Looking for mutations in PacBio cancer data: an alignment-free method
MICADo is a tool to perform variant calling on targeted (third) next-generation sequencing data. It's algorithm is based on colored de Bruijn graphs. It has been designed for high-precision variant calling for each sample in a cohort. The intuition behind the method is the substraction of systematic biases or errors present in a cohort in order to single out real mutations. We evaluated its precision on targeted sequencing data generated by a PacBio sequencer.
For any information or help running MICADo, you can get in touch with:
MICADo installation requires an Unix environment with python 2.7, it is therefore Linux and MacOS compatible. You will need a compatibility layer (bash shell) for running Linux applications on Windows.
MICADo was implemented in Python (python 2.7; http://www.python.org/) and tested under CentOS 6.6, Linux (Ubuntu 14.04 and 16.04) and Mac OS environments.
git clone https://github.com/cbib/MICADo.git
Python modules required are listed in requirement.txt. Use the following command line to install them:
cd MICADo sudo pip install -r requirements.txt
MICADo runs in a command line environment. The package contains one main script called MICADo.py that coordinates the execution of the whole process. This section describes how the user must call it.
- Fastq file of the sample of interest, targeted capture.
- Fastq files of the samples of the cohort, generated using the same library preparation and the same sequencer.
- Reference sequence of the targeted region (FASTA). This can be a multi-fasta file describing e.g. multiple isoforms.
- (Optionnal) A TSV file describing known SNPs.
/!\ Reads in fastq files must be oriented in accord to the reference fasta file. You can do it with /bin/orient_reads_in_forward_direction.py or with the cutadapt tool (http://cutadapt.readthedocs.io/en/stable/index.html).
Minimal command line
/!\ Make sure to copy the whole cohort fastq files in the folder data/fastq/
python2.7 src/MICADo.py --samplekey <sample_name> # Sample label for the results --fastq data/fastq/<experiment_name>/<sample_name.fastq> # Sample fastq file (with all the path) --experiment <experiment_name> # Experiment label corresponding to the folder name containing fastq files of the whole cohort --fasta data/reference/<reference.fasta> # Reference sequence in fasta format [options]
--snp SNP file for reference sequence (file name, with all the path) --min_support_percentage Minimum of read support percentage for node filter (default=3.0) --npermutations Number of permutations / random samples to perform (default=1000) --max_len Maximum allowed indel length (default=250) --pvalue p-value threshold for significance (default=0.001) --results Output results as JSON file (file name, with all the path) --kmer_length Size of k-mer words (default=20) --disable_cycle_breaking Do not search for k-mer values yielding a DAG
Note that k-mer length increases automatically to obtain a DAG (Directed Acyclic Graph) (stop at k >= 70). Use the --disable_cycle_breaking to allow cycle(s). In this case, this part of the graph is not analysed.
SNP input example
Use http://www.snp-nexus.org/ to generate a refseq TXT file for known SNPs containing SNP description on reference transcript.
Then use /bin/make_SNP_file.py to construct the final TSV file (example for the NM_004119 transcript of FLT3 gene):
python2.7 /bin/make_SNP_file.py NM_004119.2.fasta # Corresponding fasta file refseq_22221.txt # Refseq file obtain from snp-nexus snp_FLT3.tab # File name generated from make_SNP_file.py NM_004119 # Corresponding transcript 200 # Number of nucleotides before and after SNP
See /data/reference/snp_FLT3.tab for the final format.
Usually, results and log are displayed in the terminal. You can also write results and log in separate tsv/log files with:
python2.7 src/MICADo.py --samplekey <sample_name> --fastq data/fastq/<experiment_name>/<sample_name.fastq> --experiment <experiment_name> --fasta data/reference/<reference.fasta> > <sample_name>.tsv 2> <sample_name>.log
If you specified a SNP file, a TSV file will be created (and the direction /output/SNP/) with, for each SNP, the following informations: sample key, SNP ID, the number of reads supporting the reference nucleotide and the number of reads supporting SNP.
Detailed results JSON file (optional)
See http://www.json.org/ for complete description of json file format.
Example from the TP53 cohort (see associated paper). The pre-built library used for sampling is provided.
python2.7 src/MICADo.py --samplekey C_256_1 --fastq data/fastq/TP53_C/C_256_1.fastq --fasta data/reference/NM_000546.5.fasta --snp data/reference/snp_TP53.tab --experiment TP53_C --kmer_length 18 --results C_256_1.json &> C_256_1.log
In both C_256_1.log and C_256_1.json files, we can see that the only alteration with p-value < 0.001 and z-score > 10 is C > T at position 866 of NM_000546.5 which is the expected mutation for this sample.
- v.1.0, 2015-12-01, first release, version used in the accompanying paper
Copyright (c) 2015 Justine Rudewicz (1,2) (firstname.lastname@example.org) Hayssam Soueidan (1) (email@example.com) Macha Nikolski (1,2) (firstname.lastname@example.org) (1) CBiB - Universite Victor Segalen Bordeaux, 146, rue Leo Saignat, 33076 Bordeaux, France (2) CNRS / LaBRI, Universite Bordeaux 1, 351 cours de la Liberation, 33405 Talence Cedex, France
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