Pybedtools: a flexible Python library for manipulating genomic datasets and annotations
By Ryan K. Dale, Brent S. Pedersen, Aaron R. Quinlan. Bioinform., 2011
497 citations recorded by the source corpus.
Summary: pybedtools is a flexible Python software library for manipulating and exploring genomic datasets in many common formats. It provides an intuitive Python interface that extends upon the popular BEDTools genome arithmetic tools. The library is well documented and efficient, and allows researchers to quickly develop simple, yet powerful scripts that enable complex genomic analyses. Availability: pybedtools is maintained under the GPL license. Stable versions of pybedtools as well as documentation are available on the Python Package Index at http://pypi.python.org/pypi/pybedtools. Contact: dalerr@niddk.nih.gov; arq5x@virginia.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/7560bfb029d53514c7fb00c1e0b5b8bd8c54a08e
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Citations in the corpus
- Intervene: a tool for intersection and visualization of multiple gene or genomic region sets (2017, methodology): “The accompanying web interface is developed using Shiny (version 1.0.0), a web application framework for R. Intervene uses pybedtools [6] to perform genomic region set intersections and Seaborn (https://sea-born.pydata.org/), Matplotlib [7], UpSetR [8], and Corrplot [9] to generate figures.”
- SVIM: structural variant identification using mapped long reads (2018, methodology): “Subsequently, the diagram was generated using pybedtools [36] and matplotlib venn.”
- FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data (2017, methodology): “We developed the data generators using a combination of keras [38], pyfasta[39], pybedtools [40], and pyBigWig [41], FactorNet can accept an arbitrary number of bigWig files as input features, and we found the following signals to be highly informative for prediction:”