post-GWAS-pipeline This repository is a pipeline built for post-GWAS analysis. With GWAS summary statistics as input, the pipeline contains four major modules: Standard GWAS analysis: Manhattan plot/QQ plot/LocusZoom Heritability estimation/annotation-stratified enrichment analysis ...
GiNi-GWAS post-GWAS process: Python-based, post-GWAS processing and analysis pipeline GiNi is a automated pipeline built for post-GWAS analysis. With GWAS summary statistics as input, the pipeline processes and perform a set of comprehensive analyses. Uses brain gene-expression and splicing data ...
Details of the spatial transcriptomic analysis pipeline and R coding scripts can be found on our GitHub repository (https://github.com/wwq413/SpatialTranscriptomics)35. References Download references Acknowledgements We thank Hongyan Liu, Keke Yin, Man Jiang, Xue Song for bioinformatic assistance, samp...
a: Transcriptomic variants identified through RNA-Seq data analysis. Plot shows the average number of variants (SNPs and InDels) per patient that have been identified by applying our customized transcriptomic variant calling pipeline and filtering approaches. SNPs and InDels are considered as ...
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In the case of these legume crops, there is mainly a need of following type of platforms for genetic analysis and breeding applications: (1) high-density genotyping (> 20 K SNPs) of genetic populations for trait mapping using GWAS, (2) medium-density genotyping (2–5 K SNPs) of mappin...
GWAS analysis revealed variation in gene presence, core and accessory gene alleles, core SNPs, gene fission/fusions, and gene duplications. Five variants were most significantly associated with PI-IBS (Bonferroni correctedp-value < 7.5 × 10−5). These included genes associated with bact...