KRSA: Network-based Prediction of Differential Kinase Activity from Kinome Array Data

AbstractMotivationPhosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based approaches for measuring multiple kinases allow for the testing of differential phosphorylation between conditions for distinct sub-kinomes. While bioinformatics tools exist for processing and analyzing such kinome array data, current open-source tools lack the automated approach of upstream kinase prediction and network modeling. The presented tool, alongside other tools and methods designed for gene expression and protein-protein interaction network analyses, help the user better understand the complex regulation of gene and protein activities that forms biological systems and cellular signaling networks.ResultsWe present the Kinome Random Sampling Analyzer (KRSA), a web-application for kinome array analysis. While the underlying algorithm has been experimentally validated in previous publications, we tested the full KRSA application on dorsolateral prefrontal cortex (DLPFC) in male (n=3) and female (n=3) subjects to identify differential phosphorylation and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar patterns to the global phosphorylation signal. Additionally, kinase hits were compared to gene expression databases for in silico validation at the transcript level and showed differential gene expression of kinases.Availability and implementationKRSA as a web-based application can be found at http://bpg-n.utoledo.edu:3838/CDRL/KRSA/. The code and data are available at https://github.com/kalganem/KRSA.Supplementary informationSupplementary data are available online.

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KRSA: An R package and R Shiny web application for an end-to-end upstream kinase analysis of kinome array data

PLoS ONE ◽

10.1371/journal.pone.0260440 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0260440

Author(s):

Erica A. K. DePasquale ◽

Khaled Alganem ◽

Eduard Bentea ◽

Nawshaba Nawreen ◽

Jennifer L. McGuire ◽

...

Keyword(s):

Protein Function ◽

Web Application ◽

Kinase Activity ◽

R Package ◽

Functional Proteomics ◽

Proteomics Data ◽

Array Data ◽

Upstream Kinase ◽

R Shiny ◽

Differential Phosphorylation

Phosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based platforms for measuring reporter peptide signal levels allow for differential phosphorylation analysis between conditions for distinct active kinases. Peptide array technologies like the PamStation12 from PamGene allow for generating high-throughput, multi-dimensional, and complex functional proteomics data. As the adoption rate of such technologies increases, there is an imperative need for software tools that streamline the process of analyzing such data. We present Kinome Random Sampling Analyzer (KRSA), an R package and R Shiny web-application for analyzing kinome array data to help users better understand the patterns of functional proteomics in complex biological systems. KRSA is an All-In-One tool that reads, formats, fits models, analyzes, and visualizes PamStation12 kinome data. While the underlying algorithm has been experimentally validated in previous publications, we demonstrate KRSA workflow on dorsolateral prefrontal cortex (DLPFC) in male (n = 3) and female (n = 3) subjects to identify differential phosphorylation signatures and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar global phosphorylation signals patterns.

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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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Streamlined use of protein structures in variant analysis

10.1101/2021.09.10.459756 ◽

2021 ◽

Author(s):

Sandeep Kaur ◽

Neblina Sikta ◽

Andrea Schafferhans ◽

Nicola Bordin ◽

Mark J. Cowley ◽

...

Keyword(s):

Protein Function ◽

Molecular Mechanisms ◽

Structural Information ◽

Protein Structures ◽

Structural Data ◽

Supplementary Information ◽

3D Structures ◽

Link Type ◽

Variant Analysis ◽

Many Sources

AbstractMotivationVariant analysis is a core task in bioinformatics that requires integrating data from many sources. This process can be helped by using 3D structures of proteins, which can provide a spatial context that can provide insight into how variants affect function. Many available tools can help with mapping variants onto structures; but each has specific restrictions, with the result that many researchers fail to benefit from valuable insights that could be gained from structural data.ResultsTo address this, we have created a streamlined system for incorporating 3D structures into variant analysis. Variants can be easily specified via URLs that are easily readable and writable, and use the notation recommended by the Human Genome Variation Society (HGVS). For example, ‘https://aquaria.app/SARS-CoV-2/S/?N501Y’ specifies the N501Y variant of SARS-CoV-2 S protein. In addition to mapping variants onto structures, our system provides summary information from multiple external resources, including COSMIC, CATH-FunVar, and PredictProtein. Furthermore, our system identifies and summarizes structures containing the variant, as well as the variant-position. Our system supports essentially any mutation for any well-studied protein, and uses all available structural data — including models inferred via very remote homology — integrated into a system that is fast and simple to use. By giving researchers easy, streamlined access to a wealth of structural information during variant analysis, our system will help in revealing novel insights into the molecular mechanisms underlying protein function in health and disease.AvailabilityOur resource is freely available at the project home page (https://aquaria.app). After peer review, the code will be openly available via a GPL version 2 license at https://github.com/ODonoghueLab/Aquaria. PSSH2, the database of sequence-to-structure alignments, is also freely available for download at https://zenodo.org/record/[email protected] informationNone.

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Performance of gene expression analyses using de novo assembled transcripts in polyploid species

Bioinformatics ◽

10.1093/bioinformatics/btz620 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4314-4320 ◽

Cited By ~ 4

Author(s):

Ling-Yun Chen ◽

Diego F Morales-Briones ◽

Courtney N Passow ◽

Ya Yang

Keyword(s):

Gene Expression ◽

Reference Genes ◽

De Novo ◽

Supplementary Information ◽

Polyploid Species ◽

Network Analyses ◽

The Past ◽

Gene Expression Analyses ◽

Differential Gene

Abstract Motivation Quality of gene expression analyses using de novo assembled transcripts in species that experienced recent polyploidization remains unexplored. Results Differential gene expression (DGE) analyses using putative genes inferred by Trinity, Corset and Grouper performed slightly differently across five plant species that experienced various polyploidy histories. In species that lack recent polyploidy events that occurred in the past several millions of years, DGE analyses using de novo assembled transcriptomes identified 54–82% of the differentially expressed genes recovered by mapping reads to the reference genes. However, in species that experienced more recent polyploidy events, the percentage decreased to 21–65%. Gene co-expression network analyses using de novo assemblies versus mapping to the reference genes recovered the same module that significantly correlated with treatment in one species that lacks recent polyploidization. Availability and implementation Commands and scripts used in this study are available at https://bitbucket.org/lychen83/chen_et_al_2018_benchmark_dge/; Analysis files are available at Dryad doi: 10.5061/dryad.4p6n481. Supplementary information Supplementary data are available at Bioinformatics online.

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Expression profiling of the mature C. elegans nervous system by single-cell RNA-Sequencing

10.1101/737577 ◽

2019 ◽

Cited By ~ 20

Author(s):

Seth R Taylor ◽

Gabriel Santpere ◽

Molly Reilly ◽

Lori Glenwinkel ◽

Abigail Poff ◽

...

Keyword(s):

Gene Expression ◽

Nervous System ◽

Single Cell ◽

Web Application ◽

Model Organism ◽

Structural Motif ◽

Individual Neuron ◽

C Elegans ◽

Link Type ◽

The Individual

AbstractA single neuron and its synapses define the fundamental structural motif of the brain but the underlying gene expression programs that specify individual neuron types are poorly understood. To address this question in a model organism, we have produced a gene expression profile of >90% of the individual neuron classes in the C. elegans nervous system, an ensemble of neurons for which both the anatomy and connectivity are uniquely defined at single cell resolution. We generated single cell transcriptomes for 52,412 neurons that resolve as clusters corresponding to 109 of the canonical 118 neuron classes in the mature hermaphrodite nervous system. Detailed analysis revealed molecular signatures that further subdivide identified classes into specific neuronal subtypes. Notably, neuropeptide-related genes are often differentially expressed between subtypes of the given neuron class which points to distinct functional characteristics. All of these data are publicly available at our website (http://www.cengen.org) and can be interrogated at the web application SCeNGEA (https://cengen.shinyapps.io/SCeNGEA). We expect that this gene expression catalog will spur the goal of delineating the underlying mechanisms that define the developmental lineage, detailed anatomy, synaptic connectivity and function of each type of C. elegans neuron.

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Curatr: a web application for creating, curating, and sharing a mass spectral library

10.1101/170571 ◽

2017 ◽

Author(s):

Andrew Palmer ◽

Prasad Phapale ◽

Dominik Fay ◽

Theodore Alexandrov

Keyword(s):

Mass Spectrometry ◽

Web Application ◽

Mass Spectrometry Analysis ◽

Supplementary Information ◽

Spectral Library ◽

Mass Spectral Fragmentation ◽

Mass Spectral ◽

Link Type ◽

Manual Curation

AbstractMotivationIdentification from metabolomics mass spectrometry experiments requires comparison of fragmentation spectra from experimental samples to spectra from analytical standards. As the quality of identification depends directly on the quality of the reference spectra, manual curation is routine during the selection of reference spectra to include in a spectral library. Whilst building our own in-house spectral library we realised that there is currently no vendor neutral open access tool for for facilitating manual curation of spectra from raw LC-MS data into a custom spectral library.ResultsWe developed a web application curatr for the rapid generation of high quality mass spectral fragmentation libraries for liquid-chromatography mass spectrometry analysis. Curatr handles datasets from single or multiplexed standards, automatically extracting chromatographic profiles and potential fragmentation spectra for multiple adducts. These are presented through an intuitive interface for manual curation before being documented in a custom spectral library. Searchable molecular information and the providence of each standard is stored along with metadata on the experimental protocol. Curatr support the export of spectral libraries in several standard formats for easy use with third party software or submission to community databases, maximising the return on investment for these costly measurements. We demonstrate the use of curatr to generate the EMBL Metabolomics Core Facility spectral library which is publicly available at http://curatr.mcf.embl.de.AvailabilityThe source code is freely available at http://github.com/alexandrovteam/curatr/ along with example data.Supplementary informationA step-by step user manual is available in the supplementary information

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DNA Chisel, a versatile sequence optimizer

10.1101/2019.12.16.877480 ◽

2019 ◽

Author(s):

Valentin Zulkower ◽

Susan Rosser

Keyword(s):

Open Source ◽

Dna Sequence ◽

Web Application ◽

Optimization Problems ◽

Supplementary Information ◽

Sequence Optimization ◽

Sequence Design ◽

Optimization Framework ◽

Link Type ◽

Dna Sequence Design

AbstractMotivationAccounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine.ResultsDNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations.Availabilityas a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (code and documentation at https://github.com/Edinburgh-Genome-Foundry/DNAChisel)[email protected] informationattached.

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Performance of gene expression analyses using de novo assembled transcripts in polyploid species

10.1101/380063 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ling-Yun Chen ◽

Diego F. Morales-Briones ◽

Courtney N. Passow ◽

Ya Yang

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Reference Genes ◽

De Novo ◽

Differentially Expressed ◽

Supplementary Information ◽

Network Analyses ◽

Gene Expression Analyses ◽

Polyploidy Event ◽

Differential Gene

AbstractMotivationQuality of gene expression analyses using de novo assembled transcripts in species experienced recent polyploidization is yet unexplored.ResultsFive plant species with various polyploidy history were used for differential gene expression (DGE) analyses. DGE analyses using putative genes inferred by Trinity performed similar to or better than Corset and Grouper in precision, but lower in sensitivity. In species that lack polyploidy event in the past few million years, DGE analyses using de novo assembled transcriptome identified 50–76% of the differentially expressed genes recovered by mapping reads to the reference genes. However, in species with more recent polyploidy event, the percentage decreased to 7–30%. In addition, 7–89% of differentially expressed genes from de novo assembly are contaminations. Gene co-expression network analyses using de novo assemblies vs. mapping to the reference genes recovered the same module that significantly correlated with treatment in one of the five species tested.Availability and ImplementationCommands and scripts used in this study are available at https://bitbucket.org/lychen83/chen_et_al_2018_benchmark_dge/; Analysis files are available at Dryad doi: [email protected] informationSupplementary data are available at Bioinformatics online

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IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, Mapping and Mining ANalysis

10.1101/069104 ◽

2016 ◽

Cited By ~ 1

Author(s):

Minoo Ashtinai ◽

Payman Nickchi ◽

Soheil Jahangiri-Tazehkand ◽

Abdollah Safari ◽

Mehdi Mirzaie ◽

...

Keyword(s):

Protein Function ◽

Protein Function Prediction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Network ◽

Supplementary Information ◽

Protein Protein Interaction ◽

Link Type ◽

Supplementary Material ◽

Protein Protein Interaction Networks

SummaryIMMAN is a software for reconstructing Interolog Protein Network (IPN) by integrating several Protein-protein Interaction Networks (PPIN). Users can unify different PPINs to mine conserved common network among species. IMMAN helps to retrieve IPNs with different degrees of conservation to engage for protein function prediction analysis based on protein networks.AvailabilityIMMAN is freely available at https://bioconductor.org/packages/IMMAN, http://profiles.bs.ipm.ir/softwares/IMMAN/[email protected], [email protected], [email protected] informationSupplementary data are available online.

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Pavian: Interactive analysis of metagenomics data for microbiomics and pathogen identification

10.1101/084715 ◽

2016 ◽

Cited By ~ 25

Author(s):

Florian P. Breitwieser ◽

Steven L. Salzberg

Keyword(s):

Web Application ◽

Disease Diagnosis ◽

Supplementary Information ◽

Special Focus ◽

Web Browser ◽

R Language ◽

Interactive Analysis ◽

Link Type ◽

Metagenomics Data ◽

Flow Diagrams

AbstractSummaryPavian is a web application for exploring metagenomics classification results, with a special focus on infectious disease diagnosis. Pinpointing pathogens in metagenomics classification results is often complicated by host and laboratory contaminants as well as many non-pathogenic microbiota. With Pavian, researchers can analyze, display and transform results from the Kraken and Centrifuge classifiers using interactive tables, heatmaps and flow diagrams. Pavian also provides an alignment viewer for validation of matches to a particular genome.Availability and implementationPavian is implemented in the R language and based on the Shiny framework. It can be hosted on Windows, Mac OS X and Linux systems, and used with any contemporary web browser. It is freely available under a GPL-3 license from http://github.com/fbreitwieser/pavian. Furthermore a Docker image is provided at https://hub.docker.com/r/florianbw/[email protected] informationSupplementary data is available at Bioinformatics online.

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