YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

Abstract Summary We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. Availability and implementation YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. Supplementary information Supplementary data are available at Bioinformatics online.

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AFid: a tool for automated identification and exclusion of autofluorescent objects from microscopy images

Bioinformatics ◽

10.1093/bioinformatics/btaa780 ◽

2020 ◽

Author(s):

Heeva Baharlou ◽

Nicolas P Canete ◽

Kirstie M Bertram ◽

Kerrie J Sandgren ◽

Anthony L Cunningham ◽

...

Keyword(s):

Image Analysis ◽

Fluorescence Microscopy ◽

Hiv Transmission ◽

Supplementary Information ◽

Supplementary Data ◽

Automated Identification ◽

Immune Markers ◽

Downstream Analysis ◽

Microscopy Images ◽

Standing Problem

Abstract Motivation Autofluorescence is a long-standing problem that has hindered the analysis of images of tissues acquired by fluorescence microscopy. Current approaches to mitigate autofluorescence in tissue are lab-based and involve either chemical treatment of sections or specialized instrumentation and software to ‘unmix’ autofluorescent signals. Importantly, these approaches are pre-emptive and there are currently no methods to deal with autofluorescence in acquired fluorescence microscopy images. Results To address this, we developed Autofluorescence Identifier (AFid). AFid identifies autofluorescent pixels as discrete objects in multi-channel images post-acquisition. These objects can then be tagged for exclusion from downstream analysis. We validated AFid using images of FFPE human colorectal tissue stained for common immune markers. Further, we demonstrate its utility for image analysis where its implementation allows the accurate measurement of HIV–Dendritic cell interactions in a colorectal explant model of HIV transmission. Therefore, AFid represents a major leap forward in the extraction of useful data from images plagued by autofluorescence by offering an approach that is easily incorporated into existing workflows and that can be used with various samples, staining panels and image acquisition methods. We have implemented AFid in ImageJ, Matlab and R to accommodate the diverse image analysis community. Availability and implementation AFid software is available at https://ellispatrick.github.io/AFid. Supplementary information Supplementary data are available at Bioinformatics online.

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EpiGraph: an open-source platform to quantify epithelial organization

Bioinformatics ◽

10.1093/bioinformatics/btz683 ◽

2019 ◽

Author(s):

Pablo Vicente-Munuera ◽

Pedro Gómez-Gálvez ◽

Robert J Tetley ◽

Cristina Forja ◽

Antonio Tagua ◽

...

Keyword(s):

Image Analysis ◽

Open Access ◽

Supplementary Information ◽

Analysis Tool ◽

Distribution Analysis ◽

Supplementary Data ◽

Programming Skills ◽

User Friendly ◽

Degree Of Order

Abstract Summary Here we present EpiGraph, an image analysis tool that quantifies epithelial organization. Our method combines computational geometry and graph theory to measure the degree of order of any packed tissue. EpiGraph goes beyond the traditional polygon distribution analysis, capturing other organizational traits that improve the characterization of epithelia. EpiGraph can objectively compare the rearrangements of epithelial cells during development and homeostasis to quantify how the global ensemble is affected. Importantly, it has been implemented in the open-access platform Fiji. This makes EpiGraph very user friendly, with no programming skills required. Availability and implementation EpiGraph is available at https://imagej.net/EpiGraph and the code is accessible (https://github.com/ComplexOrganizationOfLivingMatter/Epigraph) under GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.

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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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DEsingle for detecting three types of differential expression in single-cell RNA-seq data

10.1101/173997 ◽

2017 ◽

Cited By ~ 1

Author(s):

Zhun Miao ◽

Ke Deng ◽

Xiaowo Wang ◽

Xuegong Zhang

Keyword(s):

Single Cell ◽

Differential Expression ◽

Negative Binomial ◽

Single Cells ◽

R Package ◽

Supplementary Information ◽

Binomial Model ◽

Supplementary Data ◽

Rna Seq ◽

Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

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DiscoRhythm: an easy-to-use web application and R package for discovering rhythmicity

Bioinformatics ◽

10.1093/bioinformatics/btz834 ◽

2019 ◽

Cited By ~ 2

Author(s):

Matthew Carlucci ◽

Algimantas Kriščiūnas ◽

Haohan Li ◽

Povilas Gibas ◽

Karolis Koncevičius ◽

...

Keyword(s):

Web Application ◽

Statistical Significance ◽

R Package ◽

Biological Data ◽

Supplementary Information ◽

Statistical Knowledge ◽

Health And Disease ◽

Phase Amplitude ◽

Almost All ◽

User Friendly

Abstract Motivation Biological rhythmicity is fundamental to almost all organisms on Earth and plays a key role in health and disease. Identification of oscillating signals could lead to novel biological insights, yet its investigation is impeded by the extensive computational and statistical knowledge required to perform such analysis. Results To address this issue, we present DiscoRhythm (Discovering Rhythmicity), a user-friendly application for characterizing rhythmicity in temporal biological data. DiscoRhythm is available as a web application or an R/Bioconductor package for estimating phase, amplitude, and statistical significance using four popular approaches to rhythm detection (Cosinor, JTK Cycle, ARSER, and Lomb-Scargle). We optimized these algorithms for speed, improving their execution times up to 30-fold to enable rapid analysis of -omic-scale datasets in real-time. Informative visualizations, interactive modules for quality control, dimensionality reduction, periodicity profiling, and incorporation of experimental replicates make DiscoRhythm a thorough toolkit for analyzing rhythmicity. Availability and Implementation The DiscoRhythm R package is available on Bioconductor (https://bioconductor.org/packages/DiscoRhythm), with source code available on GitHub (https://github.com/matthewcarlucci/DiscoRhythm) under a GPL-3 license. The web application is securely deployed over HTTPS (https://disco.camh.ca) and is freely available for use worldwide. Local instances of the DiscoRhythm web application can be created using the R package or by deploying the publicly available Docker container (https://hub.docker.com/r/mcarlucci/discorhythm). Supplementary information Supplementary data are available at Bioinformatics online.

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MitoTrack, a user-friendly semi-automatic software for lineage tracking in living embryos

Bioinformatics ◽

10.1093/bioinformatics/btz717 ◽

2019 ◽

Author(s):

A Trullo ◽

J Dufourt ◽

M Lagha

Keyword(s):

Graphical User Interface ◽

Transcriptional Activation ◽

Supplementary Information ◽

Supplementary Data ◽

Automatic Image Analysis ◽

Lineage Tree ◽

Automatic Software ◽

Integrated Software ◽

Lineage Tracking ◽

User Friendly

Abstract Motivation During development, progenitor cells undergo multiple rounds of cellular divisions during which transcriptional programs must be faithfully propagated. Investigating the timing of transcriptional activation, which is a highly stochastic phenomenon, requires the analysis of large amounts of data. In order to perform automatic image analysis of transcriptional activation, we developed a software that segments and tracks both small and large objects, leading the user from raw data up to the results in their final form. Results MitoTrack is a user-friendly open-access integrated software that performs the specific dual task of reporting the precise timing of transcriptional activation while keeping lineage tree history for each nucleus of a living developing embryo. The software works automatically but provides the possibility to easily supervise, correct and validate each step. Availability and implementation MitoTrack is an open source Python software, embedded within a graphical user interface (download here). Supplementary information Supplementary data are available at Bioinformatics online.

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G4Killer web application: a tool to design G-quadruplex mutations

Bioinformatics ◽

10.1093/bioinformatics/btaa057 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3246-3247

Author(s):

Vaclav Brazda ◽

Jan Kolomaznik ◽

Jean-Louis Mergny ◽

Jiri Stastny

Keyword(s):

Dna Sequences ◽

Web Application ◽

Rational Design ◽

Therapeutic Potential ◽

Supplementary Information ◽

Web Tool ◽

Web Based ◽

Dna Structures ◽

G Quadruplex ◽

User Friendly

Abstract Motivation G-quadruplexes (G4) are important regulatory non-B DNA structures with therapeutic potential. A tool for rational design of mutations leading to decreased propensity for G4 formation should be useful in studying G4 functions. Although tools exist for G4 prediction, no easily accessible tool for the rational design of G4 mutations has been available. Results We developed a web-based tool termed G4Killer that is based on the G4Hunter algorithm. This new tool is a platform-independent and user-friendly application to design mutations crippling G4 propensity in a parsimonious way (i.e., keeping the primary sequence as close as possible to the original one). The tool is integrated into our DNA analyzer server and allows for generating mutated DNA sequences having the desired lowered G4Hunter score with minimal mutation steps. Availability and implementation The G4Killer web tool can be accessed at: http://bioinformatics.ibp.cz. Supplementary information Supplementary data are available at Bioinformatics online.

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MetumpX—a metabolomics support package for untargeted mass spectrometry

Bioinformatics ◽

10.1093/bioinformatics/btz765 ◽

2019 ◽

Vol 36 (5) ◽

pp. 1647-1648 ◽

Cited By ~ 1

Author(s):

Bilal Wajid ◽

Hasan Iqbal ◽

Momina Jamil ◽

Hafsa Rafique ◽

Faria Anwar

Keyword(s):

Mass Spectrometry ◽

Data Analysis ◽

Small Molecules ◽

Software Package ◽

Life Sciences ◽

Supplementary Information ◽

Supplementary Data ◽

Software Packages ◽

Develop Software ◽

User Friendly

Abstract Motivation Metabolomics is a data analysis and interpretation field aiming to study functions of small molecules within the organism. Consequently Metabolomics requires researchers in life sciences to be comfortable in downloading, installing and scripting of software that are mostly not user friendly and lack basic GUIs. As the researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines truly speeding up the learning curve to build software workstations. Therefore, this paper aims to provide MetumpX, a software package that eases in the installation of 103 software by automatically resolving their individual dependencies and also allowing the users to choose which software works best for them. Results MetumpX is a Ubuntu-based software package that facilitate easy download and installation of 103 tools spread across the standard metabolomics pipeline. As far as the authors know MetumpX is the only solution of its kind where the focus lies on automating development of software workstations. Availability and implementation https://github.com/hasaniqbal777/MetumpX-bin. Supplementary information Supplementary data are available at Bioinformatics online.

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Temporal network alignment via GoT-WAVE

Bioinformatics ◽

10.1093/bioinformatics/btz119 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3527-3529 ◽

Cited By ~ 3

Author(s):

David Aparício ◽

Pedro Ribeiro ◽

Tijana Milenković ◽

Fernando Silva

Keyword(s):

User Interface ◽

State Of The Art ◽

Source Code ◽

Network Alignment ◽

Supplementary Information ◽

Temporal Network ◽

Temporal Networks ◽

Supplementary Data ◽

Node Similarity ◽

User Friendly

Abstract Motivation Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results On synthetic networks, GoT-WAVE improves DynaWAVE’s accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE. Availability and implementation http://www.dcc.fc.up.pt/got-wave/ Supplementary information Supplementary data are available at Bioinformatics online.

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dittoSeq: universal user-friendly single-cell and bulk RNA sequencing visualization toolkit

Bioinformatics ◽

10.1093/bioinformatics/btaa1011 ◽

2020 ◽

Author(s):

Daniel G Bunis ◽

Jared Andrews ◽

Gabriela K Fragiadakis ◽

Trevor D Burt ◽

Marina Sirota

Keyword(s):

Single Cell ◽

R Package ◽

Color Blindness ◽

Ease Of Use ◽

Supplementary Information ◽

Supplementary Data ◽

Rnaseq Data ◽

Visualization Toolkit ◽

User Friendly ◽

Publication Quality

Abstract Summary A visualization suite for major forms of bulk and single-cell RNAseq data in R. dittoSeq is color blindness-friendly by default, robustly documented to power ease-of-use and allows highly customizable generation of both daily-use and publication-quality figures. Availability and implementation dittoSeq is an R package available through Bioconductor via an open source MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

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