ArticleMolecular microbiology2020
BactMAP: An R package for integrating, analyzing and visualizing bacterial microscopy data.
Article in Molecular microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- HU promotes higher order chromosome organization and influences DNA replication rates in Streptococcus pneumoniae.Nucleic acids research · 2025Article
- Membrane staining and phospholipid tracking in Pseudomonas aeruginosa PAO1 using the phosphatidylcholine mimic propargyl-choline.Access microbiology · 2024Article
- Synthetic genetic oscillators demonstrate the functional importance of phenotypic variation in pneumococcal-host interactions.Nature communications · 2023Article
- High content quantitative imaging of Mycobacterium tuberculosis responses to acidic microenvironments within human macrophages.FEBS open bio · 2023Article
- Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation.Nature methods · 2022Article
- Amoxicillin-resistanteLife · 2022Article
- Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data.BMC bioinformatics · 2021Article
- Chromosome choreography during the non-binary cell cycle of a predatory bacterium.Current biology : CB · 2021Article
- CcrZ is a pneumococcal spatiotemporal cell cycle regulator that interacts with FtsZ and controls DNA replication by modulating the activity of DnaA.Nature microbiology · 2021Article
- Advances and opportunities in image analysis of bacterial cells and communities.FEMS microbiology reviews · 2021Article
- Inactivation of GalU Leads to a Cell Wall-Associated Polysaccharide Defect That Reduces the Susceptibility of Enterococcus faecalis to Bacteriolytic Agents.Applied and environmental microbiology · 2021Article
- Harnessing CRISPR-Cas9 for Genome Editing in Streptococcus pneumoniae D39V.Applied and environmental microbiology · 2021Article
- An Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy.Frontiers in cardiovascular medicine · 2021Article
- Single-Cell Analysis of Mycobacteria Using Microfluidics and Time-Lapse Microscopy.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Article
- BactMAP: An R package for integrating, analyzing and visualizing bacterial microscopy data.Molecular microbiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput analyses of single-cell microscopy data are a critical tool within the field of bacterial cell biology. Several programs have been developed to specifically segment bacterial cells from phase-contrast images. Together with spot and object detection algorithms, these programs offer powerful approaches to quantify observations from microscopy data, ranging from cell-to-cell genealogy to localization and movement of proteins. Most segmentation programs contain specific post-processing and plotting options, but these options vary between programs and possibilities to optimize or alter the outputs are often limited. Therefore, we developed BactMAP (Bacterial toolbox for Microscopy Analysis & Plotting), a command-line based R package that allows researchers to transform cell segmentation and spot detection data generated by different programs into various plots. Furthermore, BactMAP makes it possible to perform custom analyses and change the layout of the output. Because BactMAP works independently of segmentation and detection programs, inputs from different sources can be compared within the same analysis pipeline. BactMAP complies with standard practice in R which enables the use of advanced statistical analysis tools, and its graphic output is compatible with ggplot2, enabling adjustable plot graphics in every operating system. User feedback will be used to create a fully automated Graphical User Interface version of BactMAP in the future. Using BactMAP, we visualize key cell cycle parameters in Bacillus subtilis and Staphylococcus aureus, and demonstrate that the DNA replication forks in Streptococcus pneumoniae dissociate and associate before splitting of the cell, after the Z-ring is formed at the new quarter positions. BactMAP is available from https://veeninglab.com/bactmap.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.