This walkthrough provides a simple example of how to set-up and run the Bowtie Aligner using the web-browser accessible CloVR dashboard, as well as analyze the resulting outputs. We shall align metagenomic WGS reads extracted from the Anterior NaresÂ body site (sample SRS019215), to reference genome Staphylococcus aureus.
In most cases, this pipeline can be run locally. But for huge datasets, you may require additional computational support. This walkthrough demonstrates how to run the pipeline both locally and by using the cloud for computational support.
TheÂ Bowtie ManualÂ provides aÂ detailed description of the aligner tool.
Getting started with CloVR
Installing and setting up CloVR is a one-time process. If you have done this before, you may skip to the next step â€“Â Setting up input dataset.
CloVR is run using a local desktop client.Â Visit theÂ Getting started with CloVRÂ page to download and install the client. Once the CloVR virtual machine is set up and launched, you should see a screen similar to Figure 1.
Figure 1. CloVR desktop client
Start the CloVR web interface
First check the CloVR desktop window for the IP address of your virtual machine (VM). Then enter this IP address in a web browser as shown in Figure 2.
Figure 2. Accessing the CloVR web interface
Add cloud credentials to the pipeline
If you do not need additional computational support, you may skip to the next step â€“Â Setting up input dataset.
For additional computational support, visit theÂ Adding CredentialsÂ page for steps on how to add DIAG credentials.Â DIAGÂ is an academic cloudÂ which is free for researchers. Alternatively, you could run the pipeline on Amazon EC2 or using otherÂ cloud computing providers.Â Once the your DIAG credentials are setup, you should see it listed within the credentials tab as shown in Figure 3.
Setting up input dataset
Prepare input datasets
This pipeline requires two sets of files: reads and an index of the reference genome.
The reads file(s) should be FASTQ format. It could be just one file for single-end reads or two files for paired-end reads.
Depending on the version of pipeline you choose to use (indices or noindices), the reference index dataset is either:
- a sequence file of the reference genome (FASTA format) or,
- a pre-built index dataset (several examples can be downloaded from theÂ Bowtie website)
Next, move the input filesÂ into theÂ user_dataÂ folder located within the clovr-standard-* image directory. This will enable us to easily access the data through the CloVR dashboard.
For the “no indices” version, move the reads fastq file(s) and reference fasta file into user_data folder as shown in Figure 4.
For the “indices” version,Â create a folder namedÂ bowtie_indicesÂ inÂ user_dataÂ and move the pre-built index files there as shown in Figure 5. Also move the reads fastq file(s) toÂ user_data.
Add input datasets to the pipeline
Before starting a pipeline, you must add your datasets to the CloVR VM as â€œTagsâ€.Â To add tags, click â€œAddâ€ on the web interface.
Then click on â€œSelect file from imageâ€, which will open a sub-window where you can select one or multiple FASTQ files for upload into the VM. Alternatively, you can use â€œBrowseâ€ in the â€œUpload Fileâ€ window to find and select files from anywhere on your local computer, but multiple files have to be uploaded in separate steps.
Select â€œNucleotide FASTQâ€ from the â€œFile Typeâ€ drop-down menu and name your dataset, e.g. as â€œinput_readsâ€. For single reads select just one file to tag, and for paired reads, select both files. Add an optional description of your dataset. Click â€œTagâ€ to upload the data to CloVR. A â€œCompleted Successfullyâ€ window should appear to indicate that your datasets was added to the CloVR VM and the new dataset should be listed under â€œData Setsâ€ on the web interface.
Next, if you do not have a pre-built index, repeat the same process for the reference FASTA file. This time select â€œNucleotide FASTAâ€ from the â€œFile Typeâ€ drop-down menu and name your dataset, e.g. as â€œref_fastaâ€. Click â€œTagâ€ again to upload the data to CloVR.
If you are using a pre-built index, you do not need to tag the index files.
The tagged datasets will appear as a â€œTagâ€ on the CloVR web interface. Multiple files will listed under the same â€œTagâ€ name.
Pipeline setup and execution
To initialize a new pipeline run, click on the â€œOther Protocolsâ€ drop-down as shown in the figure below. Then select â€œclovr_align_bowtie_indicesâ€ if you uploaded bowtie pre-built index or “clovr_align_bowtie_noindices” if you uploaded a reference sequence fasta file.
This will open the pipeline configuration window. For the input datasets select the tag corresponding to the input file(s). If you are running the noindices version, also select the appropriate tag for the Reference FASTA sequence field.
Select “local” or “DIAG” credentials from the â€œAccountâ€ drop-down menu. Bowtie runs pretty quickly, so running it locally should be fine if the dataset is not too large.
Provide a name to recognize your pipeline in the web interface home page as â€œPipeline Descriptionâ€, e.g. â€œHMP_Bowtie_testâ€³.
Check your input by clicking â€œvalidateâ€. If the validation is successful, start the pipeline by clicking â€œsubmitâ€.
After a successful pipeline submission, the web interface will change to the â€œHomeâ€ page where the new pipeline will be listed as â€œStatus: running.â€
Monitoring the pipeline
Your pipeline should now appear in theÂ PipelinesÂ window in the CloVR dashboard along with its status. Occasionally, the pipeline may idle for a minute or two before running. You can click on the pipeline to get a description, input parameters, and hyperlinks to more advanced workflow interfaces like Ergatis.Â Clicking on the [Pipeline #] headers in the â€œPipeline Informationâ€ window will open the Ergatis â€œWorkflow creation and monitoring interfaceâ€ in a separate browser window, which provides useful information for troubleshooting of failed pipeline runs.
Accessing the outputs
Once the pipeline completes, the results can be downloaded from this CloVR dashboard by clicking on theÂ OutputsÂ tab (Figure 11). All results files are created as compressed archives (.tar.gz), which can be extracted using the Finder in Mac OS X, the Tar utility in Unix or programs such asÂ WinZiporÂ WinRAR, in Windows.Accessing the outputs
The CloVR-Human Contaminant Screening pipeline outputs the following files:
|alignments||Alignments in SAM format|
|unmapped||File(s) containing reads that could not be aligned|