scholarly journals CellProfiler 4: improvements in speed, utility and usability

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
David R. Stirling ◽  
Madison J. Swain-Bowden ◽  
Alice M. Lucas ◽  
Anne E. Carpenter ◽  
Beth A. Cimini ◽  
...  
Keyword(s):  
Large Scale ◽  
Biological Information ◽  
Source Image ◽  
Scale Analysis ◽  
Imaging Data ◽  
Computational Tools ◽  
Large Scale Analysis ◽  
Improved Performance ◽  
Free Open Source ◽  
Microscopy Images

Abstract Background Imaging data contains a substantial amount of information which can be difficult to evaluate by eye. With the expansion of high throughput microscopy methodologies producing increasingly large datasets, automated and objective analysis of the resulting images is essential to effectively extract biological information from this data. CellProfiler is a free, open source image analysis program which enables researchers to generate modular pipelines with which to process microscopy images into interpretable measurements. Results Herein we describe CellProfiler 4, a new version of this software with expanded functionality. Based on user feedback, we have made several user interface refinements to improve the usability of the software. We introduced new modules to expand the capabilities of the software. We also evaluated performance and made targeted optimizations to reduce the time and cost associated with running common large-scale analysis pipelines. Conclusions CellProfiler 4 provides significantly improved performance in complex workflows compared to previous versions. This release will ensure that researchers will have continued access to CellProfiler’s powerful computational tools in the coming years.

scholarly journals CellProfiler 4: Improvements in Speed, Utility and Usability

2021 ◽  
Author(s):  
David R Stirling ◽  
Madison J Swain-Bowden ◽  
Alice M Lucas ◽  
Anne E. Carpenter ◽  
Beth A Cimini ◽  
...  
Keyword(s):  
Image Analysis ◽  
User Interface ◽  
Large Scale ◽  
Source Image ◽  
Scale Analysis ◽  
Analysis Program ◽  
Computational Tools ◽  
Large Scale Analysis ◽  
Free Open Source ◽  
Microscopy Images

CellProfiler is a free, open source image analysis program which enables researchers to generate modular pipelines with which to process microscopy images into interpretable measurements. Here we describe CellProfiler 4, a new version of this software which has been ported to the Python 3 language. Based on user feedback, we have made several user interface refinements to improve the usability of the software. We introduced new modules to expand the capabilities of the software. We also evaluated performance and made targeted optimisations to reduce the time and cost associated with running common large-scale analysis pipelines. This release will ensure that researchers will have continued access to CellProfilers powerful computational tools in the coming years.

scholarly journals Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

2021 ◽  
Author(s):  
Mehdi A. Beniddir ◽  
Kyo Bin Kang ◽  
Grégory Genta-Jouve ◽  
Florian Huber ◽  
Simon Rogers ◽  
...  
Keyword(s):  
Natural Product ◽  
Large Scale ◽  
Scale Analysis ◽  
Computational Tools ◽  
Natural Product Discovery ◽  
Large Scale Analysis ◽  
Metabolite Annotation

This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.

scholarly journals REVA as a Well-curated Database for Human Expression-modulating Variants

2021 ◽  
Author(s):  
Yu Wang ◽  
Fang-Yuan Shi ◽  
Yu Liang ◽  
Ge Gao
Keyword(s):  
Large Scale ◽  
Regulatory Mechanism ◽  
State Of The Art ◽  
Scale Analysis ◽  
Computational Tools ◽  
Functional Annotations ◽  
Link Type ◽  
Large Scale Analysis ◽  
Multiple State ◽  
Limited Sensitivity

AbstractMore than 80% of disease- and trait-associated human variants are noncoding. By systematically screening multiple large-scale studies, we compiled REVA, a manually curated database for over 11.8 million experimentally tested noncoding variants with expression-modulating potentials. We provided 2424 functional annotations that could be used to pinpoint plausible regulatory mechanism of these variants. We further benchmarked multiple state-of-the-art computational tools and found their limited sensitivity remains a serious challenge for effective large-scale analysis. REVA provides high-qualify experimentally tested expression-modulating variants with extensive functional annotations, which will be useful for users in the noncoding variants community. REVA is available at http://reva.gao-lab.org.

scholarly journals Large-scale analysis, management, and retrieval of biological and medical images

2016 ◽  
Author(s):  
◽  
Jing Han
Keyword(s):  
Image Analysis ◽  
Big Data ◽  
Large Scale ◽  
Digital Pathology ◽  
Database Management System ◽  
Image Feature ◽  
Scale Analysis ◽  
Imaging Data ◽  
Data Infrastructure ◽  
Large Scale Analysis

Biomedical image data have been growing quickly in volume, speed, and complexity, and there is an increasing reliance on the analysis of these data. Biomedical scientists are in need of efficient and accurate analyses of large-scale imaging data, as well as innovative retrieval methods for visually similar imagery across a large-scale data collection to assist complex study in biological and medical applications. Moreover, biomedical images rely on increased resolution to capture subtle phenotypes of diseases, but this poses a challenge for clinicians to sift through haystacks of visual cues to make informative diagnoses. To tackle these challenges, we developed computational methods for large-scale analysis of biological and medical imaging data using simulated annealing to improve the quality of image feature extraction. Furthermore, we designed a Big Data infrastructure for the large-scale image analysis and retrieval of digital pathology images and conducted a longitudinal study of clinician's usage patterns of an image database management system (MDID) to shed light on the potential adoption of new informatics tools. This research also resulted in image analysis, management, and retrieval applications relevant to dermatology, radiology, pathology, life sciences, and palynology disciplines. These tools provide the potential to answer research questions that would not be answerable without our novel innovations that take advantage of Big Data technologies.

scholarly journals Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells

2021 ◽  
Author(s):  
Yu-jian Kang ◽  
Jing-Yi Li ◽  
Lan Ke ◽  
Shuai Jiang ◽  
De-chang Yang ◽  
...  
Keyword(s):  
Gene Transcription ◽  
Large Scale ◽  
Protein Translation ◽  
Quantitative Model ◽  
High Accuracy ◽  
Biological Information ◽  
Scale Analysis ◽  
Large Scale Analysis ◽  
Context Dependent ◽  
Key Steps

Gene transcription and protein translation are two key steps of the "central dogma". It is still a major challenge to quantitatively deconvolute factors contributing to the coding ability of transcripts in mammals. Here, we propose Ribosome Calculator (RiboCalc) for quantitatively modeling the coding ability of RNAs in human genome. In addition to effectively predicting the experimentally confirmed coding abundance via sequence and transcription features with high accuracy, RiboCalc provides interpretable parameters with biological information. Large-scale analysis further revealed a number of transcripts with a variety of coding ability for distinct types of cells (i.e., context-dependent coding transcripts, CDCTs), suggesting that, contrary to conventional wisdom, a transcript's coding ability should be modeled as a continuous spectrum with a context-dependent nature.

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