Overview

Summary

We are sequencing West Nile virus from California, with an emphasis on San Diego, Kern, and Sacramento/Yolo counties, to understand how 1) the virus spreads between regions, 2) is maintained locally between seasons, and 3) the factors that promote local outbreaks. Our goal is to generate thousands of new West Nile virus genomes from infected birds and mosquitoes. This research is part of the WestNile 4K Project.

Collaborations and data sources

The samples from San Diego county were provided by Nikos Garfield and Saran Grewal from the San Diego County Vector Control Program. The samples from all the other counties in California, including Sacramento-Yolo and Kern were provided by Ying Fang and Chris Barker from the Barker Lab, University of California, Davis and Sarah Wheeler from Sacramento-Yolo Mosquito and Vector Control Program.

Raw Data

The BAM files are available on Google Cloud.

The sequencing is being performed using an amplicon-based sequencing scheme using PrimalSeq. Our full protocol is available online here. Sequencing data is aligned using bwa and processed using iVar (Grubaguh et al. Genome Biology 2019).

Below is a table showing the count of sequenced genomes by county.

County Sequence Count
sandiego 206
kern 204
sacramento 153
yolo 42
losangeles 28
fresno 17
stanislaus 13
butte 10
kings 9
tulare 8
sanbernardino 6
riverside 6
contracosta 5
yuba 4
sutter 4
merced 4
shasta 3
placer 3
lake 3
ventura 2
solano 2
madera 2
alameda 2
sanjoaquin 1
lassen 1
glenn 1
calaveras 1
Total 740
Alignment statistics

Average depth and percent coverage of genome are available in a tsv file.

Alignment statistics

The following sequences with <50% coverage of the coding region weren't included in downstream analysis. They are highlighted in red in the figure above.

Name Length
Consensus_W052_L1_L2_L3_threshold_0_quality_20 4244
Consensus_W118_L1_threshold_0_quality_20 0
Consensus_W170_L1_threshold_0_quality_20 3526
Consensus_W251_L1_threshold_0_quality_20 1862
Consensus_W327_L1_threshold_0_quality_20 5042
Consensus_W329_L1_threshold_0_quality_20 4088
Consensus_W330_L1_threshold_0_quality_20 3255
Consensus_W336_L1_threshold_0_quality_20 1832
Consensus_W338_L1_threshold_0_quality_20 2305
Consensus_W341_L1_threshold_0_quality_20 482
Consensus_W501_L1_L2_threshold_0_quality_20 4421
Consensus_W662_L1_L2_L3_threshold_0_quality_20 4665
Consensus_W804_L1_L2_threshold_0_quality_20 2260
Consensus_W805_L1_L2_threshold_0_quality_20 2937

Multiple sequence alignment

Alignment were performed using Mafft. The PHI test was used to test for recombination and RDP4 was used to narrow down sequences with potential contamination. These sequences are in consensus_sequences/contaminated_sequences.

Name MAXCHI CHIMAERA SISCAN
W162 + - +
W301 + + +

Disclaimer. Please note that this data is still based on work in progress and should be considered preliminary. If you intend to include any of these data in publications, please let us know – otherwise please feel free to download and use without restrictions. We have shared this data with the hope that people will download and use it, as well as scrutinize it so we can improve our methods and analyses. Please contact us if you have any questions or comments – we’ll buy beers for #ResearchParasites that spot flaws and faults in the data and come up with improvements!


Andersen Lab
The Scripps Research Institute
La Jolla, CA, USA
data@andersen-lab.com

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Funding

NIH_CTSA
PEW