Cellular genetic heterogeneity is common in many biological conditions including cancer, microbiome, and co-infection of multiple pathogens. Detecting and phasing minor variants, which is to determine whether multiple variants are from the same haplotype, play an instrumental role in deciphering cellular genetic heterogeneity, but are still difficult because of technological limitations. Recently, long-read sequencing technologies, including those by Pacific Biosciences and Oxford Nanopore, have provided an unprecedented opportunity to tackle these challenges. However, high error rates make it difficult to take full advantage of these technologies. To fill this gap, we introduce iGDA, an open-source tool that can accurately detect and phase minor single-nucleotide variants (SNVs), whose frequencies are as low as 0.2%, from raw long-read sequencing data. We also demonstrated that iGDA can accurately reconstruct haplotypes in closely-related strains of the same species (divergence >= 0.011%) from long-read metagenomic data.
For optimal performance, we recommend a computer with the following specs:
RAM: 16+ GB
CPU: 8+ cores
The iGDA package is tested on Linux operating systems.
Linux: CentOS Linux release 7.6.1810 (Core)
conda install -c zhixingfeng igda
Or
conda install -c bioconda -c conda-forge -c zhixingfeng igda
if you don't have biconda or conda-forge in your channel list.
- xgboost 0.90
- bioawk 1.0
- samtools 1.10
Download the source code of iGDA from Release, unzip it, enter the directory and type "Make". Add the "bin" directory to your PATH or create a soft link to ./bin/igda in a directory that the system can find.
Download https://github.com/zhixingfeng/shell/archive/refs/tags/1.0.1.tar.gz, unzip it and add the folder to your PATH.
Convert lower case letters to upper case in reference fasta file:
fasta2upper infasta outfasta
Convert wildcard letters to N in reference fasta file:
fastaclean fastafile outfafile
Realign reads aligned to the negative strand:
(PacBio data) igda_align_pb infile(bam or sam file) reffile outfile nthread
(PacBio data with no QV, like Sequel or Sequel II) igda_align_pb_fa infile(bam or sam file) reffile outfile nthread
(Nanopore data) igda_align_ont infile(bam or sam file) reffile outfile nthread
Sort and convert realign samfile to bamfile:
sam2bam samfile nthread
(PacBio data) igda_pipe_detect -m pb bamfile reffile contextmodel outdir
(Nanopore data) igda_pipe_detect -m ont bamfile reffile contextmodel outdir
Please note:
bamfile is the aligned bam file (sorted and indexed).
reffile is the reference fasta file.
contextmodel is the context effect model trained on independent data. They can be download in https://github.com/zhixingfeng/igda_contextmodel. For PacBio, qvx_NCTC_P6_C4 is the models corresponding to different QV thresholds to mask bases with QV < x as N (Use qv0_NCTC_P6_C4 if your reads have no QV). For ONT, If your data is preprocessed by discarding reads with average QV < x and masking bases with QV < y by "N", use the model named "ont_context_effect_read_qv_x_base_qv_y". "qv_0" means no masking. The "sam_maskqv" command released with iGDA can do the low QV base masking (should be available if you install iGDA using conda), but you could also use any in-house or third-party tools. Many QC tools dealing with FASTQ files can do low average QV reads filtering.
(PacBio data) igda_pipe_phase -m pb indir(outdir of igda_pipe_detect) reffile outdir
(Nanopore data) igda_pipe_phase -m ont indir(outdir of igda_pipe_detect) reffile outdir
For detecting minor SNVs, detected_snv.vcf in outdir is the final result.
For phasing minor SNVs, contigs.sam, contigs.fa, and contigs.ann are the final results.
In the contigs.ann file, each row is a contig.
Column 1 is chromosome name.
Column 2 is the SNVs of the contig. It is encoded, for each integer x, floor(x/4) = 0-based locus, and x modulo 4 = base (0=A, 1=C, 2=G, 3=T)
Column 3 is start locus (0-based)
Column 4 is end locus (0-based)
The other columns are reserved for internal use
It is difficult to find an universally optimal parameter setting. Here are some tips for parameter tuning if the default one does not have the expected performance:
-
By default,
igda_pipe_detectdiscards reads with aligned length < 1000 because it can reduce the impact read-maping ambiguity. Useigda_pipe_detect -l 0instead if the data have lots of aligned reads shorter than 1000. -
In
igda_pipe_detect,-rand-care the two major parameters affecting the accuracy.-ris "Minimal depth for each SNV" and-cis "Minimal maximal conditional substitution rate". By default,igda_pipe_detectuses-r 25 -c 0.65, which is a conservative setting aiming to achieve a low false discover rate (FDR). These parameters might have a low sensitivity if the sequencing depth corresponding to a minor SNV is lower or close to 25. It is possible to increase sensitivity by decreasing-r, but FDR might increase.
Example data and code can be download from the following link, which includes:
- A mixture of 186 Bordetella spp. samples (PacBio sequencing)
- A mixture of 155 Escherichia coli samples (PacBio sequencing)
- A mixture of 65 Klebsiella pneumoniae samples (Oxford Nanopore sequencing)
- A mixture of 11 Borrelia burgdorferi strains and 744 other species to mimic a metagenome (PacBio sequencing)
https://www.dropbox.com/sh/umi03t3eendcktf/AABhXy2cNfQAr0wBike87Kc0a?dl=0
The run time of demo 1~3 is expected to be several minutes with 16 cores, but may vary depending on the specific machines and their working conditions. It might take about several hours to finish demo 4 with 32 cores due to its size.
Feng, Z., Clemente, J.C., Wong, B. et al. Detecting and phasing minor single-nucleotide variants from long-read sequencing data. Nat Commun 12, 3032 (2021).
DOI: https://doi.org/10.1038/s41467-021-23289-4
Zhixing Feng (冯智星)
For any questions, contact [email protected] or [email protected]