ZDOG: zooming in on dominating genes with mutations in cancer pathways

Abstract Background Inference of cancer-causing genes and their biological functions are crucial but challenging due to the heterogeneity of somatic mutations. The heterogeneity of somatic mutations reveals that only a handful of oncogenes mutate frequently and a number of cancer-causing genes mutate rarely. Results We develop a Cytoscape app, named ZDOG, for visualization of the extent to which mutated genes may affect cancer pathways using the dominating tree model. The dominator tree model allows us to examine conveniently the positional importance of a gene in cancer signalling pathways. This tool facilitates the identification of mutated “master” regulators even with low mutation frequency in deregulated signalling pathways. Conclusions We have presented a model for facilitating the examination of the extent to which mutation in a gene may affect downstream components in a signalling pathway through its positional information. The model is implemented in a user-friendly Cytoscape app which will be freely available upon publication. Availability Together with a user manual, the ZDOG app is freely available at GitHub (https://github.com/rudi2013/ZDOG). It is also available in the Cytoscape app store (http://apps.cytoscape.org/apps/ZDOG) and users can easily install it using the Cytoscape App Manager.

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PathScore: a web tool for identifying altered pathways in cancer data

10.1101/067090 ◽

2016 ◽

Cited By ~ 2

Author(s):

Stephen G. Gaffney ◽

Jeffrey P. Townsend

Keyword(s):

Web Application ◽

Somatic Mutations ◽

Supplementary Information ◽

Web Tool ◽

Cancer Data ◽

Link Type ◽

Novel Approach ◽

Supplementary Material ◽

User Friendly ◽

Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

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CancerVar: a web server for improved evidence-based clinical interpretation of cancer somatic mutations and copy number abnormalities

10.1101/2020.10.06.323162 ◽

2020 ◽

Author(s):

Quan Li ◽

Zilin Ren ◽

Yunyun Zhou ◽

Kai Wang

Keyword(s):

Clinical Significance ◽

Copy Number ◽

Somatic Mutations ◽

Relevant Information ◽

Web Interface ◽

Clinical Interpretation ◽

Link Type ◽

Command Line Tool ◽

Hotspot Mutations ◽

User Friendly

ABSTRACTSeveral knowledgebases, such as CIViC, CGI and OncoKB, have been manually curated to support clinical interpretations of somatic mutations and copy number abnormalities (CNAs) in cancer. However, these resources focus on known hotspot mutations, and discrepancies or even conflicting interpretations have been observed between these knowledgebases. To standardize clinical interpretation, AMP/ASCO/CAP/ACMG/CGC jointly published consensus guidelines for the interpretations of somatic mutations and CNAs in 2017 and 2019, respectively. Based on these guidelines, we developed a standardized, semi-automated interpretation tool called CancerVar (Cancer Variants interpretation), with a user-friendly web interface to assess the clinical impacts of somatic variants. Using a semi-supervised method, CancerVar interpret the clinical impacts of cancer variants as four tiers: strong clinical significance, potential clinical significance, unknown clinical significance, benign/likely benign. CancerVar also allows users to specify criteria or adjust scoring weights as a customized interpretation strategy, and allows phenotype-driven scoring for specific types of cancer. Importantly, CancerVar generates automated texts to summarize clinical evidence on somatic variants, which greatly reduces manual workload to write interpretations that include relevant information from harmonized knowledgebases. CancerVar can be accessed at http://cancervar.wglab.org and it is open to all users without login requirements. The command line tool is also available at https://github.com/WGLab/CancerVar.

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neoANT-HILL: an integrated tool for identification of potential neoantigens

10.1101/603670 ◽

2019 ◽

Author(s):

Ana Carolina M F Coelho ◽

André L Fonseca ◽

Danilo L Martins ◽

Lucas M da Cunha ◽

Paulo B R Lins ◽

...

Keyword(s):

Immune Responses ◽

False Positive ◽

Somatic Mutations ◽

Cancer Genome ◽

Current Work ◽

Rna Seq ◽

Link Type ◽

Technological Advances ◽

User Friendly ◽

Ngs Data

AbstractCancer neoantigens have attracted great interest in immunotherapy due to their ability to elicit antitumoral immune responses. These antigens are formed due to somatic mutations in the cancer genome that result in alterations of the original protein. Although current technological advances in neoantigen identification, it remains a challenging and a large number of false-positive continue to exist. In the current work, we present neoANT-HILL, an automatized user-friendly tool that integrates several immunogenomic analysis to improve neoantigens detection from NGS data. The program input can be a file with somatic mutations called and/or RNA-seq data. Our tool was applied on somatic mutations of melanoma dataset from TCGA and found that neoANT-HILL was able to predicted potential neoantigens. The software is available on github athttps://github.com/neoanthill/neoANT-HILL.

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MutSpot: detection of non-coding mutation hotspots in cancer genomes

10.1101/740944 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yu Amanda Guo ◽

Mei Mei Chang ◽

Anders Jacobsen Skanderup

Keyword(s):

Somatic Mutations ◽

R Package ◽

Supplementary Information ◽

Patient Specific ◽

Supplementary Data ◽

Link Type ◽

Genome Wide ◽

Cancer Genomes ◽

User Friendly ◽

Regulatory Dna

AbstractSummaryRecurrence and clustering of somatic mutations (hotspots) in cancer genomes may indicate positive selection and involvement in tumorigenesis. MutSpot performs genome-wide inference of mutation hotspots in non-coding and regulatory DNA of cancer genomes. MutSpot performs feature selection across hundreds of epigenetic and sequence features followed by estimation of position and patient-specific background somatic mutation probabilities. MutSpot is user-friendly, works on a standard workstation, and scales to thousands of cancer genomes.Availability and implementationMutSpot is implemented as an R package and is available at https://github.com/skandlab/MutSpot/Supplementary informationSupplementary data are available at https://github.com/skandlab/MutSpot/

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Pedigree and Pedigree Import Wizard

HortScience ◽

10.21273/hortsci.33.3.552g ◽

1998 ◽

Vol 33 (3) ◽

pp. 552g-553

Author(s):

Shahrokh Khandizadeh

Keyword(s):

Additional Data ◽

File Format ◽

Fruit Crops ◽

Operating Environment ◽

Agronomic Characteristics ◽

Link Type ◽

Plant Characteristics ◽

User Friendly

Pedigree for Windows is a user-friendly program that allows the user to trace agronomic characteristics, draw pedigrees, and view images of several fruit crops, including more than 1400 apple, 800 strawberry, 800 almond, 100 blackberry, 80 blueberry, 790 pear, 200 raspberry examples. Pedigree Import Wizard®© for Windows is an add-on software for users who are interested in importing their research or breeding data records of fruit, flower, and plant characteristics and any related images into Pedigree for Windows. Pedigree for Windows and Pedigree Import Wizard have been designed so that a user familiar with the Windows operating environment should have little need to refer to the documentation provided with the program. Pedigree Import Wizard uses a comma-separated value (csv) file format under the MS Excel environment. This option allows the user to add or import additional data to the existing database that are already stored in other software such as Lotus, Excel, Access, QuattroPro, WordPerfect, and MS Word tables, etc., as long as they work under the Windows environment. A free demo version of Pedigree and Pedigree Import Wizard for Windows is available from http://www.pgris.com.

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kataegis: an R package for identification and visualization of the genomic localized hypermutation regions using high-throughput sequencing

BMC Genomics ◽

10.1186/s12864-021-07696-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Xue Lin ◽

Yingying Hua ◽

Shuanglin Gu ◽

Li Lv ◽

Xingyu Li ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Somatic Mutations ◽

R Package ◽

Frequency Of Occurrence ◽

Link Type ◽

Genomic Landscape ◽

One Step ◽

Flanking Regions

Abstract Background Genomic localized hypermutation regions were found in cancers, which were reported to be related to the prognosis of cancers. This genomic localized hypermutation is quite different from the usual somatic mutations in the frequency of occurrence and genomic density. It is like a mutations “violent storm”, which is just what the Greek word “kataegis” means. Results There are needs for a light-weighted and simple-to-use toolkit to identify and visualize the localized hypermutation regions in genome. Thus we developed the R package “kataegis” to meet these needs. The package used only three steps to identify the genomic hypermutation regions, i.e., i) read in the variation files in standard formats; ii) calculate the inter-mutational distances; iii) identify the hypermutation regions with appropriate parameters, and finally one step to visualize the nucleotide contents and spectra of both the foci and flanking regions, and the genomic landscape of these regions. Conclusions The kataegis package is available on Bionconductor/Github (https://github.com/flosalbizziae/kataegis), which provides a light-weighted and simple-to-use toolkit for quickly identifying and visualizing the genomic hypermuation regions.

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DLX6-AS1: a putative lncRNA candidate in multiple human cancers

Expert Reviews in Molecular Medicine ◽

10.1017/erm.2021.17 ◽

2021 ◽

Vol 23 ◽

Author(s):

Mohsen Sheykhhasan ◽

Yaghoub Ahmadyousefi ◽

Reihaneh Seyedebrahimi ◽

Hamid Tanzadehpanah ◽

Hamed Manoochehri ◽

...

Keyword(s):

Cancer Progression ◽

Critical Analysis ◽

Molecular Mechanisms ◽

Signalling Pathways ◽

Biological Functions ◽

Initial Development ◽

Human Cancers ◽

Expression Of Genes ◽

Non Coding Rnas ◽

Research Studies

Abstract Long non-coding RNAs (lncRNAs) have important roles in regulating the expression of genes and act as biomarkers in the initial development of different cancers. Increasing research studies have verified that dysregulation of lncRNAs occurs in various pathological processes including tumorigenesis and cancer progression. Among the different lncRNAs, DLX6-AS1 has been reported to act as an oncogene in the development and prognoses of different cancers, by affecting many different signalling pathways. This review summarises and analyses the recent research studies describing the biological functions of DLX6-AS1, its overall effect on signalling pathways and the molecular mechanisms underlying its action on the expression of genes in multiple human cancers. Our critical analysis suggests that different signalling pathways associated to this lncRNA may be used as a biomarker for diagnosis, or targets of treatment in cancers.

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treeheatr: an R package for interpretable decision tree visualizations

10.1101/2020.07.10.196352 ◽

2020 ◽

Author(s):

Trang T. Le ◽

Jason H. Moore

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Feature Space ◽

R Package ◽

Tree Structure ◽

Decision Tree Model ◽

Teaching Tool ◽

Tree Model ◽

Machine Learning Methods ◽

Link Type

AbstractSummarytreeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree’s leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students’ understanding of a simple decision tree model before diving into more complex tree-based machine learning methods.AvailabilityThe treeheatr package is freely available under the permissive MIT license at https://trang1618.github.io/treeheatr and https://cran.r-project.org/package=treeheatr. It comes with a detailed vignette that is automatically built with GitHub Actions continuous [email protected]

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CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

10.1101/102848 ◽

2017 ◽

Author(s):

Venkata Manem ◽

George Adam ◽

Tina Gruosso ◽

Mathieu Gigoux ◽

Nicholas Bertos ◽

...

Keyword(s):

Stromal Cells ◽

Large Scale ◽

Network Visualization ◽

Visualization Tool ◽

Biological Processes ◽

Web Based ◽

Link Type ◽

A Cell ◽

Large Scale Networks ◽

User Friendly

ABSTRACTBackground:Over the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.Results:CrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.Conclusion:CrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.Availability:The web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.Contact:[email protected]

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COVID-Align: Accurate online alignment of hCoV-19 genomes using a profile HMM

10.1101/2020.05.25.114884 ◽

2020 ◽

Cited By ~ 2

Author(s):

Frédéric Lemoine ◽

Luc Blassel ◽

Jakub Voznica ◽

Olivier Gascuel

Keyword(s):

Daily Basis ◽

Supplementary Information ◽

Summary Statistics ◽

Evolutionary Novelty ◽

Bioinformatics Analyses ◽

Link Type ◽

Sequencing Quality ◽

User Friendly ◽

Profile Hmm ◽

New Mutations

AbstractMotivationThe first cases of the COVID-19 pandemic emerged in December 2019. Until the end of February 2020, the number of available genomes was below 1,000, and their multiple alignment was easily achieved using standard approaches. Subsequently, the availability of genomes has grown dramatically. Moreover, some genomes are of low quality with sequencing/assembly errors, making accurate re-alignment of all genomes nearly impossible on a daily basis. A more efficient, yet accurate approach was clearly required to pursue all subsequent bioinformatics analyses of this crucial data.ResultshCoV-19 genomes are highly conserved, with very few indels and no recombination. This makes the profile HMM approach particularly well suited to align new genomes, add them to an existing alignment and filter problematic ones. Using a core of ∼2,500 high quality genomes, we estimated a profile using HMMER, and implemented this profile in COVID-Align, a user-friendly interface to be used online or as standalone via Docker. The alignment of 1,000 genomes requires less than 20mn on our cluster. Moreover, COVID-Align provides summary statistics, which can be used to determine the sequencing quality and evolutionary novelty of input genomes (e.g. number of new mutations and indels).Availabilityhttps://covalign.pasteur.cloud, hub.docker.com/r/evolbioinfo/[email protected], [email protected] informationSupplementary information is available at Bioinformatics online.

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