Hunting RNA Motifs

Hunting RNA motifs

Paul Gardner

July 23, 2012

Paul Gardner Hunting RNA motifs Classiﬁcation problems

I What is this? I The second most abundant M. tuberculosis transcript I No hits to Rfam, no homologs have been identiﬁed, no known function

Mycobacteria tuberculosis genome: RNA−seq and annotations

7e+05 +ve strand R ank R eads Gene −ve strand 6e+05 1 5,741K SSU rR N A

2 689K sRNA/RUF? 5e+05 3 236K R N aseP R N A

4e+05 4 197K LSU rR N A

5 119K AT P synthase 3e+05

Read counts 6 119K HSP X

2e+05 7 115K tmR N A

CDSs ncRNAs 8 103K Secreted protein 1e+05 Pfam domain terminators

0e+00 Arnvig et al.

Rv3661 miscrna00343 (2011) PLoS HAD Rv3662c Pathog.

4099500 4100000 4100500 4101000 4101500 4102000

genome coordinate Paul Gardner Hunting RNA motifs Classiﬁcation problems

I What about this? I The seventh most abundant N. gonorrhoeae transcript I No hits to Rfam, no homologs have been identiﬁed, no known function

Neisseria gonorrhoeae genome: RNA−seq and annotations

R ank R eads Gene 80000 +ve strand −ve strand 1 206K R N aseP R N A 2 178K tR N A -Ser 60000 3 130K ZapA 3‘ U T R ?

4 120K P hage protein

40000 5‘ U T R ? 5 97K tmR N A Read counts

6 93K BRO protein 20000 CDSs ncRNAs 7 81K sR N A ? Pfam domain terminators 8 81K Hsp70 protein

0 9 76K tRNA-Asp

NGO2161 terminator12061 terminator12063 Inositol_P terminator12062 NGO2162 terminator12064 Isabella & Clark SEC−C (2011) BMC Genomics. 2137500 2138000 2138500 2139000

genome coordinate Paul Gardner Hunting RNA motifs Classiﬁcation problems

I And this? I The ninth most abundant C. diﬃcile transcript I No hits to Rfam, no homologs have been identiﬁed, no known function

Clostridium difficile genome: RNA−seq and annotations R ank R eads Gene

1 12K R N aseP R N A +ve strand 4000 −ve strand 2 12K 6S R N A 3 12K SR P R N A

4 11K ldhA 3000 5 10K tmR N A

6 6K spoV G 2000 7 6K Gly reductase Read counts 8 5K slpA

1000 9 4K sRNA/RUF? CDSs ncRNAs Pfam domain terminators 10 4K hypothetical prot.

11 4K sRNA/RUF? 0

terminator0881 Deakin, Lawley toxB toxB toxB terminator1760 trpS et al. (2012)

3014000 3014500 3015000 3015500 3016000 3016500 3017000 Unpublished. genome coordinate Paul Gardner Hunting RNA motifs Classiﬁcation problems

I And this? I The eleventh most abundant C. diﬃcile transcript I No hits to Rfam, no homologs have been identiﬁed, no known function

Clostridium difficile genome: RNA−seq and annotations R ank R eads Gene

4000 1 12K R N aseP R N A +ve strand −ve strand 2 12K 6S R N A 3 12K SR P R N A

3000 4 11K ldhA

5 10K tmR N A

6 6K spoV G 2000 7 6K Gly reductase Read counts 8 5K slpA

1000 9 4K sRNA/RUF? CDSs ncRNAs Pfam domain terminators 10 4K hypothetical prot.

11 4K sRNA/RUF? 0

Deakin, Lawley feoA2 terminator1637terminator1105 acetyltransferase et al. (2012)

1760600 1760800 1761000 1761200 1761400 1761600 Unpublished. genome coordinate Paul Gardner Hunting RNA motifs What is our next big challenge?

I Past:

I How many non-coding RNA genes are there? I Future:

I Can we determine the functions, if any, for large sets of given RNAs?

Paul Gardner Hunting RNA motifs Will a “periodic table of RNA” be useful?

I My aim is to build an analog to the Periodic Table for classifying RNA families and motifs, enabling researchers to predict function.

b ase b asep air

YR R R R UY A R A R Y G UC R R R G U C A A G A U R R R C G C U G U G A Y R Y G C G C G G C R Y R C G R Y R C A A G G U A G C A G C A G A A G R Y R Y G G A Y C U A Y C G A U A U R R R Y R C G R U R C G 5´ 5´ 5´ 5´ 5´ 5´ 5´ 5´

ANYA GNRA UNCG T loop U t u r n k t r n 1 k t r n 2 tw ist

R Y R R C Y A Y Y G R R R R G R Y G A A R Y G G A G A A U A C G G C G R A U G A Y Y R G Y A C G A Y R A Y R Y R Y C U A G A G A G R Y G U Y R A Y R Y A A R Y C G A Y R A Y G Y G C G Y R Y G C C U A R A U R Y A R C G R Y R Y R G C C A U Y A U G U A U A U C G R Y A U A U R U Y Y G R 5´ R Y A U R U 5´ R GA R RA 5´ 5´ 5´ 5´ 5´ U Y U Y 5´ U UU Y

sar r ic1 sar r ic2 UAA GAN C sr C loop d om V t er m 1 t er m 2

Y G Y C U R Y G G Y R Y Y Y U U Y Y Y Y A C R Y R Y Y A Y C C Y U G R Y R R C G Y U Y R C G C G C GU C A R A R A Y A Y R Y R A C Y A U G Y Y A Y R G R R G C Y R A G Y R C G G U U G G R Y Y R Y U A A U A R G R R Y R G C Y G U Y R Y Y G A Y C A Y Y R A U C Y C Y R Y Y R Y A A U U G A U U C G C A U C A U U R G C R C Y G R G R A U U A U Y Y R A Y G U G C U R A U G C U A G C U A C Y G Y A U Y R Y C U A U A G C G G A U G C U A A U G U A U U A A U C C G Y R U A U C G A Y Y A Y G C U A C G U R U Y G Y Y A U A U A C G U A U R R A R C G G C G C U A R C R A U U A A U U A R U A G C R Y Y R G C G A U A U A U C G Y R Y R G C G Y A U A U Y Y Y G Y R U Y A A U R R U R R Y U A U R Y U G C C G A U C G U A U A G C A C G G C G C C A Y R R U A G C C G G A A U A C C G G U A U G C G C A Y C R A R R A U U A U A U A U R R G A U A R G G Y Y A U A U C Y C A R A A A Y Y Y G C G C A U G C Y Y G U R G C R R A U U G G C U C Y G R R R C G Y A C G U R R G Y Y R U G C G U A G C C Y G Y C A U U A U A Y Y Y R Y R G R R A U C G A U U A Y U C G Y C Y G U U A G Y R G Y R R Y G C G Y Y Y C Y Y A R Y C G Y R C R R Y U A C G U A A R C G Y Y R G Y Y G R C G C G R C Y G G Y G C C G G R R R Y G R Y A U Y YY GY R C Y R G C G U R U R Y G Y U C R R U A U U Y R A U C Y G U U A G C U A A U R R G U R Y U U Y U A G C R Y R G C Y Y G G U A G C G C G R A A G A A G C G A A U U G A U U A A R A G G Y R R Y G C YG C A U Y G U A Y C C U Y C G U A G C A U R G U Y A A C R G C C G C G G C C G U G U R Y R C Y C Y U U U C G G U C G C Y R R R U Y R G U R A U C U A G C U R U Y R A C Y G G R Y R R A C G U A U G C G A G G U A A U U A G Y A G C R C U A C G R C A U U Y U C Y U A U A G Y U A R A G R Y G U A Y Y G Y A U A U U A R U G R R Y G G A Y C G U A U A R A U R G A C Y R U U A A U R C Y U R Y G A G C C G A G C Y G G C G U C G Y C G A U U G A A Y U G A U C G C U A U G C C G A U C U U Y R U A U G C G U C R C U Y U A C G C R Y R Y Y G A A R C G U A Y C G C G A U U A A U A U A U A R A U C G A C C G U A G C G Y U Y G C A U G U C G R R C Y G C Y R U A A U A U C G U A C U C G U A U G C U U G C G R A A R R A U C R Y G C G C A U A Y A G C A U C G U A Y R Y U A A U U A R Y R U C A C G G U G C G U R Y A A U C Y R G C U G R Y A U Y R R Y Y R Y R G C A U A U R U G U G C U R Y R C G Y R U A Y G Y R Y Y Y R G C U G U A R U C G U G Y R G C G C Y A C G A U C G Y G C R C G G C Y R Y R G Y R Y 5´ G C G C U A Y A A U Y Y C G G C Y R Y R G C Y 5´ 5´ AC 5´ U 5´ UC R U 5´ Y R R Y GY UYR R Y 5´ R 5´ R YY YY RRR R 5´ 5´ GC 5´ CY G Y C G G 5´ Y YY Y R R G Y YY GGY 5´ Y AA R U GG UY 5´ 5´ U U U Y 5´ Y 5´ YY 5´

TRIT IRE SECIS m ir -T A R m ir -30 m ir -9 lin -4 m ir -5 m ir -8 m ir -1 m ir -2 m ir -6 let -7 Y RNA 6S 5S t R N A R N aseP

AA U AA U A A CC C U Y Y C U

U U Y Y Y G C C C Y G CG AG Y Y Y R C C G C U YY Y G A C G RR

GG U C CU Y G C C R R C A G A R U G C A U G C C G C U R Y U A U C R GR C G C G U A G C G C G G C U G G U 5´ Y R U U UUUUUU U A G R GR G CA CGGG A

G U A U C U G C G CA G C U G C Y G C G C CC Y G GG A C R R C C A U C G G Y Y Y C A G G CGAAU U A U R A G C A U U U GG R Y R A A C Y

A A A U C Y Y R U C U U C G U A A U R Y G G C A U A G G Y G U A A A U C Y C G C C Y C C U C R Y U R C A C U A G C U G A R G C C G U U U A A U C C U G C G A G U A C G C A R R Y G A C Y A C A A C G U G C G U Y R G C U G A C G Y U A U R Y G U G A U U G A C G U U A U R U U G C G U A U Y C G Y R Y G U A C U U Y R Y ACUU UGU U G U R U

G G C Y G U G C C G A G C C G A GCUAAAR C C C C C C C GCUAAAGCACUAAAAA G G G A U U A U A G A A C G A C G U C A C C A R U A A C C G Y U C A U RR C R C C G C G A U U AG Y C G G C U A G C A U C C G G G G G G Y C C G A G A U A G U G ARA A C U U A C A G C U A Y G C A U G C A A C G U A Y G C C A C A U A G C A A R A G C A U AA A A Y C C G U R Y U U U G U G Y G C A G U R U A Y U A C G U R Y A C U U U U U A C C A A G U Y G U G A U G C C U G A U U G G C G R C R C G G A C A U A G CUCCC G U U U U Y U R U U R A A A R C A G C C G R G C A R Y R C C G G R U A GA C G C

C R U C U G G U U A G U U U A A U U UGCAU A C G

C G A U A G G Y U G Y G A C G A G U Y C G U A C G G U R A Y A G R G G C C G C C R C C U A C A A A C G 5´ UU

G G C

C A U G C A U A G C R G R G Y C UUAAA A R G C G A U G C C U

U A U C G C A U U A U C Y

A Y C C C

G G Y Y A R G A CG GCC C C C GC G G U A G Y U G U A C G R G C C G G Y A C U G G C G C R C G Y A Y U

A U A C Y A A C U G U U G G G U U C G G G A R U A U G U G G G C R U G C C G A Y A U G C Y C A C A G U

C C G A U C G C G

G U G U

G C C C C G G C U A C G U G

YC Y G A A U A U C G A U G C A U A Y AR CGC Y C U GYCG G G G G GC G C U A U U R U A G

A U G U A G C U A C G U G C C G G C A U Y C G C Y R C G G Y C RY RR U A R C U C G A U U A U Y GCG U A RYU A U A G R U G C C G Y C G U C G A U U G Y A U Y R A U C Y GA A U Y C A U A U C R U U U U U A

YY C G A U

A A U G U A U G C C G A G C C G G C C G A U R R Y G C U YG R U R U Y RY A Y Y A A G C GA U U U A 5´ A G C U 5´ GA U A G AC A GAUUU U 5´ A AUAAY A Y R 5´ 5´ YY R G RUA 5´ CGYA C 5´ C AAC R UU 5´ A U G AUU UU A C U 5´ Y RG RR R UUU 5´ 5´ R G U U 5´ YURCGUGACGAAGC A UUUU Y 5´ UUG G CG A UR U A UA A A 5´ 5´ 5´ U

SAM V sy m R C P E B 3 F in P sr oB m sr SAM a HH 3 V m nt n 3 liv K D sr A CAESAR isr K sr oD isr B 6C r sp L su h B G R C G A Y Y U R R G G G A Y Y A C R R C G A G Y RA Y

U Y G U C C C A A UUUUUUUUAUGUC G U

C U C C G A G G G U G C U C C C G G

A C U C A CGCA U A C G

A C G U C

G U A C G A CC C G U G G G A A C

U U C R R U A U U U Y A U U U C U G G G Y A A U G U A U A U U G A Y C A U U C A U Y Y U Y UUUUUU U Y G CU

C U A

C U C U U U U G G R Y C U U U U

A G G G C G A Y A G

R U Y A C U U U G R G G G G G G U C A U A A A U Y U G C C G U A A A A C C R C C U G Y A A A C U C C C C C

G G Y R G A U U C Y U C C Y Y A U Y A C C C G Y G G A R Y U A U U R C G A A Y G A A U G

C G U A U A U G G Y A A C A U C A

C A CCGG U G C U G Y G U A U A

C G C C C C G G G A A U U A U U C A U G C G Y R G A U G A G Y A C G G C

A R

C G C C U R Y G G G G U A C C G G U A A Y Y R G G C A C G R G G G C U A C U A U C R U U U AUUYCCCU C U A U A C C G G C A U G A UCCUCUU U Y G A A G C C U A C A G G C A A U YG C U U C G Y R U AUCR

U C Y

C A A A A A C C U

A G U C U C A 5´ G U A U G G U A C G A U A U C AR G C U C U GGR A R Y U G U Y Y R GC C A U U U U A C UUCCCU

A A A Y R G U A Y Y U C U G G G GYYU A G CCR Y U C U A A YAU A R Y G A U A U A G C R U C G Y G R Y U A C G G A

U U C U U A U A A A G G A U G G CAGUAU R

U U R Y C G A U A A

Y A C C U A G G C CR U A U A

U A U A G G G A A A U U G C G C G C U U G U U U A U C U G G C A C G U U A U Y G A A U G UR C

G CA A U U U A G A C U A U C U A Y G C UAGCGCAC C R C R R C C R A G G G U A A A R C G A U A C U C C G U R U U AA A A G A U A A A U G C G C U U G

A U Y Y Y C C G G G G G U G A G CA UU Y Y G C A

A C U U G U C R U C G U U A U G C Y Y C G G C U U U CGUU

Y R C C U A G R Y C Y R Y U A U A A C U C G A A G A Y G A G R C G G R C G A Y A U Y G G A G U U Y A Y Y G A Y A A UUAU G U A G G U R R C C A R R C G G A G G U A C G C A U A U U U A R Y R U C R C U C C C G C A A G 5´ CCCAGAGGUAUUGAU U C C

Y U Y U Y

U A A C U U U C U C G G G G G G R U G G CA R C R A A A U A CRRYGRGY G C A C U R R U C G A U A A R C Y Y R R G C R C C A A ACCU G CGC A U Y Y C U G C A U G C C G Y U A G Y G A U G C G R G A R A U U A C A U A G G U C C R

G Y R AAA G G Y A Y U UU

A A U C C U U C Y CC C C A A U R U U A A U A A R U U U G C G G A AGGU C GCG A U Y R R Y A A C A A C C Y G Y U C C C U U C G G G G AU G U U A A A U G G A U C G G U G A U G U A C G C U C A C G U R A A C A U A U R Y A R Y G G C U AA A A Y C U A U A C G C C G A G C A C A G C U Y U Y Y G R U A U A A U U A C U U R Y

R Y YYA G G R Y Y G U A A U C G R Y C R Y G G U R U A A U UU A U A U A U C A U U A G GGU AG R R C A G C G C G C C G U Y C G U A C G G A U U G G U A R C Y A R G C U A U A U R A U U CUU U A

U R Y Y A CRRYCY C G U A G U G C R G C U R A U G C G C G U Y Y C G G C 5´ U G C G C C G R G Y G C R C G G U U G C G C 5´ Y R G C U R R A U A GU Y G U A 5´ G AUUU C UU U R A R R 5´ ARA CGA 5´ G C 5´ U 5´ C C UU A U U 5´ UGC G A GG CY 5´ Y R Y YC A Y U UUUU U

5´ A 5´ 5´ RR R RRR RR R R G 5´ UGUA AAA A ACAU YAUUU C AAGCA 5´ * 5´

su cA S r aD sx y RNAI P u r in e S A M -C h l cd iG M P 2 A nt i-Q G ad Y r n k ld r P r fA O m r A -B R yeB t r aJ 2 S r aH 23S m et h D S -p ep ARG GG A G R C U U U U G G G G G Y A

R Y A A A C C C C C G G Y R Y C

C G U U R G A R C A Y A G A UY Y C U C A Y U A A U A A A C A A Y A R G C A U U C G A G U C U G U A U CU G U C U G A C A C U AU

U A U A G G A U A U Y G C G C URR C A U C C G A G C A C A U C G A U G C C U C G G A U G G Y U C G C C U A U Y G G C Y G C G C G A A R G G G A U A C G A U C C U R Y U C G C UUU U A A U C R C C G G Y U C C U U U U U G G G A C G G U G C U G A U U G G G C C C C U G G R Y U G U A C A A C

G A A A A A A C C C C G G G R G U Y G C C A G C U A G GGAUGUCAGGR Y C C 5´ Y R A C C R U U G C R R G U G G C C G A C C Y U A CC Y C A A C C C C A U U A U U G Y Y A Y G G G A G A

A R Y Y G C A R A U Y U C U U G C G A G AAA R G A Y A R U C U C U R U C C A G Y A U A A G G G R A G U U G U A C A G C Y G G C G U Y C A R U C U U A A U A R AU A U A G G R C G C U U Y C G U U U Y C A G C G G A A U C G C U U U C G C Y Y U G U G G Y G G R A A Y U U U G G C A A

U A

C C C U Y G G C Y G U G G A C G C R U A A A A A C A G G C U C A A C Y R U G G

Y A U U Y Y R Y U U G A U G G UA A A C C G U G C G C C A Y C Y U G U A A

A U G C YY R U Y G U U A A G UAUC R C G G Y R C C G A G C C A A R C U R A U A C U U U A Y Y R A U C C U U A U A U A G G U R G A C U G G G U A C A R Y G G R G C Y A UU A A RUU G C U U G U G R Y Y G U YA A A U R C C U C G U A U A A U U A U Y G U C G C A C A U A U A A U G Y A C U Y C U U C C U Y R R R R R C Y A U U U U G G

U U A U C U Y C Y G G U G C U A A G G C U U G

C U Y UC C G Y A U A U C U A G C C C C A G C A A U U C C C U G C G C Y G A G C A U Y Y G R Y G C UR G U G C G R Y U A U G G A C A C R Y A C G Y Y Y Y Y R G C U A R U C A A U G G G G Y Y A U U A A U A UU U G G AR A U U G Y C A G A G C G U A U A A A Y R G C G C G C U A Y A A U A A Y G C G C A U A C U U C A R G C A G C G A U U A R G C A U U G U A

G A U C G C G Y G G C C A G C C C G U U A U Y C C G C G C G C Y G A G C A G C U C U R U A A U C G U A A C G C G Y G A G C G U G R A C U U A CAG A G R Y G C G U A U U A G C G U U CA U U U G Y

R A U U U C G C G C G C Y R C G U R U A U G

U A A U G C A A A U U A A U Y R YU G A C C UGG G A U U R CU A A

A G A

G A A U U G

Y R C C C C G R Y C G U R C U G G R Y C G U C G C G A U C C G G R Y U CCGGG U Y G U G C C G G R G G C A U U A U A C A C G U A G C A C G U A A U U A G U R Y Y Y R R A U G C U C G C G G C A U C C G U Y R R Y A GUU R Y A U U U R U A C G A U G U A U C G G C G U C G Y A U G G Y G C G C G C Y G C G C G C Y A U G C G C U A C G G R U G Y C G U U A U A C G

C G R U U A U A U U A G G A Y

R C U A G C U A U A R Y U A CG 5´

A U G Y U 5´ U A U R C G U 5´ U 5´ UUCGG C CYC CC U CUGC AY 5´ C R A R Y YR G R R Y YY G C UUU UU 5´ CC Y GY CUYY G AC GUY U 5´ CCUAGCCAACUGACG G CU U UUUUUU U 5´ CUUAAU CA C A 5´ C AGAG C AUA U U CU UUC G C A U G C C G Y 5´ UC R AAAGAACA AAU 5´ Y Y Y A CC Y A 5´ C Y GG UUG GY R YU R Y RR Y AG ARR R A YA CC UUUU U U 5´ U R U G C U A A C URR R AA Y C CAUAUUAYA AAAGGAGAA AUG 5´ ** 5´

P s-R h o r n k p s M gsen s t R N A S Q r r isr C HH 1 S N R 24 T r p ld r gr eA p r eQ 12 H A R 1F T er m L eu M icC C 4 R sm Y R ib osom e

Paul Gardner Hunting RNA motifs What is a motif?

I Wiktionary deﬁnitions for motif:

I A recurring or dominant element; a theme. I For the purposes of this work:

I an RNA motif is a recurring RNA sequence and/or secondary structure found within larger structures that can be modelled by either a covariance model or a proﬁle HMM.

Escher, MC (1938) Sky and Water 1.

Paul Gardner Hunting RNA motifs What is a motif?

I The kink-turn RNA motif

I Found in rRNA, riboswitches, RNase P, snoRNAs, ... I An asymmetric internal loop I Causes a sharp kink between two ﬂanking helical regions

Cruz & Westhof (2011) Sequence-based identiﬁcation of 3D structural modules in RNA with RMDetect. Nature Methods.

Paul Gardner Hunting RNA motifs Can we build a seed alignment and CM resource of these motifs?

I For the hairpin motifs?

I YES I For the internal loop motifs?

I MAYBE I For the sequence motifs?

I Not certain yet k-turn-1 k-turn-2 GNRA Y R R R R Y G A R G G A R Y R Y G G C R Y R Y R R G U A G A G G A A G Doshi et al. (2004) Evaluation of the suitability of free- G G A C G energy minimization using nearest-neighbor energy pa- Y rameters for RNA secondary structure prediction. BMC R R Bioinformatics.

R 5´ 5´ 5´

Paul Gardner Hunting RNA motifs The RNA Motifs: RMfam

ANYA GNRA T-loop UNCG U-turn U Y A R A A A G A R R R U C U R G C U U G G A Y R C G C G A G C U A R Y R Y A U R R U 5´ 5´ 5´ 5´ 5´

k-turn-1 k-turn-2 pK-turn sarcin-ricin-1 sarcin-ricin-2 tandem-GA UAA_GAN C-loop Y R twist_up R Y R R R R R C Y A Y Y R R Y G C A A G R A Y R G R Y G A U A A R Y R C U C A Y G G C G U U A C Y R Y G C C G C R Y R Y A G C G C G R R G U A A U A A G R Y G G C A A G A U A A Y R G A A G R G A C A G A G G Y R A A Y R C G R Y Y C A Y R A R A Y A A U Y G C R R Y R Y A R C U A Y R Y R Y G C R Y R R Y R Y A R R Y G C C R Y C G Y R R Y C G A R A U R R Y C G A G G G A C G Y G C A G U G Y R 5´ R 5´ 5´ A G 5´ R A R A A 5´ R 5´ 5´ 5´ G C 5´

domain-V SRP_S_domain vapC_target terminator1 terminator2 TRIT R Csr-Rsm R A R G A G A R Y R Y R G R R Y G A G A C G G G C G C G A C C G A C Y R R Y Y C A C G A Y G Y R C G Y R Y A U Y R R Y A U G C G R Y R Y R Y Y G A U C Y C G C G A A C G C Y C G G Y C G R R G A R Y A U R C R A U R Y G C C G R Y A U A U R Y C G G U A U A U G C A U R Y A U A U R U G C G Y C G 5´ R Y Y A U R U G C 5´ 5´ 5´ A U A U G Y C 5´ U Y U Y 5´ U U U Y 5´ R Y Y Y Y R R R R 5´ R R A R G G R G R R Y A U G R R

5´ R R G G R R R Y A U G A R R

5´ A A G G R R A U G R

Paul Gardner Hunting RNA motifs Breaking Rules

I Prefer motifs found in multiple families &/or multiple locations in the same family

I Aligning analogous structures

I Motifs are allowed to overlap I Models are non-speciﬁc

I High false-positive rates I Sequence sources are I Sequences are not edited, diverse (PDB, EMBL & spliced or otherwise publications) interfered with

Paul Gardner Hunting RNA motifs An example entry

# STOCKHOLM 1.0 #=GF ID twist_up #=GF AU Gardner PP #=GF SE Gardner PP #=GF SS Published; PMID:21976732 #=GF GA 19.00 #=GF DR URL;http://rnafrabase.cs.put.poznan.pl/?act=pdbdetails&id=1S72 #=GF RN [1] #=GF RM 21976732 #=GF RT Clustering RNA structural motifs in ribosomal RNAs using #=GF RT secondary structural alignment. #=GF RA Zhong C, Zhang S #=GF RL Nucleic Acids Res. 2012;40:1307-17.

2zkr_Y/27-46 CCGAUCUCGUC-UGAUCUCGG #=GR 2zkr_Y/27-46 SS <<<<---<_____>--->>>> 2gv3_A/3-20 CCAGACUC---CCGAAUCUGG #=GR 2gv3_A/3-20 SS (((((.(.---....)))))) 3iz9_C/29-48 CGGAUCCCGUC-AGAACUCCG #=GR 3iz9_C/29-48 SS ((((...(...-.)...)))) 3bbo_B/31-50 CCAA-UCCAUCCCGAACUUGG #=GR 3bbo_B/31-50 SS .(.....<.....>.....). 1nkw_9/33-53 CCCACCCCAUGCCGAACUGGG #=GR 1nkw_9/33-53 SS (((...... ))) 1c2x_C/31-51 CUGACCCCAUGCCGAACUCAG #=GR 1c2x_C/31-51 SS ((((...<.....>...)))) 1giy_B/33-53 CCGUUCCCAUUCCGAACACGG 1S72_9/29-50 CCGUACCCAUCCCGAACACGG #=GR 1S72_9/29-50 SS ((((...(.....)...)))) #=GR 1S72_9/29-50 MT ...xxxxx.....xxxxx... #=GC SS_cons ((((...(.....)...)))) // Paul Gardner Hunting RNA motifs An example entry

# STOCKHOLM 1.0

#=GF ID twist_up #=GF AU Gardner PP #=GF SE Gardner PP #=GF SS Published; PMID:21976732 #=GF GA 19.00 #=GF DR URL;http://rnafrabase.cs.put.poznan.pl/?act=pdbdetails&id=1S72 #=GF RN [1] #=GF RM 21976732 #=GF RT Clustering RNA structural motifs in ribosomal RNAs using #=GF RT secondary structural alignment. #=GF RA Zhong C, Zhang S #=GF RL Nucleic Acids Res. 2012;40:1307-17.

R U Y C C # STOCKHOLM 1.0 C C G U A C #=GF ID 5S_rRNA G C C G C R #=GF AC RF00001 A A C G R U Y #... A R A #=GF WK 5S_ribosomal_RNA G C G R R Y G R Y G G G A U G #=GF SQ 712 C G U C Y C A R A C Y G G G A G Y U Y A U #=GF MT.G GNRA A Y G G R C C G R Y #=GF MT.s sarcin-ricin-2 G Y C G R Y #=GF MT.t twist_up Y R

5' X62858.1/62-115 GCUGCG-U-UGUGGGUGUGUACUGCGGUUUU-UUG-CUGUGGGAA--GCCCACUUCA-CUG X01588.1/64-115 UCACGU--UAGUGGG---GCCGUGGAUACCGUGAGGAUCC-GCAG-CCCCACU-AAG-CUG #=GR X01588.1/64-115 MT.0 ...... GGGGGGGGGGGGGGGGG...... X05870.1/363-414 UCACGU--UGGUGGG---GCCGUGGAUACCGUGAGGAUCC-GCAG-CCCCACU-AAG-CUG #=GR X05870.1/363-414 MT.0 ...... GGGGGGGGGGGGGGGGG...... L27170.1/63-116 CCAGCG--UCCGGCA--AGUACUGGAGUGCGCGAGCCUCUGGGAA-AUCCGGU-UCG-CCG #=GR L27170.1/63-116 MT.0 ..ssss..sssssss..ssssssssssssssssssssssssssss.sssssss.sss.ss. L27163.1/61-115 CCAGCGUUCCGGGGA---GUACUGGAGUGCGCGACCCUCUGGGAA--ACCGGGUUCG-CCG #=GR L27163.1/61-115 SS ...(((((((((...... )))))))))(((((((...((...... ))))).))).).. #=GR L27163.1/61-115 MT.0 ...... GGGGGGGGGGGG..GGGGGGG...... #=GR L27163.1/61-115 MT.1 ..sssssssssssss...sssssssssssssssssssssssssss..ssssssssss.ss. L27343.1/60-114 CCAGCGAACCAGCUA---GUACUAGAGUGGGAGACCCUCUGGGAG-CGCUGGU-UCG-CCG #=GR L27343.1/60-114 MT.0 ...... GGGGGGGGGGGGGGGGG...... #=GR L27343.1/60-114 MT.1 ....sssssssssss...sssssssssssssssssssssssssss.sssssss.sss.s.. M33891.1/66-117 CCAGCG--CCGGAGA---GUACUGCGC-GGGCAACCGCGUGGGAG--GCGAGG-CCGCUCG #=GR M33891.1/66-117 MT.0 ...... GGGG.GGGGGGGGGGGG...... X01484.1/62-114 GUCAGG--CCCAGUU--AGUACUGAGGUGGGCGACCACUUGGGAA-CACUGGG-U-G-CUG #=GR X01484.1/62-114 MT.0 ...... GGGGGGGGGGGGGGGG...... #=GR X01484.1/62-114 MT.1 ...sss..sssssss..ssssssssssssssssssssssssssss.sssssss.s.s.ss. #=GC SS_cons ::::::::::<-<<------<<-----<<____>>----->>----->>->:::::::: #=GC RF cCaGcG..cccggga...GUAcUggGGUggGcgAcCcucUGGgAA.CaCccgg.uCG.CUG //

Paul Gardner Hunting RNA motifs An example annotated Rfam alignment

R U Y C C # STOCKHOLM 1.0 C C G U A C #=GF ID 5S_rRNA G C C G C R #=GF AC RF00001 A A C G R U Y #... A R A #=GF WK 5S_ribosomal_RNA G C G R R Y G R Y G G G A U G #=GF SQ 712 C G U C Y C A R A C Y G G G A G Y U Y A U #=GF MT.G GNRA A Y G G R C C G R Y G Y #=GF MT.s sarcin-ricin-2 C G R Y #=GF MT.t twist_up Y R

Paul Gardner Hunting RNA motifs Uses for a motif DB (I): Improving a RNA alignments and structure prediction constraints

R U Y C C R U C C Y C G C C U A C U G C A G C Original Rfam Refined Rfam G C C C model model G C C R C G A A A A C G C A R U R G Y U A Y R A R A G C G G A C G

R R Y G R Y G G R G A U G C Y C G U C Y C A G R A GNRA A G C Y G G U A A G Y G C U Y U A Y R G tetraloop A Y G G R C Y G U A C C G G R Y C U C G R R G Y A A G G G G R Y G G U G Y G Y Y C C A G C G C G R Y R Y Sarcin-ricin Y R Y R loop 5' 5'

Paul Gardner Hunting RNA motifs Uses for a motif DB (II): RNA structural motifs

I The Lysine Riboswitch

R R U G A R

R A Y R 13% T-loop 21% GNRA Y R R G C Tetraloop A G G C G A 5´ 5´ R Y 11% U-turn 32% K-turn Y Y

A G A R Y R Y Y 5´ 5´ P3 R R R Y R R R Y Y Y R P4 C R G R Y R U Y R G R Y G A A U A A R G A A G C R R R G G G U Y R Y C G R R R R R R Y U Y U Y G C U C A Y G Y Y R A R R C G G G U R Y R R U A P2 G Y A A G G G P5 A G C G A U U A P1 G Y R Y 5´ R

Paul Gardner Hunting RNA motifs Uses for a motif DB (II): RNA structural motifs

I The Lysine Riboswitch

P2 P4 Actinobacillus pleuropneumoniae Haemophilus ducreyi P2 stem

Pasteurell. Haemophilus influenzae R R R

Y R Haemophilus somnus R G C R A G G A G Mannheimia haemolytica Y Kink-turn R R Mannheimia succiniciproducens R Pasteurella multocida 5´

R A Proteobacteria Vibrio parahaemolyticus U R G A C G Vibrionales G C Vibrio sp. R Y U-turn R

Vibrio splendidus 5´ Vibrio vulnificus Escherichia coli P4 stem Enterobac. Klebsiella pneumoniae A Pectobacterium atrosepticum G A Y R GNRA Serratia proteamaculans tetrloop Yersinia frederiksenii 5´

Thermoanaerobacter sp. R R R Clostridia U Thermoanaerobacter tengcongensis R Y T-loop Clostridium botulinum 5´ Lactobacillus plantarum Lactobacil. Lactobacillus brevis Firmicutes Lactococcus lactis subsp. cremoris Lactococcus lactis subsp. lactis Staphylococcus aureus Staphylococcus epidermidis Staphylococcus haemolyticus Staphylococcus saprophyticus Bacillus cereus Bacillales Bacillus halodurans Bacillus licheniformis Bacillus sp. Bacillus subtilis Listeria innocua Listeria monocytogenes Listeria welshimeri

Paul Gardner Hunting RNA motifs Uses for a motif DB (III): Functionally characterising novel RNAs

G G A R Csr-Rsm binding Y R motif Y G G A A C Y C G 5´ G G G C Y R Y R U I TwoAYGGAY sRNA: 216 homologues

R R A Y R found in Human gut metagenome G G Y G G G R A C Y C R Y A C G Y R G sequences, γ-Proteobacteria and A Y R Y G U Y A G C Y Clostridiales species

R I Weinberg Z et al. (2010) R Y ”Comparative genomics reveals 104 candidate structured RNAs from

Y bacteria, archaea and their Y R U A R metagenomes”. Genome Biol.

R Y Y R Y U Y R

5' Y R

Paul Gardner Hunting RNA motifs Csr-Rsm background

Toledo-Arana et al. (2007) Small noncoding RNAs controlling pathogenesis. Current Opinion in Microbiology.

Paul Gardner Hunting RNA motifs Uses for a motif DB (IV): Identifying and ranking ncRNAs of interest in transcriptome results

Distribution of motif scores on S. typhi ncRNAs

0.06 Known RNAs RNA−seq ncRNAs Shuffled 0.03 Density 0.00 0 50 100 150 200

Sum of motif scores (bits)

CDF of motif scores on S. typhi ncRNAs 0.8 K−S.test(Known RNA > Shuffled) => P=2.11e−37

CDF K−S.test(RNA−seq RNA > Shuffled) => P=1.47e−09 0.4 0.0

0 50 100 150 200

Sum of motif scores (bits)

Paul Gardner Hunting RNA motifs Uses for a motif DB (IV): Identifying and ranking ncRNAs of interest in transcriptome results

Distribution of motif scores on S. typhi ncRNAs alignments

Known RNAs RNA−seq ncRNAs Shuffled 0.0010 Density 0.0000 0 500 1000 1500 2000

Sum of motif scores (bits)

CDF of motif scores on S. typhi ncRNAs alignments 0.8 CDF 0.4 K−S.test(Known RNA > Shuffled) => P=8.15e−06 K−S.test(RNA−seq RNA > Shuffled) => P=0.53 0.0

0 500 1000 1500 2000

Sum of motif scores (bits)

Paul Gardner Hunting RNA motifs 80 highest scoring matches between RMfam & Rfam

TwoAYGGAYPrrB_RsmZ

RNaseP_archIntron_gpII RNaseP_bact_b CsrB GEMM_RNA_motif

U6 IRES_Aptho Qrr ● ● ● ● ● rimP ● ● Thr_leader alpha_tmRNA ● Phe_leader ● ● ● L20_leader ● ● ● L10_leader Intron_gpI ● group−II−D1D4−7 ● His_leader ● ● suhB ● group−II−D1D4−6 ● ● RNaseP_bact_a ● ● ● ● GNRA mascRNA−menRNA group−II−D1D4−5 ● tRNA ● ● ● ● ● tandem−GA tRNA−Sec group−II−D1D4−3 Csr−Rsm ● ● Domain−V cyano_tmRNA ● sarcin−ricinTerminatorT−loop ● ● ● k−turn tmRNA group−II−D1D4−1 twist_up ● ● group−II−D1D4−4 ● MOCO_RNA_motif ● ● SRP_S ● Entero_5_CRE manA ● ● FMN ● UNCG ● ● ● U−turn Cobalamin ● ● RtT ● Clostridiales−1 ● SSU_rRNA_eukarya ● serC ● SSU_rRNA_archaea ● ● SSU_rRNA_bacteria T−box ● 5S_rRNA ● ● ● ● Termite−leu ● ● HIV_FE SAM ● ● C4 ● ● ● U3 wcaG SNORA73 Plant_SRP

Archaea_SRP

Fungi_SRP

Metazoa_SRP

Bacteria_small_SRP

Bacteria_large_SRP

Paul Gardner Hunting RNA motifs What about the highly expressed ncRNAs?

C C C A G A U M_tub_RUF3 R R A G N_gon_RUF7 R G A U U G A C G U R C G C G Y R Y A U G C R A U R G C U A A G C G Possible Rho-independent G A Y A A U terminator G G Y A G C C G Y A U U R R C C C G C U A R Possible K-turn G C A G U C C A U R R A C Y C G G C A U 5´ C G G C R A Y U U C G C R U G A U U Y R A U G U C G C R U U G C C G U U A R G G A U Y C C Y G A C G A G C R U U G C C C A C G 5' UURR CC UUG U G U G Y R G G UC G A GUA C AAA G GA G C AC Y A C G A G U AG C ACGG R GG CCA C GCG A G C U A U G Y R A U U C G CCGU U CC UGG U CGC Y R U RY Y U G A A U A G C G C R U R G R C U U CC C G G C G R U C G Y G C Y C A Y C A C G C G Y R G G C G C G U U R C G U R A C G A C C U UU C C A R G C G Possible Rho-independent C G C A G U R C G G U Y R Possible Rho-independent G C C A C U A C R C C G R terminator C G G G R terminator C C C G A C G A G G Y C G Y G C G C G R 5´ Y Y AA G UAA A G C A Y R A R AA A UGC C G U CUG A G Y R C G R Y G C Y G RU UU U A GACGGC A C U G R U R Y C C U Y R C C U A U U U Y G G U G U R U G G R C G R U A R R G G 5´ U UU Y R R C C U G

Low Seq. G+C

RNA RNAcode RNAz M otifs complexity sRN A (gen.)

M .tub. RU F 3 N o R N A (prob=1.00) Terminator,k-turn N o 59% (66%)

N .gon. RU F 7 N o R N A (prob=0.94) Terminator N o 43% (52%)

C.dif. RU F 9 ? (P =0.044) R N A (prob=1.00) Terminator N o 30% (29%)

C.dif. RUF11 ? (P =0.079) R N A (prob=1.00) Terminator Y es (23/ 170nts) 26% (29%)

Possible Rho-independent C C C C_dif_RUF9 U terminator C A U U C G U A Possible Rho-independent G Y U A A terminator G U C G C G A U A U G C A A U U A R Y G C C G R Y A U U A U A C G U A A U U A A U G C 5´ YUUUUUUAU UCA A AU CAA AA AYA RG U A GUUAAYUUUUAY CU U AU Y A CYYU UR UUUUUUU A AA

C C C C_dif_RUF11 U Possible Rho-independent C A U terminator U C G U A Possible Rho-independent G Y U A A terminator G U C G C G A U A U G C A A U U A R Y G C C G R Y A U U A U A C G U A A U U A A U G C 5´ YUUUUUUAU UCA A AU CAA AA AYA RG U A GUUAAYUUUUAY CU U AU Y A CYYU UR UUUUUUU UA A R

Paul Gardner Hunting RNA motifs What about the highly expressed ncRNAs?

I Are they antisense to any 5‘ UTRs RNAup RNA−RNA energies between sRNAs and 5' UTRs

M.tub3 (native) C.dif9 (native) M.tub3 (shuffled) C.dif9 (shuffled)

0.8 N.gon7 (native) C.dif11 (native) N.gon7 (shuffled) C.dif11 (shuffled) Freq. 0.4 0.0

0 5 10 15 20 25 30

negenergy (−kcal/mol)

Difference between native and shuffled CDFs

K−S.test(M.tub3 RNA ~ Shuffled) => P=3.70e−07 K−S.test(N.gon7 RNA ~ Shuffled) => P=0.01 K−S.test(C.dif9 RNA ~ Shuffled) => P<2.2e−16

0.10 K−S.test(C.dif11 RNA ~ Shuffled) => P<2.2e−16 CDF ∆ 0.00

0 5 10 15 20 25 30

negenergy (−kcal/mol)

Paul Gardner Hunting RNA motifs What about the highly expressed ncRNAs?

I Are they antisense to any 3‘ UTRs RNAup RNA−RNA energies between sRNAs and 3' UTRs

M.tub3 (native) C.dif9 (native) M.tub3 (shuffled) C.dif9 (shuffled)

0.8 N.gon7 (native) C.dif11 (native) N.gon7 (shuffled) C.dif11 (shuffled) Freq. 0.4 0.0

0 5 10 15 20

negenergy (−kcal/mol)

Difference between native and shuffled CDFs

K−S.test(M.tub3 RNA ~ Shuffled) => P=5.13e−09 K−S.test(N.gon7 RNA ~ Shuffled) => P=8.21e−06 K−S.test(C.dif9 RNA ~ Shuffled) => P<2.2e−16

0.10 K−S.test(C.dif11 RNA ~ Shuffled) => P<2.2e−16 CDF ∆ 0.00

0 5 10 15 20

negenergy (−kcal/mol)

Paul Gardner Hunting RNA motifs Discussion points

I A useful curation tool for building alignments and structures

I Can provide testable function hypotheses, sometimes

I Limited use outside of alignments based on the Salmonella Typhi results

I Even in alignments, false-positives & negatives are common

I More motifs (Terminators, Shine-Dalgarno, ...)

I Limit trivial motifs and prevent rediscovery of old motifs

I Snapshots of the data are available from https://github.com/ppgardne/RMfam

I Can anyone here do better on MTB3, NGON7, CDIF9 & CDIF11?

Paul Gardner Hunting RNA motifs Thanks!

I Lars Barquist, Alex Bateman & Rob Knight

PPG is supported by a Rutherford Discovery Fellowship from Government funding, administered by the Royal Society of New Zealand.

Paul Gardner Hunting RNA motifs RNA motifs

ANYA GNRA UNCG T-loop U-turn U Y A R A A A G A U C R R R U R G C U G U G A C G Y R C G TwoAYGGAYPrrB_RsmZ A G C R Y R Y U A RNaseP_archIntron_gpII RNaseP_bact_b CsrB A U R GEMM_RNA_motif

U6 IRES_Aptho Qrr R U rimP ● ● ● ● ● 5´ 5´ 5´ 5´ 5´ ● ● Thr_leader alpha_tmRNA ● Phe_leader ● ● ● L20_leader ● k-turn-1 k-turn-2 sarcin-ricin-1 sarcin-ricin-2 UAA_GAN ● L10_leader Y R twist_up ● R R C Y A Y Intron_gpI ● His_leader R R Y G A A G R Y group−II−D1D4−7 ● R G A R Y ● R A U suhB G C G U C A ● U U A C ● ● RNaseP_bact_a Y R Y G C C group−II−D1D4−6 ● G C R Y R A G C G C G ● ● ● R G U A A U A GNRA mascRNA−menRNA A G G G C A ● A G A U A A Y R ● G A A G G A C A G A G group−II−D1D4−5 ● tRNA G A A ● ● C G Y R Y C A Y Y A ● R A U Y ● tandem−GA tRNA−Sec R R Y G C R Y R R Y group−II−D1D4−3 Csr−Rsm ● C G Y R C G G C ● Domain−V cyano_tmRNA A ● sarcin−ricinTerminatorT−loop ● ● R C G C G Y A G 5´ R ● k−turn tmRNA 5´ 5´ 5´ R A R A A 5´ 5´ group−II−D1D4−1 twist_up ● ● group−II−D1D4−4 C-loop TRIT ● domain-V terminator1 terminator2 ● ● R ● Csr-Rsm R MOCO_RNA_motif SRP_S Entero_5_CRE R Y R R Y R Y R manA ● G A ● FMN G R ● UNCG ● ● G A R Y U−turn C G ● ● Cobalamin C G ● C G C G RtT A R Y R R Y ● Y C G SSU_rRNA_eukarya A Y R Clostridiales−1 ● C G Y A U ● R Y A U serC SSU_rRNA_archaea G R Y ● Y R R Y Y G A U ● C G C G ● SSU_rRNA_bacteria G C ● G C Y G Y C G T−box 5S_rRNA R Y A U ● C U A R A U ● A R G C ● Termite−leu C G R Y A U ● C A U C G ● HIV_FE A U G U A U A U G C SAM ● ● A U ● ● C4 R Y G R U ● ● U3 wcaG A U A U G C Plant_SRP Y C G SNORA73 5´ R Y A U R U G C 5´ 5´ 5´ U Y U Y 5´ U U U Y 5´ R Y Y Y Y R R R R

5´ R R A R G G R G R R Y A U G R R Archaea_SRP Fungi_SRP

Metazoa_SRP

5´ R R G G R R R Y A U G A R R Bacteria_small_SRP Bacteria_large_SRP 5´ A A G G R R A U G R

Paul Gardner Hunting RNA motifs RNA classiﬁcation

Backofen et al. (2007) RNAs everywhere: genome-wide annotation of structured RNAs. Journal of Experimental Zoology.

Paul Gardner Hunting RNA motifs