CharONT2 is a Nextflow implementation of CharONT pipeline, for characterizing long genomic regions starting from ONT reads on multiple samples and across multiple infrastructures in a streamlined, portable and reproducible manner. In particular, CharONT2 is a consensus calling pipeline meant for characterizing long genomic regions - such as Short Tandem Repeats - from organisms with ploidy >= 1. Starting from ONT reads including a shared flanking sequence, it provides consensus sequences for each allele and tandem repeats annotations. In case you used an enrichment method different to PCR (e.g. CRISPR-Cas9), or performed WGS, amplicon-like sequences can be extracted in-silico based on known flanking sequences.
Prerequisites
Installation
git clone https://github.com/MaestSi/CharONT2.git
cd CharONT2
chmod 755 *
Reads assignment to each allele
After creating a preliminary consensus sequence for Allele #1, all reads are mapped to it and, based on the CIGAR string, the biggest DEL and INS are identified for each read, resulting in a bidimensional Score. At this point, reads with either component of the Score deviating from the 1st or 3rd inter quartile range (IQR) for more than IQR_outliers_coef_precl*IQR are labelled as candidate outliers, and are excluded from the k-means clustering. After clustering, IQR is computed within each cluster for both components of the Score, and reads with either component deviating from the 1st or 3rd IQR for more than IQR_outliers_coef*IQR are labelled as outliers. Scores are plotted so that the user may tune IQR_outliers_coef_precl and IQR_outliers_coef parameters according to their preferences, based on visual inspection of <sample_name>_reads_scores.png. In the provided example, reads from Allele #2 show 400 bp INS with respect to Allele #1. Notably, one read with 1000 bp INS is identified, which may indicate a somatic variant.
Also in case the SV is too big to allow mapping of reads at both 5' and 3' flanking regions, the size of the SV is estimated from soft-clipping. In particular, depending on whether soft-clipping occurs at the 5' end a) or at the 3' end b), a different formula is applied for calculating the size of the SV compared to Allele #1, used as a Reference.
The CharONT2 pipeline requires you to open CharONT2.conf configuration file and set the desired options. Then, you can run the pipeline using either docker or singularity environments just specifying a value for the -profile variable.
Usage:
nextflow -c CharONT2.conf run CharONT2.nf \
--fastq_files = "/path/to/files*.fastq" \
--scripts_dir = "/path/to/scripts_dir" \
--results_dir = "/path/to/results_dir" \
-profile docker
Mandatory argument:
-profile Configuration profile to use. Available: docker, singularity
Other mandatory arguments which may be specified in the CharONT2.conf file
--fastq_files Path to fastq files, use wildcards to select multiple samples
--results_dir Path to a folder where to store results
--num_alleles num_alleles represents the number of expected alleles; use --num_alleles="auto" for automatic num_alleles detection based on Silhouette coefficient
--PCRThr PCRThr is the identity threshold for in-silico PCR in case inSilicoPCR=true
--primerSeqOne primerSeqOne is a primer sequence used for in-silico PCR in case inSilicoPCR=true
--primerSeqTwo primerSeqTwo is a primer sequence used for in-silico PCR in case inSilicoPCR=true
--scripts_dir scripts_dir is the directory containing all scripts
--target_reads_consensus target_reads_consensus defines the maximum number of reads used for consensus calling
--target_reads_polishing target_reads_polishing defines the maximum number of reads used for consensus polishing
--max_reads_preliminary max_reads_preliminary defines the maximum number of reads used for preliminary clustering and consensus calling
--clustering_id_threshold identity threshold for clustering preliminary allele assembly
--plurality cut-off for the number of positive matches in the multiple sequence alignment below which there is no consensus
--min_maf minimum minor allele frequency; if less than min_maf*100% of reads are assigned to one allele, the allele is skipped
--IQR_outliers_coef_precl label as candidate outliers reads with score > 3rd_QR + IQR_outliers_coef_precl*IQR or score < 1st_QR - IQR_outliers_coef_precl*IQR
--IQR_outliers_coef label as outliers reads with score > 3rd_QR + IQR_outliers_coef*IQR or score < 1st_QR - IQR_outliers_coef*IQR; IQR is computed within each cluster
--fast_alignment_flag set fast_alignment_flag=1 if you want to perform fast multiple sequence alignment; otherwise set fast_alignment_flag=0
--min_clipped_len minimum number of soft-clipped bases to be considered as a DEL or INS
--sd_noise_score sd_noise_score is the standard deviation of gaussian-distributed noise with zero mean added to score
--primers_length primers_length defines how many bases are trimmed from consensus sequences
--medaka_model medaka model for consensus polishing
A test fastq dataset obtained with DeepSimulator is provided. The datasets contains 1,000 reads simulating a heterozygous short tandem repeat expansion in DMPK gene. For full pipeline testing, set the following parsmeters:
--primerSeqOne="GCTCCGCAGGGGGGGCGGGTCTGGCCGGGAGGAGGGGCGGGGAACGGGCTAGAAAGTTTGCAGCAACTTTTCTCGAGCTTGCGTCCCAGGAGCGGATGCGC"
--primerSeqTwo="TTATCTAGGGAGATCCCGGAGGGAATCTGGTGAGGCCTGAACGGAGGGAGATCTGGGGCTGAATAAAGGGCTTCTGCCCTCTAAAGTCGCAAAGACGTAGG"
If the pipeline completes successfully, the following results are produced.
If this tool is useful for your work, please consider citing our manuscript.
Alfano M., De Antoni L., Centofanti F., Visconti V. V., Maestri S., Degli Esposti C., Massa R., D'Apice M. R., Novelli G., Delledonne M., Botta A. and Rossato M. (2022) Characterization of full-length CNBP expanded alleles in myotonic dystrophy type 2 patients by Cas9-mediated enrichment and nanopore sequencing eLife 11:e80229.
For further information, please refer to the following manuscripts:
Grosso V, Marcolungo L, Maestri S, Alfano M, Lavezzari D, Iadarola B, Salviati A, Mariotti B, Botta A, D'Apice MR, Novelli G, Delledonne M and Rossato M. Characterization of FMR1 repeat-expansion and intragenic variants by indirect sequence capture. Front. Genet. doi: 10.3389/fgene.2021.743230.
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