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PubMed2025

polars-bio-fast, scalable, and out-of-core operations on large genomic interval datasets.

MOTIVATION: Genomic studies very often rely on computationally intensive analyses of relationships between features, which are typically represented as intervals along a 1D coordinate system (such as positions on a chromosome). In this context, the Python programming language is extensively used for manipulating and analyzing data stored in a tabular form of rows and columns, called a DataFrame. Pandas is the most widely used Python DataFrame package and has been criticized for inefficiencies and scalability issues, which its modern alternative-Polars-aims to address with a native backend written in the Rust programming language. RESULTS: polars-bio is a Python library that enables fast, parallel and out-of-core operations on large genomic interval datasets. Its main components are implemented in Rust, using the Apache DataFusion query engine and Apache Arrow for efficient data representation. It is compatible with Polars and Pandas DataFrame formats. In a real-world comparison (107 versus 1.2×106 intervals), our library runs overlap queries 6.5×, nearest queries 15.5×, count_overlaps queries 38×, and coverage queries 15× faster than Bioframe. On equally sized synthetic sets (107 versus 107), the corresponding speedups are 1.6×, 5.5×, 6×, and 6×. In streaming mode, on real and synthetic interval pairs, our implementation uses 90× and 15× less memory for overlap, 4.5× and 6.5× less for nearest, 60× and 12× less for count_overlaps, and 34× and 7× less for coverage than Bioframe. Multi-threaded benchmarks show good scalability characteristics. To the best of our knowledge, polars-bio is the most efficient single-node library for genomic interval DataFrames in Python. AVAILABILITY AND IMPLEMENTATION: polars-bio is an open-source Python package distributed under the Apache License available for major platforms, including Linux, macOS, and Windows in the PyPI registry. The online documentation is https://biodatageeks.org/polars-bio/ and the source code is available on GitHub: https://github.com/biodatageeks/polars-bio and Zenodo: https://doi.org/10.5281/zenodo.16374290. are available at Bioinformatics online.

Software↗
PubMed1979

Poikilotherms as reservoirs of Q-fever (Coxiella burnetii) in Uttar Pradesh.

Water snakes (Natrix natrix), rat snakes (Ptyas korros), cobras (Naja naja), pythons (Python molurus), tortoises (Kachuga sp.), plankton fish (Cirrhina mrigala), frogs (Rana tigrina), toads (Bufo sp.) and monitors (Varanus indicus) were screened for evidence of Q-fever infection by the capillary agglutination test on sera to detect antibodies and/or by attempts to demonstrate Coxiella burnetii in spleen and liver samples. Sero-reactors were observed among water and rat snakes, pythons and tortoises. The organism was isolated from the spleen and liver of the monitor, tortoise and python.

Amphibians↗
PubMed1975

Epizootic of fatal amebiasis among exhibited snakes: epidemiologic, pathologic, and chemotherapeutic considerations.

An epizootic of reptilian amebiasis seems to have caused the death of 15 to 16 large and valuable captive snakes (boas, pythons, and anacondas) occupying one of 5 large display dioramas in the Steinhart Aquarium of the California Academy of Science, Golden Gate Park, San Francisco. Subsequent review of previous snake deaths in the colony indicated that of 464 snakes that had died since early 1969, 89 snakes had intestinal or hepatic lesions, and 80 of these snakes had pathologic features which involved severe intestinal ulceration, hemorrhage, and massive enteritis, with or without hepatic necrosis and destruction, condition compatible with Entamoeba invadens infection. The present epizootic began in November, 1972, with the death by acute enteritis of a red-tailed boa constrictor (Boa constrictor amarali) and was followed by the loss of 15 other large boids and pythonids. The affected snakes became immobile, refused to feed, and began to die 10 weeks after the death of the red-tailed boa. Seven boa constrictors, 4 pythons, and 4 anacondas from the same diorama died during the ensuing 10 weeks. Entamoeba invadens trophozoites were identified in the stool of the remaining living snake, a 3-m boa constrictor, and in the liver and the intestinal tissue of 1 of the dead boas examined microscopically. The parasite was also found in the stool of a giant Burmese python (Python molurus bivittatus) that died in the adjacent diorama and in the tissues of a blue-tongued skink (Tiliqua scincoides), separately housed, that died of enteritis during this period. Amebic cysts were recovered from turtle and alligator fecal samples taken from a central "swamp," or reservoir, draining the dioramas, water that is returned to the snake display areas after passage through a biological sand-gravel filter and ultraviolet radiation exposure. Cultures from these stools were positive and proved lethal to an experimentally infected boa constrictor. Treatment of the surviving snake in the affected diorama with metronidazole at the dose rate of 275 mg/kg proved rapidly effective; toxicosis was not observed. Other snakes and lizards suspected of having the infection were similarly treated and returned to normal behavior and feeding patterns. Epidemiologic considerations review the probable mode of introduction and spread of this highly lethal snake pathogen and recommendations are made for avoiding infection, prophylactic treatment, and handling of similar epizootics when they do occur among captive reptiles in aquariums, zoos, and research laboratories.

Amebiasis↗
PubMed1976

Descending pathways from the brain stem to the spinal cord in some reptiles. II. Course and site of termination.

The course and termination of the pathways descending from the brain stem to the spinal cord have been studied by tracing the ensuing anterograde fiber degeneration, following appropriate lesions in the reptiles Testudo hermanni, Tupinambis nigropunctatus and Python reticulatus. In these reptiles the presence of interstitiospinal, vestibulospinal and reticulospinal pathways has been demonstrated. A crossed rubrospinal tract has been shown in the turtle and lizard, but could not be demonstrated in the Python. The presence of a tectospinal pathway of any importance could not be shown. However, the tectum mesencephali has been found to project profusely to the brain stem reticular formation. The interstitiospinal tract projects predominantly to the ipsilateral side of the spinal cord. The vestibulospinal projection, arising from the large-celled nucleus vestibularis ventrolateralis, comprises a large uncrossed and a small decussating component. The rubrospinal pathway terminates in a particular area of the spinal gray, i.e., the intermediate zone, whereas the interstitiospainal, reticulospinal and vestibulospinal tracts all terminate in the medial part of the ventral horn. It appeared that the classification of descending pathways as advocated in mammals by Kuypers ('64) into lateral and medial systems can be readily applied to reptiles. The lateral system terminates in the dorsal and lateral parts of the intermediate zone, the medial system predominantely in the dorsomedial part of the ventral horn. This classification renders it likely that the absence of a lateral focus of termination as well as the absence of a rubrospinal tract in the Python, is correlated to the absence of limbs. A comparison of experimental data concerning the systems descending from the brain stem to the spinal cord in amphibians, reptiles, birds and mammals suggests that these systems with regard to origin, course and termination have a basic pattern in common.

Animals↗
PubMed2025

Parsing GTF and FASTA files using the eccLib Library.

SUMMARY: Leveraging the Python/C API, eccLib was developed as a high-performance library designed for parsing genomic files and analysing genomic contexts. To the best of the authors' knowledge, it is the fastest Python-based solution available. With eccLib, users can efficiently parse GTF/GFFv3 and FASTA files and utilize the provided methods for additional analysis. AVAILABILITY AND IMPLEMENTATION: This library is implemented in C and distributed under the GPL-3.0 licence. It is compatible with any system that has the Python interpreter (CPython) installed. The use of C enables numerous optimizations at both the implementation and algorithmic levels, which are either unachievable or impractical in Python.

Software↗
PubMed2026

Uchimata: a toolkit for visualization of 3D genome structures on the web and in computational notebooks.

SUMMARY: Uchimata is a toolkit for visualization of 3D structures of genomes. It consists of two packages: a Javascript library facilitating the rendering of 3D models of genomes, and a Python widget for visualization in Jupyter Notebooks. Main features include an expressive way to specify visual encodings, and filtering of 3D genome structures based on genomic semantics and spatial aspects. Uchimata is designed to be highly integratable with biological tooling available in Python. AVAILABILITY AND IMPLEMENTATION: Uchimata is released under the MIT License. The Javascript library is available on NPM, while the widget is available as a Python package hosted on PyPI. The source code for both is available publicly on Github (https://github.com/hms-dbmi/uchimata and https://github.com/hms-dbmi/uchimata-py) and Zenodo (https://doi.org/10.5281/zenodo.17831959 and https://doi.org/10.5281/zenodo.17832045). The documentation with examples is hosted at https://hms-dbmi.github.io/uchimata/.

Software↗
PubMed2026

BioNeuralNet: a graph neural network based Multi-Omics network data analysis tool.

SUMMARY: Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical challenges. Network-based approaches have advanced multi-omics research by effectively capturing biologically relevant relationships among molecular features (e.g., genes, proteins, metabolites). While these methods are powerful for representing molecular interactions, there remains a need for tools specifically designed to effectively utilize these network representations across diverse downstream analyses. To fulfill this need, we introduce BioNeuralNet, a flexible and modular Python framework tailored for end-to-end network-based multi-omics data analysis. BioNeuralNet leverages Graph Neural Networks (GNNs) to learn biologically meaningful low-dimensional representations from multi-omics networks, converting these complex molecular networks into versatile embeddings. BioNeuralNet supports all major stages of multi-omics network analysis, including several network construction techniques, generation of low-dimensional representations, and a broad range of downstream analytical tasks. Its extensive utilities, including diverse GNN architectures, and compatibility with established Python packages (e.g., scikit-learn, PyTorch, NetworkX), enhance usability and facilitate quick adoption. BioNeuralNet is an open-source, user-friendly, and extensively documented framework designed to support flexible and reproducible multi-omics network analysis in precision medicine. AVAILABILITY AND IMPLEMENTATION: The BioNeuralNet library is available via The Python Package Index (PyPI). Source code, documentation, tutorials, and workflows are hosted at https://bioneuralnet.readthedocs.io. Code archived at https://doi.org/10.5281/zenodo.17503083.

Graph Neural Networks↗
PubMed2025

Uchimata: a toolkit for visualization of 3D genome structures on the web and in computational notebooks.

SUMMARY: Uchimata is a toolkit for visualization of 3D structures of genomes. It consists of two packages: a Javascript library facilitating the rendering of 3D models of genomes, and a Python widget for visualization in Jupyter Notebooks. Main features include an expressive way to specify visual encodings, and filtering of 3D genome structures based on genomic semantics and spatial aspects. Uchimata is designed to be highly integratable with biological tooling available in Python. AVAILABILITY AND IMPLEMENTATION: Uchimata is released under the MIT License. The Javascript library is available on NPM, while the widget is available as a Python package hosted on PyPI. The source code for both is available publicly on Github (https://github.com/hms-dbmi/uchimata and https://github.com/hms-dbmi/uchimata-py). The documentation with examples is hosted at https://hms-dbmi.github.io/uchimata/. CONTACT: david_kouril@hms.harvard.edu or nils@hms.harvard.edu.

Journal Article↗
PubMed1976

Descending pathways from the brain stem to the spinal cord in some reptiles. I. Origin.

In the present study of the origin of the pathways descending from the brain stem to the spinal cord has been investigated in the reptiles Testudo hermanni, Pseudemys scripta elegans, Tupinambis nigropunctatus and Python reticulatus. These reptiles, using highly different types of progression, have been selected, because fundamental variations in the organization of the central motor apparatus are to be expected. The origin of the descending pathways has been demonstrated by recording the occurrence of retrograde cell changes following hemicordotomies and by searching for labeled cells following injection into the spinal cord of the enzyme horseradish peroxidase. In the reptiles studies the presence of interstitiospinal, vestibulospinal and reticulospinal pathways could be demonstrated. A crossed rubrospinal tract has been shown in the turtles and in the lizard, but could not be demonstrated in the Python. The presence of a direct tectospinal pathway could not be shown.

Animals↗
PubMed1988

Riboflavin-binding protein from reptiles: a comparison with avian riboflavin-binding proteins.

1. Riboflavin-binding protein (RBP) has been isolated for the first time from reptilian sources. 2. RBP from eggs of Python molurus (Indian python) and Chrysemys picta (painted turtle) has been isolated and compared to RBP from Gallus gallus domesticus (chicken), a well-characterized protein, and a newly isolated RBP from Cairina moschata (Muscovy duck). 3. Each of the proteins is phosphorylated and glycosylated. 4. The ratio of riboflavin binding to protein is 1:1 and the KD for each protein is between 1-3 nM. 5. The mol. wts, different for each species, range from 30,000-40,000, with the reptilian proteins being approx. 10,000 larger than the avian proteins.

Amino Acids↗
PubMed2025

GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretation.

SUMMARY: GeneFEAST, implemented in Python, is a gene-centric functional enrichment analysis summarization and visualization tool that can be applied to large functional enrichment analysis (FEA) results arising from upstream FEA pipelines. It produces a systematic, navigable HTML report, making it easy to identify sets of genes putatively driving multiple enrichments and to explore gene-level quantitative data first used to identify input genes. Further, GeneFEAST can juxtapose FEA results from multiple studies, making it possible to highlight patterns of gene expression amongst genes that are differentially expressed in at least one of multiple conditions, and which give rise to shared enrichments under those conditions. Thus, GeneFEAST offers a novel, effective way to address the complexities of linking up many overlapping FEA results to their underlying genes and data, advancing gene-centric hypotheses, and providing pivotal information for downstream validation experiments. AVAILABILITY AND IMPLEMENTATION: GeneFEAST GitHub repository: https://github.com/avigailtaylor/GeneFEAST; Zenodo record: 10.5281/zenodo.14753734; Python Package Index: https://pypi.org/project/genefeast; Docker container: ghcr.io/avigailtaylor/genefeast.

Software↗