DNA Affinity Purification Sequencing (DAP-seq) is an epigenomic sequencing technology that has emerged in the field of plant research in recent years, which is capable of focusing on a protein (e.g., various transcription factor proteins involved in gene transcription), studying the DNA sequences to which such proteins are bound, analyzing the binding characteristics, and identifying the binding pattern of the proteins to target the genes that are regulated by the proteins. The biggest advantage of DAP-seq technology is that it uses in vitro expression of tagged proteins to bind the expression product to the target genome sequence, which perfectly avoids the dilemma of lack of protein antibodies that the plant research field has been facing for a long time in the past.
Advantages of DAP-seq Technology
DNA Affinity Purification Sequencing (DAP-seq) represents a contemporary breakthrough in the exploration of transcriptional regulatory sites, having gained prominence in recent years. This innovative approach involves the use of in vitro constructs for expressing transcription factor (TF) proteins. These proteins then selectively bind to target genomic fragments, enabling the sequencing of protein-bound gene fragments for subsequent library construction and analysis of TF binding sequences.
The advent of DAP-seq technology addresses historical limitations associated with the scarcity of TF antibodies and suboptimal binding efficiency. By overcoming these constraints, DAP-seq significantly broadens the horizons of TF factor research. This transformative technology not only resolves past challenges but also facilitates a more expansive and nuanced exploration of transcription factor dynamics, marking a substantial leap forward in the field.
Overview of the DAP-seq Experimental Process
(1) Feasibility Assessment: Initiate the process by providing the CDS sequence of the transcription factor for assessment.
(2) Sample Preparation: Ensure an ample supply of samples, following the guidelines outlined in the Sample Delivery Guidelines. Additionally, include plasmids containing the target transcription factors, supported by provided sequencing results.
(3) Sample Duplication: Avoid setting up duplicate samples for the same transcription factor. If duplication is necessary, it is advisable to establish two to three duplicate samples and conduct the duplication experiment simultaneously.
(4) Preparation of Analyzed Information: Assemble mature genomic information, including genomic fa file, gff file, and pep.fa file, tailored to the species under investigation.
Remarks: While theoretically, all plant transcription factors can undergo DAP-seq, the feasibility of non-plant samples is also considered. However, since DAP is primarily designed for plant expression systems, subsequent results for non-plant samples are not guaranteed. It's important to note that proteins of non-transcription factors cannot undergo feasibility analysis, and the reliability of follow-up results is not assured.
The primary steps encompass DNA library construction, protein expression, binding reactions between the protein and library, library PCR with splicing and quantitative detection, online sequencing, and raw data analysis.
(1) Whole Genomic DNA Extraction and Library Construction:
- Begin by extracting the entire genomic DNA from the tissue material.
- Subsequently, construct the DNA library.
(2) In Vitro Expression Plasmid Construction for Transcription Factors
- Develop the Halo-tag in vitro expression plasmid for transcription factors.
- Utilize the wheat embryo system for protein expression.
(3) Protein-Library Interaction and Enrichment
- Combine the protein and library.
- Employ magnetic beads to enrich DNA fragments bound to the target protein.
(4) Washing and Complex Purification
- Conduct multiple washes to eliminate non-specifically bound chromatin.
- Purify the resulting complex.
(5) DNA Fragment Purification, Sequencing, and Raw Data Analysis
- Purify DNA fragments.
- Perform sequencing analysis on an online platform.
- Analyze raw sequencing data for meaningful insights.
DAP-seq Data Analysis
- Quality Control and Data Filtering
Initial processing involves quality control and filtering of raw data, addressing joint issues and eliminating low-quality data.
Subsequent quality control ensures reliability by scrutinizing the filtered clean data.
- Reference Genome Sequence Comparison
Utilizing clean data, perform a comprehensive comparison with the reference genome sequence.
Identify the genomic locations where the sequenced reads are mapped.
- Peak Calling and Bam File Generation
Generate a bam file post-comparison.
Employ software to execute peak calling on the bam file, identifying significant read accumulations that denote binding locations of the studied transcription factors.
Conduct a detailed analysis of obtained peaks, considering factors such as peak count, length, genome distribution, and distribution across gene functional elements.
- Functional Annotation and Enrichment Analysis
Apply GO and KEGG annotation to genes associated with the peaks, unraveling their functions.
Perform enrichment analysis to gain insights into the functional significance of these genes.
Predict plant and animal transcription factors associated with the genes, providing additional layers of understanding.
A pivotal step involves motif analysis to identify potential binding features of transcription factors.
Predict motifs to enhance comprehension of transcription factor binding characteristics, facilitating subsequent validation.
Bioinformatics workflow of DAP-Seq – CD Genomics
Integrated Transcriptome and DAP-seq Analysis in Plant Research
In the realm of plant sciences, there is a growing interest in combining transcriptome analysis and DAP-seq technology to unravel intricate biological processes. This novel approach involves utilizing the transcriptome to identify intergroup gene differentials. Simultaneously, DAP-seq is employed to scrutinize the specific genes targeted by transcription factors, thereby elucidating the comprehensive regulatory network.
This integrated analysis offers a holistic understanding, linking transcription factor binding targets to the emergence of differentially expressed genes and their ensuing functional distinctions. Subsequent biological experiments, such as gene knockdown or overexpression, serve as validation tools. By correlating data from these two genomic perspectives, a synergistic enhancement in the depth of transcriptome research is achieved. This comprehensive approach provides insights into the regulatory expression network of genes, shedding light on their upstream regulatory mechanisms.
Please refer to our article How to Use DAP-Seq for Non-Model Organism Transcription Factor Studies? for a case.
The connection between these two methodologies revolves around the orientations of transcription factors and their respective target genes.
- Transcription Factor Identification
Functional transcription factors often manifest in RNA-seq data, exhibiting differential expression between samples or core gene status within the gene regulatory network.
Transcription factors meeting these criteria become candidates for further in-depth investigation and serve as pivotal targets for subsequent DAP-seq experiment construction.
The information extracted through DAP-seq unveils the DNA sequence recognition preferences of transcription factors, essentially exposing potential binding sites.
Predicting the regulatory relationship between transcription factors and target genes is achievable by leveraging these potential binding sites.
Validation of the actual impact of target genes on phenotype occurs at the transcriptional level and is conducted through RNA-seq data.
The convergence of findings from these two methodologies often identifies the ultimate target gene contributing to the observed phenotype, providing a comprehensive understanding of transcriptional regulation.
For Research Use Only. Not for use in diagnostic procedures.
Sample Submission Guidelines
