1. Illumin8er: Software for the Illumina GAII Ian Carr, Joanne Morgan, Phil Chambers, Alex Markham, David Bonthron& Graham Taylor Leeds Institute of Molecular Medicine, Leeds Teaching Hospitals & Cancer Research UK

2. Sipping from the hosepipe The cost of DNA sequencing is plummeting Current sequence output from an Illumina GAII is over 1 Gigabase per day Managing the data is the single biggest challenge to bringing the benefits to patients and cost savings to to the Healthcare budget The next biggest challenge is optimising the workflow to achieve cost efficiency

3. What should the software do? Scan for and report mutations against a defined reference sequence. Be able to handle bar-code sequence tags Be easy to use Report on data quality Export to a database

4. Why Illumina? Cost: 0002p per base Capacity: 3.5 Gigabase per run Simplicity: library>cluster station>sequence>data

5. 500,000,000 bases per channel

6. Software requirements Runs in MS Windows User definable reference sequence Quality scores Automatic mutation calling SNPs Indels Speed

7. Initial data manipulation Illuminator can transform data in prb.txt or seq.txt in to fasta files If tagged data is used each tag is separated in to an individual file. The prb.txt files can be filtered for low quality data

8. Reference files Reference files are created from plain text files of the genomic sequence and a cDNA sequence in either a plain text file or a genbank web page. If a genbank page is used the SNP data in the page is also imported with cDNA sequence. The reference file contains the position of the exons and ORF relative to the genomic sequence to aid mutation annotation.

9. Indexing the reference sequence Each octamer in the reference sequence is mapped to an array of 65537 octamers (the extra one is for unmapped rubbish such as ‘nnnnnnnn’) Some octamers have no positions in the reference while others have several. GCTGGTGAGGGGTGGGGCAGGAGTGCTTGGGTTGTGGTGAAACATTGG aaaaaaaa aaaaaaac aaaaaaat aaaaaaag aaaaaaca aaaaaacc tttttttt tttttttc tttttttg ~65000 nnnnnnnn

10. Mapping reads with 3’ mismatches TGAGGGGTGGGGCAGGAGTGCTTGGGTTGTGGGAAA Position where octamer is found in ref seq 606 2900 5000 614 8900 306 622 1400 1830 2500 Match up positions where octamer increase by 8 606 2900 5000 614 8900 306 622 1400 NA not +8bp 3’ mismatches have a run of 3 foot prints with the last octomer missing. This goes in to array 2 (phase 2) GCTGGTGAGGGGTGGGGCAGGAGTGCTTGGGTTGTGGTGAAACATTGG

11. Mapping reads with 5’ mismatches GTGAGGGGGGGGCAGGAGTGCTTGGGTTGTGGTGAA Position where octamer is found in ref seq 5700 614 8900 306 622 1400 630 Match up positions where octamer increase by 8 NA614 8900 306 622 1400 630 +8bp GCTGGTGAGGGGTGGGGCAGGAGTGCTTGGGTTGTGGTGAAACATTGG not +8bp 5’ mismatches have a run of 3 foot prints with the first octomer missing. This goes in to array 3 (phase 3)

12. Mapping reads with internal mismatches TGAGGGGTGGGGCAGAAGTGCTTGGGTTGTGGTGAA Position where octamer is found in ref seq 606 2900 5000 1664 5900 306 622 1400 630 Match up positions where octamer increase by 8 606 2900 5000 1664 5900 306 622 1400 630 +8bp not +8bp GCTGGTGAGGGGTGGGGCAGGAGTGCTTGGGTTGTGGTGAAACATTGG not +8bp internal mismatches have a run of 3 foot prints with either the second or third octamer out of phase. This goes in to array 4 (phase 4) +16bp

13. What each phase is used for Phase 1 = perfect matches Phase 2 = indels and small mutations at end of a read Phase 3 = indels and small mutations at start of a read Phase 4 = small mutations in the middle of read

14. Small changes These are found by looking at Phase 4 data. Homozygous mutation are in Phase 4 but not phase 1 (seen as a hole) Heterozygous variants are in seen in phase 4 and wt seen in phase 1 data. WT in Phase 1 data Mut in Phase 4 Data. (The wt allele Is present due to seq errors elsewhere in the read.)

15. InDels Phase 2 data gets indels from end of the read while Phase 3 gets them from the start of the read. In a perfect world Phase 2 and 3 data should mirror each other.

16. Global view Data for a PCR product containing two exons; blue = exonic DNA pink = protein coding DNA The red and blue lines show the read depth of forward and reverse reads. The lower panel shows the reference and deduced sequences around the a point on the upper panel selected by clicking on the panel with the mouse

17. Data view Forward and Reverse sequences Patient sequence Patient’s other allele sequence Score for each nucleotide Reference genomic, cDNA and protein sequence Read depth Heterozygous base

18. Indel interface Forward and Reverse sequences Reference sequence Patient sequences with indel at start and end of read Consensus sequence of patient reads across indel Alignment of patient and reference sequence to identify indel

19. Data export The program can both export and import the alignment data as a plain text file Create an updatable library of sequence variants Export sequence variants as a text file Create a LOVD import file for the sequence variants

20. Validation: BRCA1&BRCA2 Illuminator detected all the mutations previously identified by dye terminator Sanger sequencing of the exons in BRCA1 and 2 of 10 individuals. Each nucleotide had a read depth of at least 75 reads (approximately 6.6x10 3 sequences per gene). The alignment and mutation annotation took ~50 seconds per gene per person

21. Conclusions Illumin8er is Easy to use Rapid Runs on Windows desktop Uses standard Illumina output files Reports mutations in a sensitive and specific manner

22. Next steps.. Make freely available by download http://dna.leeds.ac.uk/illumin8er/ Design compatible LOVD Large scale validation trial