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Updated: Apr 19, 2026

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Published on: July 29, 2021

LAILAPS: the plant science search engine.

Maria Esch¹, Jinbo Chen², Christian Colmsee²

¹Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, D-06466 Stadt Seeland, Germany Maria.Esch@ipk-gatersleben.de.

Plant & Cell Physiology

|December 7, 2014

Summary

This summary is machine-generated.

This study introduces LAILAPS, a novel information retrieval system connecting plant genomic data with phenotypic traits. It facilitates efficient discovery of candidate genes for forward genetic research.

Keywords:

Functional gene annotation Information retrieval Integrative search engine Plant genomics resources Traits

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Last Updated: Apr 19, 2026

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

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Published on: July 29, 2021

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Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Published on: August 5, 2020

Area of Science:

Bioinformatics
Plant Genomics
Computational Biology

Background:

Increasing number of sequenced plant genomes and gene annotations.
Growing interest in linking genes to phenotypic traits.
Scattered information across multiple databases hinders comprehensive analysis.

Purpose of the Study:

To develop an information retrieval (IR) system, LAILAPS, for linking plant genomic data with phenotypic attributes.
To facilitate detailed forward genetic research by providing a unified view of distributed data.
To improve the efficiency and comprehensiveness of knowledge extraction in plant genomics.

Main Methods:

Developed LAILAPS, an IR system indexing over 65 million documents from >13 life science databases.
Implemented a fuzzy querying search engine for candidate gene discovery linked to traits.
Integrated query assistance, evidence-based annotation, and an artificial neural network for relevance sorting.

Main Results:

LAILAPS links plant genomic resources with phenotypic data across a distributed system.
The system enables fuzzy querying for candidate genes associated with specific traits.
Performance comparison with other systems and validation in maize and barley demonstrated its utility.

Conclusions:

LAILAPS provides a time-efficient and comprehensive solution for plant genomic information retrieval.
The system effectively links genes to traits, supporting forward genetic research.
LAILAPS enhances knowledge discovery in plant genomics through its integrated approach.