Maximal Exact Matchings similar to (local) alignment, but only - - PowerPoint PPT Presentation

S.Will, 18.417, Fall 2011

Maximal Exact Matchings

• similar to (local) alignment, but only identify parts that are

exactly identical (no gaps)

• exact matches must be connected at sequence or structure

level

• faster than structure alignment: O(n2) time & space

S.Will, 18.417, Fall 2011

Maximal Exact Matchings

expaRNA: compute alignments fast with help of exact matchings

• step 1: compute matchings
• step 2: chaining of matchings (select “chain” of compatible

matchings, i.e. no overlap, no crossing)

• step 3: compute alignment using chain of matchings as

anchor constraints in LocARNA → speed-up over LocARNA Steffen Heyne, Sebastian Will, Michael Beckstette, Rolf Backofen, Lightweight Comparison of RNAs Based on Exact Sequence-Structure Matches, Bioinformatics 2009

S.Will, 18.417, Fall 2011

Bralibase 2.1-Benchmark

20 40 60 80 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Sequence Identity SPS reference Locarna ExpLoc−P (heuristic) ExpLoc−P (suboptimal) ExpLoc (minsize10) ExpLoc (minsize9) RAF

• ExpLoc: Exact matchings as anchors in LocARNA; 4.4, 5.4 times

speed-up

• ExpLoc-P: Exact matchings from structure ensembles as anchors in

LocARNA (submitted RECOMB’12; speed-up: 4.9, 6.0)

• RAF: Do et al., Bioinformatics 2008; speed-up 15.9

S.Will, 18.417, Fall 2011

Whole Genome Realignment for ncRNA Prediction

whole genome alignment

1

stable loci

realign based on sequence and structure

realigned loci

2 3

slice and filter by thermodynamic stability

• f single RNA structures

estimate ncRNA likelihood

predictions (q-values)

unstable conserved structure of original locus alignment stable conserved structure

............................................................ DroMel_CAF1

• ----------------------TTTGAGTG-TTTCTTGTGTTCATTAAGGTTTAATGAA 36

DroSim_CAF1

• ----------------------TTTTAGTG-TTTCTTGGGTTCATTAAGGTTTAATGAA 36

DroYak_CAF1

• ----------------------TTTGATGG-TTACTTTGCTTCATCAAGGTTTAATGGT 36

DroEre_CAF1

• ----------------------TTTGATGG-TTTCTTTGCTTCATCAAGTTTTAATGAT 36

DroPse_CAF1 GGGCCATGGCCTCCTCTGATCGATTAG-GGGTTTTCTTGCTTGATTTATCGGTTGATGGA 59 DroPer_CAF1 GGGCCATGGCCTCCTCTGATCGATTAG-GGGTTTTCTTGCTTGATTTATCGGTTGATGGA 59 .........10........20........30........40........50......... ..((((..((.((....((((........))))....)).))..))))............ DroMel_CAF1 TCTATGGAGCGAGTAATGCGCTTGAAGCTGTGTTTATCTGGTCACATGTAT---TGA--A 91 DroSim_CAF1 TCTATGGAGCGAGTACTGGGCTTGAAGCTGGGCTTATCTGGTCACATGTAT---TGA--A 91 DroYak_CAF1 TCTATGGAGCGAGTATTGGGCTTGAAGCTGTGTGTTTCTGGTCGCATGTAT---TGA--A 91 DroEre_CAF1 TCTATGAAGCGAGTATTGCGCTTGAAGCTGTGTGTTTCTGGTCACATGTAT---TGA--A 91 DroPse_CAF1 GCAATGGGGTG----ATGCTAGTGA--GTGGGTGATTCTGGCCATGGCCATAGGTGAATA 113 DroPer_CAF1 GCAATGGGGTG----ATGCTAGTGA--GTGGGTGATTCTGGCCATGGCCATAGGTGAATA 113 .........70........80........90........100.......110........

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ U U U G A G G G _ U U U C UUGGC U U C A U U A A G G U U U A AUG A A U C U A U G G A G CG A G UA A UG C G C U U GA A G C U G G G U G U U U C U G G U C A C A U G U A U _ _ _ U G A _ _ A G U U A U A C G G C G G G C

structure- based realignment − − −− →

.((((.......((((((.((((((((((((......))))..)))))))).)))))).. DroMel_CAF1 UUUGAG------UGUUUCUUGUGUUCAUUAAG---GUUUAA--UGAAUCUAUGGAGCGAG 49 DroSim_CAF1 UUUUAG------UGUUUCUUGGGUUCAUUAAG---GUUUAA--UGAAUCUAUGGAGCGAG 49 DroYak_CAF1 UUUGAU------GGUUACUUUGCUUCAUCAAG---GUUUAA--UGGUUCUAUGGAGCGAG 49 DroEre_CAF1 UUUGAU------GGUUUCUUUGCUUCAUCAAG---UUUUAA--UGAUUCUAUGAAGCGAG 49 DroPse_CAF1 GGGCCAUGGCCUCCUCUGAUCGAUUAGGGGUUUUCUUGCUUGAUUUAUCGGUUGAUGGAG 60 DroPer_CAF1 GGGCCAUGGCCUCCUCUGAUCGAUUAGGGGUUUUCUUGCUUGAUUUAUCGGUUGAUGGAG 60 .........10........20........30........40........50......... .....((((.(((((....)))..)).)))).......))))........... DroMel_CAF1 UAAUGCGCUUGAAGCUGU-GUUUAUCUGGUCACAUGUAUUGA----------A 91 DroSim_CAF1 UACUGGGCUUGAAGCUGG-GCUUAUCUGGUCACAUGUAUUGA----------A 91 DroYak_CAF1 UAUUGGGCUUGAAGCUGU-GUGUUUCUGGUCGCAUGUAUUGA----------A 91 DroEre_CAF1 UAUUGCGCUUGAAGCUGU-GUGUUUCUGGUCACAUGUAUUGA----------A 91 DroPse_CAF1 CAAUGGGGUGAUGCUAGUGAGUGGGUGAUUCUGGCCAUGGCCAUAGGUGAAUA 113 DroPer_CAF1 CAAUGGGGUGAUGCUAGUGAGUGGGUGAUUCUGGCCAUGGCCAUAGGUGAAUA 113 .........70........80........90........100.......110.

UU U G A A _ _ _ _ _ _ C G U U U C U U C G A U U C A U C A A G _ _ _ G U U U A A _ _ U G A A U C U A U G G A G C G A G U A A U G G G C U U G A A G C U G U _ G U U U A U C U G G U C A C A U G U A U U G A _ _ _ _ _ _ _ _ _ _ A U G G C G U A U G U U C C G U A A U A U A C C G U A

S.Will, 18.417, Fall 2011

RNA Shapes : Idea

• A more coarse-grained look at RNA structure
• intuition: often general shape of RNA is more important for

RNA function than “details”

• example: cloverleaf structure of tRNAs

Shape can be considered at different levels of abstraction Robert Giegerich, Bj¨

• rn Voss, Marc Rehmsmeier, Abstract

shapes of RNA, Nucleic Acids Research, 2004

S.Will, 18.417, Fall 2011

RNA Shapes : different Shape Types

• 5 Most abstract - helix nesting pattern and no unpaired

regions

• 4 Helix nesting pattern in internal loops and multiloops
• 3 Nesting pattern for all loop types but no unpaired regions
• 2 Nesting pattern for all loop types and unpaired regions in

bulges, internal loops, and multiloops

• 1 Most accurate - all loops and all unpaired

RNAshapes: Computes shape probabilities for a sequence (+ Shrep = representative structure for each shape)