HTS data file

1. HTS data file • Sequence and quality information are recorded as multi-FASTQ files. • For efficient storage and transmission, they are transformed into SRA (Sequence Read Archives) format. Practice: transform the SRA file to fastq. • “$ fastq-dump.2path_to_sra_file”

2. HTS - Recording sequence and quality information FASTQ format = FASTA + Quality • @HWI-EAS209_0006_FC706VJ:5:58:5894:21141#ATCACG/1 • TTAATTGGTAAATAAATCTCCTAATAGCTTAGATNTTACCTTNNNNNNNNNNTAGTTTCTT • +HWI-EAS209_0006_FC706VJ:5:58:5894:21141#ATCACG/1 • !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQabdefghadfda • Two identification lines (@, +) for each sequence. • Identification line format depends on specific sequencing platform. • Quality line using characters representing integer values.

3. HTS data – map to genome • “bwa” or “bowtie” are the two most popular software that implement a similar strategy (Burrows-Wheeler Transform). • Can benefit from multi-processor . Practice: map the data to hg19. • “ bowtie2 [options]* -x <bt2-idx> {-1 <m1> -2 <m2> | -U <r>} [-S <sam>]”

4. Representation of HTS data • The importance of a reference genome • All coordinates are only meaningful for a given genome assembly. • One assembly may have multiple releases (annotations). • You need to know which reference genome was used to generate the BED file.

5. Representation of (HTS) data – BED (Browser Extensible Data) file Chrom. Start End name Scor Strand chr210000192 10000217 U0 0 + chr210000227 10000252 U1 0 - chr210000310 10000335 U2 0 + chr310000496 10000521 U1 0 - chr2 10000556 10000581 U2 0 + With the completion of the genome, there is no need to record the base pair identity (if it is the same as the reference genome). Detailed description of genomic data formats: http://genome.ucsc.edu/FAQ/FAQformat.html

6. How to gain knowledge from HTS data • Visualization of HTS data. • Discovering genomic patterns. • Identifying novel mechanism – hypothesis generation.

7. Visualization of HTS data. Simple visualization - distribution of tags (or normalized values). Barski et al. (2007) Cell Chr. ChrStartChrEndValue chr4 0 200 0 chr4 200 400 2 chr4400 600 13 chr4600 800 35 chr4800 1000 27 BedGraph file (Wig)

8. Visualizing Deep Seq data with UCSC genome browser Practice & Observe I: Load the PolII.H99.Bed file as custom track to the browser by copy/past the URL link. View ‘dense’ and then ‘full’ presentation of the track.

9. Visualizing Deep Seq data with UCSC genome browser Practice & Observe II: Save the landmark.bed file to your local computer. View the contents with Notepad. Load the local file to the UCSC browser. Edit the color value, save, resubmit, and observe the differences.

10. Visualization of HTS data. Shifting sequence tag position may be necessary to reflect nucleosome positions. In this example the mapping positions were shifted +73bp for forward strain and -73bp for reverse strain to reflect the midpoint of the nucleosome. Jiang & Pugh, Nat. Rev. Genet., 2009

11. Visualization of HTS data. Advanced visualization – depending on purpose of comparison. Example - Circos plot depicts genomic location, chromosomal copy number (red, copy gain; blue, copy loss). Inter-chromosomal translocations (purple) and intra-chromosomal (green) rearrangements observed in primary prostate cancers Berger et al. (2011) Nature

12. Discovering genomic patterns Barski et al. (2007) Cell Usually requires some programming (scripting). As a biologist, you need to clearly define your question, and the logic to obtain the data summary.

13. Discovering genomic patterns Q: Is H3K4me3 associated with TSS? Is such an association related to gene expression status? Logic: Group genes based on expression levels obtained with a microarray study (Su et al, 2004). For each gene, obtain the normalized H3K4me3 ChIP-Seq counts within [-2k, +2k] of the TSS. For each of the expression group, plot the average value along the [-2k, +2k] interval.

14. Functional Analysis of HTS data • Gene Ontology – http://www.geneontology.org/ • Regulatory pathways. • Modeling & Systems Biology.

15. Gene Ontology – hierarchical framework of terms / concepts

16. Gene Ontology Goal – “produce a dynamic controlled vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing” – GO consortium Ontology: “ The branch of metaphysics that deals with the nature of being” – The American Heritage Dictionary

17. Implications of Gene Ontology (I) Monitoring biological processes or molecular functions beyond individual gene. • Example: • 1.) Which biological process (mol. Function) is activated/suppressed following a treatment?

18. Gene Expression Profile Differences between the two long cancer cell lines A549 and H23

19. Implications of Gene Ontology (II) Basis for cross genome comparison and integrating knowledge from different model systems.

20. Tools associated with GO • A comprehensive list at GO web site. • Tools for browsing, AmiGO, QuickGO at EBI, etc. • Tools for analyzing array data such as FuncAssociate, etc.

21. Using GO to gain comprehensive understanding of cellular differences Practice: Load a probe set list to FuncAssociate to identify over-represented GO