gene level ase in matlab (MathWorks Inc)
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gene level ase in matlab
Gene Level Ase In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 325 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/matlab+2014b/MATLAB+Compiler/pmc08591365-60-37-40
Average 96 stars, based on 325 article reviews
Gene Level Ase In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 325 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/matlab+2014b/MATLAB+Compiler/pmc08591365-60-37-40
Average 96 stars, based on 325 article reviews
gene level ase in matlab - by Bioz Stars,
2026-10
96/100 stars
Images
1) Product Images from "Identifying tumorigenic non-coding mutations through altered cis -regulation"
Article Title: Identifying tumorigenic non-coding mutations through altered cis -regulation
Journal: STAR Protocols
doi: 10.1016/j.xpro.2021.100934
Figure Legend Snippet: Screenshot of how to add the Driver_ASE scripts into the MATLAB global path
Techniques Used:
Figure Legend Snippet: Sample MATLAB commands to run ASE-Mut association on BRCA data
Techniques Used:
![Evaluation of ASE-Mutation associations using the MATLAB function ‘ M1_Import_ASE_and_Mutation_data.m ’ Typing ‘Top_assoc’ in MATLAB terminal shows the top associations (false discovery rate [FDR]<=0.2 and raw association p value [p]<=0.05) across 18 different regulatory or genomic features after running the scripts in this vignette with default settings. All association results are also included in the MATLAB structure ‘All_Assoc’.](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_1365/pmc08591365/pmc08591365__gr10.jpg "Evaluation of ASE-Mutation associations using the MATLAB function ‘ M1_Import_ASE_and_Mutation_data.m ’ ...")
Figure Legend Snippet: Evaluation of ASE-Mutation associations using the MATLAB function ‘ M1_Import_ASE_and_Mutation_data.m ’ Typing ‘Top_assoc’ in MATLAB terminal shows the top associations (false discovery rate [FDR]<=0.2 and raw association p value [p]<=0.05) across 18 different regulatory or genomic features after running the scripts in this vignette with default settings. All association results are also included in the MATLAB structure ‘All_Assoc’.
Techniques Used: Mutagenesis
Figure Legend Snippet: The directory tree of the final ASE-Mutation results (A) The ASE-Mutation associations are saved in the ‘BRCA’ directory that includes 3 subdirectories: ‘Driver_Beds’, ‘hits’, and ‘mut_ase_auto’. After running the pipeline the ‘Driver_beds’ directory will contain one text file of all associations FDR<0.2 (driver_top_fdr0.2), and a bed file for each association between a mutated cis-regulatory element and gene-level ASE. For example, the upper box shows an association between RALGPS1 and mutations within 10kb of its TSS and gene body that is found using the demo data of 46 BRCA samples. The bed files of putative driver mutations can be visualized with the UCSC or alternate genome browsers (hg19). The directory ‘hits’ will contain all ASE-Mut association results as shown in the lower panel. (B) The raw association p-values (assoc_P_all.tab), FDR values (fdr_all.tab), mutation enrichment p-values for each feature with each gene (fm_all.tab), and information for samples harboring these regulatory mutations (mut_all.tab) are output into the ‘hits’ directory. The ‘mut_ase_auto’ directory contains the ‘mutation x regulatory-feature’ MATLAB matrix.
Techniques Used: Mutagenesis
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