There are 316 articles for you to read.

Reproduce the plots in the 'Methods' part

Author: gene_x

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

Nextflow RNAseq

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

RNA-seq processing for control-LT-LTtr-K331A

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

Native elongating transcript sequencing (NET-seq)

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

SPANDx Genomic Profiling: Verifying LTtr and K331A RNASeq Mutations

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

域名预订类型

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

LIMMA pipeline processing proteomics (MS)

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

GSVA calculation for Mass spectrometry (MS)-based proteomics

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

Ordinary vs. Moderated P-values: A Key Comparison in limma Differential Expression Analysis

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

GSVA calculation for NanoString data

Author: gene_x

Abstract: library("rmarkdown") library("tidyverse") library(rmarkdown) library("GeomxTools") library("GeoMxWorkflows") library("NanoStringNCTools") setwd("/home/jhuang/DATA/Data_Susa


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