Search PubMedSearch

SEARCH · Search PubMed

Results for “python”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources

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

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

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

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

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

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

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

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

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

Predicting coarse-grained representations of biogeochemical cycles from metabarcoding data.

MOTIVATION: Taxonomic analysis of environmental microbial communities is now routinely performed thanks to advances in DNA sequencing. Determining the role of these communities in global biogeochemical cycles requires the identification of their metabolic functions, such as hydrogen oxidation, sulfur reduction, and carbon fixation. These functions can be directly inferred from metagenomics data, but in many environmental applications metabarcoding is still the method of choice. The reconstruction of metabolic functions from metabarcoding data and their integration into coarse-grained representations of biogeochemical cycles remains a difficult bioinformatics problem today. RESULTS: We developed a pipeline, called Tabigecy, which exploits taxonomic affiliations to predict metabolic functions constituting biogeochemical cycles. In a first step, Tabigecy uses the tool EsMeCaTa to predict consensus proteomes from input affiliations. To optimize this process, we generated a precomputed database containing information about 2404 taxa from UniProt. The consensus proteomes are searched using bigecyhmm, a newly developed Python package relying on Hidden Markov Models to identify key enzymes involved in metabolic function of biogeochemical cycles. The metabolic functions are then projected on coarse-grained representation of the cycles. We applied Tabigecy to two salt cavern datasets and validated its predictions with microbial activity and hydrochemistry measurements performed on the samples. The results highlight the utility of the approach to investigate the impact of microbial communities on biogeochemical processes. AVAILABILITY AND IMPLEMENTATION: The Tabigecy pipeline is available at https://github.com/ArnaudBelcour/tabigecy. The Python package bigecyhmm and the precomputed EsMeCaTa database are also separately available at https://github.com/ArnaudBelcour/bigecyhmm and https://doi.org/10.5281/zenodo.13354073, respectively.

Metagenomics

RabbitSketch: a high-performance sketching library for genome analysis.

SUMMARY: We present RabbitSketch, a highly optimized library of sketching algorithms such as MinHash, OrderMinHash, and HyperLogLog that can exploit the power of modern multi-core CPUs. It provides significant speedups compared to existing implementations, ranging from 2.30× to 49.55×, as well as flexible and easy-to-use interfaces for both Python and C++. As a result, the similarity analysis of 455GB genomic data can be completed in only 5 minutes using RabbitSketch with merely 20 lines of Python code. As a case study, we enhanced RabbitTClust by integrating RabbitSketch's Kssd algorithm, resulting in a 1.54× speedup with no loss in accuracy. AVAILABILITY AND IMPLEMENTATION: RabbitSketch is available at https://github.com/RabbitBio/RabbitSketch with an archived version at Zenodo: https://doi.org/10.5281/zenodo.14903962. Detailed API documentation is available at https://rabbitsketch.readthedocs.io/en/latest.

Software

FuNTB: a functional network clustering tool for the analysis of genome-wide genetic variants in Mycobacterium tuberculosis.

MOTIVATION: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), still claims around 1.25 million lives each year. The growing threat of drug resistance-often driven by single‑nucleotide polymorphisms (SNPs) in Mtb genomes underscores the need for high‑quality genomic data and powerful bioinformatics tools. We present FuNTB, a python‑based pipeline that detects non‑synonymous SNPs in Mtb and builds functional network clusters to reveal genotype-phenotype relationships. RESULTS: FuNTB profiles non‑synonymous SNPs at the gene level across user‑defined phenotypes, pinpointing both shared and unique mutations. It ingests annotated Variant Call Format (VCF) files or MTBseq outputs and merges them with clinical metadata to produce network‑XML files compatible with Cytoscape and Gephi. When applied to the CRyPTIC Mtb collection, FuNTB rapidly recovered established resistance genes and surfaced novel candidates, validating its utility for mapping genotype-phenotype associations. AVAILABILITY AND IMPLEMENTATION: FuNTB is implemented in Python 3.8+ and is freely available under the MIT license at https://doi.org/10.5281/zenodo.15399917.

Mycobacterium tuberculosis

Fast and flexible minimizer digestion with digest.

SUMMARY: Minimizer digestion is an increasingly common component of bioinformatics tools, including tools for de Bruijn graph assembly and sequence classification. We describe a new open source tool and library to facilitate efficient digestion of genomic sequences. It can produce digests based on the related ideas of minimizers, modimizers or syncmers. Digest uses efficient data structures, scales well to many threads, and produces digests with expected spacings between digested elements. AVAILABILITY AND IMPLEMENTATION: Digest is implemented in C++17 with a Python API, and is available open-source at https://github.com/VeryAmazed/digest. The python library is available on Bioconda. Rust bindings are available as a public crate at https://crates.io/crates/digest-rs.

Software

Tsbrowse: an interactive browser for ancestral recombination graphs.

SUMMARY: Ancestral recombination graphs (ARGs) represent the interwoven paths of genetic ancestry of a set of recombining sequences. The ability to capture the evolutionary history of samples makes ARGs valuable in a wide range of applications in population and statistical genetics. ARG-based approaches are increasingly becoming a part of genetic data analysis pipelines due to breakthroughs enabling ARG inference at biobank-scale. However, there is a lack of visualization tools, which are crucial for validating inferences and generating hypotheses. We present tsbrowse, an open-source, web-based Python application for the interactive visualization of the fundamental building blocks of ARGs, i.e. nodes, edges and mutations. We demonstrate the application of tsbrowse to various data sources and scenarios, and highlight its key features of browsability along the genome, user interactivity, and scalability to very large sample sizes. AVAILABILITY AND IMPLEMENTATION: Tsbrowse is installed as a Python package from PyPI (https://pypi.org/project/tsbrowse/), while a development version is maintained at https://github.com/tskit-dev/tsbrowse. Documentation is available at https://tskit.dev/tsbrowse/docs/. Source code is archived on Zenodo with DOI, https://doi.org/10.5281/zenodo.15683039.

Software

ShortCake: an integrated platform for efficient and reproducible single-cell analysis.

SUMMARY: Recent advances in single-cell analysis have introduced new computational challenges. Researchers often need to use multiple analysis tools written in different programming languages while managing version conflicts between related packages within a single workflow. For the research community, minimizing the time spent on environment setup and installation issues is essential. We present ShortCake, a containerized platform that integrates a suite of single-cell analysis tools written in R and Python. ShortCake isolates competing Python tools into separate virtual environments that can be easily accessed within a Jupyter notebook. This enables users to effortlessly transition between various environments, including R, even within a single notebook. Additionally, ShortCake offers multiple "flavors," enabling users to select container images tailored to their specific needs. ShortCake provides a unified environment with fixed versions of various tools, thus streamlining workflows, reducing setup time, and improving reproducibility. AVAILABILITY AND IMPLEMENTATION: The ShortCake image is available on DockerHub (https://hub.docker.com/r/rnakato/shortcake) and Zenodo (DOIs: 10.5281/zenodo.17116765 and 10.5281/zenodo.17118158). The source code is available on GitHub (https://github.com/rnakato/ShortCake).

Single-Cell Analysis

Tomtom-lite: accelerating Tomtom enables large-scale and real-time motif similarity scoring.

SUMMARY: Pairwise sequence similarity is a core operation in genomic analysis, yet most attention has been given to sequences made up of discrete characters. With the growing prevalence of machine learning, calculating similarities for sequences of continuous representations, e.g. frequency-based position-weight matrices (PWMs) and attribution-based contribution-weight matrices, is taking on newfound importance. Tomtom has previously been proposed as an algorithm for identifying pairs of PWMs whose similarity is statistically significant, but the implementation remains inefficient for both real-time and large-scale analysis. Accordingly, we have re-implemented Tomtom as a numba-accelerated Python function that is natively multi-threaded, avoids cache misses, more efficiently caches intermediate values, and uses approximations at compute bottlenecks. Here, we provide a detailed description of the original Tomtom method and present results demonstrating that our re-implementation can achieve over a 1000-fold speedup compared with the original tool on reasonable tasks. AVAILABILITY AND IMPLEMENTATION: Our implementation of Tomtom is freely available as a Python package at https://github.com/jmschrei/memesuite-lite, which can be downloaded via pip install memelite or at https://zenodo.org/records/17008952.

Software

DNAvi: integration, statistics, and visualization of cell-free DNA fragment traces.

SUMMARY: DNAvi is a Python-based tool for rapid grouped analysis and visualization of cell-free DNA fragment size profiles directly from electrophoresis data, overcoming the need for sequencing in basic fragmentomic screenings. It enables normalization, statistical comparison, and publication-ready plotting of multiple samples, supporting quality control and exploratory fragmentomics in clinical and research workflows. AVAILABILITY AND IMPLEMENTATION: DNAvi is implemented in Python and freely available on GitHub at https://github.com/anjahess/DNAvi under a GNU General Public License v3.0, along with source code, documentation, and examples. An archived version is available under https://doi.org/10.5281/zenodo.18401705.

Software

ORFannotate: reproducible coding sequence annotation of transcriptome assemblies.

SUMMARY: Accurate annotation of coding sequences and translational features within transcript models is essential for interpreting assembled transcriptomes and their functional potential. Existing open reading frame (ORF) prediction tools typically operate on transcript FASTA files and do not reintegrate coding sequence (CDS) information back into transcript models, limiting their utility in long-read sequencing workflows where GTF/GFF annotations are the primary output. We present ORFannotate, a lightweight, GTF-native Python command-line tool that predicts ORFs from transcript annotations and reinserts precise, exon-aware CDS and UTR features into the original GTF/GFF file. In addition, ORFannotate provides biologically informative translational context by annotating Kozak sequence strength, detecting non-overlapping upstream ORFs (uORFs) with coding probabilities, characterising 5' and 3' untranslated regions (UTRs), and predicting nonsense-mediated decay (NMD) susceptibility. All annotations are consolidated in a transcript-level summary to support downstream analysis. By generating GTF files with accurate CDS annotations, ORFannotate facilitates reproducible analysis of both long- and short-read transcriptomes and integrates seamlessly with visualization tools, genome browsers, and comparative transcript analysis workflows. ORFannotate is fast, scalable and provides a practical solution for transcriptome annotation beyond coding potential prediction alone. AVAILABILITY AND IMPLEMENTATION: ORFannotate is implemented in Python and freely available under the GNU General Public License v3 (GPL-3.0) at: https://github.com/egustavsson/ORFannotate (DOI: https://doi.org/10.5281/zenodo.16812866).

Open Reading Frames