PyABraOM is an API developed to facilitate the access to human variant data from ABraOM database to access data from GRCh37/hg19 and GRCh38/hg38 genome version. The package offers support to three kinds of different searches and the possibility to search in batches.For plotting the data, please take a look at the BIOVARS package. If you have scientific interests or want to use our package in formal reports, we kindly ask you to cite us in your publication: Carneiro, P., Colombelli, F., Recamonde-Mendoza, M., and Matte, U. (2022). Pynoma, PyABraOM and BIOVARS: Towards genetic variant data acquisition and integration. bioRxiv.
Currently there is not a PyPI version for the PyABraOM API, so the installation needs that you clone this repository and install it as local package.
$ git clone https://github.com/bioinfo-hcpa/pyABraOM.git
$ pip install -e pyABraOM #outside the directory
There are 3 kinds of search by using pyABraOM and all returned in the same format: Gene, Genomic Region and Variant search.
Since the ABraOM database has the two latest human genome versions, the user must specify the version in GRCh37/hg19 or GRCh38/hg38 to retrieve data from the database for the specific genome version. Besides, when searching by genes, genomic regions or variant for chromosomes X or Y, a new column "Number of Hemizygous" will be added to the output dataframe, so the user should have caution when performing pandas concatenation operations, resulting in table cells with NaN values. Futhermore, the GATK and CEGH filter provided from ABraOM are available, but latter is not in the default output dataframe.
The search method expects the gene name to be considered in the function Search_gene(version:str,gene:str,CEGH_Filter= False,Variant_ID= False, Process= True).
- version (str):the reference genome version (either "GRCh37/hg19" or "GRCh38/hg38")
- gene (str): the gene name to search in database
- CEGH_Filter (bool): whether to provide in-house quality control filter CEGH-USP
- Variant_ID (bool): whether to provide variant identifier
- Process (bool): whether to provide data in JSON format or pandas dataframe
import pyabraom
from pyabraom import Seach_gene
df = Search_gene('hg38', 'ACE2')The genomic region method expects the genome region to be considered in the function Search_region(version:str,chromosome,start,end,CEGH_Filter= False,Variant_ID= False, Process= True). The X and Y chromosome must be provided as character in the chromosome parameter.
- version (str):the reference genome version (either "GRCh37/hg19" or "GRCh38/hg38")
- chromosome(obj): chromosome
- start (int): where the region of interest starts
- end (int):where the region of interest ends
- CEGH_Filter (bool): whether to provide in-house quality control filter CEGH-USP
- Variant_ID (bool): whether to provide variant identifier
- Process (bool): whether to provide data in JSON format or pandas dataframe
import pyabraom
from pyabraom import Search_region
df = Search_region('hg38',4,980883,984868)The variant search method expects the variant identifier rsID i the function Variant_ID(version:str,variant:str,CEGH_Filter= False,Process= True):
- version (str):the reference genome version (either "GRCh37/hg19" or "GRCh38/hg38")
- variant (str): the variant identifier rsID to search in database
- CEGH_Filter (bool): whether to provide in-house quality control filter CEGH-USP
- Variant_ID (bool): whether to provide variant identifier
- Process (bool): whether to provide data in JSON format or pandas dataframe
import pyabraom
from pyabraom import Variant_ID
df = Variant_ID('hg38','rs115134980')The batch search method expects as parameter a list of one provided type of search in the function Searches(searches_list:list)
import pyabraom
df = Searches([Search_gene('hg38','ACE2'),Search_gene('hg38','TMPRSS2')])Not available yet.
- Variant Filter
@article {Carneiro2022.06.07.495190,
author = {Carneiro, Paola and Colombelli, Felipe and Recamonde-Mendoza, Mariana and Matte, Ursula},
title = {Pynoma, PyABraOM and BIOVARS: Towards genetic variant data acquisition and integration},
elocation-id = {2022.06.07.495190},
year = {2022},
doi = {10.1101/2022.06.07.495190},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2022/06/09/2022.06.07.495190},
eprint = {https://www.biorxiv.org/content/early/2022/06/09/2022.06.07.495190.full.pdf},
journal = {bioRxiv}
}
This research was supported by the National Council for Scientific and Technological Development (CNPq) and the Research Incentive Fund (FIPE) from Hospital de Clínicas de Porto Alegre.
Naslavsky, Scliar, Yamamoto, et al., 2022. Whole-genome sequencing of 1,171 elderly admixed individuals from São Paulo, Brazil.
