scholarly journals SNPnexus: a web server for functional annotation of human genome sequence variation (2020 update)

2020 ◽  
Vol 48 (W1) ◽  
pp. W185-W192 ◽  
Author(s):  
Jorge Oscanoa ◽  
Lavanya Sivapalan ◽  
Emanuela Gadaleta ◽  
Abu Z Dayem Ullah ◽  
Nicholas R Lemoine ◽  
...  
Keyword(s):  
Sequence Variation ◽  
Molecular Processes ◽  
Sequencing Data ◽  
Web Based ◽  
Data Annotation ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Visualization Tools ◽  
Clinically Significant ◽  
Complete Overhaul

Abstract SNPnexus is a web-based annotation tool for the analysis and interpretation of both known and novel sequencing variations. Since its last release, SNPnexus has received continual updates to expand the range and depth of annotations provided. SNPnexus has undergone a complete overhaul of the underlying infrastructure to accommodate faster computational times. The scope for data annotation has been substantially expanded to enhance biological interpretations of queried variants. This includes the addition of pathway analysis for the identification of enriched biological pathways and molecular processes. We have further expanded the range of user directed annotation fields available for the study of cancer sequencing data. These new additions facilitate investigations into cancer driver variants and targetable molecular alterations within input datasets. New user directed filtering options have been coupled with the addition of interactive graphical and visualization tools. These improvements streamline the analysis of variants derived from large sequencing datasets for the identification of biologically and clinically significant subsets in the data. SNPnexus is the most comprehensible web-based application currently available and these new set of updates ensures that it remains a state-of-the-art tool for researchers. SNPnexus is freely available at https://www.snp-nexus.org.

scholarly journals Proteogenomic analysis of pancreatic cancer subtypes

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257084
Author(s):  
Doris Kafita ◽  
Panji Nkhoma ◽  
Mildred Zulu ◽  
Musalula Sinkala
Keyword(s):  
Pancreatic Cancer ◽  
Notch Pathway ◽  
Public Health Problem ◽  
The Cancer Genome Atlas ◽  
Molecular Processes ◽  
Sequencing Data ◽  
Proteomics Data ◽  
Cancer Subtypes ◽  
Molecular Alterations ◽  
Significant Public Health

Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. Therefore, there is a need to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset, including proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis based on the proteomics data, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. In addition, we explore the clinical characteristics of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways previously associated with pancreatic cancer are altered among both subtypes of pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes that can be leveraged to devise more effective treatments.

scholarly journals Proteogenomic Analysis of Pancreatic Cancer Subtypes

2020 ◽  
Author(s):  
Doris Kafita ◽  
Panji Nkhoma ◽  
Mildred Zulu ◽  
Musalula Sinkala
Keyword(s):  
Pancreatic Cancer ◽  
Notch Pathway ◽  
Public Health Problem ◽  
The Cancer Genome Atlas ◽  
Molecular Processes ◽  
Sequencing Data ◽  
Cancer Subtypes ◽  
Molecular Alterations ◽  
Significant Public Health ◽  
Whole Exome Sequencing Data

AbstractPancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. There is a need, therefore, to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset that includes proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. We explore the clinical characteristic of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways are altered among pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes which can be leveraged to devise more effective treatments.

eMap: A Web Application for Identifying and Visualizing Electron or Hole Hopping Pathways in Proteins

2019 ◽  
Author(s):  
Ruslan N. Tazhigulov ◽  
James R. Gayvert ◽  
Melissa Wei ◽  
Ksenia B. Bravaya
Keyword(s):  
Web Application ◽  
Shortest Paths ◽  
Coarse Grained ◽  
Decay Parameter ◽  
Electron Hole ◽  
Web Based ◽  
Aromatic Amino Acid Residue ◽  
Pathways Model ◽  
Visualization Tools ◽  
Effective Decay

<p>eMap is a web-based platform for identifying and visualizing electron or hole transfer pathways in proteins based on their crystal structures. The underlying model can be viewed as a coarse-grained version of the Pathways model, where each tunneling step between hopping sites represented by electron transfer active (ETA) moieties is described with one effective decay parameter that describes protein-mediated tunneling. ETA moieties include aromatic amino acid residue side chains and aromatic fragments of cofactors that are automatically detected, and, in addition, electron/hole residing sites that can be specified by the users. The software searches for the shortest paths connecting the user-specified electron/hole source to either all surface-exposed ETA residues or to the user-specified target. The identified pathways are ranked based on their length. The pathways are visualized in 2D as a graph, in which each node represents an ETA site, and in 3D using available protein visualization tools. Here, we present the capability and user interface of eMap 1.0, which is available at https://emap.bu.edu.</p>

scholarly journals Inferring perturbation profiles of cancer samples

2021 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Indirect Evidence ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Patient Specific ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Incomplete Coverage ◽  
Gene Perturbations

Abstract Motivation Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations. Results We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas. Availability and implementation The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi. Supplementary information Supplementary data are available at Bioinformatics online.

A Web-Based Tool for Visualization of Biomolecular Network Comparison

2014 ◽  
Vol 556-562 ◽  
pp. 5482-5487
Author(s):  
Hui Ran Zhang ◽  
Xiao Long Shen ◽  
Jiang Xie ◽  
Dong Bo Dai
Keyword(s):  
Open Source ◽  
Undirected Graphs ◽  
Web Based ◽  
Network Comparison ◽  
Biomolecular Networks ◽  
Biomolecular Network ◽  
Visualization Tools ◽  
Similarities And Differences ◽  
Adaptive Visualization

Analyzing similarities and differences between biomolecular networks comparison through website intuitively could be a convenient and effective way for researchers. Although several network comparison visualization tools have been developed, none of them can be integrated into websites. In this paper, a web-based tool kit named dynamically adaptive Visualization of Biomolecular Network Comparison (Bio-NCV) is designed and developed. The proposed tool is based on Cytyoscape.js – a popular open-source library for analyzing and visualizing networks. Bio-NCV integrates arjor.js which including the Barnes-Hut algorithm and the Traer Physics library for processing in order to express the dynamically adaptive initialization. In addition, in order to maintain consistency, the counterparts in other networks will change while the nodes and edges in one of the compared networks change. Furthermore, Bio-NCV can deal with both directed and undirected graphs.

scholarly journals Codon usage bias creates a ramp of hydrogen bonding at the 5′-end in prokaryotic ORFeomes

2019 ◽  
Author(s):  
Juan C. Villada ◽  
Maria F. Duran ◽  
Patrick K. H. Lee
Keyword(s):  
Hydrogen Bonding ◽  
Codon Usage ◽  
Codon Usage Bias ◽  
Translation Efficiency ◽  
Molecular Processes ◽  
Molecular Feature ◽  
Web Based ◽  
Synonymous Codons ◽  
Double Stranded Dna ◽  
Codon Positions

Codon usage bias exerts control over a wide variety of molecular processes. The positioning of synonymous codons within coding sequences (CDSs) dictates protein expression by mechanisms such as local translation efficiency, mRNA Gibbs free energy, and protein co-translational folding. In this work, we explore how codon variants affect the position-dependent content of hydrogen bonding, which in turn influences energy requirements for unwinding double-stranded DNA. By analyzing over 14,000 bacterial, archaeal, and fungal ORFeomes, we found that Bacteria and Archaea exhibit an exponential ramp of hydrogen bonding at the 5′-end of CDSs, while a similar ramp was not found in Fungi. The ramp develops within the first 20 codon positions in prokaryotes, eventually reaching a steady carrying capacity of hydrogen bonding that does not differ from Fungi. Selection against uniformity tests proved that selection acts against synonymous codons with high content of hydrogen bonding at the 5′-end of prokaryotic ORFeomes. Overall, this study provides novel insights into the molecular feature of hydrogen bonding that is governed by the genetic code at the 5′-end of CDSs. A web-based application to analyze the position-dependent hydrogen bonding of ORFeomes has been developed and is publicly available (https://juanvillada.shinyapps.io/hbonds/).

scholarly journals KiMoSys 2.0: an upgraded database for submitting, storing and accessing experimental data for kinetic modeling

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Hugo Mochão ◽  
Pedro Barahona ◽  
Rafael S Costa
Keyword(s):  
Experimental Data ◽  
Metabolic Networks ◽  
Model Simulation ◽  
Web Interface ◽  
Concentration Data ◽  
Web Based ◽  
Share Data ◽  
Visualization Tools ◽  
Filter Mechanism ◽  
Machine Readable

Abstract The KiMoSys (https://kimosys.org), launched in 2014, is a public repository of published experimental data, which contains concentration data of metabolites, protein abundances and flux data. It offers a web-based interface and upload facility to share data, making it accessible in structured formats, while also integrating associated kinetic models related to the data. In addition, it also supplies tools to simplify the construction process of ODE (Ordinary Differential Equations)-based models of metabolic networks. In this release, we present an update of KiMoSys with new data and several new features, including (i) an improved web interface, (ii) a new multi-filter mechanism, (iii) introduction of data visualization tools, (iv) the addition of downloadable data in machine-readable formats, (v) an improved data submission tool, (vi) the integration of a kinetic model simulation environment and (vii) the introduction of a unique persistent identifier system. We believe that this new version will improve its role as a valuable resource for the systems biology community. Database URL:  www.kimosys.org

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