OME-Zarr: a cloud-optimized bioimaging file format with international community support.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.

OME-Zarr: a cloud-optimized bioimaging file format with international community support.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

PHi-C2: interpreting Hi-C data as the dynamic 3D genome state.

SUMMARY: High-throughput chromosome conformation capture (Hi-C) is a widely used assay for studying the three-dimensional (3D) genome organization across the whole genome. Here, we present PHi-C2, a Python package supported by mathematical and biophysical polymer modeling that converts input Hi-C matrix data into the polymer model's dynamics, structural conformations and rheological features. The updated optimization algorithm for regenerating a highly similar Hi-C matrix provides a fast and accurate optimal solution compared to the previous version by eliminating the factors underlying the inefficiency of the optimization algorithm in the iterative optimization process. In addition, we have enabled a Google Colab workflow to run the algorithm, wherein users can easily change the parameters and check the results in the notebook. Overall, PHi-C2 represents a valuable tool for mining the dynamic 3D genome state embedded in Hi-C data. AVAILABILITY AND IMPLEMENTATION: PHi-C2 as the phic Python package is freely available under the GPL license and can be installed from the Python package index. The source code is available from GitHub at https://github.com/soyashinkai/PHi-C2. Moreover, users do not have to prepare a Python environment because PHi-C2 can run on Google Colab (https://bit.ly/3rlptGI). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BD5: An open HDF5-based data format to represent quantitative biological dynamics data.

BD5 is a new binary data format based on HDF5 (hierarchical data format version 5). It can be used for representing quantitative biological dynamics data obtained from bioimage informatics techniques and mechanobiological simulations. Biological Dynamics Markup Language (BDML) is an XML (Extensible Markup Language)-based open format that is also used to represent such data; however, it becomes difficult to access quantitative data in BDML files when the file size is large because parsing XML-based files requires large computational resources to first read the whole file sequentially into computer memory. BD5 enables fast random (i.e., direct) access to quantitative data on disk without parsing the entire file. Therefore, it allows practical reuse of data for understanding biological mechanisms underlying the dynamics.

Phenotype Analysis Method for Identification of Gene Functions Involved in Asymmetric Division of Caenorhabditis elegans.

In gene function analysis, it is arduous to identify gene function individually, and the way to screen out all involved genes according to a particular phenotype or disease usually shows us little information for a specific problem. We present a data-driven analysis system based on wild type (WT) embryos to study the concrete function of each gene associated with certain category of abnormal phenotypes. It can be applied to genes with very few RNAi embryos. Instead of presupposing the particular function of a gene, its function is confirmed by the statistical testing of built models. The scheme includes the following five: first, verify the to be detected genes and determine related recognized features according to the given category; second, compute the value of each feature based on WT embryos and merge them by principal component analysis (PCA); third, for each of the selected components of PCA, build a normal distribution and verify its normality; fourth, project the RNAi embryos to each component and probe them; and finally, analyze the more detailed functions of each gene based on the physical or biological meaning of each component. Choosing the first-round asymmetric division process of Caenorhabditis elegans as the phenotype, experimental results show that on the different aspects of the asymmetric division process, par-2, par-3, and let-754 are related to scalar differences; dcn-1 and mcm-5 are associated with the divergences of scalar variation, which may reflect the disaccord in development; and dcn-1, par-2, and par-3 are involved with morphological discrepancies.

SSBD: a database of quantitative data of spatiotemporal dynamics of biological phenomena.

MOTIVATION: Rapid advances in live-cell imaging analysis and mathematical modeling have produced a large amount of quantitative data on spatiotemporal dynamics of biological objects ranging from molecules to organisms. There is now a crucial need to bring these large amounts of quantitative biological dynamics data together centrally in a coherent and systematic manner. This will facilitate the reuse of this data for further analysis. RESULTS: We have developed the Systems Science of Biological Dynamics database (SSBD) to store and share quantitative biological dynamics data. SSBD currently provides 311 sets of quantitative data for single molecules, nuclei and whole organisms in a wide variety of model organisms from Escherichia coli to Mus musculus The data are provided in Biological Dynamics Markup Language format and also through a REST API. In addition, SSBD provides 188 sets of time-lapse microscopy images from which the quantitative data were obtained and software tools for data visualization and analysis. AVAILABILITY AND IMPLEMENTATION: SSBD is accessible at http://ssbd.qbic.riken.jp CONTACT: sonami@riken.jp.

Biological Dynamics Markup Language (BDML): an open format for representing quantitative biological dynamics data.

MOTIVATION: Recent progress in live-cell imaging and modeling techniques has resulted in generation of a large amount of quantitative data (from experimental measurements and computer simulations) on spatiotemporal dynamics of biological objects such as molecules, cells and organisms. Although many research groups have independently dedicated their efforts to developing software tools for visualizing and analyzing these data, these tools are often not compatible with each other because of different data formats. RESULTS: We developed an open unified format, Biological Dynamics Markup Language (BDML; current version: 0.2), which provides a basic framework for representing quantitative biological dynamics data for objects ranging from molecules to cells to organisms. BDML is based on Extensible Markup Language (XML). Its advantages are machine and human readability and extensibility. BDML will improve the efficiency of development and evaluation of software tools for data visualization and analysis. AVAILABILITY AND IMPLEMENTATION: A specification and a schema file for BDML are freely available online at http://ssbd.qbic.riken.jp/bdml/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

WDDD: Worm Developmental Dynamics Database.

During animal development, cells undergo dynamic changes in position and gene expression. A collection of quantitative information about morphological dynamics under a wide variety of gene perturbations would provide a rich resource for understanding the molecular mechanisms of development. Here, we created a database, the Worm Developmental Dynamics Database (http://so.qbic.riken.jp/wddd/), which stores a collection of quantitative information about cell division dynamics in early Caenorhabditis elegans embryos with single genes silenced by RNA-mediated interference. The information contains the three-dimensional coordinate values of the outlines of nuclear regions and the dynamics of the outlines over time. The database provides free access to 50 sets of quantitative data for wild-type embryos and 136 sets of quantitative data for RNA-mediated interference embryos corresponding to 72 of the 97 essential embryonic genes on chromosome III. The database also provides sets of four-dimensional differential interference contrast microscopy images on which the quantitative data were based. The database will provide a novel opportunity for the development of computational methods to obtain fresh insights into the mechanisms of development. The quantitative information and microscopy images can be synchronously viewed through a web browser, which is designed for easy access by experimental biologists.

A proof of the DBRF-MEGN method, an algorithm for deducing minimum equivalent gene networks.

BACKGROUND: We previously developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method, which deduces the most parsimonious signed directed graphs (SDGs) consistent with expression profiles of single-gene deletion mutants. However, until the present study, we have not presented the details of the method's algorithm or a proof of the algorithm. RESULTS: We describe in detail the algorithm of the DBRF-MEGN method and prove that the algorithm deduces all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants. CONCLUSIONS: The DBRF-MEGN method provides all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.

DBRF-MEGN method: an algorithm for deducing minimum equivalent gene networks from large-scale gene expression profiles of gene deletion mutants.

MOTIVATION: Large-scale gene expression profiles measured in gene deletion mutants are invaluable sources for identifying gene regulatory networks. Signed directed graph (SDG) is the most common representation of gene networks in genetics and cell biology. However, no practical procedure that deduces SDGs consistent with such profiles has been developed. RESULTS: We developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method in which an algorithm deduces the most parsimonious SDGs consistent with expression profiles of gene deletion mutants. Positive (or negative) directed edges representing positive (or negative) gene regulations are deduced by comparing the gene expression level between the wild-type and mutant. The most parsimonious SDGs are deduced using graph theoretical procedures. Compensation for excess removal of edges by restoring a minimum number of edges makes the method applicable to cyclic gene networks. Use of independent groups of edges greatly reduces the computational cost, thus making the method applicable to large-scale expression profiles. We confirmed the applicability of our method by applying it to the gene expression profiles of 265 Saccharomyces cerevisiae deletion mutants, and we confirmed our method's validity by comparing the pheromone response pathway, general amino acid control system, and copper and iron homeostasis system deduced by our method with those reported in the literature. Interpretation of the gene network deduced from the S. cerevisiae expression profiles by using our method led to the prediction of 132 transcriptional targets and modulators of transcriptional activity of 18 transcriptional regulators. AVAILABILITY: The software is available on request.