard. All the related documentation is accessible at http://www.psidev.info/mzidentml., (© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.)

The 2017 EuBIC Winter School was held from January 10th to January 13th 2017 in Semmering, Austria. This meeting gathered international researchers in the fields of bioinformatics and proteomics to discuss current challenges in data analysis and biological interpretation. This article outlines the scientific program and exchanges that took place on this occasion and presents the current challenges of this ever-growing field., Biological Significance: The EUPA bioinformatics community (EuBIC) organized its first winter school in January 2017. This successful event illustrates the growing need of the bioinformatics community in proteomics to gather and discuss current and future challenges in the field. In addition to the organization of yearly meetings, the young and active EuBIC community aims to develop new collaborative open source projects, spread bioinformatics knowledge in Europe, and actively promote data sharing through public repositories., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Cell signaling and functions heavily rely on post-translational modifications (PTMs) of proteins. Their high-throughput characterization is thus of utmost interest for multiple biological and medical investigations. In combination with efficient enrichment methods, peptide mass spectrometry analysis allows the quantitative comparison of thousands of modified peptides over different conditions. However, the large and complex datasets produced pose multiple data interpretation challenges, ranging from spectral interpretation to statistical and multivariate analyses. Here, we present a typical workflow to interpret such data.

Unlabelled: MS-based proteomic studies aiming for the discovery of acute myeloid leukemia (AML) biomarkers require sample processing that can assure an optimal proteome coverage and identification of PTMs. We evaluated different in-solution and filter-aided sample preparation (FASP) proteomic workflows and different enrichment strategies of phosphorylated peptides. The FASP protocols in the label-free and SILAC (stable isotope labelling with amino acids in cell culture) approaches were selected for producing the highest number of quantified proteins with reduced number of missed cleavages. The IMAC method was selected for producing the highest number of quantified phosphopeptides from SILAC-labelled peptides prepared with FASP. Using these selected workflows, we studied the effect of liquid nitrogen storage on the proteome and phosphoproteome of four AML patients. Our results showed that although there was not a major global proteome and phosphoproteome change when compared to their freshly processed counterparts, the freezing appeared to influence the abundance of mitochondrial proteins involved in the respiratory chain transport and affect the phosphorylation of apoptosis related proteins, cell surface interactors, ERK/MAPK targets and proteins involved in thrombin signalling. Our results encourage the assessment of current procedures of AML sample collection and preservation that could be used in future AML biomarker discovery studies., Biological Significance: Proteomic studies aiming to identify potential cancer biomarkers need to utilize the best sample preparation workflows on the samples of interest to achieve maximal proteome coverage. We have tested the most popular and recent proteomic and phosphoproteomic methods on cell lysates from patients with AML and systematically evaluated their performance. Our study shows the relevance of selecting the patient sample procedure giving the highest protein and PTM coverage. Moreover, we assessed how the proteome and phosphoproteome were affected by the conventional liquid nitrogen storage compared to cell lysis of fresh material, using the methods that worked best in our hands. For potential biomarkers that could be used for AML diagnostic and prognostic, it is of great importance to study the behaviour during sample conservation in order to avoid artefactual findings. Our results recommend caution in data interpretation when using different protocols of sample collection and conservation for proteomic and phosphosproteomic research., (Copyright © 2016 Elsevier B.V. All rights reserved.)

The first step in identifying proteins from mass spectrometry based shotgun proteomics data is to infer peptides from tandem mass spectra, a task generally achieved using database search engines. In this chapter, the basic principles of database search engines are introduced with a focus on open source software, and the use of database search engines is demonstrated using the freely available SearchGUI interface. This chapter also discusses how to tackle general issues related to sequence database searching and shows how to minimize their impact.

Shotgun proteomics is a high throughput technique for protein identification able to identify up to several thousand proteins from a single sample. In order to make sense of this large amount of data, proteomics analysis software is needed, aimed at making the data intuitively accessible to beginners as well as experienced scientists. This chapter provides insight on where to start when analyzing shotgun proteomics data, with a focus on explaining the most common pitfalls in protein identification analysis and how to avoid them. Finally, the move to seeing beyond the list of identified proteins and to putting the results into a bigger biological context is discussed.

In quantitative proteomics, large lists of identified and quantified proteins are used to answer biological questions in a systemic approach. However, working with such extensive datasets can be challenging, especially when complex experimental designs are involved. Here, we demonstrate how to post-process large quantitative datasets, detect proteins of interest, and annotate the data with biological knowledge. The protocol presented can be achieved without advanced computational knowledge thanks to the user-friendly Perseus interface (available from the MaxQuant website, www.maxquant.org ). Various visualization techniques facilitating the interpretation of quantitative results in complex biological systems are also highlighted.

Targeting subproteomes is a good strategy to decrease the complexity of a sample, for example in body fluid biomarker studies. Glycoproteins are proteins with carbohydrates of varying size and structure attached to the polypeptide chain, and it has been shown that glycosylation plays essential roles in several vital cellular processes, making glycosylation a particularly interesting field of study. Here, we describe a method for the enrichment of glycosylated peptides from trypsin digested proteins in human cerebrospinal fluid. We also describe how to perform the data analysis on the mass spectrometry data for such samples, focusing on site-specific identification of glycosylation sites, using user friendly open source software.

Proteogenomic studies ally the omic fields related to gene expression into a combined approach to improve the characterization of biological samples. Part of this consists in mining proteomics datasets for non-canonical sequences of amino acids. These include intergenic peptides, products of mutations, or of RNA editing events hypothesized from genomic, epigenomic, or transcriptomic data. This approach poses new challenges for standard peptide identification workflows. In this chapter, we present the principles behind the use of peptide identification algorithms and highlight the major pitfalls of their application to proteogenomic studies.

Protein identification via database searches has become the gold standard in mass spectrometry based shotgun proteomics. However, as the quality of tandem mass spectra improves, direct mass spectrum sequencing gains interest as a database-independent alternative. In this chapter, the general principle of this so-called de novo sequencing is introduced along with pitfalls and challenges of the technique. The main tools available are presented with a focus on user friendly open source software which can be directly applied in everyday proteomic workflows.

In biological and medical sciences, high throughput analytical methods are now commonly used to investigate samples of different conditions, e.g., patients versus controls. Systemic functional analyses emerged as a reference method to go beyond a list of regulated compounds, and identify activated or inactivated biological functions. This approach holds the promise for a better understanding of biological systems, of the mechanisms involved in disease progression, and thus improved diagnosis, prognosis, and treatment. In this chapter, we present a simple workflow to conduct pathway analyses on biological data using the freely available Reactome platform (http://www.reactome.org).

One of the most important physiological platelet inhibitors is endothelium-derived prostacyclin which stimulates the platelet cyclic adenosine monophosphate/protein kinase A (cAMP/PKA)-signaling cascade and inhibits virtually all platelet-activating key mechanisms. Using quantitative mass spectrometry, we analyzed time-resolved phosphorylation patterns in human platelets after treatment with iloprost, a stable prostacyclin analog, for 0, 10, 30, and 60 seconds to characterize key mediators of platelet inhibition and activation in 3 independent biological replicates. We quantified over 2700 different phosphorylated peptides of which 360 were significantly regulated upon stimulation. This comprehensive and time-resolved analysis indicates that platelet inhibition is a multipronged process involving different kinases and phosphatases as well as many previously unanticipated proteins and pathways.

With the advent of mass spectrometry based proteomics, the identification of thousands of proteins has become commonplace in biology nowadays. Increasingly, efforts have also been invested toward the detection and localization of posttranslational modifications. It is furthermore common practice to quantify the identified entities, a task supported by a panel of different methods. Finally, the results can also be enriched with functional knowledge gained on the proteins, detecting for instance differentially expressed gene ontology terms or biological pathways. In this study, we review the resources, methods and tools available for the researcher to achieve such a quantitative functional analysis. These include statistics for the post-processing of identification and quantification results, online resources and public repositories. With a focus on free but user-friendly software, preferably also open-source, we provide a list of tools designed to help the researcher manage the vast amount of data generated. We also indicate where such applications currently remain lacking. Moreover, we stress the eventual pitfalls of every step of such studies. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan., (© 2013.)

Antiplatelet treatment is of fundamental importance in combatting functions/dysfunction of platelets in the pathogenesis of cardiovascular and inflammatory diseases. Dysfunction of anucleate platelets is likely to be completely attributable to alterations in posttranslational modifications and protein expression. We therefore examined the proteome of platelets highly purified from fresh blood donations, using elaborate protocols to ensure negligible contamination by leukocytes, erythrocytes, and plasma. Using quantitative mass spectrometry, we created the first comprehensive and quantitative human platelet proteome, comprising almost 4000 unique proteins, estimated copy numbers for ∼ 3700 of those, and assessed intersubject (4 donors) as well as intrasubject (3 different blood samples from 1 donor) variations of the proteome. For the first time, our data allow for a systematic and weighted appraisal of protein networks and pathways in human platelets, and indicate the feasibility of differential and comprehensive proteome analyses from small blood donations. Because 85% of the platelet proteome shows no variation between healthy donors, this study represents the starting point for disease-oriented platelet proteomics. In the near future, comprehensive and quantitative comparisons between normal and well-defined dysfunctional platelets, or between platelets obtained from donors at various stages of chronic cardiovascular and inflammatory diseases will be feasible.

Quantitative proteomic analysis can help elucidating unexplored biological questions; it, however, relies on highly reproducible experiments and reliable data processing. Among the existing strategies, iTRAQ is known as an easy to use method allowing relative comparison of up to eight multiplexed samples.Once the data is acquired it is important that the final protein quantification reflects the actual amounts in the samples. Data interpretation must thus be achieved with a constant focus on quality. Here, we describe a workflow for processing iTRAQ data in user-friendly environments with emphasis on quality control.