ATAC-seq analysıs
Transposase-accessible chromatin with high-throughput sequencing, or ATAC-Seq, is a technique which measures genome-wide chromatin accessibility by sequencing the open chromatin regions captured by the loaded transposase. The data generated by ATAC-Seq can be used to study the epigenetic landscape of an organism or biological system and give insight into the different regulatory mechanisms in varying conditions such as in disease or at different life stages.
Case Study
Can we identify the accessible chromatin regions across the genome of liver tumour tissues?
By using ATAC-Seq on liver tumour tissues, we can identify accessible chromatin regions across the genome. These accessible regions could represent binding sites of regulatory factors such as transcription factors that may regulate gene expression in liver tumours. ATAC-Seq data can be visualized as genome tracts with open/accessible chromatin regions indicated by peaks (Figure 1.1). Accessible chromatin regions can also be further categorized based on where they are present in the genome (Figure 1.2).
Figure 1.1: Chromatin accessibility profile showing genome tracts representing a region in Chromosome 5 of liver tumour tissues and adjacent normal tissues. Peaks indicate open/accessible chromatin regions.
Figure 1.2: Classification of open/accessible chromatin regions in liver tumour tissues.
Can we identify if there are accessible chromatin regions specific to only liver tumour tissues that are not present in adjacent normal tissues?
By comparing the whole genome chromatin accessibility profile of liver tumour tissues and adjacent normal tissues, we can study the overall difference in accessibility signals at potential regulatory sites such as transcriptional start sites, TSS (Figure 2.1). Specific peaks/regions with accessibility difference specific to tumor can be further determined (Figure 2.2) for downstream analysis.
Figure 2.1: Overall chromatin accessibility profile at whole genome TSS. Chromatin at TSS found to be more open/accessible in liver tumour tissues as compared to adjacent normal tissues. This could give insight to the regulatory mechanism specific to liver tumours progression.
Figure 2.2: A specific chromatin region (red arrow) that is found to be more open/accessible in liver tumour tissues as compared to adjacent normal tissues.
Can we determine the regulatory factors that may potentially bind to or regulate the differential open/accessible chromatin regions identified?
By performing motif enrichment analysis at the identified open/accessible chromatin regions, we can identify transcription factors and other chromatin binding proteins that could regulate a certain phenotype (Figure 3.1). Additional downstream analysis can also be done to increase the specificity of target regions, such as overlapping with other omics data and filtering out the peaks present in adjacent normal liver tissues (Figure 3.2).
Figure 3.1: Different motifs significantly present in the open/accessible chromatin regions specific to liver tumour tissues.
Figure 3.2: Identification of open/accessible chromatin regions that could serve as candidate regulatory regions of liver cancer.
Standard Analysis Workflow:
- FREE initial consulation includes discussion on project objectives and result expectation, analysis method proposal, timeline and cost estimation.
- Standard data analysis includes primary sequencing reads quality control to downstream reads quatification and functional profiling for up to 12 samples. Please refer to analysis catalog for detail analysis workflow and result description.
- Publication ready figures/plots includes customize processing of analysis result plots such as spliting by samples group/conditions, color selection, highligting specific components within the plot etc. Only applies to analysis result of samples in package analysis.
- Publication submission consulation includes compilation of publication ready analysis result, scientific writing review, scientific plots review or customization, consultation for reviewer queries for up to 5 hours in total.
- Data integration includes intergration of existing pre-processed data for up to two applications between RNA-Seq, ChIP-Seq, or ATAC-Seq.
- TCGA differential expression and correlation analysis profiles various cancer types and identifies the most associated genes and pathways related to gene of interest. Please refer to our TCGA Analysis page for more information.
- Customize bioinformatic analysis: USD 370 / hour
- Standard data analysis: USD 110 / sample
- Bioinformatics consulation: USD 110 / hour
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