Bioinformatics Challenge Day

1. Bioinformatics Challenge Day Peter Carr 2/2/2013 This work is sponsored by the Defense Threat Reduction Agency under Air Force Contract #FA8721-05-C-0002.  Opinions, interpretations, recommendations and conclusions are those of the authors and are not necessarily endorsed by the United States Government.

2. Bioinformatics Challenge Days The problem: drowning in complex data, very hard to make sense of it all • Approach: A one day hack-a-thon • Innovate: tackle huge challenges in bioinformatics • Educate: bring in specialists from diverse fields, participants in DoD bioinformatics interests • Investigate: what this short format can accomplish • Aggregate: bring people together • The Challenges: • Can you determine the cause of an infection? • Can you invent a new way to visualize complex bioinformatics data? • Can you spot the signs of genetic engineering? Can you figure out what an engineered organism does? DNA sequencing MAGE engineering • Sponsor: Defense Threat Reduction Agency (DTRA) • Organizer: MIT Lincoln Laboratory (MIT LL)

3. Cast of Characters Darrell Ricke (MIT Lincoln Laboratory) • Bioinformatics Peter Carr (MIT Lincoln Laboratory) • Synthetic Biology, Biochemistry Anna Shcherbina (MIT Lincoln Laboratory) • Bioengineering, Electrical Engineering Nancy Burgess (Defense Threat Reduction Agency) • Chemical and Biological Defense

4. Some Big Hammers • Sequencing • Complete genome sequences • Mixed populations • Expression (RNA species) • Interaction (ChIP-seq) • Mass spectroscopy • Protein/peptide fingerprinting • Metabolites • Interaction (cross-linking) • Other tools • Microarrays • High-throughput screening (e.g. fluorescence)

5. Now and Future • Data galore: Omics approaches are generating massive amounts of increasingly complex measurement data • How do we best make sense of this information? • Some fundamental development areas • Processing • Visualizing/analyzing • Storing/accessing

6. The Challenges • Metagenomic Visual Developing visualization methods to facilitate analysis of metagenomic data with unknown numbers of genomes at varying concentrations • Genome Assembly for the Clinic Performing de novo assembly from clinical samples with an emphasis on pathogen identification • Genetic Engineering ID and interpret the signatures of genetic engineering

7. What can your efforts today produce? • Analysis, answers to questions • Heuristics, algorithms • Specific software tools • Roadmap for future work

8. What to get out of this? • A deeper understanding of the field • Tools • Approaches • Concerns/challenges • Ideas and experiences that may motivate future work • Connection to others with similar interests

9. What We Hope to See From You Creativity (innovative ideas and efforts) Energy (intensity and focus) Communication (results, feedback)

10. Theme: Flexibility • You can work alone, come with a team, or team up on-site • You can use any of the resources we have provided, any you have access to (including tools you code yourself ahead of time or today) • You keep what you make (DTRA and MIT LL make no claims to what you produce)

11. Schedule 8:00 AM               Breakfast/check-in 9:00 AM               Welcome (Pete) 9:15 AM               Overview and logistics (Pete) 9:45 AM               The Challenges: 1. Metagenomic Visual (Anna) 2. Genome Assembly for the Clinic (Darrell) 3. Genetic Engineering (Pete) 10:45 AM             Coffee/Break into project groups 12:30 PM             Lunch served (groups can continue to work) 3:30 PM              Snack (groups can continue to work) 6:30 PM              Progress updates ready by dinnertime 6:30 PM              Dinner and progress reports 8:00 PM+Groups can continue to work

12. Getting Started • On the USB sticks: • Data for the three challenges (FASTA, FASTQ, CSV) • Software (Mac, Windows, Linux) • Local wifi access • Teaming

13. Questions?

14. Challenge 3: Genetic Engineering • Background: a sample has been dug from the back of a lab freezer, and subjected to Ion Torrent sequencing • We would like to know what it is: • Simple or complex? • Natural or engineered? • If engineered, how? (what techniques) • For what purpose? • Will the design work? • [No surprise: yes, there is an (in silico) engineered component. Find it! And figure out as much as you can about it.] • We have a lot of great questions, but may not have all the answers

15. What Do We Design For? • Investigation (answer a biological question) • Production (make a drug, a fuel) • Serve a specialized role • Protect against infection • Detect dangerous chemicals • Environmental remediation • Creatively explore an interesting design space

16. How Do We Produce These?

17. Getting DNA In • Transformation/transfection can be via natural, chemical, or electrical methods

18. Old School: Conjugation • Transfer “in vivo” protects fragile DNA • An entire genome can be transferred • Transfer to other species • Requires an origin of replication, pilus protein donor (sender) recipient (receiver)

19. Old School: Phage Transduction • Phage/virus can replicate independently, or integrate into genome • DNA or RNA, single- or double-stranded • Examples: • Lentivirus (mammalian) • Lambda, T4, T7, P1, M13 (E. coli)

20. Old School: Mutagenesis • Natural mutation rates (mutations accumulate slowly over time) • Exposure to damaging effects (chemicals, radiation) • Mutator strains: cells defective for one or more natural repair mechanisms

21. Revolution 1: Restriction Enzymes • Specific sites: often 6 bp, but can be longer or shorter • “Outside cutters” cut some distance away from recognition site • Homing nucleases (longer ~30 bp sites, can be unique in a genome) • Multiple Cloning Site (MCS) often engineered into cloning vector

22. Plasmids • Circular • Contain origin of replication • Single copy • Low to high copy (hundreds) • Selection gene (1 or more) • MCS and other features common • Extension: BACs and YACs

23. Selection and Screening • Almost all approaches give a mix of successes and failures • Screening searches for what you want • Selection kills off what you don’t want

24. Revolution 2: PCR Polymerase Chain Reaction • Simple scheme made it possible to manipulate DNA in new ways • Used not just to make more DNA, but to modify it • Dependent on oligonucleotide synthesis and enzyme (DNA polymerase)

25. Site-Directed Mutagenesis • Perform on DNA in vitro (higher background error rates than in vivo) • Employs a synthetic oligo and an enzyme (polymerase) • Users typically screen clones with PCR or restriction, then sequencing • Rest of the plasmid typically not re-sequenced

26. Gibson Assembly • Can bring together many pieces of DNA at once • Based on identical sequence overlaps • 3-enyzme reaction • Intrinsically scar-less • Often relies on PCR (& thus oligos) to produce each segment http://www.youtube.com/watch?v=WCWjJFU1be8

27. Golden Gate Assembly • “Outside cutter” restriction enzymes • Little or no scar at joining point • Segments may or may not be produced by PCR

28. Recombination • Site-specific • attB (Gateway) • Cre/lox • Homologous • Natural (B. Subtilis, RecA) • Engineered (lambda red) • Directed by double-stranded break repair • Zn finger nucleases • TALENs • CRISPRs

29. DNA Synthesis to Genome Assembly • Oligo synthesis (building blocks) using organic chemistry • Assemble to genes using biochemistry (in vitro) • Assemble to genomes (small ones for starters) using biology (in vivo) • Each of these processes can carry their own error signature, but can also be counteracted by sequencing-based screening, post-repair, etc.

30. MAGE: Multiplexed Automatable Genome Engineering Generation of genome edits at many targeted chromosomal locations Much like site-directed mutagenesis, but on a chromosome Wang, Isaacs, Carr et al. (2009)Nature460(7257):894-8

31. MAGE • A lot like site-directed mutagenesis—but on the genome of living cells • Uses long oligos • Does not require selection markers (but can use them) • Other than the desired change (as small as a DNA base, as large as a multi-gene deletion) there is no obvious sign • BUT there can be secondary signs: • Oligo-mediated defects within 50-100 bp of the edited site • Higher background mutation rates (mismatch repair deactivated)

32. CAGE: Conjugative Assembly Genome Engineering • Conjugation now employed with controlled precision • But DNA crossover points not always perfectly defined Isaacs, Carr, Wang, ... (2011) Science

33. Genetic Circuits: DNA Parts • Make use of DNA “parts” libraries for constructing more advanced genetic designs • Fundamental concept in synthetic biology, inspired by electrical engineering • Basis of the iGEMcompetetion (International Genetically Engineered Machines)

34. Genetic Circuits: Bacteria • Repressilator an early example of synthetic biology circuits • Three inverters in series (circular) made a ring oscillator) Elowitz and Liebler (2000) Nature

35. Genetic Circuits: Yeast • Adapted a signaling system from plants • Used to engineer communication between yeast cells • Basic features can be installed in a variety of organisms Chen and Weiss (2005) Nature Biotechnology

36. Genetic Circuits: Mammalian Concept: insert DNA circuit into cells  ID cancer and/or kill it Overview Genetic Circuits DNA for classifier circuit cancer cell normal cell no match match cell death no effect Xie et al. (2011) Science (Weiss, Benenson labs)

37. Increasingly Alien • Codon usage • Adapt how often codons are used to match target organism • New amino acids (Tirrell, Schultz) • New genetic codes (Church, Carr) • Minimal life • Engineering by subtraction (Blattner) • Compose from the ground up (Forster/Church) • New DNA bases • Alternate hydrogen-bonding (Benner) • Hydrophobic bases (Schultz) • Mirror-image life