scholarly journals piNET: a versatile web platform for downstream analysis and visualization of proteomics data

2020 ◽  
Vol 48 (W1) ◽  
pp. W85-W93 ◽  
Author(s):  
Behrouz Shamsaei ◽  
Szymon Chojnacki ◽  
Marcin Pilarczyk ◽  
Mehdi Najafabadi ◽  
Wen Niu ◽  
...  
Keyword(s):  
Quantitative Proteomics ◽  
Large Scale ◽  
Model Organism ◽  
Proteomics Data ◽  
Primary Input ◽  
Network Analyses ◽  
Enzyme Substrate ◽  
Chemical Inhibitors ◽  
Downstream Analysis ◽  
Web Platform

Abstract Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

scholarly journals piNET: a versatile web platform for downstream analysis and visualization of proteomics data

2019 ◽  
Author(s):  
Behrouz Shamsaei ◽  
Szymon Chojnacki ◽  
Marcin Pilarczyk ◽  
Mehdi Najafabadi ◽  
Chuming Chen ◽  
...  
Keyword(s):  
Protein Level ◽  
Expression Profiles ◽  
Scientific Discovery ◽  
Biological Knowledge ◽  
Proteomics Data ◽  
Gene And Protein Expression ◽  
Downstream Analysis ◽  
Modern Mass ◽  
The Impact ◽  
Web Platform

ABSTRACTLarge proteomics data, including those generated by mass spectrometry, are being generated to characterize biological systems at the protein level. Computational methods and tools to identify and quantify peptides, proteins and post-translational modifications (PTMs) that are captured in modern mass spectrometers have matured over the years. On the other hand, tools for downstream analysis, interpretation and visualization of proteomics data sets, in particular those involving PTMs, require further improvement and integration to accelerate scientific discovery and maximize the impact of proteomics studies by connecting them better with biological knowledge across not only proteomics, but also other Omics domains. With the goal of addressing these challenges, the piNET server has been developed as a versatile web platform to facilitate mapping, annotation, analysis and visualization of peptide, PTM, and protein level quantitative data generated by either targeted, shotgun or other proteomics approaches. Building on our experience with large scale analysis of gene and protein expression profiles as part of the Library of Integrated Network Cellular Signatures (LINCS) project, piNET has been designed as a fast, versatile and easy to use web-based tool with three modules that provide mapping from peptides (with PTMs) to proteins, from PTM sites to modifying enzymes that target those sites, and finally from proteins (with PTMs) to pathways, and for further mechanistic insights to LINCS signatures of chemical and genetic perturbations. piNET is freely available at http://www.pinet-server.org.

scholarly journals Listeriomics: an Interactive Web Platform for Systems Biology of Listeria

mSystems ◽  
2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Christophe Bécavin ◽  
Mikael Koutero ◽  
Nicolas Tchitchek ◽  
Franck Cerutti ◽  
Pierre Lechat ◽  
...  
Keyword(s):  
Systems Biology ◽  
Noncoding Rna ◽  
Model Organism ◽  
Regulation Mechanism ◽  
Data Sets ◽  
Omics Data ◽  
Sources Of Information ◽  
Proteomics Data ◽  
Rna Regulation ◽  
Web Platform

ABSTRACT In the last decades, Listeria has become a key model organism for the study of host-pathogen interactions, noncoding RNA regulation, and bacterial adaptation to stress. To study these mechanisms, several genomics, transcriptomics, and proteomics data sets have been produced. We have developed Listeriomics, an interactive web platform to browse and correlate these heterogeneous sources of information. Our website will allow listeriologists and microbiologists to decipher key regulation mechanism by using a systems biology approach. As for many model organisms, the amount of Listeria omics data produced has recently increased exponentially. There are now >80 published complete Listeria genomes, around 350 different transcriptomic data sets, and 25 proteomic data sets available. The analysis of these data sets through a systems biology approach and the generation of tools for biologists to browse these various data are a challenge for bioinformaticians. We have developed a web-based platform, named Listeriomics, that integrates different tools for omics data analyses, i.e., (i) an interactive genome viewer to display gene expression arrays, tiling arrays, and sequencing data sets along with proteomics and genomics data sets; (ii) an expression and protein atlas that connects every gene, small RNA, antisense RNA, or protein with the most relevant omics data; (iii) a specific tool for exploring protein conservation through the Listeria phylogenomic tree; and (iv) a coexpression network tool for the discovery of potential new regulations. Our platform integrates all the complete Listeria species genomes, transcriptomes, and proteomes published to date. This website allows navigation among all these data sets with enriched metadata in a user-friendly format and can be used as a central database for systems biology analysis. IMPORTANCE In the last decades, Listeria has become a key model organism for the study of host-pathogen interactions, noncoding RNA regulation, and bacterial adaptation to stress. To study these mechanisms, several genomics, transcriptomics, and proteomics data sets have been produced. We have developed Listeriomics, an interactive web platform to browse and correlate these heterogeneous sources of information. Our website will allow listeriologists and microbiologists to decipher key regulation mechanism by using a systems biology approach.

BIOLOGICAL SYSTEMS ANALYSIS BY A NETWORK PROTEOMICS APPROACH AND SUBCELLULAR PROTEIN PROFILING

2006 ◽  
Vol 09 (04) ◽  
pp. 299-314
Author(s):  
CHRISTIAN V. FORST ◽  
LAWRENCE CABUSORA ◽  
KWASI G. MAWUENYEGA ◽  
XIAN CHEN
Keyword(s):  
Cell Wall ◽  
Large Scale ◽  
Organizational Structures ◽  
Protein Profiling ◽  
Proteomics Data ◽  
Systematic Analysis ◽  
Network Analyses ◽  
Its Gene ◽  
Proteomics Approach ◽  
Related Proteins

We provide a systematic analysis of a biological system, the microbial pathogen Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and biocomputational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane and cytosol) of Mtb. We report the compartmentalization of 1,044 identified proteins using proteomics methods. Genome-based biological network analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.

scholarly journals amica: an interactive and user-friendly web-platform for the analysis of proteomics data

2021 ◽  
Author(s):  
Sebastian Didusch ◽  
Moritz Madern ◽  
Markus Hartl ◽  
Manuela Baccarini
Keyword(s):  
Quantitative Proteomics ◽  
Web Application ◽  
Biological Network ◽  
Life Sciences ◽  
Proteomics Data ◽  
Web Based ◽  
User Input ◽  
User Friendly ◽  
Different Sources ◽  
Web Platform

Quantitative proteomics has become an increasingly prominent tool in the study of life sciences. A substantial hurdle for many biologists are, however, the intricacies involved in the associated high troughput data analysis. In order to facilitate this task for users with little background knowledge in proteomics, we have developed amica, a freely available open-source web-based software that accepts proteomic input files from different sources and provides quality control, differential expression, biological network and over-representation analysis on the basis of minimal user input. Scientists can use amica interactively to compare proteins across multiple groups, create customized output graphics, and ultimately export the results in a tab-separated format that can be shared with collaborators. Availability and Implementation: The code for the application, input data and documentation can be accessed online at https://github.com/tbaccata/amica and is also incorporated in the web application. A freely available version of amica is available at https://bioapps.maxperutzlabs.ac.at/app/amica.

scholarly journals In Vivo Production of RNA Aptamers and Nanoparticles: Problems and Prospects

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1422
Author(s):  
Ousama Al Shanaa ◽  
Andrey Rumyantsev ◽  
Elena Sambuk ◽  
Marina Padkina
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Large Scale ◽  
Potential Model ◽  
Model Organism ◽  
Rna Aptamers ◽  
Early Stages ◽  
Near Future

RNA aptamers are becoming increasingly attractive due to their superior properties. This review discusses the early stages of aptamer research, the main developments in this area, and the latest technologies being developed. The review also highlights the advantages of RNA aptamers in comparison to antibodies, considering the great potential of RNA aptamers and their applications in the near future. In addition, it is shown how RNA aptamers can form endless 3-D structures, giving rise to various structural and functional possibilities. Special attention is paid to the Mango, Spinach and Broccoli fluorescent RNA aptamers, and the advantages of split RNA aptamers are discussed. The review focuses on the importance of creating a platform for the synthesis of RNA nanoparticles in vivo and examines yeast, namely Saccharomyces cerevisiae, as a potential model organism for the production of RNA nanoparticles on a large scale.

scholarly journals PEDRERA. Positive Energy District Renovation Model for Large Scale Actions

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2833
Author(s):  
Paolo Civiero ◽  
Jordi Pascual ◽  
Joaquim Arcas Abella ◽  
Ander Bilbao Figuero ◽  
Jaume Salom
Keyword(s):  
Simulation Model ◽  
Performance Indicators ◽  
Large Scale ◽  
Key Performance Indicators ◽  
Positive Energy ◽  
Design Phase ◽  
Multiple Sources ◽  
Reliable Prediction ◽  
Sensitive Analysis ◽  
Web Platform

In this paper, we provide a view of the ongoing PEDRERA project, whose main scope is to design a district simulation model able to set and analyze a reliable prediction of potential business scenarios on large scale retrofitting actions, and to evaluate the overall co-benefits resulting from the renovation process of a cluster of buildings. According to this purpose and to a Positive Energy Districts (PEDs) approach, the model combines systemized data—at both building and district scale—from multiple sources and domains. A sensitive analysis of 200 scenarios provided a quick perception on how results will change once inputs are defined, and how attended results will answer to stakeholders’ requirements. In order to enable a clever input analysis and to appraise wide-ranging ranks of Key Performance Indicators (KPIs) suited to each stakeholder and design phase targets, the model is currently under the implementation in the urbanZEB tool’s web platform.

scholarly journals Oxonium Ion Guided Analysis of Quantitative Proteomics Data Reveals Site-Specific O-Glycosylation of Anterior Gradient Protein 2 (AGR2)

2021 ◽  
Vol 22 (10) ◽  
pp. 5369
Author(s):  
Martina Pirro ◽  
Yassene Mohammed ◽  
Arnoud H. de Ru ◽  
George M. C. Janssen ◽  
Rayman T. N. Tjokrodirijo ◽  
...  
Keyword(s):  
Cell Lines ◽  
Quantitative Proteomics ◽  
Glycosylation Site ◽  
Total Cell ◽  
Tandem Mass ◽  
Proteomics Data ◽  
Site Specific ◽  
Oxonium Ions ◽  
Tandem Mass Tag ◽  
Colorectal Cancer Cell Lines

Developments in mass spectrometry (MS)-based analyses of glycoproteins have been important to study changes in glycosylation related to disease. Recently, the characteristic pattern of oxonium ions in glycopeptide fragmentation spectra had been used to assign different sets of glycopeptides. In particular, this was helpful to discriminate between O-GalNAc and O-GlcNAc. Here, we thought to investigate how such information can be used to examine quantitative proteomics data. For this purpose, we used tandem mass tag (TMT)-labeled samples from total cell lysates and secreted proteins from three different colorectal cancer cell lines. Following automated glycopeptide assignment (Byonic) and evaluation of the presence and relative intensity of oxonium ions, we observed that, in particular, the ratio of the ions at m/z 144.066 and 138.055, respectively, could be used to discriminate between O-GlcNAcylated and O-GalNAcylated peptides, with concomitant relative quantification between the different cell lines. Among the O-GalNAcylated proteins, we also observed anterior gradient protein 2 (AGR2), a protein which glycosylation site and status was hitherto not well documented. Using a combination of multiple fragmentation methods, we then not only assigned the site of modification, but also showed different glycosylation between intracellular (ER-resident) and secreted AGR2. Overall, our study shows the potential of broad application of the use of the relative intensities of oxonium ions for the confident assignment of glycopeptides, even in complex proteomics datasets.

scholarly journals A primary human T-cell spectral library to facilitate large scale quantitative T-cell proteomics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Harshi Weerakoon ◽  
Jeremy Potriquet ◽  
Alok K. Shah ◽  
Sarah Reed ◽  
Buddhika Jayakody ◽  
...  
Keyword(s):  
T Cell ◽  
Large Scale ◽  
Large Data ◽  
Spectral Library ◽  
Proteomics Data ◽  
Cell Library ◽  
Public Resource ◽  
Human T Cell ◽  
Theoretical Mass ◽  
Current Resource

AbstractData independent analysis (DIA) exemplified by sequential window acquisition of all theoretical mass spectra (SWATH-MS) provides robust quantitative proteomics data, but the lack of a public primary human T-cell spectral library is a current resource gap. Here, we report the generation of a high-quality spectral library containing data for 4,833 distinct proteins from human T-cells across genetically unrelated donors, covering ~24% proteins of the UniProt/SwissProt reviewed human proteome. SWATH-MS analysis of 18 primary T-cell samples using the new human T-cell spectral library reliably identified and quantified 2,850 proteins at 1% false discovery rate (FDR). In comparison, the larger Pan-human spectral library identified and quantified 2,794 T-cell proteins in the same dataset. As the libraries identified an overlapping set of proteins, combining the two libraries resulted in quantification of 4,078 human T-cell proteins. Collectively, this large data archive will be a useful public resource for human T-cell proteomic studies. The human T-cell library is available at SWATHAtlas and the data are available via ProteomeXchange (PXD019446 and PXD019542) and PeptideAtlas (PASS01587).

scholarly journals Quantitative Proteomics Analysis Reveals the Function of the Putative Ester Cyclase UvEC1 in the Pathogenicity of the Rice False Smut Fungus Ustilaginoidea virens

2021 ◽  
Vol 22 (8) ◽  
pp. 4069
Author(s):  
Xiaoyang Chen ◽  
Zhangxin Pei ◽  
Pingping Li ◽  
Xiabing Li ◽  
Yuhang Duan ◽  
...  
Keyword(s):  
Quantitative Proteomics ◽  
Fungal Pathogens ◽  
Protein Localization ◽  
Proteomics Data ◽  
Ustilaginoidea Virens ◽  
Rice False Smut ◽  
Oxidative Stresses ◽  
Tandem Mass Tag ◽  
Cyclase Domain ◽  
False Smut

Rice false smut is a fungal disease distributed worldwide and caused by Ustilaginoidea virens. In this study, we identified a putative ester cyclase (named as UvEC1) as being significantly upregulated during U. virens infection. UvEC1 contained a SnoaL-like polyketide cyclase domain, but the functions of ketone cyclases such as SnoaL in plant fungal pathogens remain unclear. Deletion of UvEC1 caused defects in vegetative growth and conidiation. UvEC1 was also required for response to hyperosmotic and oxidative stresses and for maintenance of cell wall integrity. Importantly, ΔUvEC1 mutants exhibited reduced virulence. We performed a tandem mass tag (TMT)-based quantitative proteomic analysis to identify differentially accumulating proteins (DAPs) between the ΔUvEC1-1 mutant and the wild-type isolate HWD-2. Proteomics data revealed that UvEC1 has a variety of effects on metabolism, protein localization, catalytic activity, binding, toxin biosynthesis and the spliceosome. Taken together, our findings suggest that UvEC1 is critical for the development and virulence of U. virens.

scholarly journals Meeting Highlights: Genome Sequencing and Biology 2001

2001 ◽  
Vol 2 (4) ◽  
pp. 243-251
Author(s):  
Jo Wixon
Keyword(s):  
Comparative Genomics ◽  
Functional Genomics ◽  
Genome Sequencing ◽  
Large Scale ◽  
Model Organism

We bring you a report from the CSHL Genome Sequencing and Biology Meeting, which has a long and prestigious history. This year there were sessions on large-scale sequencing and analysis, polymorphisms (covering discovery and technologies and mapping and analysis), comparative genomics of mammalian and model organism genomes, functional genomics and bioinformatics.

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