edms 0.2.4.post0

Endogenous Deep Mutational Scans

Endogenous Deep Mutational Scans (EDMS)

Command Line Interface

edms -h # or edms <TAB>

Package Organization

• gen: input/output, data wrangling, generating plots, and statistics.

  import edms.gen.cli as cli
  import edms.gen.image as im
  import edms.gen.io as io
  import edms.gen.plot as p
  import edms.gen.stat as st
  import edms.gen.tidy as t
  

• bio: molecular biology & tissue culture workflows.

  import edms.bio.clone as cl
  import edms.bio.fastq as fq
  import edms.bio.genbank as gb
  import edms.bio.ngs as ngs
  import edms.bio.pe as pe
  import edms.bio.pegLIT as pegLIT
  import edms.bio.primedesign as primedesign
  import edms.bio.qPCR as qPCR
  import edms.bio.sanger as sanger
  import edms.bio.signature as signature
  import edms.bio.transfect as tf
  

• dat: interacting with databases.

  import edms.dat.cosmic as co
  import edms.dat.cvar as cv
  import edms.dat.ncbi as ncbi
  

PyPI Instructions

Install

1. Install edms from PyPI

   pip install edms
   

2. Check edms install

   edms -h
   

3. Optional: Set up edms autocomplete

   edms autocomplete
   # Follow CLI instructions
   

4. Optional: edms fastq {extract_umis,trim_motifs,make_sams,make_bams,bam_umi_tags,group_umis,consensus_umis,bam_to_fastq} need umi_tools, cutadapt, samtools, bowtie2, and fgbio in a seperate environment

   conda create -n umi_tools umi_tools cutadapt samtools bowtie2 fgbio
   

Update

1. Update edms from PyPI

   pip install --upgrade edms
   

GitHub Instructions

Install

1. Download Anaconda:
   • Mac: https://docs.anaconda.com/anaconda/install/mac-os/
   • Windows: https://docs.anaconda.com/anaconda/install/windows/
   • Linux: https://docs.anaconda.com/anaconda/install/linux/
2. Download Git: https://github.com/git-guides/install-git
3. Clone edms from github:

   cd ~
   mkdir git
   cd git
   git clone https://github.com/marczepeda/edms.git
   cd edms 
   

4. Make the environment and install edms:

   conda env create -f edms.yml # When conda asks you to proceed, type "y"
   conda activate edms
   pip install -e . # Include the "."
   edms autocomplete # Optional: set up edms autocomplete; follow CLI instructions
   conda deactivate
   

5. Optional: edms fastq {extract_umis,trim_motifs,make_sams,make_bams,bam_umi_tags,group_umis,consensus_umis,bam_to_fastq} need umi_tools, cutadapt, samtools, bowtie2, and fgbio in a seperate environment

   conda create -n umi_tools umi_tools cutadapt samtools bowtie2 fgbio
   

Update

1. Enter the environment and uninstall edms:

   cd ~/git/edms
   conda activate edms
   pip uninstall -y edms
   rm -rf build/ dist/ *.egg-info
   

2. Pull latest version from github and install edms:

   git pull origin main
   pip install -e . # Include the "."
   conda deactivate
   

PE Strategies

Strategy	Description	Reference
PE1	Cas9(H840A) - M-MLV RT + pegRNA	Search-and-replace genome editing without double-strand breaks or donor DNA
PE2	Cas9(H840A) – M-MLV RT(D200N/L603W/T330P/T306K/W313F) + pegRNA	Search-and-replace genome editing without double-strand breaks or donor DNA
PE3	Cas9(H840A) – M-MLV RT(D200N/L603W/T330P/T306K/W313F) + ngRNA (targets non-edited strand)	Search-and-replace genome editing without double-strand breaks or donor DNA
PE4	Cas9(H840A) – M-MLV RT(D200N/L603W/T330P/T306K/W313F) + MLH1dn (MMR evasion)	Enhanced prime editing systems by manipulating cellular determinants of editing outcomes
PE5	Cas9(H840A) – M-MLV RT(D200N/L603W/T330P/T306K/W313F) + MLH1dn (MMR evasion) + ngRNA (targets non-edited strand)	Enhanced prime editing systems by manipulating cellular determinants of editing outcomes
PE6a-d	Cas9(H840A) – ... PEa: ... - evo-Ec48 RT PEb: ... - evo-Tf1 RT PEc: ... - Tf1 RT variant PEd: ... - M-MLV RT variant	Phage-assisted evolution and protein engineering yield compact, efficient prime editors
PE6e-f	Cas9(H840A) variants – ... M-MLV RT(ΔRNAseH)	Phage-assisted evolution and protein engineering yield compact, efficient prime editors
PE7	Cas9(H840A) – M-MLV RT(D200N/L603W/T330P/T306K/W313F) - La (RNA binding protein that stabilizes pegRNA) +/- ngRNA (targets non-edited strand)	Improving prime editing with an endogenous small RNA-binding protein
PEmax	Mammalian codon-optimized PE	Enhanced prime editing systems by manipulating cellular determinants of editing outcomes
pegRNA	spacer - scaffold - RTT - PBS (makes the edit)	Search-and-replace genome editing without double-strand breaks or donor DNA
epegRNA	spacer - scaffold - RTT - PBS - linker - tevoPreQ (makes the edit; more stable pegRNA)	Engineered pegRNAs improve prime editing efficiency
ngRNA	spacer - scaffold (targets non-edited strand)	Search-and-replace genome editing without double-strand breaks or donor DNA
MLH1dn	Dominant negative MLH1 (MMR evasion)	Enhanced prime editing systems by manipulating cellular determinants of editing outcomes
silent mutations	Larger prime edits are more efficient through bypassing MMR	Enhanced prime editing systems by manipulating cellular determinants of editing outcomes
La	Small RNA binding protein that stabilizes pegRNA	Improving prime editing with an endogenous small RNA-binding protein
PE-eVLP	Engineered Virus-Like Particle for Prime Editors	Engineered virus-like particles for transient delivery of prime editor ribonucleoprotein complexes in vivo
dNTPs	HSCs have low dNTP levels, limiting reverse transcription	Enhancing prime editing in hematopoietic stem and progenitor cells by modulating nucleotide metabolism
Vpx	HSCs express SAMHD1 (triphosphohydrolase), which depletes dNTPs. Accessory lentiviral protein Vpx, encoded by HIV-2 and simian immunodeficiency viruses (SIVs), associates with the CRL4-DCAF1 E3 ubiquitin ligase to target SAMHD1 for proteasomal degradation.	Enhancing prime editing in hematopoietic stem and progenitor cells by modulating nucleotide metabolism
MLH-SB	Small protein binder that disrupts MLH1 & PMS2 binding (MMR evasion)	AI-generated small binder improves prime editing (Preprint)