SNIPs In Nanopore reads of Gene Libraries
SINGLe computes consensus sequence of DNA reads by (noisy) nanopore sequencing. It is focused on long amplicons sequencing, and it aims to the reads of gene libraries, typically used in directed evolution experiments.
SINGLe takes advantage that gene libraries are created from an original wild type or reference sequence, and it characterizes the systematic errors made by nanopore sequencing. Then, uses that information to correct the confidence values (QUAL) assigned to each nucleotide read in the mutants library.
Finally, given that you can identify which variant was read in each case (for example by the use of unique molecular identifiers or DNA barcodes), SINGLe groups them and computes the consensus sequence by weighting the frequencies with the corrected confidence values.
For more details, please refer to our pre-print "Accurate gene consensus at low nanopore coverage" doi: https://doi.org/10.1101/2020.03.25.007146 for more information.
Using bioconductor:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("single")
Or from git-hub:
require(devtools)
install_github("rocioespci/single")
To use SINGLe you must have the following data:
- A fasta file with the reference sequence (ex. a wild type from which you generated the library) (REF.fasta).
- Nanopore reads of a reference sequence (REF_READS.fastq).
- Nanopore reads of a gene libraries (LIB_READS.fastq).
- Identification of each read in the gene library with a gene variant (ex via the use of DNA barcodes on your experiment) (BC_TABLE.txt).
Align nanopore reads to the reference sequence and create a sorted bam file.
For the reads of the reference:
minimap2 -ax map-ont --sam-hit-only REF.fasta REF_READS.fastq > REF_READS.sam
samtools view -S -b REF_READS.sam > REF_READS.bam
samtools sort REF_READS.bam -o REF_READS.sorted.bam
And for the reads of the library:
minimap2 -ax map-ont --sam-hit-only REF.fasta LIB_READS.fastq >LIB_READS.sam
samtools view -S -b LIB_READS.sam > LIB_READS.bam
samtools sort LIB_READS.bam -o LIB_READS.sorted.bams
Recommendation: SINGLe works better if you work separately with nanopore reads of the forward and the reverse strand separately. To do that, you can add --for-only and --rev-only in minimap2 options, and follow the downstream analysis independently for each set of aligned reads.
SINGLe consists on three steps: train model, evaluate model, compute consensus. As it can be time consuming, I will only analyze a subset of positions
library(single)
pos_start <- 1
pos_end <- 10
refseq_fasta <- system.file("extdata", "ref_seq.fasta", package = "single")
ref_seq <- Biostrings::subseq(Biostrings::readDNAStringSet(refseq_fasta), pos_start,pos_end)
First, train the model using nanopore reads of the reference (wild type).
REF_READS <- system.file("extdata", "train_seqs_500.sorted.bam",package = "single")
train <- single_train(bamfile=REF_READS,
output="train",
refseq_fasta=refseq_fasta,
rates.matrix=mutation_rate,
mean.n.mutations=5.4,
pos_start=pos_start,
pos_end=pos_end,
verbose=FALSE,
save_partial=FALSE,
save_final= FALSE)
print(head(train))
Second, evaluate model: use the fitted model to evaluate the reads of your library, and re-weight the QUAL (quality scores).
LIB_READS <- system.file("extdata","test_sequences.sorted.bam",package ="single")
corrected_reads <- single_evaluate(bamfile = LIB_READS,
single_fits = train,
ref_seq = ref_seq,
pos_start=pos_start,pos_end=pos_end,
verbose=FALSE,
gaps_weights = "minimum",
save = FALSE)
corrected_reads
Finally, use the reads of the library with the corrected QUAL scores to compute a weighted consensus sequences in subsets of reads. The sets of reads corresponding to each variant are indicated in a table (here BC_TABLE) of two columns: SeqID (name of the read) and BCid (barcode or group identity).
BC_TABLE = system.file("extdata", "Barcodes_table.txt",package = "single")
consensus <- single_consensus_byBarcode(barcodes_table = BC_TABLE,
sequences = corrected_reads,
verbose = FALSE)
consensus
Use pileup to create a data.frame with counts by position nucleotide and quality score
counts_pnq <- pileup_by_QUAL(bam_file=REF_READS,
pos_start=pos_start,
pos_end=pos_end)
head(counts_pnq)
Compute a priori probability of making errors
p_prior_errors <- p_prior_errors(counts_pnq=counts_pnq,
save=FALSE)
p_prior_errors
Compute a priori probability of having a mutation
p_prior_mutations <- p_prior_mutations(rates.matrix = mutation_rate,
mean.n.mut = 5,ref_seq = ref_seq,save = FALSE)
head(p_prior_mutations)
Fit SINGLe logistic regression using the prior probabilities and the counts
fits <- fit_logregr(counts_pnq = counts_pnq,ref_seq=ref_seq,
p_prior_errors = p_prior_errors,
p_prior_mutations = p_prior_mutations,
save=FALSE)
head(fits)
Use the fits to obtain the replacement Qscores after SINGLe fit, for all possible QUAL, nucleotide and position values
evaluated_fits <- evaluate_fits(pos_range = c(1,5),q_range = c(0,10),
data_fits = fits,ref_seq = ref_seq,
save=FALSE,verbose = FALSE)
head(evaluated_fits)
Compute one consensus sequence weighted by QUAL values.
data_barcode = data.frame(
nucleotide=unlist(sapply(as.character(corrected_reads),strsplit, split="")),
p_SINGLe=unlist(1-as(Biostrings::quality(corrected_reads),"NumericList")),
pos=rep(1:Biostrings::width(corrected_reads[1]),length(corrected_reads)))
consensus_seq <- weighted_consensus(df = data_barcode, cutoff_prob = 0.9)
consensus_seq
another_consensus_seq <- weighted_consensus(df = data_barcode, cutoff_prob = 0.999)
another_consensus_seq
list_mismatches(ref_seq[[1]],another_consensus_seq)