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Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

Published on: August 26, 2014

BLIND ordering of large-scale transcriptomic developmental timecourses.

Leon Anavy¹, Michal Levin, Sally Khair

¹Department of Biology, Technion - Israel Institute of Technology, Haifa 32000, Israel.

Development (Cambridge, England)

|February 8, 2014

Summary

This summary is machine-generated.

We developed a new method called basic linear index determination of transcriptomes (BLIND) to automatically order biological samples, like embryos, by their developmental stage using RNA-Seq data.

Keywords:

Amphimedon transcriptomic timecourse Developmental timecourse Large-scale datasets Principal components analysis Single-embryo RNA-Seq Traveling salesman problem

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Area of Science:

Developmental Biology
Bioinformatics
Genomics

Background:

RNA-Seq is a powerful tool for transcriptome sequencing but analyzing developmental samples is challenging due to difficulties in morphological staging.
Automatic stage identification is crucial for advancing developmental studies, especially for organisms with asynchronous development.

Purpose of the Study:

To present the basic linear index determination of transcriptomes (BLIND) method for ordering transcriptomic samples by developmental stage.
To enable previously infeasible experimental designs in developmental biology.

Main Methods:

BLIND utilizes a traveling salesman algorithm applied to principal components analysis of transcriptome data.
Transcriptomic entropy is used to establish the directionality of the developmental timecourse.

Main Results:

BLIND successfully recovered the annotated developmental order for frog, mosquito, fly, and zebrafish embryonic transcriptomic timecourses.
The method was validated by ordering 59 sponge embryos (Amphimedon queenslandica) by developmental stage.

Conclusions:

BLIND is an effective tool for establishing the temporal order of samples in large transcriptomic datasets.
The method is particularly relevant for studying organisms with asynchronous development or when morphological staging is difficult.