SynaMer A New Application for Rapid Identification of Overlapping n-mers From Sequence Reads

1. SynaMer A New Application for Rapid Identification of Overlapping n-mers From Sequence Reads June 2006

2. Synamatix team - Introductions • Colin Hercus • CTO • Poh Yang Ming • Bioinformatics Research Team Member • Arif Anwar • VP

3. Summary of Agenda • Overview of Genome assembly • Key bottlenecks • Introducing SynaMer: • A solution for rapidly finding longer overlapping n-mers • The method • The results • Discussion

4. Ab initio • genome assembly • Overlap-layout-consensus • Needs high • sequence coverage • No requirement for • closely related genome • Comparative genome • assembly • Alignment-layout-consensus • Requires a closely • related genome • High speed sequence read • to genome mapping is • required • Less dependent • on overlap finding 2 major approaches

5. Identified bottlenecks for Ab initio • Typical genome assembly process flow • Sequence reads/Fragments • Vector trimming • Overlapping • Contig/Supercontig/Scaffold generation • Finishing • Final Genome • User* identified major bottleneck in n-mer finding: • Performance • Preference for longer n-mers • IT Hardware requirements User* - Major US Genome Research Institute

6. Task to accomplish • Original user data set and requirement was: • To find all overlapping exact 100-mers in 50million 1kb sequencing reads – i.e. 50 Billion bp • Report n-mers that have a frequency >2 and <m • Using conventional software and approaches the user took 500hrs and 1.5TB of disc space to find all 100-mer overlaps • Hence standard approach limits usage to 32mers • Longer mers help bridge repetitive and low-complexity regions

7. Long v Short n-mersadvantages and disadvantages 100 mer +ve Fewer false positives Improvement in final assembly Errors in reads may lead to false negatives Slow to process with conventional software -ve

8. Explanation of advantages A A shorter overlap results in more false positives Low-complexity region B A longer overlap results in less false positives Final assembly improved

9. Synamer: A solution for rapid identification of longer n-mers • Synamer finds overlapping sequences given a defined “n” with a range of frequency of occurrence in the sequence set • It is similar to a class of tools in genome assembly called “overlappers” • 2 well known overlappers are: • UMD Overlapper • Roberts  M et. al.(2004) Bioinformatics 20(18):3363-3369 • KI Overlapper • Tammi MT et.al., (2003) NAR 31(15):4663-4672

10. How Synamer works • Given a mer length of “n” • Extract a n-mer at each position within a read • Compare the n-mer and reverse complement, to report palindromes • Index n-mers and their location within reads • For each n-mer within a user defined frequency range report the n-mer and locations

11. SynaMer data • Input: • Text file of the reads • Parameters: • n (default 96, maximum of 128) • Frequency range (default of 2 to 50) • Memory usage (Default to available memory) • Temporary file location • Output Format: • Text or binary • n-mer Frequency Palindrome direction read ID:location

12. Test cases • User case 1: 30million 1kb reads finding exact 96mer took approximately 5hrs to process, with less than 200GB temporary disk space on a dual CPU Itanium • Compared to 500hrs and over 1.5TB of disk space • Use case 2: • Brucella_suis 1330, 36080 900bp reads (http://www.tigr.org/tdb/benchmark/) • Tests were conducted with a range of n-mer with frequency of minimum of 2 to 120. • n-mer range of: 12, 24, 36, 48, 60, 72, 84, 96, 108, 120 • Average execution time measured with 6 replicates

13. Brucella suis - results • Majority of the patterns are at frequency of 2-50 • More pattern at higher n-mer • Longer n-mer would be more specific and less false positive

14. Distribution of overlapping sequence with frequency Higher level of repeats in more complex genomes leads to increased benefits from using longer n-mers

15. Using SynaMer there is no time increase with longer n-mers

16. Sample Output • At 96-mer: • TTTCATAAAGCCGCTTTGCACCATAAAGCGCGTCGCCGGTGCTGCCTGTGGTGCCGTAGAAAGTCCAGCCTTCCTCCGCCATCAGGAAATCAACCACTGAAACGGAAA 5 33984:395 25036:255 17186:435 -5741:85 5184:181 • TTTCATAAACCTGACCCTGATTCGCCGCACCATCGCCGAAATAGGTCAGCGAAACGGATTTATTCTCACGATAGTGATTGGCGAAGGCCAACCCCGTACCGAGCGAAA 8 30929:163 28279:329 25051:228 -22556:257 -14554:249 -12303:286 15820:325 6770:434 • TTTCATAAAACCTAAATAATATAGAATATATTTTTTAATTTACTCCCACAAAAATTGATATTTATAAAATAAAAAATCCCAATCTGTAAATCCCAATAATTTTACAAA 4 32618:184 -9587:456 9891:617 8902:369 • TTTCAGTTTCTCAAGCAAACCCTTTATGACATTGCATCTTTGCTGGTGTTTTTCGCCAATGTTGCATTTTGTTTCTCAATTGTAGCGCAAGCAAATGCGGCTTGAAAA 5 26073:487 -21045:262 22952:244 12603:19 6640:383 • The numbers before the “:” are the ordinal position of the reads in the file

17. Sample contig • To show validity of the result • GBUAS15TR and GBUCA37TF • Detected overlap at 96-mer – shown below: • At position 188 on GBUAS15TR and 811 on GBUCA37TF • They can be joined to a 1.5kbp contig, with consensus

18. Conclusions • For 30million 1kb reads took 5 hours on a dual CPU itanium machine, with temporary file size less than 200GB • Time consumed to find overlapping sequences for 33000 900bp reads of a bacterial WGSS reads took less than 20s • 100 fold faster than conventional method • Allows use of longer n-mers • Potentially increases quality of assembly • SynaMer will be made released as a product later this Summer

19. Questions and Follow up • Please send questions to: tech@synamatix.com • Webcast will be available online in 24hrs at • www.mgrc.com.my • Paper accompanying this webcast will be sent to all attendees • If you are interested in testing SynaMer when it is released please email: enquries@synamatix.com

20. Thank you! tech@synamatix.com