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CHIP-SEQ DATA ANALYSIS

Quickly go from raw data to comprehensive ChIP-Seq data analysis using an easy-to-use wizard and powerful post-assembly analysis tools. PRICING DOWNLOAD FREE TRIAL

Use Lasergene Genomics to easily assemble and perform peak detection on your ChIP-Seq data, getting you to comprehensive ChIP-Seq data analysis quicker.

ChIP-Seq data analysis is often a cumbersome process, requiring tedious project setup and time-consuming, complicated data manipulation. Lasergene Genomics makes it quick and easy to set up your ChIP-Seq analysis project, by offering an easy-to-use wizard that guides you through project setup in minutes. The software also includes several different customizable options for variant detection, normalization, and peak detection. Select binding proteins from the Transcription Factor Database, by using a type-in pattern, or by using a position weight matrix to identify binding sites. Following your ChIP-Seq data assembly, easily analyze genes near peaks using live, interactive views and comprehensive filtering tools. Utilize gene ontology to identify the relationship between genes with particular biological functions or to determine the relative importance of genes in specific processes. Visualize peaks to interpret gene expression and regulation in GenVision Pro, and create high-quality, highly customizable images for publication. Use the ChIP-Seq analysis tools in Lasergene Genomics to achieve comprehensive results quicker!

ChIP-Seq Peak Data Analysis
ChIP-Seq peaks from two experiments and one control (green) visualized in GenVision Pro.

ChIP-Seq data analysis in 4 simple steps

ChIP-Seq Data Analysis Step 1

Step 1

Set up and run assembly

ChIP-Seq Data Analysis Step 2

Step 2

Filter to find genes of interest

ChIP-Seq Data Analysis Step 3

Step 3

Identify relationships using gene ontology

ChIP-Seq Data Analysis Step 4

Step 4

Visualize peaks to interpret gene expression and regulation

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Learn more about ChIP-Seq Data Analysis

Resources | Tutorials | FAQs | Benchmarks | User Guide

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Resources

Please see our resources below for more information on ChIP-Seq data analysis.

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Automating Workflows in DNASTAR’s Lasergene Genomics Suite for High-Throughput Applications

View Poster

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Considerations for Next-Gen Sequence Assembly and Analysis Software Selection

Read White Paper

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Cloud Assemblies for NGS Sequences

Watch Webinar

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Free NGS Assembly and Alignment for Genomic Sequencing Data

Read Blog Post

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Q&A: NGS Sequence Assembly on the Cloud

Read Blog Post

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Is Open Source NGS Software for You?

Read Blog Post

Tutorials

Watch one of our videos or check out one of our written tutorials to learn more about ChIP-Seq data analysis.

Creating Gene Sets and Comparing in the Venn Diagram

See various ways to filter and create gene sets of interest, then compare the gene sets in the Venn diagram in ArrayStar.

Visualizing Genes and SNPS from ArrayStar in GenVision Pro

This quick tip video demonstrates how to visualize groups of genes or SNPs from ArrayStar in GenVision Pro.

Using the Gene Ontology View in ArrayStar

See how to use the Gene Ontology View in ArrayStar, which enables you to determine the biological significance of genes and identify relationships between genes with particular biological functions.

Using the Gene Table in ArrayStar

See how to use the Gene Table in ArrayStar, which allows you to view, sort, and search detailed information about each gene in your project. View statistical data, expression levels, annotations, and much more.

FAQs

What peak detection options are available for ChIP-Seq data analysis?

Lasergene Genomics offers three peak detection methods for ChIP data analysis: MACS, ERANGE2 and ERANGE3. You can specifiy the ChIP-Seq peak detection method you want to use within the Analysis Options screen in SeqMan NGen.

What options are available for defining the known protein binding motifs for my ChIP-Seq data?

When you are setting up your ChIP-Seq project in SeqMan NGen, you can select binding proteins from the JASPAR (PWM) or Transcription Factor Database (PubMed), by using a type-in pattern, or by using a position weight matrix. If the binding site is “unknown”, you can select that as an option as well.

How do I analyze my ChIP-Seq peaks?

Most of the ChIP-Seq peak analysis and comparison tools are in ArrayStar where you can choose to compare detected peaks in the Peak Table or the DNA fragments that correlate to those peaks in the Fragment Table. Peak data tracks can be visualized in GenVision Pro and exported into common image file formats (PDF, PowerPoint, Bitmap).

How do I export my ChIP-Seq peaks to visualize them in GenVision Pro?

In ArrayStar, select your fragments or peaks of interest, then choose File > Send Selection To > GenVision Pro. In GenVision Pro, select the desired peak or IP fragment from the Places tab to visualize their tracks in the Analysis view.

What types of read technology are supported for ChIP-Seq data analysis?

Lasergene Genomics supports Illumina, 454, Ion Torrent, and Sanger technology for ChIP-Seq data analysis.

Where do I get the reference sequence for aligning ChIP-Seq data?

If your organism of interest is a commonly-studied species, you can select Download Genome Package within the SeqMan NGen wizard to download a DNASTAR genome template package or a reference sequence from NCBI.

If I have multiple ChIP-Seq samples, can I run them as separate assemblies?

Yes. In the SeqMan NGen wizard screen where you browse to your sample data, select “Multi-Sample” from the Experiment Setup field. You will be prompted to group and name each experiment, which you can do automatically or manually.

Benchmarks

Lasergene Genomics Reference-Guided Genome Alignment Benchmarks
Data SetSequence TechnologyCoverageAssembly Time
Yeast Genome (ERX009559)*Illumina22X2 Minutes
Yeast Genome (srx1484451)*Ion Torrent22X3 Minutes
Drosophila Genome (SRX1961013)*Illumina23X23 Minutes
Human Exome (SRR701474)*Illumina81X53 Minutes
Mouse Rna-seq (SRX2013345)*Ion Torrent ProtonNA1.3 Hours
Human Rna-seq (SEQC-UHRR & Brain)IlluminaNA1.6 Hours
Rice Genome (Srx179262)*Illumina34X1.6 Hours
Human Exome (SRX1873412)*Ion Torrent Proton162X3.9 Hours
Human Genome (SRP000239)*Illumina36X23 Hours

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Brenda Oppert, USDA ARS CGAHR

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