1. High Throughput Sequence (HTS) data analysis 1.Storage and retrieving of HTS data. 2.Representation of HTS data. 3.Visualization of HTS data. 4.Discovering genomic patterns from HTS data.

2. Large Data Set Analysis. Hardware considerations: 1.) Data storage.  FASTA record of a protein (1,000 aa) ~ 1 KB.  Human proteome, or Chromosome 21 ~ 50 MB  Human genome ~ 1.5 GB  HTS transcriptome analysis (4 samples @ 40 million reads each) original and derived data sets ~ 200 GB

3. Large Data Set Analysis. Hardware considerations: 2.) Processors and RAM.  Comparison: tbalstn of 5 protein sequences against 1.2GB genome, ~15 sec CPU time. Map a single 10 M reads illumina run to human genome ~15,000 CPU sec (> 4 hours).  When RAM < data size, the computer will come to a crawl.

4. Large Data Set Analysis. Hardware considerations: 3.) Operating system determines the availability of tools.  Linux is the default development system for most bioinformatics groups. It is also the OS of the UFHPC.  Easy control and automation.  Most tools are portable to Mac OSX, but often requires recompiling the source code.

5. Observe: demanding computation for large data set analysis.

6. High Throughput DNA-Sequencing (HTS) data analysis 1.Sources and representation of HTS data. 2.Visualization of HTS data. 3.Discovering genomic pattern from HTS data. 4.Integrated data analysis and hypothesis- generating exploration.

7.  Your own ( sequencing service ).  Public databases, such as NCBI/GEO.  Major genomic /epigenomic projects, such as ENCODE (ENCylopedia Of DNA Elements); the Cancer Genome Project, etc.  Other internet sources. Source of HTS data

8. Practice: log into UFHPC / Linux server. Mac user, type in terminal: $ ssh username@gator.hpc.ufl.edu If you do not have an HPC acct: $ ssh gms6014@159.178.28.30gms6014@159.178.28.30 Windows, Open in Putty: gator.hpc.ufl.edu or 159.178.28.30159.178.28.30 First step

9. Practice: log into a Linux system (such as UFHPC). Connect and log into the system with Putty. Make a directory by typing “mkdir your_name” Type “ls” or “ls -l” to verify the folder. Download data file with “wget –c URL” First step

10. Navigating the Linux command line environment: User rights ~ Program can not run unless you have the rights to read/write/execute the file. Basic commands to survive.

11. Recoding sequence information – sequence file format FASTA format– suitable for single gene or genomic region, pre-genomic era. > Gene_name or accession, (other info) ACTGGGTTTATGACGTGTCATGCATGCA ATGTAGCTAGATGCTAGCTAGATGCTAG CTAGATGCTA…. Defined format is necessary for computers to identify and process the information.

12. Recording sequence reads from the machine – FASTQ FASTA: >My_sequence AATTACGCGCGATACGAT FASTQ: @My_sequence AATTACGCGCGATACGAT +My_sequence quality efcfffffcfeeYBBsdf Recording of quality assessment allows filtering based on sequence quality.

13. Paint the sequence reads to the genome HTS reads @reads_1 AATTACGCGCGATACGAT + efcfffffcfeeYBBsdf @reads_2 ACCGAGGCGCGTATGTCT + efcfffffcfeeYBBsea …. @reads_1,000,001 Corresponding location on the genome ELAND (Illumina) Bowtie, etc. ChIP-Seq; RNA-Seq De novo assembly of genomes, chromatin conformation, genomic abnormality, etc…

14. 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.

15. 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. Observe: transform the SRA file to fastq. “$ fastq-dump.2 path_to_sra_file”

16. Representation of (HTS) data – BED (Browser Extensible Data) file chr2 1000019210000217U00+ chr21000022710000252U10- chr21000031010000335U20+ chr31000049610000521U10- chr21000055610000581U20+ Chrom.Start EndnameScorStrand 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 http://genome.ucsc.edu/FAQ/FAQformat.html

17. 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 {-1 - 2 | -U } [-S ]”

18. 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.

19. Retrieving HTS data  Retrieving HTS data from the web using wget.  Loading to and unloading data from UFHPC (check with HPC instructions).

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

21. Visualization of HTS data. Simple visualization - distribution of tags (or normalized values). Barski et al. (2007) Cell chr4 02000 chr42004002 chr4400600 13 chr460080035 chr4800100027 Chr.ChrStart ChrEndValue BedGraph file (Wig)

22. 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

23. Visualization of HTS data. Advanced visualization – depending on purpose of comparison. Berger et al. (2011) Nature 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

24. Manipulating Deep Seq data with Galaxy Practice & Observe: 1.Load the PolII.H99.Bed file to Galaxy with the Get Data tool. Select “D. melanogaster Apr. 006 (BDGP R5/dm3) (dm3)” as the database 2.Sort data based on chromosome location c2. 3.Filter out lines with U0 with the expression c4!=‘U2’ 4.Extract genomic sequences.

25. Visualizing Deep Seq data with UCSC genome browser Practice & Observe I: 1.Load the PolII.H99.Bed file as custom track to the browser by copy/past the URL link. 2.View ‘dense’ and then ‘full’ presentation of the track.

26. Visualizing Deep Seq data with UCSC genome browser Practice & Observe II: 1.Save the landmark.bed file to your local computer. View the contents with Notepad. 2.Load the local file to the UCSC browser. 3.Edit the color value, save, resubmit, and observe the differences.