Experiments and analysis performed by Caroline Kikawa, using method and analysis developed by the Bloom lab and described in Loes et al (2024) and Kikawa et al (2025).
- The viruses included in the library are outlined here:
- Nucleotide and protein HA ectodomain only sequences in CSV format in data/ha_sequences/flu-seqneut-2025-library.csv
- Protein HA ctodomain only sequences FASTA format are placed in data/ha_sequences/library_2025_HA_ectodomain_protein_sequences.fasta
- The library design is outlined in detail in non-pipeline_analyses/library_design/
- The sera we will assay and their associated metadata are placed in data/sera_metadata/
- The Seattle Children's Hospital (
SCH) cohort in Seattle, Washington, United States of America data/sera_metadata/SCH_metadata.csv - The University of Washington Medical Center (
UWMC) cohort in Seattle, Washington, United States of America data/sera_metadata/UWMC_metadata.csv - The National Institutes of Infectious Disease (
NIID) cohort in Tokyo, Japan data/sera_metadata/NIID_metadata.csv - The EPI-HK cohort (
EPIHK) at Hong Kong University in Hong Kong data/sera_metadata/EPIHK_metadata.csv - Innoculated ferrets from studies at the Francis Crick Institute (
FCI) in London, United Kingdom data/sera_metadata/FCI_metadata.csv
- The Seattle Children's Hospital (
This repository contains an analysis of the data using the Bloom lab software seqneut-pipeline as a submodule. See that repository for intstructions on how to use Github submodules, including seqneut-pipeline.
The configuration for the analysis is in config.yml and the analysis itself is run by snakemake using Snakefile.
Again, see seqneut-pipeline for more description of how the pipeline works.
To run the pipeline, build the seqneut-pipeline conda environment from the environment.yml in seqneut-pipeline.
Then run the pipeline using:
snakemake -j <n_jobs> --software-deployment-method conda
To run on the Hutch cluster, you can use the Bash script run_Hutch_cluster.bash