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:

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:

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

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

Journal: Biomedicines

Article Title: Sulforaphane’s Role in Osteosarcoma Treatment: A Systematic Review and Meta-Analysis of Preclinical Studies

doi: 10.3390/biomedicines13051048

Figure Lengend Snippet: Forest plots showing the effect of 20 μΜ SFN on the viability of ( a ) MG-63 and ( b ) U-2 OS cells. Data from the included studies were categorized into subgroups based on the duration of the 20 μM SFN treatment. The green squares represent the estimated mean difference for each individual study and the black diamond represents the pooled mean difference and the associated 95% CI. The studies included in these forest plots are: Ferreira de Oliveira et al. (2014b) , Kim et al. (2011) , Jeong et al. (2017) , Li et al. (2016) .

Article Snippet: Cell viability , Kim et al., 2011 [ ]; Ferreira de Oliveira et al., 2014b [ ]; Li et al., 2016 [ ] , MG-63 (human) , 5 μΜ , 24 48 72 , −3.13 (−18.02, 11.75) −38.93 (−78.30, 0.45) −23.89 (−31.41, −16.37) , −26.05 (−53.30, 1.19) , Mean difference, IV, Random effects, 95% CI , Chi 2 = 130.38, df = 3, p < 0.00001, I 2 = 98% , Z = 1.87, p = 0.06.

Techniques:

Journal: Biomedicines

Article Title: Sulforaphane’s Role in Osteosarcoma Treatment: A Systematic Review and Meta-Analysis of Preclinical Studies

doi: 10.3390/biomedicines13051048

Figure Lengend Snippet: Forest plots showing the effect of 20 μΜ SFN on the distribution of MG-63 cells in ( a ) G0/G1, ( b ) S, and ( c ) G2/M phases of the cell cycle. Data from the included studies were categorized into subgroups based on the duration of the 20 μM SFN treatment. The green squares represent the estimated mean difference for each individual study and the black diamond represents the pooled mean difference and the associated 95% CI. The studies included in these forest plots are: Ferreira de Oliveira et al. (2014b) , Matsui et al. (2007) , Kim et al. (2011) .

Article Snippet: Cell viability , Kim et al., 2011 [ ]; Ferreira de Oliveira et al., 2014b [ ]; Li et al., 2016 [ ] , MG-63 (human) , 5 μΜ , 24 48 72 , −3.13 (−18.02, 11.75) −38.93 (−78.30, 0.45) −23.89 (−31.41, −16.37) , −26.05 (−53.30, 1.19) , Mean difference, IV, Random effects, 95% CI , Chi 2 = 130.38, df = 3, p < 0.00001, I 2 = 98% , Z = 1.87, p = 0.06.

Techniques:

Journal: Biomedicines

Article Title: Sulforaphane’s Role in Osteosarcoma Treatment: A Systematic Review and Meta-Analysis of Preclinical Studies

doi: 10.3390/biomedicines13051048

Figure Lengend Snippet: Forest plots showing the effect of 20 and 15 μΜ SFN on the percentage of apoptotic MG-63 cells. Data from the included studies were categorized into subgroups based on the duration of the SFN treatment. The green squares represent the estimated std. mean difference for each individual study and the black diamond represents the pooled std. mean difference and the associated 95% CI. The studies included in this forest plot are: Ferreira de Oliveira et al. (2014b) , Li et al. (2016) .

Article Snippet: Cell viability , Kim et al., 2011 [ ]; Ferreira de Oliveira et al., 2014b [ ]; Li et al., 2016 [ ] , MG-63 (human) , 5 μΜ , 24 48 72 , −3.13 (−18.02, 11.75) −38.93 (−78.30, 0.45) −23.89 (−31.41, −16.37) , −26.05 (−53.30, 1.19) , Mean difference, IV, Random effects, 95% CI , Chi 2 = 130.38, df = 3, p < 0.00001, I 2 = 98% , Z = 1.87, p = 0.06.

Techniques: