scholarly journals A framework for exhaustive modelling of genetic interaction patterns using Petri nets

2019 ◽  
Vol 36 (7) ◽  
pp. 2142-2149
Author(s):  
Annika Jacobsen ◽  
Olga Ivanova ◽  
Saman Amini ◽  
Jaap Heringa ◽  
Patrick Kemmeren ◽  
...  
Keyword(s):  
Petri Nets ◽  
Drug Response ◽  
Genetic Interaction ◽  
Regulatory Mechanisms ◽  
Supplementary Information ◽  
Biological Processes ◽  
Interaction Patterns ◽  
Supplementary Data ◽  
Gene Pairs ◽  
Computational Analyses

Abstract Motivation Genetic interaction (GI) patterns are characterized by the phenotypes of interacting single and double mutated gene pairs. Uncovering the regulatory mechanisms of GIs would provide a better understanding of their role in biological processes, diseases and drug response. Computational analyses can provide insights into the underpinning mechanisms of GIs. Results In this study, we present a framework for exhaustive modelling of GI patterns using Petri nets (PN). Four-node models were defined and generated on three levels with restrictions, to enable an exhaustive approach. Simulations suggest ∼5 million models of GIs. Generalizing these we propose putative mechanisms for the GI patterns, inversion and suppression. We demonstrate that exhaustive PN modelling enables reasoning about mechanisms of GIs when only the phenotypes of gene pairs are known. The framework can be applied to other GI or genetic regulatory datasets. Availability and implementation The framework is available at http://www.ibi.vu.nl/programs/ExhMod. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

2021 ◽  
Author(s):  
Zachary B Abrams ◽  
Dwayne G Tally ◽  
Lynne V Abruzzo ◽  
Kevin R Coombes
Keyword(s):  
Large Scale ◽  
International System ◽  
Genetic Data ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Computational Tools ◽  
Text Format ◽  
Karyotype Analyses ◽  
Computational Analyses

Abstract Summary Cytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology. Availability and Implementation Freely available at https://CRAN.R-project.org/package=RCytoGPS. The code for the underlying CytoGPS software can be found at https://github.com/i2-wustl/CytoGPS. Supplementary information There is no supplementary data.

scholarly journals GlycanFormatConverter: a conversion tool for translating the complexities of glycans

2018 ◽  
Vol 35 (14) ◽  
pp. 2434-2440 ◽  
Author(s):  
Shinichiro Tsuchiya ◽  
Issaku Yamada ◽  
Kiyoko F Aoki-Kinoshita
Keyword(s):  
Open Source ◽  
Source Code ◽  
Supplementary Information ◽  
Biological Processes ◽  
Supplementary Data ◽  
Unique Representation ◽  
Open Source Tool ◽  
Living Organisms ◽  
Conversion Tool ◽  
Complex Glycan

Abstract Motivation Glycans are biomolecules that take an important role in the biological processes of living organisms. They form diverse, complicated structures such as branched and cyclic forms. Web3 Unique Representation of Carbohydrate Structures (WURCS) was proposed as a new linear notation for uniquely representing glycans during the GlyTouCan project. WURCS defines rules for complex glycan structures that other text formats did not support, and so it is possible to represent a wide variety glycans. However, WURCS uses a complicated nomenclature, so it is not human-readable. Therefore, we aimed to support the interpretation of WURCS by converting WURCS to the most basic and widely used format IUPAC. Results In this study, we developed GlycanFormatConverter and succeeded in converting WURCS to the three kinds of IUPAC formats (IUPAC-Extended, IUPAC-Condensed and IUPAC-Short). Furthermore, we have implemented functionality to import IUPAC-Extended, KEGG Chemical Function (KCF) and LinearCode formats and to export WURCS. We have thoroughly tested our GlycanFormatConverter and were able to show that it was possible to convert all the glycans registered in the GlyTouCan repository, with exceptions owing only to the limitations of the original format. The source code for this conversion tool has been released as an open source tool. Availability and implementation https://github.com/glycoinfo/GlycanFormatConverter.git Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

2021 ◽  
Author(s):  
Zachary B. Abrams ◽  
Dwayne G. Tally ◽  
Lynne V. Abruzzo ◽  
Kevin R. Coombes
Keyword(s):  
Large Scale ◽  
International System ◽  
Genetic Data ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Computational Tools ◽  
Text Format ◽  
Karyotype Analyses ◽  
Computational Analyses

AbstractSummaryCytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology.Availability and ImplementationFreely available at https://CRAN.R-project.org/package=RCytoGPSSupplementary informationSupplementary data are available at Bioinformatics online.

Characterizing therapeutic signatures of transcription factors in cancer by incorporating profiles in compound treated cells

2020 ◽  
Author(s):  
Jinmyung Jung
Keyword(s):  
Transcription Factors ◽  
Tumor Suppressors ◽  
Therapeutic Strategy ◽  
Therapeutic Targets ◽  
Supplementary Information ◽  
Biological Processes ◽  
Supplementary Data ◽  
Colon Cancer Cells ◽  
Gene Expressions ◽  
Substantial Discrepancy

Abstract Motivation Cancers are promoted by abnormal alterations in biological processes, such as cell cycle and apoptosis. An immediate reason for those aberrant processes is the deregulation of their involved transcription factors (TFs). Thus, the deregulated TFs in cancer have been experimented as successful therapeutic targets, such as RARA and RUNX1. This therapeutic strategy can be accelerated by characterizing new potential TF targets. Results Two kinds of therapeutic signatures of TFs in A375 (skin) and HT29 (colon) cancer cells were characterized by analyzing TF activities under effective and ineffective compounds to cancer. First, the therapeutic TFs (TTs) were identified as the TFs that are significantly activated or repressed under effective compared to ineffective compounds. Second, the therapeutically correlated TF pairs (TCPs) were determined as the TF pairs whose activity correlations show substantial discrepancy between the effective and ineffective compounds. It was facilitated by incorporating (i) compound-induced gene expressions (LINCS), (ii) compound-induced cell viabilities (GDSC) and (iii) TF–target interactions (TRUST2). As a result, among 627 TFs, the 35 TTs (such as MYCN and TP53) and the 214 TCPs (such as FOXO3 and POU2F2 pair) were identified. The TTs and the proteins on the paths between TCPs were compared with the known therapeutic targets, tumor suppressors, oncogenes and CRISPR-Cas9 knockout screening, which yielded significant consequences. We expect that the results provide good candidates for therapeutic TF targets in cancer. Availability and implementation The data and Python implementations are available at https://github.com/jmjung83/TT_and_TCP. Supplementary information Supplementary data are available at Bioinformatics online.

PPaxe: easy extraction of protein occurrence and interactions from the scientific literature

2018 ◽  
Vol 35 (14) ◽  
pp. 2523-2524 ◽  
Author(s):  
S Castillo-Lara ◽  
J F Abril
Keyword(s):  
Protein Interactions ◽  
Web Application ◽  
Scientific Literature ◽  
Supplementary Information ◽  
Command Line ◽  
Biological Processes ◽  
Supplementary Data ◽  
Protein Protein Interactions ◽  
Command Line Version

Abstract Motivation Protein–protein interactions (PPIs) are very important to build models for understanding many biological processes. Although several databases hold many of these interactions, exploring them, selecting those relevant for a given subject and contextualizing them can be a difficult task for researchers. Extracting PPIs directly from the scientific literature can be very helpful for providing such context, as the sentences describing these interactions may give insights to researchers in helpful ways. Results We have developed PPaxe, a python module and a web application that allows users to extract PPIs and protein occurrence from a given set of PubMed and PubMedCentral articles. It presents the results of the analysis in different ways to help researchers export, filter and analyze the results easily. Availability and implementation PPaxe web demo is freely available at https://compgen.bio.ub.edu/PPaxe. All the software can be downloaded from https://compgen.bio.ub.edu/PPaxe/download, including a command-line version and docker containers for an easy installation. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ccNetViz: a WebGL-based JavaScript library for visualization of large networks

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  
Keyword(s):  
Systems Biology ◽  
Complex Networks ◽  
Open Source ◽  
High Speed ◽  
A Priori ◽  
Supplementary Information ◽  
Network Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Epidemiological modeling in StochSS Live!

2021 ◽  
Author(s):  
Richard Jiang ◽  
Bruno Jacob ◽  
Matthew Geiger ◽  
Sean Matthew ◽  
Bryan Rumsey ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Epidemiological Model ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Based ◽  
Epidemiological Modeling ◽  
Modeling Simulation ◽  
Wide Range ◽  
Biochemical Systems

Abstract Summary We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results. Availability and implementation StochSS Live! is freely available at https://live.stochss.org/ Supplementary information Supplementary data are available at Bioinformatics online.

KEC: unique sequence search by K-mer exclusion

2021 ◽  
Author(s):  
Pavel Beran ◽  
Dagmar Stehlíková ◽  
Stephen P Cohen ◽  
Vladislav Čurn
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Source Code ◽  
Unique Sequence ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Laptop Computers ◽  
Sequence Search ◽  
Target Sequences ◽  
Cross Reference

Abstract Summary Searching for amino acid or nucleic acid sequences unique to one organism may be challenging depending on size of the available datasets. K-mer elimination by cross-reference (KEC) allows users to quickly and easily find unique sequences by providing target and non-target sequences. Due to its speed, it can be used for datasets of genomic size and can be run on desktop or laptop computers with modest specifications. Availability and implementation KEC is freely available for non-commercial purposes. Source code and executable binary files compiled for Linux, Mac and Windows can be downloaded from https://github.com/berybox/KEC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Protein lysine crotonylation: past, present, perspective

2021 ◽  
Vol 12 (7) ◽  
Author(s):  
Gaoyue Jiang ◽  
Chunxia Li ◽  
Meng Lu ◽  
Kefeng Lu ◽  
Huihui Li
Keyword(s):  
Cross Talk ◽  
Tissue Injury ◽  
Regulatory Mechanisms ◽  
Biological Processes ◽  
Histone Proteins ◽  
Physiological Functions ◽  
Neuropsychiatric Disease ◽  
Enzymatic Mechanisms ◽  
Substrate Proteins ◽  
Tools And Methods

AbstractLysine crotonylation has been discovered in histone and non-histone proteins and found to be involved in diverse diseases and biological processes, such as neuropsychiatric disease, carcinogenesis, spermatogenesis, tissue injury, and inflammation. The unique carbon–carbon π-bond structure indicates that lysine crotonylation may use distinct regulatory mechanisms from the widely studied other types of lysine acylation. In this review, we discussed the regulation of lysine crotonylation by enzymatic and non-enzymatic mechanisms, the recognition of substrate proteins, the physiological functions of lysine crotonylation and its cross-talk with other types of modification. The tools and methods for prediction and detection of lysine crotonylation were also described.

scholarly journals CorGAT: a tool for the functional annotation of SARS-CoV-2 genomes

2020 ◽  
Author(s):  
Matteo Chiara ◽  
Federico Zambelli ◽  
Marco Antonio Tangaro ◽  
Pietro Mandreoli ◽  
David S Horner ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Ad Hoc ◽  
State Of The Art ◽  
Supplementary Information ◽  
Genomic Sequences ◽  
Supplementary Data ◽  
Evolutionary Patterns ◽  
Genomic Variants ◽  
Art Methods ◽  
Available Resources

Abstract Summary While over 200 000 genomic sequences are currently available through dedicated repositories, ad hoc methods for the functional annotation of SARS-CoV-2 genomes do not harness all currently available resources for the annotation of functionally relevant genomic sites. Here, we present CorGAT, a novel tool for the functional annotation of SARS-CoV-2 genomic variants. By comparisons with other state of the art methods we demonstrate that, by providing a more comprehensive and rich annotation, our method can facilitate the identification of evolutionary patterns in the genome of SARS-CoV-2. Availabilityand implementation Galaxy   http://corgat.cloud.ba.infn.it/galaxy; software: https://github.com/matteo14c/CorGAT/tree/Revision_V1; docker: https://hub.docker.com/r/laniakeacloud/galaxy_corgat. Supplementary information Supplementary data are available at Bioinformatics online.

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