Epigenetic Analysis Platform

PEAKS: Integrated Analysis Platform for Epigenome Data

About PEAKS
PEAKS System developed by Rhelixa is a platform-based integrated analysis for epigenome data obtained via NGS techniques. The program analyzes the data and helps provide optimal information that is best-fit for each users’s purpose. PEAKS engine is built with three primary functions:

  1. Selectively extracting information from epigenome data: Deduce essential features of the obtained data using our own machine learning algorithm
  2. Constructing adaptive pipeline for epigenetic analysis: Create optimal analysis pipeline based on data quantity
  3. Compressing large data efficiently: Store and compact large epigenome data using compression algorithms

PEAKS on Illumina Basespace

Rhelixa has implemented ATAC-seq-oriented, novel epigenome analysis pipeline “PEAKS.motif” on the cloud system “BaseSpace Sequence Hub ” operated by Illumina, a leading manufacturer and supplier of next-generation sequencing systems . The ATAC-seq data input to the PEAKS.motif looks, in particular, at enhancer and promoter region to predict TF binding-motif (GATA, etc.)

De novo sequence motifs sequence: An example of sequence data obtained from ATAC-seq peaks.

Searching database for binding motifs: TF binding motif from reference database can be used to compare to the obtained data

Co-occurence rate of TF binding motifs : Quantifying the rate of de novo sequence motif for co-occurrence

Implementing epigenetic analysis software system

Enhancing ChIP-Atlas

We collaborated with the main developer of ChIP-Atlas, Oki Maaya, assistant professor of Developmental Regeneration Medicine, Kyushu University Medical Research Institute, for the purpose of improving the use of epigenome integrated database “ChIP-Atlas”. ChIP – Atlas can simultaneously compare numerous ChIP-seq data existing in the public, and can analyze the synergistic effect of transcription factors and the characteristics of its binding region during specific cell state.
In this project, we 1) improved the feasibility of software usage using natural language processing and machine learning, and 2) created pipeline for analyzing bisulfite sequencing data.

(Learn more about ChIP-Atlas here.)

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