Pynoma, PyABraOM and BIOVARS: Towards genetic variant data acquisition and integration
Carneiro, P.; Colombelli, F.; Recamonde-Mendoza, M.; Matte, U.
Show abstract
MotivationAdvances in genomic sequencing of human populations have generated a large amount of genomics data deposited in multiple sources. Programmatic batch searches executed at once are of great scientific interest to ease genomic investigations by retrieving and integrating this massive and decentralized data with little manual intervention. ResultsPynoma and PyABraOM APIs were developed to offer multiple queries in gnomAD and ABraOM databases, respectively. A centralized search in these databases with data integration is offered by a third API, BIOVARS, which combines the resulting information with statistical and graphical visualizations. The implemented features are demonstrated in a case study using ACE2, ADAM17 and TMPRSS2 genes, which presents a generalizable workflow that shows how our APIs facilitate the access and integration of valuable biological data. AvailabilityAll the APIs are written in Python 3. Graphical visualizations for the retrieved data are provided by using the R language version 4.1. The source codes are publicly available and hosted on GitHub (github.com/bioinfo-hcpa). Contactumatte@hcpa.edu.br Supplementary informationSupplementary data is available at github@nbioinfo-hcpa.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Variant Graph Craft (VGC): A Comprehensive Tool for Analyzing Genetic Variation and Identifying Disease-Causing Variants. 97%
- ggcoverage: an R package to visualize and annotate genome coverage for various NGS data 96%
- chromoMap: An R package for Interactive Visualization and Annotation of Chromosomes 95%
Similar papers in this journal
- Machado: open source genomics data integration framework 97%
- DivBrowse - interactive visualization and exploratory data analysis of variant call matrices 97%
- SnpHub: an easy-to-set-up web server framework for exploring large-scale genomic variation data in the post-genomic era with applications in wheat 96%
Similar papers in this journal
- MSABrowser: dynamic and fast visualization of sequence alignments, variations, and annotations 95%
- RCX - an R package adapting the Cytoscape Exchange format for biological networks 94%
- The network makeup artist (NORMA-2.0): Distinguishing annotated groups in a network using innovative layout strategies 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.