Oryza CLIMtools

Interactive climate ↔ genome (G×E) association apps over the rice landrace collection — explore which environmental variables track natural genetic variation.

Open Oryza CLIMtools ↗ Jump to examples

README — what it is & what it hosts

Three R/Shiny applications:

App	Answers
OryzaCLIM	What is each accession's local environment? (per-accession geo-environmental variables)
Oryza GenoCLIM	Which climate variables associate with my gene?
Oryza CLIMGeno	Which genotypes associate with an environmental variable?

CLIMtools has no REST API. The supported programmatic path is its downloadable result tables (read in pandas/R); the application source is on GitHub (Apache 2.0).

Tutorial — GenoCLIM (MADS14 / MADS50)

Open Oryza GenoCLIM and enter a gene (e.g. MADS14).
Read the ranked climate-association profile; the top variable is BIO6 (minimum temperature of the coldest month).
Download the association table.
In OryzaCLIM, enter an accession to view its full geo-environmental vector.

Workflow — a gene's top climate driver, then test it

Goal: get the climate variable most associated with heading date for a candidate gene, and the per-accession climate to test it. GenoCLIM gene query → download table → join OryzaCLIM/CLIMGeno per-accession climate to the phenotype. See Workflow A.

Examples

import pandas as pd
genoclim = pd.read_csv("climtools_genoclim.tsv", sep="\t")   # exported / precomputed table
mads14 = genoclim[genoclim["gene"].str.contains("MADS14", case=False)]
mads14.sort_values("P").head()                                # top variable: BIO6

genoclim <- read.csv("climtools_genoclim.tsv", sep = "\t")
subset(genoclim, grepl("MADS14", gene, ignore.case = TRUE))

Access & cite

Public Interactive web apps + downloadable tables; no login. Source: github.com/CLIMtools (Apache 2.0).

Cite: Ferrero-Serrano et al. (2024) Plant Communications — Oryza CLIMtools.