Now that you have assembled the data into contigs next natural step to do is annotation of the data, i.e. finding the genes and doing functional annotation of those. For gene finding a range of programs are available (Metagene Annotator, MetaGeneMark, Orphelia, FragGeneScan), here we will use Prodigal which is very fast and has recently been enhanced for metagenomics. We will use the -p flag which instructs Prodigal to use the algorithm suitable for metagenomic data. We will use a dataset consisting of 11 samples from a time series sampling of surface water in the Baltic Sea. Sequencing was done with Illumina MiSeq here generating on average 835,048 2 x 250 bp reads per sample. The reads can be found here:
The first four numbers in the filename represent a date. All samples are from 2012. R1 and R2 both contain one read of a pair. They are ordered, so the first four lines in R1 are paired with the read in the first four lines of R2. They are in CASAVA v1.8 format (http://en.wikipedia.org/wiki/FASTQ_format).
A coassembly has already been made with Ray using all reads to save you some time. You can find the contigs from a combined assembly on reads from all samples here:
They have been constructed with Ray using a kmer of 41 and no scaffolding. Only contigs >= 1000 are in this file. The reason a coassembly is used is that we can get an idea of the entire metagenome over multiple samples. By mapping the reads back per sample we can compare coverages of contigs between samples.
Question: What could be a possible advantage/disadvantage for the assembly process when assembling multiple samples at one time?
Question: Can you think of other approaches to get a coassembly?
Note that all solutions (i.e. the generated outputs) for the exercises are also in:
In all the following exercises you should again use the virtual environment to get all the necessary programs (unless you already loaded it ofc):
It’s time to run Prodigal. First create an output directory with a copy of the contig file:
mkdir -p ~/metagenomics/cfa/prodigal cd ~/metagenomics/cfa/prodigal cp /proj/g2014113/metagenomics/cfa/assembly/baltic-sea-ray-noscaf-41.1000.fa .
Then run Prodigal on the contig file (~2m20):
prodigal -a baltic-sea-ray-noscaf-41.1000.aa.fa \ -d baltic-sea-ray-noscaf-41.1000.nuc.fa \ -i baltic-sea-ray-noscaf-41.1000.fa \ -f gff -p meta \ > baltic-sea-ray-noscaf-41.1000.gff
This will produce 3 files:
-da fasta file with the gene sequences (nucleotides)
-aa fasta file with the protein sequences (aminoacids)
stdouta gff file
The gff format is a standardised file type for showing annotations.It’s a tab delimited file that can be viewed by e.g.
Pass the option -S to less if you don’t want lines to wrap
An explanation of the gff format can be found at http://genome.ucsc.edu/FAQ/FAQformat.html
Question: How many coding regions were found by Prodigal? Hint: use grep -c
Question: How many contigs have coding regions? How many do not?