1. Proposal for the RNA Alignment Ontology Rob Knight Dept Chem & Biochem CU Boulder

2. What do we want to do? Represent detailed structural info and other metadata on alignment Avoid horizontal and vertical expansion Explicitly annotate correspondences at the level where they occur

3. Homology is problematic… Fundamental problem: systems that are homologous at one level are not necessarily homologous at other levels E.g. bat wings and bird wings: homologous as pentadactyl limbs, but not homologous as wings Homology is hierarchical and can partially overlap at any level (e.g. Griffiths 2006) Ridley “Evolution” 3rd ed. Bat forelimbs Bird forelimbs Frog forelimbs Rodent forelimbs Mammal forelimbs Tetrapod forelimbs

4. …and correspondence need not be homology at all! Example from SELEX: hammerhead ribozymes independently evolved at least three times: in nature, and in Jack Szostak and Ron Breaker’s labs However, we still want to be able to align the functionally equivalent sequences although there is not evolutionary relationship

5. So what are going to use the alignment ontology for?

6. Use case 1: aligning rRNA

7. Problem: have millions of fragments, want to align (incl. noncanonical pairs) + assign named regions

8. Solution Use existing alignment, try to fit new seqs in Would be improved if we could explicitly annotate helices, noncanonical pairs, etc. on the sequence overall For display, need to easily show/hide groups of sequences and/or regions of the sequence

9. Use case 2: SELEX From large number of unaligned sequences, want to identify motifs like this (Majerfeld & Yarus 2005)

10. How is this currently done? Find regions that are similar in more sequences than chance Group these sequences centered on the “motif” See if the parts of the motif can be related by helices See if anything else is reliably found by the motif Repeat for other families and see if there are relationships between them Group these families together, then iterate

11. e.g. here we discovered unpaired G important

12. So how do we handle all this? A proposal Entities: sequence_region: a thing that defines a set of bases relative to some sequence (i.e. with indices for each base) stem: two regions linked by pairs unbroken_stem: two regions completely paired base: region that consists of single nucleotide base_pair: region that consists of two, paired bases canonical_base_pair: base pair that is cis-WW terminal_loop: contiguous sequence_region stretching from i to j such that i-1 and j+1 are a base pair internal_loop: unpaired region that interrupts one unbroken_stem junction: unpaired region that connects two or more stems

13. So how do we handle all this? A proposal (cont’d) Relationships: correspondence: relation among set of sequence_regions implying all share a feature (with metadata about how determined) homology: correspondence implying continuous chain of descent preserving the relation sequence_similarity: correspondence implying regions are similar in primary sequence two_d_structure_similarity: correspondence implying regions are similar in 2D structure, i.e. nested canonical base pairs secondary_structure_similarity: correspondence implying regions are similar in secondary structure, i.e. incl. pseudoknots/noncanonicals tertiary_structure_similarity: correspondence implying regions are similar in 3D structure

14. So how do we handle all this? A proposal (cont’d) Relationships: pairing: relation that asserts that two sequence_regions each have parts of at least one base_pair that connects them stem_pairing: pairing that includes several base_pairs (not necessarily contiguous) between two sequence_regions unbroken_stem_pairing: stem_pairing that includes no bases in the sequence_regions that are not paired with the other sequence_region, in order base_pairing: pairing that connects exactly two bases, annotated with the Leontis-Westhof classification More exotic uses for alignment: microrna_target: pairing relation in which one member is a miRNA and the other is an mRNA according to SO same_microrna_target: a relation among a set of sequences that have microrna_target relation to the same miRNA

15. Definitions Correspondence: A relation between regions of an RNA alignment, which can occur between molecules or within a molecule. These relations are reflexive, symmetric and transitive. Region: Consists of a single RNA nucleotide or a set of RNA nucleotides. Regions can be continuous spans of nucleotides or discontinuous collections of contiguous spans. Single base pairs, terminal loops, junctions, etc. are all examples of regions. Homology: A correspondence that implies descent from a common ancestor with evolutionary continuity. Similarity: A correspondence that can be defined in terms of a quantitative measurement, typically at some structural level. Sequence similarity: A similarity defined at the primary sequence level, e.g. 95% sequence identity. Secondary structure similarity: A similarity defined at the secondary structure level, e.g. 50% of base pairs in common. 3D structure similarity: A similarity defined at the 3D structure level, e.g. 3 Angstrom RMSD. Basepairing: A relation between two RNA nucleotides, defined by base-base hydrogen-bonding interactions. Function: The properties of a biological entity for which it is maintained by evolutionary selection

16. Acknowledgements RNA Alignment Ontology working group: James. W. Brown Fabrice Jossinet Rym Kachouri B. Franz. Lang Neocles Lenotis Gerhard Steger Jesse Stombaugh Eric Westhof Other coauthors: Amanda Birmingham Paul Griffiths Franz Lang NSF RCN grant # 0443508 Knight Lab members: Cathy Lozupone Micah Hamady Chris Lauber Jesse Zaneveld Jeremy Widmann Elizabeth Costello Jens Reeder Daniel McDonald Anh Vu Ryan Kennedy Julia Goodrich Meg Pirrung Reece Gesumaria Tony Walters Bob Larsen Trp project: Irene Majerfeld Jana Chocholousova Vikas Malaiya Matthew Iyer Mike Yarus