Genome assembly

Challenge

Your professor has sequenced a bacterial isolate using PacBio and Nanopore sequencing methods and has got the FASTQ files from both technologies. Now he needs to know the quality and quantity of these data before start any other analysis and ask you to assess the data. He needs to know how many sequences there are, how many base pairs (in GB) are there and the N50. He is also interested in see a visualization of the i) number of bases vs. sequence lengths (log transformed) and ii) the read length vs. read quality vs. read number.

He ask you to document every step and to conclude what data should be used.

Download the reads

wget

Assess read qualities

When using illumina fastqc is a very fast alternative. For nanopore nanplot will do the job.

fastqc

Exploring assemblers

One of the most popular genome assemblers for NGS is spades whereas for TGS flye has been widely used

Shovill: spades under the hood

shovill is a pipeline that enables pre and post processing of genomic data. It can be tunned to several tools for the processing steps and also to select different standalone assemblers

shovill --outdir MxanthusIllumina\
        --R1 R1.fq.gz\
        --R2 R2.fq.gz\
        --trim\
        --cpus 32

Dragonflye: flye under the hood

Similar to shovill (and inspired by it) dragonflye is a pipeline that enables several processing steps of genomic data be

dragonflye --outdir MxanthusNanopore\
           --gsize 9Mb\
           --trim\
           --reads ont-readsfastq.gz\
           --racon 5

wget

Since we are trying to assemble a bacterial genome, computer memory appears to be a limiting features of a local machine. Then, a computer cluster with high performance turns out to be an important need.

First we need environment installations, therefore its important to have conda environments with the assemblers and other programs (conda create -c bioconda dragonflye dragonflye and conda create -c bioconda shovill shovill). That way both assemblers pipelines will lie in separate environment avoiding possible dependenies problems

We will use Apolo computer cluster which uses Slurm as the computer system workload manager (i.e a program that manages the time and resources of the computer).