Overview

As has been mentioned several times, variants are anything that is different from a reference genome, but large insertions of novel DNA represent something of an unknown unknown. If a read doesn't map is it because its some kind of contamination, or is it because something new has entered into the sample. This tutorial is an attempt to use tools you are already familiar with to identify such novel DNA mutations.

Learning Objectives

1. Extract reads where one or both reads do not map to reference
2. de novo assemble reads

Relationship to other tutorials

1. As presence of adapter can cause problems with assembly, make sure adapters have been trimmed
2. Reads will be mapped with bowtie2
3. non-mapped reads will be extracted, consider checking quality of these reads and possible additional trimming as non-mapped reads may have not mapped due to low quality, and thus could be further improved.
4. Reads will then be assembled

None of these other tutorials are required to complete this tutorial, but additional information about individual steps may be found there.

Identification of a novel plasmid

One example of novel DNA being present is when a given sample may have a virus or plasmid associated with a sample. Here we will take a sample known to have a high copy plasmid associated with it, but map the reads against only the genome. Unaligned reads would then be expected to be able to assemble into a plasmid.

Get some data

mkdir $SCRATCH/GVA_novel_DNA
cd $SCRATCH/GVA_novel_DNA
cp $BI/gva_course/novel_DNA/* .
ls

^ above transfers 2 fastq files and a reference file.

Map read using bowtie2

This is the same process used in the read mapping tutorial, and therefore presented with little comment except to remark on differences. Refer to that tutorial for more in-depth information. Recall we installed bowtie2 in our GVA2021 environment

1. activate conda envrionment

   conda activate GVA2021
   bowtie2 --version

   You should see version 2.3.5.1

2. Convert reference to fasta

   module load bioperl
   bp_seqconvert.pl --from genbank --to fasta < CP009273.1_Eco_BW25113.gbk > CP009273.1_Eco_BW25113.fasta

   
   

   
   

3. Remember to make sure you are on an idev done

   For reasons discussed numerous times throughout the course already, please be sure you are on an idev done. It is unlikely that you are currently on an idev node as copying the files while on an idev node seems to be having problems as discussed. Remember the hostname command and showq -u can be used to check if you are on one of the login nodes or one of the compute nodes. If you need more information or help re-launching a new idev node, please see this tutorial.

   Index the reference

   mkdir bowtie2
   bowtie2-build CP009273.1_Eco_BW25113.fasta bowtie2/CP009273.1_Eco_BW25113

   
   

   
   

4. The following command will take ~7 minutes to complete. Before you run the command execute 'bowtie2 -h' so while the command is running you can try to figure out what the different options are doing that we did not include in our first tutorial.

   Map reads

   bowtie2 --very-sensitive-local -t -p 68 -x bowtie2/CP009273.1_Eco_BW25113 -1 SRR4341249_1.fastq -2 SRR4341249_2.fastq -S bowtie2/SRR4341249-vsl.sam --un-conc SRR4341249-unmapped-vsl.fastq

    Click here to explain the new options.

   option	effect
   --very-sensitive-local	map in very sensitive local alignment mode
   -t	print time info
   -p 68	use 68 processors
   -x bowtie2/CP009273.1_Eco_BW25113	index
   -1 SRR4341249_1.fastq -2 SRR4341249_2.fastq	reads used for mapping
   -S bowtie2/SRR4341249-vsl.sam	Sam file detailing mapping
   --un-conc SRR4341249-unmapped-vsl.fastq	print reads which do not map to the genome to the file SRR4341249-unmapped-vsl.fastq

    What percent of reads mapped?

   The stdoutput of the program listed:

   65.74% overall alignment rate

    How many reads are in our unmapped fastq file?

   grep -c "^+SRR4341249"  SRR4341249-unmapped-vsl.1.fastq

   returns: 441842

   Note that head or tail of the fastq file shows that our 3rd line is not just "+", hence why we have to add the additional sequence ID to the grep command

   
   

   
   

Assemble unmapped reads

This is the same process used in the plasmid assembly portion of the genome assembly tutorial, and therefore presented with little comment except to remark on differences. Refer to that tutorial for more in-depth information.

1. Attempt to assemble with plasmidspades.py which expects to find circularized plasmid type sequences. Command expected to take ~15 minutes

   run plasmid spades

   conda activate GVA-SPAdes
   plasmidspades.py -t 68 -o unmapped_plasmid -1 SRR4341249-unmapped-vsl.1.fastq -2 SRR4341249-unmapped-vsl.2.fastq

2. Additionally attempt to assemble with base spades.py command which uses different internal settings to potentially identify different types of novel DNA from our unmapped reads. Command expected to take ~15 minutes. 

   run plasmid spades

   spades.py -t 68 -o unmapped_full -1 SRR4341249-unmapped-vsl.1.fastq -2 SRR4341249-unmapped-vsl.2.fastq

Compare contigs generated from different assemblies

Recall from genome assembly tutorial the file we are most interested in is the contigs.fasta file in each output directory (unmapped_plasmid and unmapped_full).

1.  How many contigs were generated in each case?

   2 for the plasmid and 16 for the full

   Where does the answer come from? Expand source

   grep -c "^>" unmapped*/contigs.fasta

2.  Are any of the contigs the same?

   Yes, both contigs detected in the plasmid mode were also detected in the full mode

   Where does the answer come from? Expand source

   grep "^>" unmapped*/contigs.fasta
   # lists identical lengths and coverages for 2 plasmid contig

3.  What sizes are the contigs?

   5441

   3170

   14 others less than 500bp

   Where does the answer come from? Expand source

   grep "^>" unmapped*/contigs.fasta
   # same command as above, just focusing on the length value

4.  which is most likely to be our plasmid?

   The contig that is 3170.

   Where does the answer come from? Expand source

   # From above, I stated this was a high copy plasmid, it has a coverage value of 12,698 compared to 98 for the larger contig

5.  Is that actually our plasmid?

   Yes! The actual plasmid reference locus line stats:

   LOCUS       GFP_Plasmid_Sequ        3115 bp    DNA     circular UNA 18-NOV-2013

    Why might the sizes not agree?

   My thought is that this was raw fastq files that were fed into the assembly, not trimmed files. Leads me to hypothesize that the difference in size is related to the presence of adapter sequence.

   
   

Next steps

Here we have presented a proof of concept that unmapped reads can be used to find something that we actually did know was present. We also found something that was even longer that wasn't expected.

Additional questions are:

1.  How might we go about finding out what an assembled product actually is when it truly is novel rather than a positive control?

   blast. In fact I did just that and identified it as an artifact of sequencing. The contig corresponds to phiX.

   Steps to identify phiX

   copy full 5441 bp of sequence
   Go to https://blast.ncbi.nlm.nih.gov/Blast.cgi
   large list of results, including vast majority listing phiX or genome assembly/scaffold

   Why does seeing phiX (link to screen shot of blast results) tell me that it is an artifact? phiX is used as a loading control for illumina runs to both tell the difference between a failed run because of bad libraries and a failed run due to poor base diversity.

2.  How would we decide if it was real or important if we hadn't recognized it?

   Depends on blast results, how high coverage is compared to genome, gene content
3.  In other systems what else might you find?

   viruses, mobile genetic elements, evidence of microbiome, mitochondria, chloroplast
4.  How does this effect mapping?

   Consider advanced read mapping with multiple references tutorial
5.  Do you expect to find more novel DNA in a highly accurate reference file, or a "similar' reference file?

   Similar. The fact that the reference is not as accurate will lower the alignment scores across the board, potentially dropping below thresholds to be able to anchor the match at all. Look deeper at the bowtie2 mapping command where you used --very-sensative-local mode the documentation tells you about tolerated mismatches etc. The more reads you have that don't match, the more novel DNA inserts you are likely to deal with.

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