Cargando…

Streamlined computational pipeline for genetic background characterization of genetically engineered mice based on next generation sequencing data

BACKGROUND: Genetically engineered mice (GEM) are essential tools for understanding gene function and disease modeling. Historically, gene targeting was first done in embryonic stem cells (ESCs) derived from the 129 family of inbred strains, leading to a mixed background or congenic mice when crosse...

Descripción completa

Detalles Bibliográficos
Autores principales: Farkas, C., Fuentes-Villalobos, F., Rebolledo-Jaramillo, B., Benavides, F., Castro, A. F., Pincheira, R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373082/
https://www.ncbi.nlm.nih.gov/pubmed/30755158
http://dx.doi.org/10.1186/s12864-019-5504-9
_version_ 1783394900016889856
author Farkas, C.
Fuentes-Villalobos, F.
Rebolledo-Jaramillo, B.
Benavides, F.
Castro, A. F.
Pincheira, R.
author_facet Farkas, C.
Fuentes-Villalobos, F.
Rebolledo-Jaramillo, B.
Benavides, F.
Castro, A. F.
Pincheira, R.
author_sort Farkas, C.
collection PubMed
description BACKGROUND: Genetically engineered mice (GEM) are essential tools for understanding gene function and disease modeling. Historically, gene targeting was first done in embryonic stem cells (ESCs) derived from the 129 family of inbred strains, leading to a mixed background or congenic mice when crossed with C57BL/6 mice. Depending on the number of backcrosses and breeding strategies, genomic segments from 129-derived ESCs can be introgressed into the C57BL/6 genome, establishing a unique genetic makeup that needs characterization in order to obtain valid conclusions from experiments using GEM lines. Currently, SNP genotyping is used to detect the extent of 129-derived ESC genome introgression into C57BL/6 recipients; however, it fails to detect novel/rare variants. RESULTS: Here, we present a computational pipeline implemented in the Galaxy platform and in BASH/R script to determine genetic introgression of GEM using next generation sequencing data (NGS), such as whole genome sequencing (WGS), whole exome sequencing (WES) and RNA-Seq. The pipeline includes strategies to uncover variants linked to a targeted locus, genome-wide variant visualization, and the identification of potential modifier genes. Although these methods apply to congenic mice, they can also be used to describe variants fixed by genetic drift. As a proof of principle, we analyzed publicly available RNA-Seq data from five congenic knockout (KO) lines and our own RNA-Seq data from the Sall2 KO line. Additionally, we performed target validation using several genetics approaches. CONCLUSIONS: We revealed the impact of the 129-derived ESC genome introgression on gene expression, predicted potential modifier genes, and identified potential phenotypic interference in KO lines. Our results demonstrate that our new approach is an effective method to determine genetic introgression of GEM. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5504-9) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6373082
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-63730822019-02-25 Streamlined computational pipeline for genetic background characterization of genetically engineered mice based on next generation sequencing data Farkas, C. Fuentes-Villalobos, F. Rebolledo-Jaramillo, B. Benavides, F. Castro, A. F. Pincheira, R. BMC Genomics Research Article BACKGROUND: Genetically engineered mice (GEM) are essential tools for understanding gene function and disease modeling. Historically, gene targeting was first done in embryonic stem cells (ESCs) derived from the 129 family of inbred strains, leading to a mixed background or congenic mice when crossed with C57BL/6 mice. Depending on the number of backcrosses and breeding strategies, genomic segments from 129-derived ESCs can be introgressed into the C57BL/6 genome, establishing a unique genetic makeup that needs characterization in order to obtain valid conclusions from experiments using GEM lines. Currently, SNP genotyping is used to detect the extent of 129-derived ESC genome introgression into C57BL/6 recipients; however, it fails to detect novel/rare variants. RESULTS: Here, we present a computational pipeline implemented in the Galaxy platform and in BASH/R script to determine genetic introgression of GEM using next generation sequencing data (NGS), such as whole genome sequencing (WGS), whole exome sequencing (WES) and RNA-Seq. The pipeline includes strategies to uncover variants linked to a targeted locus, genome-wide variant visualization, and the identification of potential modifier genes. Although these methods apply to congenic mice, they can also be used to describe variants fixed by genetic drift. As a proof of principle, we analyzed publicly available RNA-Seq data from five congenic knockout (KO) lines and our own RNA-Seq data from the Sall2 KO line. Additionally, we performed target validation using several genetics approaches. CONCLUSIONS: We revealed the impact of the 129-derived ESC genome introgression on gene expression, predicted potential modifier genes, and identified potential phenotypic interference in KO lines. Our results demonstrate that our new approach is an effective method to determine genetic introgression of GEM. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5504-9) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-12 /pmc/articles/PMC6373082/ /pubmed/30755158 http://dx.doi.org/10.1186/s12864-019-5504-9 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Farkas, C.
Fuentes-Villalobos, F.
Rebolledo-Jaramillo, B.
Benavides, F.
Castro, A. F.
Pincheira, R.
Streamlined computational pipeline for genetic background characterization of genetically engineered mice based on next generation sequencing data
title Streamlined computational pipeline for genetic background characterization of genetically engineered mice based on next generation sequencing data
title_full Streamlined computational pipeline for genetic background characterization of genetically engineered mice based on next generation sequencing data
title_fullStr Streamlined computational pipeline for genetic background characterization of genetically engineered mice based on next generation sequencing data
title_full_unstemmed Streamlined computational pipeline for genetic background characterization of genetically engineered mice based on next generation sequencing data
title_short Streamlined computational pipeline for genetic background characterization of genetically engineered mice based on next generation sequencing data
title_sort streamlined computational pipeline for genetic background characterization of genetically engineered mice based on next generation sequencing data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373082/
https://www.ncbi.nlm.nih.gov/pubmed/30755158
http://dx.doi.org/10.1186/s12864-019-5504-9
work_keys_str_mv AT farkasc streamlinedcomputationalpipelineforgeneticbackgroundcharacterizationofgeneticallyengineeredmicebasedonnextgenerationsequencingdata
AT fuentesvillalobosf streamlinedcomputationalpipelineforgeneticbackgroundcharacterizationofgeneticallyengineeredmicebasedonnextgenerationsequencingdata
AT rebolledojaramillob streamlinedcomputationalpipelineforgeneticbackgroundcharacterizationofgeneticallyengineeredmicebasedonnextgenerationsequencingdata
AT benavidesf streamlinedcomputationalpipelineforgeneticbackgroundcharacterizationofgeneticallyengineeredmicebasedonnextgenerationsequencingdata
AT castroaf streamlinedcomputationalpipelineforgeneticbackgroundcharacterizationofgeneticallyengineeredmicebasedonnextgenerationsequencingdata
AT pincheirar streamlinedcomputationalpipelineforgeneticbackgroundcharacterizationofgeneticallyengineeredmicebasedonnextgenerationsequencingdata