Author: gene_x

Abstract: Volcano plots are graphical representations of the significance versus fold-change for values obtained from a particular data analysis. They are especially useful in high-throughput studies, such as g

Author: gene_x

Abstract: 1. a dashed vertical line dividing two sets of rectangular bars. [](/static/reproducing_plots_in_Methods/plot1.png "plot1.png"

Author: gene_x

Abstract: 1. Merge re-sequenced FastQ files (cat) 2. Sub-sample FastQ files and auto-infer strandedness (fq, Salmon) 3. Read QC (FastQC) 4. UMI extraction (UMI-tools) 5. Adapter and quality trimming (Trim Galor

Author: gene_x

Abstract: #nextflow run rnaseq --reads '/home/jhuang/DATA/Data_Denise_tx_epi_MCPyV/Raw_Data_RNAseq_K331A/*.fastq.gz' --fasta /home/jhuang/REFs/Homo_sapiens/UCSC/hg38/Sequence/WholeGenomeFasta/genome.fa --gt

Author: gene_x

Abstract: 2023-10-10 16:18:07 星期二 Net-seq（原位延伸转录测序）是一种用于以核苷酸分辨率跨基因组分析RNA聚合酶（Pol）活性的方法。以下是Net-seq的简要概述： 目的: Net-seq旨在捕获并测序仍与RNA聚合酶结合在一起的活跃转录过程中的RNA的3'末端。这有助于创建跨基因组的活跃转录位点的高分辨率图。 工作原理: * 从细胞中提取RNA聚合酶及其绑定的RN

Author: gene_x

Abstract: 1. Set up the directory for raw data. #Replace "p600" with "control", "p602" with "LT", "p605" with "LTtr", and "p783" with "K331A". Please note that the RNAseq data from the LT_K331A_d8 repl

Author: gene_x

Abstract: snapnames、namejet、dropcatch、youdot、mediaon、namecatch、name、domainmonster、hexonet、xz、epik、pool、asiaregister、dynadot、pheenix2、backorder、godaddy、ename、flappy、hupo、hooyoo、Juming、west263、yijie、cndns、bizcn、z

Author: gene_x

Abstract: Statistical analysis on LC-MS data In order to detect significant changes between two experimental groups we performed statistical analysis on LC-MS data. We have chosen LIMMA moderated t test statis

Author: gene_x

Abstract: library("rmarkdown") library("tidyverse") library(rmarkdown) setwd("/home/jhuang/DATA/Data_Susanne_Carotis_MS/LIMMA-pipeline-proteomics/Results_20231006_165122") # -1. prepare

Author: gene_x

Abstract: In the context of differential expression analysis, limma is a popular R package that originally was designed for microarray data but has since been adapted for RNA-seq data (using voom transformation