15_sordella

Code and processed data reported in Senturk et al.(2016), Nat. Commun. [in press]

DATA

The raw sequence data for this study is available on the Sequence Read Archive under accession number SRP078612. The first 10K reads for each of the 8 samples are provided in the FASTQ files in the data_tiny/ directory. The Cas9-targeted region of p53, as well as the primers use to amplify this locus for sequencing, are given in data/regions.txt. The content of each of the 8 sequencing samples is described in data/samples.txt.

PUBLISHED RESULTS

The published results are provided in the directory published/.

• all_unique_seqs.txt shows all of the unique reconstructed sequences for the p53 locus described in this paper.
• all_summarized_seqs.txt shows summary information for each unique sequence.
• stats.txt shows the number and percentage of sequences in each sample that could be successfully reconstructed.
• alignment.txt shows an alignment of the most prevalent sequences, along with their observed counts in the 8 samples.
• rates.pdf is the bar chart shown in Fig. 2D
• mutations.pdf is the bar chart shown in Fig. 2E

RUNNING THE PIPELINE ON SAMPLE DATA

1. Download this repository and change to the top-level directory 15_sordella/
2. Exectue $ ./run_tiny.py, which will run the pipeline on the small sample datasets in data_tiny/. The results will be stored in output/results/
3. Exectue $ ./make_plots.py, which will create the correspondign plots and store them in output/results/
4. The results of this pipeline will be in output/results/

RUNNING THE PIPELINE ON THE FULL DATASETS.

1. Download the eight paired-end read datasets from SRP078612. Save the resulting 16 files (8 for read1, 8 for read2) in fastq format in the directory data/.
2. Edit run.py so that the variables read1_file_glob, read1_file_glob, and split_file_globs point to these fastq files.
3. Set the variable use_multiple_nodes = False in run.py to run the analysis in single node mode. To run analysis on multiple nodes, set use_multiple_nodes = True. To get the analysis on multiple nodes working in your cluster environment, however, you might have to edit the function submit_and_complete_jobs(), defined in pipleine.py
4. Exectue $ ./run.py, which will run the pipeline on the small sample datasets in data_tiny/. The results will be stored in output/results/
5. Exectue $ ./make_plots.py, which will create the correspondign plots and store them in output/results/
6. The results of this pipeline will be in output/results/