# 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)

1. Open Oryza GenoCLIM and enter a gene (e.g. `MADS14`).
2. Read the ranked climate-association profile; the top variable is BIO6 (minimum temperature of the coldest month).
3. Download the association table.
4. 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.