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A few best practices for ATAC-seq assays are suggested as follows:

• Digest away background DNA (medium/dead cells) using DNase I²²
  
• Use fresh/cyropreserved cells/tissues to isolate nuclei^7,9
  
• Reduce mitochondrial/chloroplast DNA contamination as much as possible by using the Omni-ATAC protocol or other methods^22–25
  
• Optimize the ratio of the amount of Tn5 enzyme to the number of nuclei
  
• Optimize the number of PCR cycles¹⁹
  
• Perform Paired-end (PE) sequencing, e.g., 2 x 50 to 100 bp
  
• Sequence > 50 M PE reads (~200 M for footprinting analysis)⁷

A few best practices for ATAC-seq data analysis are suggested as follows:

• Perform raw read QC using FASTQC before alignment
• Perform post-alignment QC using ATACSeqQC¹⁰
  
• Perform peak calling using a peak caller, such as MACS2²⁶ in narrowdPeak mode with option settings: “shift -s and extend 2s”, Genrich, or HMMRATAC.²⁷
• Perform post-peak calling QC
  • Annotate peaks and generate peak distribution among genomic features using ChIPpeakAnno²⁸
    
  • Obtain functions of genes associated to peaks using the Genomic Regions Enrichment of Annotations Tool (GREAT)²⁹

Copied From: https://haibol2016.github.io/ATACseqQCWorkshop/articles/ATACseqQC_workshop.html

I am very excited to have begun a new position this July as a Bioinformatics Specialist within the Iowa Institute for Human Genetics (IIHG).

I am fortunate to be working  with a very talented team of expert bioinformaticians, researchers, and programmers.   I have a lot to learn about my new field, but I’m enthusiastic about the challenge and looking forward to participating in the fast-moving and cutting-edge research at the nexus of computer science, data analysis, genetics, and biology.