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OptimAssembly

Viruses are characterized by, among other things, much smaller genome with respect to bacteria. As a consequence, the coverage obtained in massively parallel sequencing tends to be very high. This is not always good for genome assembly. In fact, too many reads can decrease the quality of the assembly (and increase the time/memory requirements), due to the abundance of sequencing errrors.

This simple tool filters the raw reads and takes a sample of them in order to reach a coverage that should give optimal results in genome assembly.

Sampling reads multiple times increases the chances that different contigs spanning the whole genome will be reconstructed. These are aligned and then the consensus is generated.

Dependencies

  • Python;
  • Biopython for the sequence manipulation;
  • seqtk for the trimming;
  • velvet for the assembly;
  • needle and cons from EMBOSS, for alignment and consensus building;
  • muscle for MSA.

Usage

usage: optimassembly.py [-h] [-f FASTQ] [-r REFERENCE] [-l EXP_LENGTH]

Optimise de novo assembly for short, viral genomes

optional arguments:
  -h, --help            show this help message and exit
  -f FASTQ, --fastq FASTQ input file in fastq format <>
  -r REFERENCE, --reference REFERENCE closest known genome reference
  -l EXP_LENGTH, --length EXP_LENGTH expected length <10000>