PEChyron

A repository for PE Chyron code base

To analyze peCHYRON loci by nanopore sequencing:

Start with demultiplexed fastq data.

We use the Maple pipeline (github.com/gordonrix/maple) for demultiplexing, then convert the outputted .bam files to .fastq using the bamtofastq utility in bedtools.

First, run extract_insertion_sequences_and_report_indel_sizes_and_signatures.py.

Then, if analyzing overall efficiency of editing is necessary, run calculate_number_edits_from_nanopore_data.py.

To analyze peCHYRON loci by Illumina sequencing:

First, we demultiplex using the separate_by_barcodes_modified.py script from the liusynevolab/CHYRON-NGS repository. Then, if desired we combine forward and reverse reads using PEAR, as described in the CHYRON-NGS repository. However, we normally use forward reads only, as the repetitive nature of the peCHYRON locus can lead to errors in the combination process.

Next, we analyze in the same way as nanopore data.

For samples in which each recording locus integrated into the cell line is tagged with a unique static barcode, if desirable the peCHYRON insertions statistics for each static barcode can be calculated, and specific static barcodes can be filtered. For example, to determine the edits acquired in one biological replicate of iMEF cells, as shown in Extended Data Figure 6d, you would run "grab_static_and_insertion.py," then "static_stats.py," then "good_statics_only.py," on the iMEF_biorep1 fastq files in this repository.

Please find in other repositories at github.com/liusynevolab:

Plasmid maps.

Flow cytometry data.

Analysis pipeline underlying Figure 5c.

Analysis pipeline underlying Figure 5b and Extended Data Figures 7-9.