See Buenrostroet al., 2015, ENCODE - ATAC-seq Data Standards and Prototype Processing Pipeline, and Harvard FAS Informatics - ATAC-seq Guidelines for details. 两个或更多的生物学重复样本 每个重复样本包含单端测序的2500万条非重复、非线粒...
ATAC-seq general workflow 非常详细的 ATAC-seq 数据分析指导资源 Title and author Notes link ATAC-seq data analysis: from FASTQ to peaks Yiwei Niu,Last updated: 2019 Blog style walkthrough of generalized ATAC-seq data analysis. https://yiweiniu.github.io/blog/2019/03/ATAC-seq-data-analysis-fr...
ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) has gained wide popularity as a fast, straightforward, and efficient way of generating genome-wide maps of open chromatin and guiding identification of active regulatory elements and inference of DNA protein binding locations. Given ...
ATAC-Seq可以用来: 生成表观基因组学特征 在不同组织或条件下绘制可及染色质图谱 检索核小体位置 识别重要的转录因子 生成转录因子的占用特征(足迹分析) 2.实验设计 See Buenrostroet al., 2015, ENCODE - ATAC-seq Data Standards and Prototype Processing Pipeline, and Harvard FAS Informatics - ATAC-seq Gu...
ATAC-seq Data Standards and Processing Pipeline in ENCODE ATAC-seq数据分析实战 Harvard FAS Informatics - ATAC-seq Guidelines 第一篇文章是我学习ATAC-seq的首选文章。它非常耐心细致地讲解了从raw data到ATAC-seq 的peak数据的分析流程,非常建议读一读。 第二篇文章是ENCODE官网的文章,比较晦涩一些,但里边不仅...
ENCODE - ATAC-seq Data Standards and Prototype Processing Pipeline:https://www.encodeproject.org/atac-seq/ 以下是ENCODE所使用的标准,以下将详细解释每个术语的具体描述。 当前标准: 实验需要有>=2个生物学重复 每个重复数据量至少:25M 非重复re...
ATAC-seq Data Standards and Processing Pipeline ENCODE-DCC/atac-seq-pipeline 1. 安装pipeline step 1: 进入conda module loadminiconda3/38_4.9.2 source ~/***/conda.sh #可通过whereis 查找conda.sh的路径 condo activate base step2: 安装pipeline ...
The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing ...
Existing approaches to scoring single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) feature matrices from sequencing reads are inconsistent, affecting downstream analyses and displaying artifacts. We show that, even with sparse single-cell data, quantitative counts are info...
ATACProc is a pipeline to analyze ATAC-seq data. Currently datasets involving one of the four reference genomes, namely hg19, hg38, mm9 and mm10 are supported. Important features of this pipeline are: Supports single or paired-end fastq or BAM formatted data. ...