There are 326 articles for you to read.
Author: gene_x
Abstract: The study revolves around the evaluation of the proximity of integration sites to peaks in the human genome, using a permutation-based approach. It involves three primary steps: 1. Observed Data: We
Author: gene_x
Abstract: [![error_bar](/static/images/error_bar.png "error_bar")](/static/images/error_bar.png "error_bar") How to run the software package PiCRUST2 (Phylogenetic Investigation of Communities by Reconstructio
Author: gene_x
Abstract: Install and configure that the packages for Docker (docker-ce, docker-ce-cli, and containerd.io): 1. Update your system's package information: sudo apt-get update 2. Uninstall any old versions of
Author: gene_x
Abstract: [![Venn_Diagram_NHDF_vs_HEK293_vs_PFSK-1](/static/venn_diagrams/Venn_Diagram_NHDF_vs_HEK293_vs_PFSK-1.png "Venn_Diagram_NHDF_vs_HEK293_vs_PFSK-1")](/static/venn_diagrams/Venn_Diagram_NHDF_vs_HEK293_vs
Author: gene_x
Abstract: 详细解释:梅尔克细胞多瘤病毒(Merkel cell polyomavirus,MCPyV)的小肿瘤抗原(small tumor antigen,sT)是该病毒的一个重要组分,通过干扰类型I干扰素(type I interferon)信号传导来促进免疫逃逸。 类型I干扰素是免疫系统中的一类重要信号分子,对抗病毒感染和肿瘤发展起着关键作用。然而,MCPyV的小肿瘤抗原通过干扰类型I干扰素信号的正常
Author: gene_x
Abstract: #!/usr/bin/env python3 #python3 plot_peaks.py peaks.bed /home/jhuang/REFs/gencode.v43.annotation.gtf.db /ref/Homo_sapiens/UCSC/hg38/Sequence/WholeGenomeFasta/genome.fa #~/Tools/csv2xls-0.4/csv_to_x
Author: gene_x
Abstract: For SNP visualization in Python, we can consider using the following packages: * Matplotlib: Matplotlib is a popular plotting library in Python that can be used to create various types of visualizati
Author: gene_x
Abstract: 1. construct DESeqDataSet from Matrix library("AnnotationDbi") library("clusterProfiler") library("ReactomePA") library("org.Hs.eg.db") library(DESeq2) library(gplots) library(ggplot2)
Author: gene_x
Abstract: 1. Integration and analysis of multi-omics data: Explore the integration of different types of omics data (e.g., genomics, transcriptomics, proteomics) from public repositories to uncover novel biolog
Author: gene_x
Abstract: 1. generate_promter_sequences #!/usr/bin/env python3 #./1_generate_promoter_sequences.py gencode.v43.annotation.gtf.db import gffutils from pyfaidx import Fasta import argparse from Bio i
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