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Fitness Browser

kescience_fitnessbrowser

Primary

Philosophy

Connect genotype to phenotype through systematic mutagenesis. Understand which genes are essential for growth, which provide advantages under specific conditions, and how genetic background affects fitness.

Data sources: RB-TnSeq experiments KEGG MetaCyc SEED

Citation & Attribution

Provider: Price Lab, LBNL

Cite as: Price MN et al. (2018) Mutant phenotypes for thousands of bacterial genes of unknown function. Nature 557:503-509

DOI: 10.1038/s41586-018-0124-0

Website: https://fit.genomics.lbl.gov/

Scale

40+
organisms
1000+
experiments
90+
tables

Schema Browser

genefitness 27,410,721 rows

Gene fitness scores per experiment.

Column Type Description
orgId string FK -> `organism.orgId`
locusId string FK -> `gene.locusId`
expName string FK -> `experiment.expName`
fit string Fitness score (log2 ratio)
t string T-statistic for fitness score

Sample Queries

Get organisms in the fitness browser

SELECT *
FROM kescience_fitnessbrowser.organism
LIMIT 20

Get fitness scores for a gene

SELECT
  e.condition,
  f.fitness,
  f.t_score
FROM kescience_fitnessbrowser.fitness f
JOIN kescience_fitnessbrowser.experiment e
  ON f.experiment_id = e.experiment_id
WHERE f.gene_id = 'YOUR_GENE_ID'
ORDER BY ABS(f.fitness) DESC
LIMIT 20

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Access the full Fitness Browser data through BERDL JupyterHub.

Open JupyterHub