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gene level ase in matlab  (MathWorks Inc)


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    MathWorks Inc gene level ase in matlab
    Screenshot of how to add the <t>Driver_ASE</t> scripts into the <t>MATLAB</t> global path
    Gene Level Ase In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 310 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/gene level ase in matlab/product/MathWorks Inc
    Average 96 stars, based on 310 article reviews
    gene level ase in matlab - by Bioz Stars, 2026-05
    96/100 stars

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    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

    Screenshot of how to add the Driver_ASE scripts into the MATLAB global path
    Figure Legend Snippet: Screenshot of how to add the Driver_ASE scripts into the MATLAB global path

    Techniques Used:

    Sample MATLAB commands to run ASE-Mut association on BRCA data
    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’.
    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

    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.
    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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    gene level ase in matlab  (MathWorks Inc)
    96
    MathWorks Inc gene level ase in matlab
    Screenshot of how to add the <t>Driver_ASE</t> scripts into the <t>MATLAB</t> global path
    Gene Level Ase In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/gene level ase in matlab/product/MathWorks Inc
    Average 96 stars, based on 1 article reviews
    gene level ase in matlab - by Bioz Stars, 2026-05
    96/100 stars
    		  Buy from Supplier

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    Screenshot of how to add the Driver_ASE scripts into the MATLAB global path

    Journal: STAR Protocols

    Article Title: Identifying tumorigenic non-coding mutations through altered cis -regulation

    doi: 10.1016/j.xpro.2021.100934

    Figure Lengend Snippet: Screenshot of how to add the Driver_ASE scripts into the MATLAB global path

    Article Snippet: The pipeline uses different perl scripts to download SNP array, RNA-Seq and WGS BAMs, and then calculate gene-level ASE and call somatic mutations; these results are then converted to associate somatic mutations in different regulatory elements and gene-level ASE in MATLAB (at least version 2014b or using version 90 of the freely available MATLAB runtime ( https://www.mathworks.com/products/compiler/matlab-runtime.html ).

    Techniques:

    Sample MATLAB commands to run ASE-Mut association on BRCA data

    Journal: STAR Protocols

    Article Title: Identifying tumorigenic non-coding mutations through altered cis -regulation

    doi: 10.1016/j.xpro.2021.100934

    Figure Lengend Snippet: Sample MATLAB commands to run ASE-Mut association on BRCA data

    Article Snippet: The pipeline uses different perl scripts to download SNP array, RNA-Seq and WGS BAMs, and then calculate gene-level ASE and call somatic mutations; these results are then converted to associate somatic mutations in different regulatory elements and gene-level ASE in MATLAB (at least version 2014b or using version 90 of the freely available MATLAB runtime ( https://www.mathworks.com/products/compiler/matlab-runtime.html ).

    Techniques:

    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’.

    Journal: STAR Protocols

    Article Title: Identifying tumorigenic non-coding mutations through altered cis -regulation

    doi: 10.1016/j.xpro.2021.100934

    Figure Lengend 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’.

    Article Snippet: The pipeline uses different perl scripts to download SNP array, RNA-Seq and WGS BAMs, and then calculate gene-level ASE and call somatic mutations; these results are then converted to associate somatic mutations in different regulatory elements and gene-level ASE in MATLAB (at least version 2014b or using version 90 of the freely available MATLAB runtime ( https://www.mathworks.com/products/compiler/matlab-runtime.html ).

    Techniques: Mutagenesis

    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.

    Journal: STAR Protocols

    Article Title: Identifying tumorigenic non-coding mutations through altered cis -regulation

    doi: 10.1016/j.xpro.2021.100934

    Figure Lengend 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.

    Article Snippet: The pipeline uses different perl scripts to download SNP array, RNA-Seq and WGS BAMs, and then calculate gene-level ASE and call somatic mutations; these results are then converted to associate somatic mutations in different regulatory elements and gene-level ASE in MATLAB (at least version 2014b or using version 90 of the freely available MATLAB runtime ( https://www.mathworks.com/products/compiler/matlab-runtime.html ).

    Techniques: Mutagenesis