Hello from a french bio-informatician looking for help with NextSeq500 Illumina data

[berthenet]

Hi everyone!

It doesn't look like there are a lot of answers to the presentation threads, but I'm going to introduce myself anyway. Feel free to jump in and have a chat with me if you feel like it!

I was originally an engineer in biotechnologies, and I am currently finishing off my PhD while starting a new job as a bioinformatician.
I started doing bioinformatics during my thesis, because I was working on the genome of a bacteria, doing gene-by-gene approaches on different datasets of whole-genome sequenced strains. I'll get into details if you're interested.

I have always worked with either published data or data from our own in-house sequencing platform, so I have never had to really assess the quality of a sequence before working with them.

It's a different story in my new job, because our lab ordered sequencing and assembly for strains, which I received, and I now have to work from them, first step being to validate them. I am a bit lost with that, so I was hoping to find some help here, as you guys seem to work a lot with sequencing data.

The sequenging was achieved using NextSeq500 (Illumina), and the assembly was made with SPAdes. So if anyone is an expert in those, please shout under this post!

[GenoMax]

Welcome to SA!

You can use FastQC for assessment of quality of initial read data. This is pretty much the de facto program people use. The lab that made FastQC has many informative blog posts about data quality/observations at their QC Fail site.

Quast is what you would want to use for assessment of assemblies.

If these are bacterial genomes then Mauve allows you to do genome-wide alignments to quickly identify rearrangements.\

I recommend BBMap suite (multiple threads here on various tools included). This suite has many tools that allow you to work with NGS data.

[berthenet]

Just lost my post, I've been logged out during writing it. Note to myself: always copy my answer in my clipboard before posting...

So, let's right it all again.

Thanks for the links you shared. Some of them I knew of, but some of them I'll go and have a look. I do work with bacterial genomes.

So most of my assemblies look fine in terms of number of contigs (<100 contigs) once I filter out the smallest ones (<1000bp). However, for some of them the number of contigs remain really high, and when I check the length of the complete genome, I obain 3 genomes of more than 2.4Mb when I expect 1.65Mb approximately. I checked the 30 largest contigs for one of these outsider strain by doing a nblast against the NCBI database. I noticed that some of the contigs don't match the species of interest. These contigs have a low coverage value (indicated in the name of the contig): around 1, against more than 200 for contigs matching the species of interest.

Do you usually filter your contigs based on this coverage value? Is that why I have weird sizes?