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ONT_BACTERIAL_ANALYSIS

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Pipeline summary:

Create for a multiplex input must to add a file genome_size.csv with size of each genome (bp) per barcode. :

e.g

barcode,genome_size
barcode01,3000000
barcode02,4500000
barcode03,5000000
barcode04,4000000
barcode05,3200000
barcode06,3500000

polishing process

The optimal number of polishing rounds is determined automatically using the CART algorithm. The prediction is based on multiple parameters, including error rate, N50/L50, genome coverage, Total Length of Matches, Average Occurrences, Distinct Minimizers, and processing time per round.

Source Parameter Description
Minimap2 DistinctMinimizers Number of unique minimizers found (Minimap2 value),change < 0.1% in distinct minimizers
AverageOccurrences Average occurrences of minimizers (Minimap2),change < 0.01 in average occurrences
TotalLengthMatches Total length of aligned matches,change < 0.1%
ProcessingTime Total execution time per round (Racon or Minimap2), change < 5%
RACON Processing Time Change < 5%
QUAST N50/L50 Minimum contig length that covers 50% of the assembly, change < 100 bp
QUAST/MEDAKA ErrorRate Error rate in the sequence after each polishing round
BUSCO Completeness (BUSCO) Change < 1% in complete genes
Target Value Optional Rounds Optimal number of rounds needed to achieve convergence

COMAND LINE

nextflow run main.nf --mode assemble --genome_size_file barcode_info.csv -profile <docker/singularity/conda>

--mode : assemble / hybrid_amr / hybrid_vc -profile:

REFERENCE

Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data How low can you go? Short-read polishing of Oxford Nanopore bacterial genome assemblies

Evaluation of the accuracy of bacterial genome reconstruction with Oxford Nanopore R10.4.1 long-read-only sequencing

Assembling the perfect bacterial genome using Oxford Nanopore and Illumina sequencing

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