How to Analyze ChIP-Seq Data with ROSALIND

Empowering Epigenetics Data Analysis & Interpretation

WHY STUDY PROTEIN INTERACTIONS WITH DNA 

ChIP-sequencing, also known as ChIP-seq, is a method used to analyze protein interactions with DNA. ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. Previously, ChIP-on-chip was the most common technique utilized to study these protein–DNA relations.

ChIP-seq is primarily used to determine how transcription factors and other chromatin-associated proteins like histone marks influence phenotype-affecting mechanisms. Determining how proteins interact with DNA to regulate gene expression is essential for fully understanding many biological processes and disease states. This epigenetic information is complementary to genotype and expression analysis.

Specific DNA sites in direct physical interaction with transcription factors and other proteins can be isolated by chromatin immunoprecipitation. ChIP produces a library of target DNA sites bound to a protein of interest in vivo. Massively parallel sequence analyses are used in conjunction with whole-genome sequence databases to analyze the interaction pattern of any protein with DNA or the pattern of any epigenetic chromatin modifications like histone marks. This method can be applied to the set of ChIP-able proteins and modifications, such as transcription factors, polymerases and transcriptional machinery, structural proteins, protein modifications,histone marks and DNA modifications.[3] As an alternative to the dependence on specific antibodies, different methods have been developed to find the superset of all nucleosome-depleted or nucleosome-disrupted active regulatory regions in the genome, like DNase-Seq, FAIRE-Seq or ATAC-seq.

OVERVIEW

ROSALIND is a cloud platform that connects researchers to experiment design to quality control, peak overlaps, differential binding and pathway exploration in a real-time collaborative environment.

Scientists of every skill level benefit from ROSALIND since no programming or bioinformatics are required. By accepting raw FASTQ sequence data as well as processed counts data, ROSALIND enables powerful downstream analysis and truly insightful visualizations on gene expression datasets. Receive same-day results with every experiment in an interactive experience designed for ease of use and saving valuable time.

FIVE STEPS TO SUCCESS WITH CHIP-SEQ DATA ANALYSIS

ROSALIND simplifies data analysis and works like a data hub interconnecting every stage of data interpretation.  The ROSALIND DNA-Protein interactions discovery experience enables visual exploration and self-investigation of experiment results to give researchers the freedom to explore peak overlaps and differential binding. The peak overlap experience provides visibility into the presence of DNA-Protein interactions regions anywhere in the genome and across multiple comparison groups. The differential binding experience focuses on DNA-Protein interactions on the proximal promoter region of genes regions within the transcription start site (TSS) and gene promoter regions. Based on the differential binding in their promoter region, each gene is displayed with visualization similar to gene expression, including heatmaps, volcano plots, MA plots, bar graphs and box plots. Within this interactive environment, one may adjust cut-offs, add comparisons and even find patterns across multiple datasets and experiment types - such as ChIP-seq, ATAC-seq or RNA-seq multi-omics analyses. There are five easy steps to performing ChIP-seq data analysis on ROSALIND.

1. EXPERIMENT DESIGN

Starting a ChIP-seq data analysis begins with creating a new experiment and capturing the experiment design. ROSALIND walks through the key aspects of an experiment in a guided experience to record biological objectives, sample attributes and analysis parameters. These details become the basis of the experiment discovery dashboard. Researchers who publish papers and work with NCBI public data know the importance of natively supporting NCBI data models. ROSALIND fully supports the NCBI BioProject and BioSample models for metadata assignment and sample attribute descriptions. ROSALIND also enables scientists to create custom attributes to describe biological behaviors in terms relevant to the experiment. The setup of comparisons is simplified by describing and annotating samples using these familiar terms. This methodology minimizes the risk of differential accessibility errors when selecting samples for comparison.

For ChIP-seq data analysis, ROSALIND analyzes the raw FASTQ files produced by high throughput sequencing. ROSALIND streamlines data analysis using an advanced pipeline for analysis that includes intelligent quality control with automatic contamination detection, identification of binding sites or chromatin modification regions and deep pathway interpretation of the genes close by. Visit the technical specifications section to learn more about the ROSALIND ChIP-seq data analysis pipeline and available reference materials.  

For proper ChIP-seq results, an analysis pipeline must adjust for sample preparation and proprietary differences in library preparation kits used in the experiment. Not only is the kit selection important for targeting and capturing the desired binding regions, but the analysis pipeline also adjusts and optimizes for the kit’s unique characteristics, such as presence of strandedness, strand direction, any unique molecular identifiers (UMIs) as well as the adapters used. ROSALIND integrates and supports a broad library of sample and library preparation kits, automatically calibrating each analysis with the appropriate details. ROSALIND will also need to know the list of antibodies against proteins and/or histone marks used in the experiment To learn more about supported kits and antibodies, visit the technical specifications section. Featured kits and instrument partners are also listed below.

2. CHIP-SEQ QUALITY CONTROL

Researchers must be confident in the quality control phase before gathering insights from an ChIP-seq experiment, otherwise, the results of the analysis should not be trusted. Biology’s mysteries are elusive and complex. Time should not be lost chasing corrective measures for outliers, contamination, swapped samples and the many other errors that can occur in the course of a well-designed experiment.

Some of the most important Quality Control metrics to verify are Q30 scores, alignment rates, duplicate rates, number of opened regions detected, sample correlation, fraction of reads in peaks, genomic regions and TSS plot for all samples. When ROSALIND detects low alignment, non-aligning reads are evaluated for possible contamination. For best results with Illumina sequencers, Q30 values should exceed 85% with alignment rates over 80% for the target species. Additional QC metrics, such as duplicate rates, should be less than 25% with fewer than 10% of reads trimmed. Researchers can eliminate offending samples and the deleterious effects on results by identifying the sample as an outlier and move confidently into the discovery and exploration phase of results interpretation. 

ROSALIND Quality Control Intelligence identifies potential data quality issues and triages the data before presenting the results. This eliminates the need for researchers to be experts in Sequencing quality control issues. Learn how researchers gain confidence in their results through Quality Control Intelligence.

3. UNLOCKING RESULTS

After a researcher has reviewed the quality control phase the interactive presentation of results is ready to begin. The next step is to unlock the experiment. ROSALIND calculates the quantity of Analysis Units (“AU”) required to unlock the results. This is generally 1 AU per single-sample FASTQ file for ChIP-seq experiments, however, this may differ based on counts files or other experiment parameters. Account balances and quick links for acquiring more AU are directly accessible from the unlock screen. To learn more about Analysis Units, check out the Q&A in the section below, or visit the ROSALIND Store.  

4. ANALYSIS & DISCOVERY

A typical ChIP-seq analysis provides a list of differentially binding regions or histone modifications, generally in the form of a massive and obtuse CSV file. Unfortunately, this often results in more questions than answers for scientists. Multiple applications may also need to be used to generate this CSV file. Such applications often have a wide range of complexity with non-standard input/output formats, many of which are command-line tools requiring advanced knowledge in programming — an exercise well beyond the level of most biologists.

ROSALIND moves beyond the CSV file by providing a comprehensive dashboard for differential binding and interpretation of ChIP-seq data. Researchers begin with a list of significant differentially binding regions determined by a calculated cut-off filter. Default settings for the filter begin with a fold change of +/- 1.5 with a p-Adjust lower than 0.05. Further adjustments to achieve a significant set of regions are performed by ROSALIND if needed. Researchers may also create an unlimited set of their own customized filters using fold changes and P-value parameters. Convenient on-screen controls are easily accessible for modifying filters, applying gene lists and signatures, and adjusting plot color palettes. The ROSALIND differential binding and histone modifications experience features deep interpretation of top pathways, gene ontology diseases, and drug interactions, as rich interactive plots that fill the screen and respond to interactions from the scientist, showing customizable heatmaps, volcano and MA plots as well as box and bar plots. 

New comparisons and meta-analysis may be added at any time. Comparisons are created using BioProject attributes. Meta-analyses created can be cross experiments and multi-omic. Each of these perspectives are available within minutes of setup, reducing internal bioinformatic workload and enabling scientists to react fluidly by focusing directly on the science of the experiment.

5. COLLABORATION & DATA SHARING

The discovery process rarely ends with a single point of view from a single researcher’s opinion. ROSALIND Spaces enables true scientist-to-scientist collaboration through virtual data rooms where scientists and collaborators can come together on related datasets anywhere in the world to interactively explore shared experiments much like working with Google Docs. Researchers access a consistent version of the data, without the need to transfer unwieldy files or reinterpret origin files. All changes are interactive, instantly available, and viewable everywhere in the world (as authorized by the organization) with real-time activity feeds and historical reports. Spaces participants can add experiments, explore pathways, change cut-offs, add meta-analyses and add new comparisons all within the shared collaborative environment.

Spaces are virtual meeting rooms where scientists meet with niche experts, clients and supporting teams to maximize the discovery value of every experiment and prepare for the next one.