Automated cell tracking using StarDist and TrackMate

10.1101/2020.09.22.306233 ◽

2020 ◽

Author(s):

Elnaz Fazeli ◽

Nathan H. Roy ◽

Gautier Follain ◽

Romain F. Laine ◽

Lucas von Chamier ◽

...

Keyword(s):

Open Source Software ◽

Large Scale ◽

Cell Tracking ◽

Quantitative Imaging ◽

Immune Surveillance ◽

Supplementary Information ◽

Large Scale Analysis ◽

Deep Learning Network ◽

Cellular Locomotion ◽

Automatically Tracking

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline’s usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

Automated cell tracking using StarDist and TrackMate

F1000Research ◽

10.12688/f1000research.27019.1 ◽

2020 ◽

Vol 9 ◽

pp. 1279

Author(s):

Elnaz Fazeli ◽

Nathan H. Roy ◽

Gautier Follain ◽

Romain F. Laine ◽

Lucas von Chamier ◽

...

Keyword(s):

Open Source Software ◽

Large Scale ◽

Cell Tracking ◽

Quantitative Imaging ◽

Immune Surveillance ◽

Supplementary Information ◽

Large Scale Analysis ◽

Deep Learning Network ◽

Cellular Locomotion ◽

Automatically Tracking

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences

Bioinformatics ◽

10.1093/bioinformatics/btab025 ◽

2021 ◽

Author(s):

Ting-Hsuan Wang ◽

Cheng-Ching Huang ◽

Jui-Hung Hung

Keyword(s):

Open Source Software ◽

Large Scale ◽

A Priori ◽

Supplementary Information ◽

Supplementary Data ◽

Comparable Accuracy ◽

Meta Analyses ◽

Next Generation Sequencing Ngs ◽

Adapter Trimming ◽

Generation Sequencing

Abstract Motivation Cross-sample comparisons or large-scale meta-analyses based on the next generation sequencing (NGS) involve replicable and universal data preprocessing, including removing adapter fragments in contaminated reads (i.e. adapter trimming). While modern adapter trimmers require users to provide candidate adapter sequences for each sample, which are sometimes unavailable or falsely documented in the repositories (such as GEO or SRA), large-scale meta-analyses are therefore jeopardized by suboptimal adapter trimming. Results Here we introduce a set of fast and accurate adapter detection and trimming algorithms that entail no a priori adapter sequences. These algorithms were implemented in modern C++ with SIMD and multithreading to accelerate its speed. Our experiments and benchmarks show that the implementation (i.e. EARRINGS), without being given any hint of adapter sequences, can reach comparable accuracy and higher throughput than that of existing adapter trimmers. EARRINGS is particularly useful in meta-analyses of a large batch of datasets and can be incorporated in any sequence analysis pipelines in all scales. Availability and implementation EARRINGS is open-source software and is available at https://github.com/jhhung/EARRINGS. Supplementary information Supplementary data are available at Bioinformatics online.

From Aristotle to Ringelmann: a large-scale analysis of team productivity and coordination in Open Source Software projects

Empirical Software Engineering ◽

10.1007/s10664-015-9406-4 ◽

2015 ◽

Vol 21 (2) ◽

pp. 642-683 ◽

Cited By ~ 19

Author(s):

Ingo Scholtes ◽

Pavlin Mavrodiev ◽

Frank Schweitzer

Keyword(s):

Open Source ◽

Open Source Software ◽

Large Scale ◽

Scale Analysis ◽

Software Projects ◽

Large Scale Analysis ◽

Team Productivity

CellTracker (not only) for dummies

Bioinformatics ◽

10.1093/bioinformatics/btv686 ◽

2015 ◽

Vol 32 (6) ◽

pp. 955-957 ◽

Cited By ~ 46

Author(s):

Filippo Piccinini ◽

Alexa Kiss ◽

Peter Horvath

Keyword(s):

Graphical User Interface ◽

Open Source Software ◽

Phase Contrast ◽

Cell Tracking ◽

Source Code ◽

Software Tool ◽

Time Lapse ◽

Supplementary Information ◽

Differential Interference Contrast ◽

User Friendly

Abstract Motivation: Time-lapse experiments play a key role in studying the dynamic behavior of cells. Single-cell tracking is one of the fundamental tools for such analyses. The vast majority of the recently introduced cell tracking methods are limited to fluorescently labeled cells. An equally important limitation is that most software cannot be effectively used by biologists without reasonable expertise in image processing. Here we present CellTracker, a user-friendly open-source software tool for tracking cells imaged with various imaging modalities, including fluorescent, phase contrast and differential interference contrast (DIC) techniques. Availability and implementation: CellTracker is written in MATLAB (The MathWorks, Inc., USA). It works with Windows, Macintosh and UNIX-based systems. Source code and graphical user interface (GUI) are freely available at: http://celltracker.website/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Free and Open Source Software organizations: A large-scale analysis of code, comments, and commits frequency

PLoS ONE ◽

10.1371/journal.pone.0257192 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257192

Author(s):

Tadeusz Chełkowski ◽

Dariusz Jemielniak ◽

Kacper Macikowski

Keyword(s):

Open Source ◽

Open Source Software ◽

Large Scale ◽

Source Code ◽

Business Strategies ◽

Scale Analysis ◽

Large Scale Analysis ◽

Global Corporations ◽

Frequency Source

As Free and Open Source Software (FOSS) increases in importance and use by global corporations, understanding the dynamics of its communities becomes critical. This paper measures up to 21 years of activities in 1314 individual projects and 1.4 billion lines of code managed. After analyzing the FOSS activities on the projects and organizations level, such as commits frequency, source code lines, and code comments, we find that there is less activity now than there was a decade ago. Moreover, our results suggest a greater decrease in the activities in large and well-established FOSS organizations. Our findings indicate that as technologies and business strategies related to FOSS mature, the role of large formal FOSS organizations serving as intermediary between developers diminishes.

ClinVAP: a reporting strategy from variants to therapeutic options

Bioinformatics ◽

10.1093/bioinformatics/btz924 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2316-2317

Author(s):

Bilge Sürün ◽

Charlotta P I Schärfe ◽

Mathew R Divine ◽

Julian Heinrich ◽

Nora C Toussaint ◽

...

Keyword(s):

Large Scale ◽

Downstream Processing ◽

Supplementary Information ◽

Driver Gene ◽

Single Nucleotide Variants ◽

Large Scale Analysis ◽

Variant Information ◽

Personalized Oncology ◽

Gene Status ◽

Cancer Diagnosis And Therapy

Abstract Motivation Next-generation sequencing has become routine in oncology and opens up new avenues of therapies, particularly in personalized oncology setting. An increasing number of cases also implies a need for a more robust, automated and reproducible processing of long lists of variants for cancer diagnosis and therapy. While solutions for the large-scale analysis of somatic variants have been implemented, existing solutions often have issues with reproducibility, scalability and interoperability. Results Clinical Variant Annotation Pipeline (ClinVAP) is an automated pipeline which annotates, filters and prioritizes somatic single nucleotide variants provided in variant call format. It augments the variant information with documented or predicted clinical effect. These annotated variants are prioritized based on driver gene status and druggability. ClinVAP is available as a fully containerized, self-contained pipeline maximizing reproducibility and scalability allowing the analysis of larger scale data. The resulting JSON-based report is suited for automated downstream processing, but ClinVAP can also automatically render the information into a user-defined template to yield a human-readable report. Availability and implementation ClinVAP is available at https://github.com/PersonalizedOncology/ClinVAP. Supplementary information Supplementary data are available at Bioinformatics online.

Morphing projections: a new visual technique for fast and interactive large-scale analysis of biomedical datasets

Bioinformatics ◽

10.1093/bioinformatics/btaa989 ◽

2020 ◽

Author(s):

Ignacio Díaz ◽

José M Enguita ◽

Ana González ◽

Diego García ◽

Abel A Cuadrado ◽

...

Keyword(s):

Data Analytics ◽

Domain Knowledge ◽

Large Scale ◽

User Interaction ◽

Machine Learning Algorithms ◽

Supplementary Information ◽

High Dimensional ◽

Biomedical Data ◽

Efficient Manner ◽

Large Scale Analysis

Abstract Motivation Biomedical research entails analyzing high dimensional records of biomedical features with hundreds or thousands of samples each. This often involves using also complementary clinical metadata, as well as a broad user domain knowledge. Common data analytics software makes use of machine learning algorithms or data visualization tools. However, they are frequently one-way analyses, providing little room for the user to reconfigure the steps in light of the observed results. In other cases, reconfigurations involve large latencies, requiring a retraining of algorithms or a large pipeline of actions. The complex and multiway nature of the problem, nonetheless, suggests that user interaction feedback is a key element to boost the cognitive process of analysis, and must be both broad and fluid. Results In this article, we present a technique for biomedical data analytics, based on blending meaningful views in an efficient manner, allowing to provide a natural smooth way to transition among different but complementary representations of data and knowledge. Our hypothesis is that the confluence of diverse complementary information from different domains on a highly interactive interface allows the user to discover relevant relationships or generate new hypotheses to be investigated by other means. We illustrate the potential of this approach with three case studies involving gene expression data and clinical metadata, as representative examples of high dimensional, multidomain, biomedical data. Availability and implementation Code and demo app to reproduce the results available at https://gitlab.com/idiazblanco/morphing-projections-demo-and-dataset-preparation. Supplementary information Supplementary data are available at Bioinformatics online.

HOW DO OSS PROJECTS CHANGE IN NUMBER AND SIZE? A LARGE-SCALE ANALYSIS TO TEST A MODEL OF PROJECT GROWTH

Advances in Complex Systems ◽

10.1142/s0219525915500083 ◽

2014 ◽

Vol 17 (07n08) ◽

pp. 1550008 ◽

Cited By ~ 6

Author(s):

FRANK SCHWEITZER ◽

VAHAN NANUMYAN ◽

CLAUDIO J. TESSONE ◽

XI XIA

Keyword(s):

Size Distribution ◽

Open Source Software ◽

Large Scale ◽

Firm Growth ◽

Data Set ◽

Monthly Data ◽

Large Scale Data ◽

Large Scale Analysis ◽

Time Period ◽

Scale Data

Established open source software (OSS) projects can grow in size if new developers join, but also the number of OSS projects can grow if developers choose to found new projects. We discuss to what extent an established model for firm growth can be applied to the dynamics of OSS projects. Our analysis is based on a large-scale data set from SourceForge (SF) consisting of monthly data for 10 years, for up to 360,000 OSS projects and up to 340,000 developers. Over this time period, we find an exponential growth both in the number of projects and developers, with a remarkable increase of single-developer projects after 2009. We analyze the monthly entry and exit rates for both projects and developers, the growth rate of established projects and the monthly project size distribution. To derive a prediction for the latter, we use modeling assumptions of how newly entering developers choose to either found a new project or to join existing ones. Our model applies only to collaborative projects that are deemed to grow in size by attracting new developers. We verify, by a thorough statistical analysis, that the Yule–Simon distribution is a valid candidate for the size distribution of collaborative projects except for certain time periods where the modeling assumptions no longer hold. We detect and empirically test the reason for this limitation, i.e., the fact that an increasing number of established developers found additional new projects after 2009.

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

10.1101/285965 ◽

2018 ◽

Author(s):

Alexander R. Gawronski ◽

Michael Uhl ◽

Yajia Zhang ◽

Yen-Yi Lin ◽

Yashar S. Niknafs ◽

...

Keyword(s):

Protein Binding ◽

Protein Interactions ◽

Large Scale ◽

Mechanistic Explanation ◽

Supplementary Information ◽

Target Pair ◽

P Value ◽

Binding Prediction ◽

Large Scale Analysis ◽

Rna Interaction

AbstractMotivationLong non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nucleotides that do not get translated into proteins. Often these transcripts are processed (spliced, capped, polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA.ApproachHere we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined p-value.ResultsWe applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29, ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays.AvailabilityMechRNA is available for download athttps://bitbucket.org/compbio/[email protected],[email protected] informationSupplementary data are available atBioinformaticsonline.