1. Advanced ChIPseq Identification of consensus binding sites for the LEAFY transcription factor

2. Scientific Objective The LEAFY transcription factor has been shown (Moyroud et al. 2011) to bind a dimer of the motif CCANTG[G/T] We will use data from a chromatin immunoprecipitation assay on the LEAFY protein to: Identify LEAFY binding targets Attempt confirmation of the binding site

3. A Few Known LEAFY targets Gene NameLocus APETALA (AP1)AT1G69120.1 AGAMOUS (AG)AT4G18960.1 LMI2AT3G61250.1 LMI3AT5G49770.1 LMI4AT5G60630.1 LMI5AT1G16070.1 Look for LEAFY enrichment at these loci in IGV 2.0

4. 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-typeap1

5. 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-typeap1

6. ChIPseq Conceptual Overview

7. 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 One can use NCBI SRA Import to rapidly copy SRA accession SRP003928 over ASCP into the iPlant Data Store.

9. Import SRA data from NCBI SRA Extract FASTQ files from the downloaded SRA archives

10. 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

11. Import SRA data from NCBI SRA Extract FASTQ files from the downloaded SRA archives

12. 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 was developed and used by the Human 1000 genomes project due to its speed and accuracy. BWA mem 0.7.4 is a fast variant of BWA able to use long reads. It is newly available in the iPlant DE

13. 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 All SAMTools functions are in the DE

14. Align FASTQ files to Arabidopsis genome using BWA Merge and index BAM files using SAMtools apps

15. 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/

16. 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.

17. 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

18. Wiggle file BED file

19. 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/

20. Using IGV in Atmosphere 1.Launch an instance of RNA-Seq Visualization (or any image that has IGV) from the Atmosphere App list 2.Use VNClient to connect to your remote desktop

21. Using IGV in Atmosphere 1.Configure iDrop 2.Copy.wig and.bed files from the PeakRanger output to your Atmosphere instance desktop

22. Using IGV in Atmosphere 1.Launch IGV (Integrative Genomics Viewer) 2.Change the current genome to A. thaliana (TAIR10)

23. 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

24. Using IGV in Atmosphere Enrichment region and alignment peak at promoter region of APETALA (AP1)

25. 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

26. 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 entire BEDtools suite is now integrated 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

27. 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 equidistant BEDTools fastaFromBed, Arabidopsis genome DREME 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 Peak regions from PeakRanger and/or MACS IntersectBed peak regions Peaks found by both codes

28. DREME Run DREME on 100bp windows surrounding LEAFY peaks Download results

29. DREME results CCANTG(G/T)! Success!

30. 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

31. Cyberinfrastructure Overview ComponentWhat we didWhy we used it iPlant Data StoreImported data from SRA. Stored results of analyses. Downloaded results. Fast, flexible storage for large bioinformatics data. Discovery EnvironmentData import. NGS Alignment. Peak Finding. Data organization. One interface. Multiple bioinformatics applications. Easy to manage work products. AtmosphereLoaded results into desktop client application. Avoid downloading large files to personal computer. Easy access to powerful desktop environment.

32. On to the Exercise