Advanced ChIP-seq Identification of consensus binding sites for the LEAFY transcription factor.
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Transcript of Advanced ChIP-seq Identification of consensus binding sites for the LEAFY transcription factor.
Import
from
SRA
Export
to F
ASTQ
Align to
Gen
ome:
BW
A
Mer
ge re
plicat
es
Find p
eaks
: Pea
kRan
ger
Expan
d to 1
00 b
p win
dows
Extra
ct F
ASTA
Filter
bes
t pea
ks
Find m
otifs:
DREM
E
Insp
ect r
esults
: IGV 2
.0
The NCBI SRA
• NCBI SRA is a repository for NGS sequence reads
• Data is stored in association with basic metadata explaining experimental technique and inter-sample relationships
• Data format is NCBI-specific SRA and SRA-lite format. “Universal” lossless format.
• Upload and download is offered via FTP and HTTP but also via Aspera ASCP– Fast, parallel protocol similar in performance to
iRODS iput/iget commands used in iPlant Data Store• Use NCBI SRA Import to rapidly copy SRA
accession SRP003928 over ASCP into the iPlant Data Store.
NCBI SRA Toolkit
• SRA data format is a universal format, but no downstream apps can accept it natively.
• Need to export SRA to FASTQ, SFF, etc.
• These are the standard file formats for representing sequence.
• Use the NCBI SRA Toolkit fastq-dump to export FASTQ sequence files from SRA files so we can process them
BWA
• BWA is one of many applications whose objective is to efficiently align short sequence reads to a reference genome sequence
• Other alternatives are BOWTIE, MAQ, TopHat, Stampy, Novoalign, etc.
• BWA is used by the Human 1000 genomes project due to its speed and accuracy.
Outputs from BWA
• BWA emits alignments in the SAM format
• SAM is a universal system for describing next-gen sequences and their corresponding genome alignments
• SAMTools is a suite of applications for manipulating SAM files– Sort, Merge, Index, and more– Emit as binary BAM file
PeakRanger
• PeakRanger is a fast, optimized algorithm for detecting enrichment peaks in ChIPseq data sets
• PeakRanger was developed at OICR in partnership between modENCODE and iPlant and is now maintained at UTSW
• It’s not the only option for peak finding:– MACS– ChIPseq Peak Finder– CisGenome– FindPeaks
http://ranger.sourceforge.net/
Use PeakRanger with the BAM files from the Control and Sample assays to find
LEAFY enrichment
NOTE: Many parameters to tweak. You are recommended to read the PeakRanger paper.
NOTE: Many parameters to tweak. You are recommended to read the PeakRanger paper.
• Wiggle (.wig) files: Density map of sequence reads across the reference genome for control and sample BAM alignments
• Region (.bed) file: Feature file containing the significantly enriched domains in the genome
• Summit (.bed) file: Feature file containing the single base maximum of each peak
Outputs from PeakRanger
Integrative Genomics Viewer
The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.
Use IGV to inspect outputs from PeakRanger
http://www.broadinstitute.org/igv/
Using IGV in Atmosphere
1. Launch an instance of NGS Viewers from the Atmosphere App list
2. Use VNClient to connect to your remote desktop
Using IGV in Atmosphere
1. Configure iDrop
2. Copy .wig and .bed files from the PeakRanger output to your Atmosphere instance desktop
Using IGV in Atmosphere
1. Launch IGV (Integrative Genomics Viewer)
2. Change the current genome to A. thaliana (TAIR10)
Using IGV in Atmosphere
1. Open igvtools and convert .wig file to .tdf
2. Load the .tdf and .bed files into the IGV window
3. Inspect loci by entering their name into search box
Using IGV in Atmosphere
Enrichment region and alignment
peak at promoter region of
APETALA (AP1)
Enrichment region and alignment
peak at promoter region of
APETALA (AP1)
AP1 (APETALA) Mutant
Why do we even care about LEAFY? Well, it activates AP1. If API is not active, Arabidopsis can’t make flowers and instead makes cauliflowers!
Wild-type ap1
Some Known LEAFY targets
Gene Name Locus
APETALA (AP1) AT1G69120.1
AGAMOUS (AG) AT4G18960.1
LMI2 AT3G61250.1
LMI3 AT5G49770.1
LMI4 AT5G60630.1
LMI5 AT1G16070.1
Look for LEAFY enrichment at these loci in IGV 2.0
Filtering the PeakRanger summits file
The statiscally best summits from PeakRanger have P-values of Zero. If you look at the summits.bed file you can see this is embedded in the name of the features. So, if we filter the summits.bed for only lines matching pval_0, we will generate a BED file containing summits most likely to be near true LEAFY binding sites.
This identical to running
egrep “pval_0” peakranger_summit.bed > peakranger_summit_best.bed
on a command line
This identical to running
egrep “pval_0” peakranger_summit.bed > peakranger_summit_best.bed
on a command line
Find Lines Matching a Regular Expression
BEDTools for Interval Operations
The BEDTools utilities allow one to address common genomics tasks such as finding feature overlaps and computing coverage. The utilities are largely based on four widely-used file formats: BED, GFF/GTF, VCF, and SAM/BAM. Using BEDTools, one can develop sophisticated pipelines that answer complicated research questions by "streaming" several BEDTools together.
The BEDTools utilities allow one to address common genomics tasks such as finding feature overlaps and computing coverage. The utilities are largely based on four widely-used file formats: BED, GFF/GTF, VCF, and SAM/BAM. Using BEDTools, one can develop sophisticated pipelines that answer complicated research questions by "streaming" several BEDTools together.
* The entire BEDtools suite is slated for itegration into the iPlant DE. Follow us on Twitter @iPlantCollab to learn when new tools become available.
slopBed – Expand the coordinates of features in a BED file by a a defined number of bases
fastaFromBed – Extract a multiFASTA file from a reference sequence using a BED file of features
Best Summits BED File(single base pair features)Best Summits BED File
(single base pair features)
100 bp Region BED File(100 bp centered on peak centers)
100 bp Region BED File(100 bp centered on peak centers)
FASTA file of 100 bp regions(likely to contain consensus motifs)
FASTA file of 100 bp regions(likely to contain consensus motifs)
BEDTools slopBed, 50bp equidistantBEDTools slopBed, 50bp equidistant
BEDTools fastaFromBed, Arabidopsis genomeBEDTools fastaFromBed, Arabidopsis genome
DREMEDREME
Filter summits.bed on pval_0 Filter summits.bed on pval_0
Objective
Go from BED file of single-base peak summits to a FASTA file containing the 100 bp surrounding those summits that can be used for motif hunting
Potential Next Steps
• Identify all consensus LEAFY sites in the genome that fall in promoters
• Extract all the promoters where LEAFY has significant binding and associate them with genes.
• Generate a simple gene list and run Ontology Term enrichment analysis to find classes of genes influenced by LEAFY
Cyberinfrastructure OverviewComponent What we did Why we used it
iPlant Data Store Imported data from SRA. Stored results of analyses. Downloaded
results.
Fast, flexible storage for large bioinformatics
data.
Discovery Environment Data import. NGS Alignment. Peak
Finding. Data organization.
One interface. Multiple bioinformatics
applications. Easy to manage work products.
Atmosphere Loaded results into desktop client application.
Avoid downloading large files to personal
computer. Easy access to powerful desktop
environment.