Begin the priming and loading the quality checked flow cell by flipping back the sequencing device lid and sliding the priming port cover clockwise, visualizing the priming port. To remove air bubbles, set a P 1000 pipette to 200 microliters and insert the pipette tip vertically into the priming port. Turn the wheel until a small volume entering the pipette tip is seen.
Load 800 microliters of pre-prepared flow cell priming mix into the flow cell via the priming port to avoid introducing bubbles. Lift the sample port cover and load 200 microliters of the remaining priming mix into the flow cell via the priming port. To ensure the mixing of the loading beads, resuspend the library master mix by pipetting and dropwise, load 75 microliters to the flow cell via the sample port.
Replace the sample port cover gently ensuring the bung enters the sample port. Close the priming port and replace the sequencing device lid. For live base calling, use Rampart.
Use the Arctic RabV environment and work in the directory created for the Rampart output. Then type the Rampart command to navigate to the required paths. First, the Rampart specific scheme protocol, and next base called path, the mino fastq pass output folder for the run.
Open a browser window and navigate to local host 3000 in the URL box. Wait for sufficient data to be base called before results appear on the screen. The top three panels show summary plots for the whole run.
Plot one shows the depth of coverage of mapped reads for each barcode per nucleotide position on the index reference genome. Plot two shows mapped reads from all barcodes over time, and plot three shows mapped reads per barcode. Lower panels show rows of plots per barcode.
The left shows the depth of coverage of mapped reads per nucleotide position on the index reference genome. The length distribution of mapped reads is in the middle. The proportion of nucleotide positions on the index reference genome obtaining 10x, 100x, and 1000x coverage of mapped reads over time is seen in the right corner.
For lineage assignment of consensus sequences, use MadDog. Pull the MadDog repository from GitHub to ensure working with the latest version. Create a folder within the previously created local MadDog repository.
Inside the folder, add the fastA file containing the consensus sequences. Also, add a metadata file to the folder. Ensure that this file is a CSV with four columns called ID, country, year and assignment.
Pull the MadDog repository from GitHub to ensure working with the latest version. In the command line interface, activate the conda environment with a conda activate MADDOG command. In the command line interface, navigate to the MadDog repository folder.
First, perform lineage assignment on sequences to check for potential abnormalities and identify if running the longer lineage designation step is appropriate by running the sh assignment. sh command. When prompted, enter Y to confirm that the repository is pulled and working with MadDog's latest version.
When prompted, enter the folder name containing the fastA file within the MadDog repository. When the lineage assignment is complete, check the output file in the folder. If the output is as expected and multiple sequences are assigned to the same lineage, then run the lineage designation.
While running lineage designation, delete the assignment output file just created. In the terminal inside the MadDog repository folder, run the command sh designation.sh. When prompted, enter Y to indicate that the repository is pulled and the work is being done with the most up-to-date version of MadDog.
When prompted, enter the folder name within the MadDog repository folder containing the fastA file and metadata. The sample to sequence to interpretation workflow for rabies virus RABV was successfully used in different laboratory conditions in endemic countries such as Tanzania, Kenya, Nigeria, and the Philippines. The live base calling using Rampart showed the real-time generation of reads and the percent coverage per sample.
A lineage classification and nomenclature system, MadDog, used to compile and interpret resulting RABV sequences showed the higher resolution classification of local lineages following the MadDog assignment.
