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This Nextflow pipeline analyzes the results of the MiSeq sequencing of a collection of circular DNA vectors (paired ends). The input is a pair of fastq files per sample (a vector) and a fasta file of inserts introduced in the vectors. For the input detail see description of the params.config file below. For each vector, the pipeline outputs an image of the DNA map with annotation and the GenBank file.

Software Requirements

Docker (https://www.docker.com/) or Singularity (https://www.sylabs.io/) Linux containers and NextFlow workflow manager (https://www.nextflow.io/). On Mac OS, Docker can be installed, with the Homebrew manager (https://brew.sh), as:

 brew cask install docker

After Docker is installed, to be able to run the pipeline, you need to launch Docker (e.g., on Mac, clicking on it from the Launchpad; for the first time launch, Docker will ask you to register on its website).

NextFlow needs both Java RE and Java SE Developer Kit (JDK) version 1.8 or later (JDK is not automatically updated on MAC; it needs to be manually installed; check its version with "java -version"). To install NextFlow:

 curl -s https://get.nextflow.io | bash 

To test it:

./nextflow run hello

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Download VectorQC (current version 1.8)

curl -s -L https://github.com/biocorecrg/vectorQC/archive/v1.8.tar.gz > v1.8.tar.gz
tar -zvxf v1.5.tar.gz

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Install VectorQC

 sh INSTALL.sh 

This downloads the BioNextflow library and the file conf_tools.txt containing information about tools used by the pipeline.

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Modify nextflow.config and Dockerfile (optional)

The config file nextflow.config provides the computational parameters (memory, CPUs, run time) that you might want to change; if the pipeline is run on the cluster, the batch system parameters might need to be provided (e.g., queue names). By default, the Docker container is used (see Dockerfile); although Singularity can be used instead using the nextflow parameter -with-singularity (in this case, the Docker image will be converted to the Singularity image). In Dockerfile, you can change versions of software used by the pipeline.

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Check required parameters and modify params.config

To check the required parameters type

nextflow run main.nf --help

For the test run, the only parameter you might want to change is the email address if you want to recieve an email upon finishing the run. The e-mail will arrive with the MultiQC report attached.

Below all parameters in params.config are explained in detail.

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Input

Reads (param reads in the file params.config)

This parameters indicate the path where the fastq files are stored. Each file pairs matching a glob pattern indicated by the asterisk will be analyzed in parallel. !!Important!! when specifying the parameters reads by command line you should use "quotation marks". Be careful with file names as the naming can vary among facilities, instruments, etc.

Inserts (param inserts in the file params.config)

A custom fasta file with the header containing the name of the inserted genes/DNA. No whithe-spaces are allowed in the fasta header!

An example can be found in:

examples/inserts/genes.fa

Features (param features in the file params.config)

The fasta file (in the folder db) downloaded using the Plasmapper tool (http://wishart.biology.ualberta.ca/PlasMapper/). The fasta header is formatted in this way:

lpp_promoter[PRO]{lpp},30 bases, 1123 checksum.

• [] is a category. HYB (hyper activation binding doamin), LOC (locus), ORI (Origin of replication), OTH (other gene), PRO (promoter), REG (regulatory sequence), REP (reporter gene), SEL (gene for selection), TAG (affinity TAG) and TER (terminator).
• {} contains a small string of the sequence decription that is shown on the plot.
• Sequence length.

Common enzymes (param commonenz in the file params.config)

A list of restriction enzymes for the vectors (the file db/common.ids).

Parameter for reads trimming (using skewer)

The following parameters (in the file params.config) are used by the skewer algorithm for trimming reads (for further detail on the algorithm, see https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-182):

• adapter [in skewer: -x Adapter sequence/file] (by default, the universal Illumina adapter is used),
• minsize [ in skewer: -l, --min The minimum read length allowed after trimming; (18)],
• trimquality [in skewer: -q, --end-quality Trim 3' end until specified or higher quality reached; (0)], and
• meanquality [in skewer: -Q, --mean-quality The lowest mean quality value allowed before trimming; (0)].

Output (param output in the file params.config)

The output folder. Default is output. Outputs of the pipelines run with different parameters can be saved in different folders.

Email (param email in the file params.config)

This parameter is useful to receive an e-mail once the process is finished or crashed.

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Run VectorQC test example

First, simulate paired reads for vectors in ./examples, running:

 nextflow run ./simulate/simulate.nf

The result of running this pipeline is the fastq files in ./simulate/output. The parameters for the coverage and reads are provided and can be changed in ./simulate/params.config. For detail, see ./simulate/README.md.

Now, the pipeline can be run on these simulated fastq files:

 nextflow run main.nf

The run takes 3-5 mins. The results are in ./output folder. For more detail on the output, see the description of the pipeline below.

To override the parameters, run the pipeline with those parameters specified in the command line; for example, to simulate reads of the length 250bp, run

 nextflow run ./simulate/simulate.nf --size 250 --output simulate_250

The pipeline can be resumed and run in the background

 nextflow run main.nf --reads "./simulate/simulate_250/*{1,2}.fq" --output output_250 -resume -bg > log_250.txt

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Pipeline

1. QC. Run FastQC [1] on raw reads. Results are in the folder QC.
2. Trimming reads. Remove the adapter by using skewer [2]. Results are in the folder QC.
3. QC of trimmed reads. Results are in the folder QC.
4. Indexing features. Index the fasta file of features using makeblastdb from NCBI BLAST+ toolbox [3].
5. Read assembly. Assemble trimmed reads and merged them, if needed (default is no) using the SPAdes assembler [4]. If the parameter merge = "yes", the FLASH algorithm [5] is used to merge overlapping paired reads.
6. Assembly evaluation. Evaluate and merge assembled contigs using the in-house script evaluateAssembly.py (in ./bin). If more than one contig was assembled for a vector, contigs are merged into a circular DNA randomly. Contigs with coverage lower than 50% or more than 150% than the average are removed. Results are in the folder Refined_Assembly, while the original assembly is stored in the folder Assembly.
7. Alignment. Align assembled scaffolds to the feature database using BLAST [6]. Results are stored in the folder Blast.
8. Annotation of the restriction enzyme sites. The scaffolds are scanned for the presence of RE sites using the EMBOSS tool restrict [7] over the REBASE database [8] and the list of common enzymes specified in the parameter commonenz in params.config. Results are in the folder REsites.
9. Generation of results. Make a vector map using the Circular Genome Viewer (http://wishart.biology.ualberta.ca/cgview/) [9] and the GenBank-formatted file for each sample. Results are in the folders
10. Variant calling. If reference sequences are provided, the pipeline will align the assembled vectors to the refence one by using BWA[10] and it will call the variants with BCFtools[11]. Variants are then stored within the folder Variants Plots and GenBank. Generate the MultiQC [12] report and send an e-mail.

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Pipeline output

An example of the MultiQC Report

An example of the vector map:

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DAG graph

Run VectorQC on AWS Batch

For running VectorQC on AWS Batch, we provide a sample profile in nextflow.config. You need to adapt your parameters according to your deployment. For more detail, see this blogpost.

Once your parameters are set, you can run the pipeline by using this commandline from an EC2 instance:

 nextflow run main.nf -profile awsbatch -bucket-dir s3://mys3bucket/work

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References

1. Andrews, S., https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. 2010.
2. Jiang, H., et al., Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics, 2014. 15: p. 182.
3. Camacho, C., et al., BLAST+: architecture and applications. BMC Bioinformatics, 2009. 10: p. 421.
4. Bankevich, A., et al., SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol, 2012. 19(5): p. 455-77.
5. Magoč, T. and S.L. Salzberg, FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics, 2011. 27(21): p. 2957-63.
6. Altschul, S.F., et al., Basic local alignment search tool. J Mol Biol, 1990. 215(3): p. 403-10.
7. Rice, P., I. Longden, and A. Bleasby, EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet, 2000. 16(6): p. 276-7.
8. Roberts RJ, Vincze T, Posfai J, Macelis D. REBASE--a database for DNA restriction and modification: enzymes, genes and genomes. Nucleic Acids Res. 2015 Jan;43(Database issue) http://rebase.neb.com/rebase/rebase.html
9. Stothard, P. and D.S. Wishart, Circular genome visualization and exploration using CGView. Bioinformatics, 2005. 21(4): p. 537-9.
10. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010 Mar 1;26(5):589-95.
11. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009 Aug 15;25(16):2078-9
12. Ewels, P., et al., MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 2016. 32(19): p. 3047-8.