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Related Experiment Video

Updated: Mar 10, 2026

Pattern-based Search of Epigenomic Data Using GeNemo

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

SeqPlots - Interactive software for exploratory data analyses, pattern discovery and visualization in genomics.

Przemyslaw Stempor¹, Julie Ahringer¹

¹The Gurdon Institute and the Department of Genetics, University of Cambridge, Cambridge, CB2 1QN, UK.

Wellcome Open Research

|December 6, 2016

Summary

This summary is machine-generated.

SeqPlots is a new software that simplifies genomic data visualization for researchers. It enables quick generation of profile plots and heatmaps from high-throughput sequencing data, aiding in the discovery of novel genomic associations.

Keywords:

aggregate gene profile plot hierarchical cluster k-means cluster self-organizing maps unsupervised machine learning

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

High-throughput sequencing experiments are crucial for studying chromatin function and gene expression.
Analyzing coverage signals often involves profile plots and heatmaps, but existing tools are cumbersome.
Limited computational expertise can hinder effective data exploration and visualization.

Purpose of the Study:

To develop user-friendly software for exploratory data analysis (EDA) of genomic data.
To provide efficient visualization tools for high-throughput sequencing data, including profile plots and heatmaps.
To facilitate wider data exploration and the discovery of novel genomic associations.

Main Methods:

Developed SeqPlots, a graphical user interface (GUI) based software.
SeqPlots accepts major genomic file formats and supports user-defined motif densities.
Offers configurable profile plots and heatmaps with various clustering algorithms and batch processing capabilities.

Main Results:

SeqPlots generates average signal profile plots and clustered heatmaps rapidly via its GUI.
The software is available as a standalone application (Mac, Windows, Linux) and an R/Bioconductor package.
It can be deployed on servers for remote and collaborative data analysis.

Conclusions:

SeqPlots significantly enhances the ease and speed of genomic data exploration.
Its user-friendly interface lowers the barrier for researchers with limited computational training.
The software promotes comprehensive analysis, potentially leading to new discoveries in genomics.