Installation

First, clone up-to-date versions of this repo and of two dependencies from github:

git clone [email protected]:jeffhussmann/hits.git
git clone [email protected]:jeffhussmann/knock-knock.git
git clone [email protected]:jeffhussmann/dCas9-fusions.git

Then, in the order hits, then knock-knock, then dCas9-fusions, go into each cloned directory and install the corresponding python using pip by running this command:

pip install -e ./

You will also need to install some non-python dependencies. In a conda environment, run:

conda install blast
conda install samtools

Running

Installation will have put an executable script count_dCas9_fusion_domains in your PATH. Identify a directory BASE_DIR that you want to hold data and results.

First run

count_dCas9_fusion_domains setup $BASE_DIR

substituting your actual directory for $BASE_DIR. This will install references sequences for the domain libraries into $BASE_DIR/target:

$ tree $BASE_DIR/target
$BASE_DIR/target
├── pTwist-SFFV_Library1
│   ├── manifest.yaml
│   ├── ptwist-sffv.gb
│   ├── refs.fasta
│   ├── refs.fasta.fai
│   ├── refs.gff
│   ├── TwistLibrary1.fasta
│   └── xten16-2xnls-dcas9-xten80.gb
└── pTwist-SFFV_Library2
    ├── manifest.yaml
    ├── ptwist-sffv.gb
    ├── refs.fasta
    ├── refs.fasta.fai
    ├── refs.gff
    ├── TwistLibrary2.fasta
    └── xten16-2xnls-dcas9-xten80.gb

Then setup input data in $BASE_DIR/data, organized into subdirectories for each batch of samples. For example, here are the contents for a single batch 220610_pL1_pL2:

$ tree $BASE_DIR/data
$BASE_DIR/data
└── 220610_pL1_pL2
    ├── pL1_trimmed.fastq.gz
    ├── pL2_trimmed.fastq.gz
    └── sample_sheet.yaml

The fastq.gz files should be the CCS reads from Pacbio output (i.e. corresponding to files originally named demux/A02.demux.ccs_kinetics.pL1--pL1.fastq.gz and demux/A02.demux.ccs_kinetics.pL2--pL2.fastq.gz from this batch) that have had barcodes trimmed from the beginning and ends, so that each read is expected to start and end with the relevant parts of the amplicon primers (CATTGTCGATCCTACCATCCACTCGAC and CCATCCGCACGCATCTGGAATAAG). Example code for how to do this trimming can be found in notebooks/trimming_example.ipynb

Each batch subdirectory should contain sample_sheet.yaml that associates sample names with fastq files and indicates which library was used for each sample:

$ cat sample_sheet.yaml
pL1:
    CCS_fastq_fn: pL1_trimmed.fastq.gz
    target_info: pTwist-SFFV_Library1

pL2:
    CCS_fastq_fn: pL2_trimmed.fastq.gz
    target_info: pTwist-SFFV_Library2

Then run count_dCas9_fusion_domains parallel to count occurences of domain pairs in each sample in parallel, with syntax:

$ count_dCas9_fusion_domains parallel -h
usage: count_domains parallel [-h] [--batch BATCH] [--stages STAGES]
                              base_dir max_procs

positional arguments:
  base_dir         the base directory to store input data, reference
                   annotations, and analysis output for a project
  max_procs        maximum number of samples to process at once

optional arguments:
  -h, --help       show this help message and exit
  --batch BATCH    if specified, the single batch name to process; if not
                   specified, all groups will be processed
  --stages STAGES

After processing is finished, you can access a DataFrame with counts for each domain pair for each experiment as follows:

$ ipython

In [1]: import dCas9_fusions.experiment

In [2]: base_dir = $BASE_DIR

In [3]: exps = dCas9_fusions.experiment.get_all_experiments(base_dir)

In [4]: exps
Out[4]:
{('220610_pL1_pL2',
 'pL1'): dCas9FusionExperiment: batch=220610_pL1_pL2, sample_name=pL1, base_dir=$BASE_DIR,
('220610_pL1_pL2',
 'pL2'): dCas9FusionExperiment: batch=220610_pL1_pL2, sample_name=pL2, base_dir=$BASE_DIR}

In [5]: exps['220610_pL1_pL2', 'pL1'].domain_counts
Out[5]:
        count  length  count_with_pseudocount  log10_count  fraction  fraction_with_floor  log10_fraction
N1  C1      13    4968                    13.1     1.117271  0.000890             0.000890       -3.050796
   C2      31    5946                    31.1     1.492760  0.002121             0.002121       -2.673378
   C3      24    6000                    24.1     1.382017  0.001642             0.001642       -2.784528
   C4       1    8001                     1.1     0.041393  0.000068             0.000068       -4.164739
   C5      12    5229                    12.1     1.082785  0.000821             0.000821       -3.085558
...        ...     ...                     ...          ...       ...                  ...             ...
N39 C35      1    7920                     1.1     0.041393  0.000068             0.000068       -4.164739
   C36      1    8526                     1.1     0.041393  0.000068             0.000068       -4.164739
   C37      8    7290                     8.1     0.908485  0.000547             0.000547       -3.261649
   C38      0    7299                     0.1    -1.000000  0.000000             0.000001       -6.000000
   C39      4    8274                     4.1     0.612784  0.000274             0.000274       -3.562679

[1369 rows x 7 columns]