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.

An atlas and database of neuropeptide gene expression in the adult zebrafish forebrain.

Zebrafish is a useful model organism in neuroscience; however, its gene expression atlas in the adult brain is not well developed. In the present study, we examined the expression of 38 neuropeptides, comparing with GABAergic and glutamatergic neuron marker genes in the adult zebrafish brain by comprehensive in situ hybridization. The results are summarized as an expression atlas in 19 coronal planes of the forebrain. Furthermore, the scanned data of all brain sections were made publicly available in the Adult Zebrafish Brain Gene Expression Database (https://ssbd.riken.jp/azebex/). Based on these data, we performed detailed comparative neuroanatomical analyses of the hypothalamus and found that several regions previously described as one nucleus in the reference zebrafish brain atlas contain two or more subregions with significantly different neuropeptide/neurotransmitter expression profiles. Subsequently, we compared the expression data in zebrafish telencephalon and hypothalamus obtained in this study with those in mice, by performing a cluster analysis. As a result, several nuclei in zebrafish and mice were clustered in close vicinity. The present expression atlas, database, and anatomical findings will contribute to future neuroscience research using zebrafish.

NeuroGT: A brain atlas of neurogenic tagging CreER drivers for birthdate-based classification and manipulation of mouse neurons.

Neuronal birthdate is one of the major determinants of neuronal phenotypes. However, most birthdating methods are retrospective in nature, allowing very little experimental access to the classified neuronal subsets. Here, we introduce four neurogenic tagging mouse lines, which can assign CreER-loxP recombination to neuron subsets that share the same differentiation timing in living animals and enable various experimental manipulations of the classified subsets. We constructed a brain atlas of the neurogenic tagging mouse lines (NeuroGT), which includes holistic image data of the loxP-recombined neurons and their processes across the entire brain that were tagged on each single day during the neurodevelopmental period. This image database, which is open to the public, offers investigators the opportunity to find specific neurogenic tagging driver lines and the stages of tagging appropriate for their own research purposes.

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.