Summary of the latest findings on the viral genome

The post will only be a summary of some knowledge that the team gain during the past week or so about the phage and the genome of the phage that we were assigned.

First of all, out of the ten assemblies we decided to continue with the one that was assembled with SPAdes, where all reads where given as input and the setting careful was used. This resulted in an assembly with 34 contigs, but where two of the contigs make up the majority of the total sizes. One of these contigs is 90000 bp and the other one is 76000 bp. The rest of the contigs are about 3000 bp or shorter. This makes us think that maybe one of the two larger contigs might be the phage genome. Later it was confirmed by Anders Nilsson that both of these contigs are phage genomes and from two different phages. The the smaller contig is the genome that the research group was looking for, and so in the continued work of this project we have started to characterize and annotate this genome. Anders also mentioned that this phage is virulent and so we expect and have to some extent confirmed that this phage has its own enzymes and mechanisms for replication. This was done by using BLAST against other phage genomes. We also were informed by Anders that this phage belongs to the family of P7 phages. Further annotation should, thus, be proceeded by using BLAST against genomes belonging this this family of phages, as far as possible.

We also performed BLASTs against human and E. coli genomes and found matches against the genomes of these species that could not be found in bacteriophages. We thus concluded that there is contamination from these species in the samples. These are non the less irrelevant as we have been provided confirmation that the 76000 bp contig is the genome of the phage of interest. But this contig should be BLASTed against E. coli and human to assess if there might be reads from these species that might have been incorporated into the contig.

We decided to divided the work among us, where one of us would do research on the phage biology of our phage of interest and compare it to other phages as one means of characterization of our phage genome, one would research the ORFs of the phage genomes to try and predict unique genes for this phage, and one of us would do the research and testing necessary to find the terminal repeats of the genome. For my part I was given the task of finding the terminal repeats. This work will be conducted by researching the literature to find phages of the same family where the terminals have already been found to find clues about the terminal repeats, and also the coverage of the reads back to the genome should give clues about the position of the terminals, since it can be assumed that the repeats of the terminal have a higher coverage compared to the rest of the genome.

We have been looking for a tool to visualize the contigs and allow us to work with the genome in a visual manner, and Anders recommended the software Geneious. This software is a commercial software, but there is a possibility to get a 14 days free trial version. This is the software I will use to explore the terminal repeats.

Assembly and more quality controls

Last week we focused on doing more quality controls, and we started with doing alignments. After the adapter of the reads had been removed a quality control of the reads with FastQC clearly showed an increase in the quality of the data.

Then we proceeded to do trim and filter the reads with FastX. Trimming is when bases with poor quality are removed, and filtering is when entire reads of poor quality are removed from the dataset, either due to poor average quality, ambiguous base calling or short length. FastX is a collection of command line tools for pre-processing short reads. Using FastX to filter the data did not result in any changes of the quality of the data. The file sizes of the fastq files before and after remained the same and analyzing the files with FastQC confirmed this since there were no changes in the plots compared to the plots before filtering. The per base quality plot below is given as an example of no changes to before the filtering.

The per base quality plot after filtering the reads.

We decided to continue without the filtered reads.

Next we tried assembling the reads into contigs with two different assemblers: Velvet and SPAdes. After discussion with Anders Nilsson and consulting documentation from Illumina it was decided to assemble the reads with Velvet with three different k-mer sizes: 21, 41 and 61. SPAdes has an algorithm for calculating the k-mer size. In the literature it is also recommended to assemble with a lower coverage that is common when assembling phage genomes. This is because phage genomes are small and with a high coverage there is a risk that systematic errors would be treated as natural variations. For this reason the reads were also assembled with only 10% of the reads, for both Velvet and for SPAdes. In addition, when the reads were assembled with SPAdes they were assemble with the setting “careful” on and off. In total ten assemblies were made using SPAdes and Velvet.

The software Quast was used to analyze and require metrics for all assemblies. The result reported by Quast was (only contigs > 500 bp reported):

Velvet 21 k-mer:
Number of contigs: 32
Largest contig: 2926 bp
N50: 749 bp
Total length: 24391 bp

Velvet 41 k-mer:
Number of contigs: 43
Largest contig: 1730 bp
N50: 711 bp
Total length: 32159 bp

Velvet 61 k-mer:
Number of contigs: 63
Largest contig: 1241 bp
N50: 660 bp
Total length: 42473 bp

Velvet 21 k-mer (10% coverage):
Number of contigs: 63
Largest contig: 5390 bp
N50: 1862 bp
Total length: 85004 bp

Velvet 41 k-mer (10% coverage):
Number of contigs: 64
Largest contig: 7011 bp
N50: 2777 bp
Total length: 100054 bp

Velvet 61 k-mer (10% coverage):
Number of contigs: 60
Largest contig: 14272 bp
N50: 2194 bp
Total length: 103048 bp

SPAdes (careful):
Number of contigs: 34
Largest contig: 90035 bp
N50: 76572 bp
Total length: 193763 bp

SPAdes (uncareful):
Number of contigs: 34
Largest contig: 90035 bp
N50: 76700 bp
Total length: 193891 bp

SPAdes (10% coverage and careful):
Number of contigs: 8
Largest contig: 90035 bp
N50: 90035 bp
Total length: 169704 bp

SPAdes (10% coverage and uncareful):
Number of contigs: 7
Largest contig: 90035 bp
N50: 90035 bp
Total length: 169705 bp

We are unsure how to interpret which of these assemblies are the best based on these metrics, but the genome of the phage is expected to be 80-90 kb in total. None of the SPAdes assemblies fall within this range. Out of the Velvet assemblies only the 21 k-mer with 10% coverage fall with in the expected genome size. But the best assembly still remains to be discussed.

Literature research on methods and tools for assembly of viral genomes

Have been doing literature research to find out more about the general approach of assembling and the corresponding software tools used in each step. One recent paper gives the overview of the approaches to assembling  viral genomes (R.J. Orton et al.).

The steps that are recommended for the de novo assembly and annotation of a viral genome according to R.J. Orton et al. would be first of all to put the raw read through a quality control to remove primers/adapter from the reads. Cutadapt and Trimmomatic are two widely used tools to remove adapters. The reads are also usually trimmed to remove poor-quality bases from the ends of reads. In addition to trimming the read they are also filtered, which means the complete removal of some reads because of low quality, short length or ambiguous base calling. For de novo assembly it is also recommended to remove exact read duplicates. Two widely used tools for filtering and trimming are Trim Galore! and PRINSEQ. Because phage samples often are contaminated with the host genome it is also recommended to “run a host sequence depletion step”. This means that the reads are first aligned to the host genome and only the unmapped reads are used for de novo assembly. But, in the meeting with Anders Nilsson, he said that phage genomes might contain sequences that are the same as the host genome, so a host sequence depletion step can probably not be performed thoughtlessly.

The next step is the assembly. For this step R.J. Orton et al. emphases the importance of removing adapters and trimming bases of low quality, since a very low amount of the DNA will be viral it will be important to have high quality yields. The most common algorithms for de novo assembly are overlap layout consensus (OLC) and de Bruijn graphs. They mention the assemblers MIRA (OLC), Edena (OLC), AbySS (de Bruijn) and Velvet (de Bruijn). One big issue with de novo assemblies are that they consist of a multitude of contigs and not the complete genome. This is because of “sequencing errors, repeat regions and areas with low converage”. The recommended way of joining contigs is to align them to a related reference genome. This will probably not be possible in this case, though, since phages evolve to fast which makes it impossible to use a reference genome. In discussions with Anders it was advised that this strategy might be possible to do for some of the genes, but not any longer stretches of the phage genome. If a reference genome is not available for alignment of the gaps R.J. Orton et al. recommends using paired-end reads or mate-pair reads to scaffold the contigs into the correct linear order. This should be possible to do in this case since the data is paired-ends. If the assembler does not do the scaffolding inherently there are stand-alone scaffolders such as Bambus2 and BESST. For paired-end data gap filling software such as IMAGE and GapFiller may also be used to close some of the gaps.

After the genome assembly draft is completed it is recommended to inspect the draft genome, for example by mapping the reads to the completed draft genome and looking for issues, such as miscalled bases, indels and regions of no coverage. Tools exist to help in this inspection process, such as ICORN2.

SPAdes is a recommended tool that can perform most of the steps of de novo assembly and the following quality control steps and corrections.