benasque rna 2012: 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 transcriptI No hits to Rfam, no homologs have been identified, no known

function

0e+00

1e+05

2e+05

3e+05

4e+05

5e+05

6e+05

7e+05

Mycobacteria tuberculosis genome: RNA−seq and annotations

Rea

d co

unts

+ve strand−ve strand

CDSsPfam domain

ncRNAsterminators

4099500 4100000 4100500 4101000 4101500 4102000

genome coordinate

Rv3661

HAD

miscrna00343

Rv3662c

Rank Reads Gene

1 5,741K SSU rRNA

2 689K sRNA / RUF?

3 236K RNaseP RNA

4 197K LSU rRNA

5 119K ATP synthase

6 119K HSPX

7 115K tmRNA

8 103K Secreted protein

Arnvig et al.

(2011) PLoS

Pathog.



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

function

0

20000

40000

60000

80000

Neisseria gonorrhoeae genome: RNA−seq and annotations

Rea

d co

unts


CDSsPfam domain

ncRNAsterminators

2137500 2138000 2138500 2139000

genome coordinate

NGO2161

Inositol_P

terminator12061

terminator12062 NGO2162

SEC−C

terminator12063

terminator12064

Rank Reads Gene

1 206K RNaseP RNA

2 178K tRNA -Ser

3 130K ZapA 3‘ UTR?

4 120K Phage protein

5‘ UTR?

5 97K tmRNA

6 93K BRO protein

7 81K sRNA?

8 81K Hsp70 protein

9 76K tRNA -Asp

Isabella & Clark(2011) BMCGenomics.



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

function

0

1000

2000

3000

4000

Clostridium difficile genome: RNA−seq and annotations

Rea

d co

unts


CDSsPfam domain

ncRNAsterminators

3014000 3014500 3015000 3015500 3016000 3016500 3017000

genome coordinate

toxB toxB toxB

terminator0881

terminator1760 trpS

Rank Reads Gene

1 12K RNaseP RNA

2 12K 6S RNA

3 12K SRP RNA

4 11K ldhA

5 10K tmRNA

6 6K spoVG

7 6K Gly reductase

8 5K slpA

9 4K sRNA / RUF?

10 4K hypothetical prot.

11 4K sRNA / RUF?

Deakin, Lawley

et al. (2012)

Unpublished.



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

function

0

1000

2000

3000

4000

Clostridium difficile genome: RNA−seq and annotations

Rea

d co

unts


CDSsPfam domain

ncRNAsterminators

1760600 1760800 1761000 1761200 1761400 1761600

genome coordinate

feoA2 terminator1637terminator1105 acetyltransferase

Rank Reads Gene

1 12K RNaseP RNA

2 12K 6S RNA

3 12K SRP RNA

4 11K ldhA

5 10K tmRNA

6 6K spoVG

7 6K Gly reductase

8 5K slpA

9 4K sRNA / RUF?

10 4K hypothetical prot.

11 4K sRNA / RUF?

Deakin, Lawley

et al. (2012)

Unpublished.


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?


Will a “periodic table of RNA” be useful?

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

base basepair

R

AU

AGA

U YA

C

AU

U5´

Y

GA

A

R

5´

CU

U CG

G

5´

R

UR R R

Y

5´

R

RGC

GU

R ARA

GCY

5´

RYGGAGY

R RR

RC R

RGA

R

R

5´

CGAAGYY

R

Y

Y RR

GG

GRUGGAG

5´

CCRA

YCC

CRU C

CG

AA

CUYGG

5´

A N Y A G N R A U N C G T loop U turn k t rn1 k t rn2 tw ist

R

CYRG

GAA

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RC R

UYA

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C

GG

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Y

YY

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U A

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GAA

RR

R

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GR

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A YYA

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G

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A

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5ÁAAAAGCYRYY R

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U Y U Y5´RRARR Y

YUUU

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sar r ic1 sar r ic2 U A A G A N C sr C loop domV term1 term2

R Y Y Y Y

GCGAGCAGACGCARAA

CRCCCRR

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R

R

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UUYUGCGUCUGCUCGC

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CA

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Y GACUGGAAGCUG

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5´

T R I T I R E SE C I S m ir -T A R m ir -30 m ir -9 l in -4 m ir -5 m ir -8 m ir -1 m ir -2 m ir -6 let -7 Y R N A 6S 5S tR N A R N aseP

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AUCUC

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CG

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CCYY

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SA M V symR C P E B 3 F inP sroB msr SA M a H H 3 V mntn3 l ivK D srA C A E SA R isrK sroD isrB 6C r spL suhB

UYGC

AUCCGCYAA

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CU

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AAYYYCRRRAA

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Y Y Y G R R AG

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

GU

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RR R5´

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CCCAU

UA C C

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CA

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C A R G C U C U G G R AG UUC

GUYCCRGAGYYUGYYGGAARGGUUUUCCGUGUCCAG

5´

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R

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RR

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CCC

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AU A

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UU

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AU

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AA

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UC

AG

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UC

AU

CR

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GA

GY

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UU

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AGCGUGAYU

UU

CUAUCAAC

AGC U A A C

AAUUGUUA

UUACU

G CCUAA

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AA G G G U A A

UUUUAA

AA

AAGGG C

GAUA

AAA

A ACGAUUG G GG

GAUGAGA

YA

UGAAC

GCU

C A A G C A5´

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

U RRCAY

YU C U

RUGYUY

A

UUY A

UUR

CACCA

A C C U G C G C RGAUGCGCAGGU

UUUUUUU

5´

ARR

R Y YY

YY

AA

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YC

AA

CY

UU

UA

GC

GC

AC

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A A G A G C CA

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CC

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AGGA

A U YG U U U G G Y C U

UU

UU

UU

5´

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UG

UG

AA

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GCY

AC

C

GCA

A GC

YG

RU

A

CY

CU

UC

AC Y

Y Y C CUUA U U

CG C

U

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GGR

AUCYUGCUC

U G C G A G G C Y5´

GU

GC

RR

YC

YR

AU

UY

YR

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AU

CA

YA A R A

UA

CG G C R C R R C

CA

CR

AU

UU

CC

CU

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AU U CG C G C A C C

CC

GG

UC

UA

CC

5´

Y

UUYRYURRUUU

YAUCA

RAY

C U GUU

UGAURRAAGYUARYGAR

R Y Y C A Y UAAC

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Y G

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AC

CCGAG

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U U U U U5´

RA

CG

UU

CA

YCCYYY

R G G RC

GC

AY

RA

YCARRYCAYGG

AA

C G GG G

RY Y U G R R

5´

sucA SraD sxy R N A I P ur ine SA M -C hl cd iG M P 2 A nt i -Q G adY rnk ldr P r fA O mrA -B R yeB t raJ 2 SraH 23Smeth D S-pep

U U C G G C C Y CGCRRCG

YU U Y

UY

CGYYGC

C C U C U G C A YGCCGU CGCCG

ACGCAY

UCC

YAUUC

GA A Y Y G U

GCGAUC C U

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YUC

CU

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5´ CGYRGC

GC

UUGU

UA U U

URYY

G C UG

UG

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C

G

U

CY

YR A R Y Y R G R R Y Y Y

AAACCCCGCCY

UU Y

GGCGGGGUUUU

G C U U U U U5´ ** CUUACCGGAGGY

RUAUGGACC

CU

G A UCC C A

CY C C UCUCCC

C GA

UGG

AG

AAU

YYYU

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C C Y G Y C U Y YRCUGYYUUAC

CG

G UGY

GUAAGGCAGU

G A C G U Y U5´GGRAG

RYR

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GGU G R

Y

CG

GC

UU

C A AA

CC

GR

Y GR

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YR

Y

YY

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5´ UGACCCU

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UA R

CCR

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

UA

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YY A

UGUUCAC A

RAUAR

GCCAAUCGC

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C U U U U U U U U U5´ C U U A A URAACAA

GA

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AR C GUACYUUC

CY C

CUGA

G UU

CAGGC

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AG C U R

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

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CAGUGA

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CG

AU

GAAU

GU

CGG

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C A C5´

RUYY

RCU

GAYG

A GUCC

CA

A AUA

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AAA C G C

GCGUCY

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5´ CUCCAUGU

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U GGGA

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AG

C UGU

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CUCCC UU

CCU

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

UA

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AG

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UC

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UU

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AU

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GU

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GGUGU RUYAC

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

RR

R AGCGGGCU

U U U U U5´

GUCAUAC

U A CG

GU

GCA

AYG

YR R

AA

AGU A

AAC

GAUGAC

C C YARG

AACUCYR

G G U AA A A

URCR

UAUCAAAAUGYAAAAUUG

UY U G A C C U G G G

R U

YY

UCCGGGUYRGYUYUUUU

5´

U R U G C U A A C U R R R A A YGUUGY

A U

RYAAC

CCUUGRYGC

UUA

U YCCUU

URYCAAG

C A U A U U A Y A

RCG

RU

CGYY

A A A G G A G A A A U G5Ú C R A A A G A A C A

UGAAAU

GGAGGAGAAAUU

ACAG

C A A U U UAU

C ARC U

GA

AA

UUA

UAG

GU

GUA

G AC

AC A

UG

UCAG

C R G UGGAA

ACAGU

UU

C U A UCA A A A UU A A A

GUA

UUUAG

AGAUUUUC

CUC A

AAUUUCA

A A U5´

AC

AG

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

RU

UU

CUC

AARUYGGRGYA

AA Y C C G R U U G RA

RU

AU

AR

AG

GA

RG

5´ CG

YG

UU

AUAUGCCU U U A

UU G U

CA

CA

RU

UY

UU

UU

UY

YGYUGR

YC

AUUG

GY

AY

YA U U R A U

UY

C C A G CR

AU

AA

AY

GAC

AAGCCCGAACRY U G U U C G G G C U U U U

UU

UU

RR

UY

A5´

Y Y Y AUGGYGG

Y

GR

GGGR

RCCUU

YG GG Y

YGCCGGUU

CCY

Y R

CCGGU Y U RCCA

ACCC

YY

R

CYRCCA

C C Y5´ AU

GG

AY

RU

GCGCAGG

A A G C G C RA

AG

AC

AR

AC

AG

GG

AC

AC

RY

AG

GR

ACC

C G G AU

GG

YG

GR

RY

AG

GA

UG

UC

AG

GR

AA

CA

GU

CU

GCAAAGCCCCGCYY

Y G G C G G G G U U U U5´

P s-R ho rnk ps M gsens tR N A S Q r r isrC H H 1 SN R 24 T rp ldr greA preQ 12 H A R 1F T ermL eu M icC C 4 R smY R ibosome


What is a motif?

Escher, MC (1938) Sky and Water 1.

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 structurefound within larger structures thatcan be modelled by either acovariance model or a profile HMM.


What is a motif?

I The kink-turn RNA motifI Found in rRNA, riboswitches, RNase P, snoRNAs, ...I An asymmetric internal loopI Causes a sharp kink between two flanking helical regions

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


Can we build a seed alignment and CM resource of thesemotifs?

I For the hairpin motifs?I YES

I For the internal loopmotifs?

I MAYBE

I For the sequence motifs?I Not certain yet

GNRA

Y

GA

A

R

5´

k-turn-1

RYGGAGY

R RR

RC R

RGA

R

R

5´

k-turn-2

CGAAGYY

R

Y

Y RR

GG

GRUGGAG

5´

Doshi et al. (2004) Evaluation of the suitability of free-energy minimization using nearest-neighbor energy pa-rameters for RNA secondary structure prediction. BMCBioinformatics.


The RNA Motifs: RMfamANYA

R

AU

A

GA

U YA

C

AU

U5´

GNRA

Y

GA

A

R

5´

T-loop

R

UR R R

Y

5´

UNCG

CU

U CG

G

5´

U-turn

R

RGC

GU

R ARA

GCY

5´

k-turn-1

RYGGAGY

R RR

RC R

RGA

R

R

5´

k-turn-2

CGAAGYY

R

Y

Y RR

GG

GRUGGAG

5´

pK-turn

A GGRR

RY

RR

Y

R C YC

G

YY

RY

YYC

5´

sarcin-ricin-1

RACCYRRYGAAC

UGA

AR C

AUCU

CA GUAR

YRG AGG

A R A A5´

sarcin-ricin-2

Y

Y

AGUA

G Y RA

G

GAA

R

5´

tandem-GA

G CRRUGA

RRRR

AYA

AG

A

RARYRY

YYYRGAG

5´

twist_up

CCGA

YCC

CAU C

CG

AA

CUCGG

5´

UAA_GAN

RY

GR

YAA

Y

C

RY

A Y

YA

G

RG

AA

YC

5´

C-loop

GA

CAC U

G

Y

R

Y R Y R RR

R

CAR

U

Y

5´

Csr-Rsm

R

CAG G A

G

Y5´

domain-V

RAGC

R

C

GR

A

GY A

YG

YYRGUUY

5´

SRP_S_domain

R

GRAC Y

YRY

CAGG

Y

GR A

A

R

AGC

AGYR

AA

Y

5´

vapC_target

A U A UYG

R

RC

RC

C

R

GYCGC RY

Y

G Y C5´

terminator1

AAAAAGCYRYY R

RYGGYUUUUU

U Y U Y5´

terminator2

RRARR Y

YUUU

U U U Y5´

TRIT

R Y Y Y Y

GCGAGCAGACGCARAA

CRCCCRR

Y

R

R

YGGGYG

UUYUGCGUCUGCUCGC

R R R R5´

R R A R G G R G R R Y A U G R R5´

R R G G R R R Y A U G A R R5´

A A G G R R A U G R5´


Breaking Rules

I Prefer motifs found inmultiple families &/ormultiple locations in thesame family

I Aligning analogousstructures

I Motifs are allowed tooverlap

I Models are non-specificI High false-positive rates

I Sequences are not edited,spliced or otherwiseinterfered with

I Sequence sources arediverse (PDB, EMBL &publications)


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 CCGUUCCCAUUCCGAACACGG1S72_9/29-50 CCGUACCCAUCCCGAACACGG#=GR 1S72_9/29-50 SS ((((...(.....)...))))#=GR 1S72_9/29-50 MT ...xxxxx.....xxxxx...#=GC SS_cons ((((...(.....)...))))//


An example annotated Rfam alignment

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

X62858.1/62-115 GCUGCG-U-UGUGGGUGUGUACUGCGGUUUU-UUG-CUGUGGGAA--GCCCACUUCA-CUGX01588.1/64-115 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//

Y

RCGRC

CAUA

CR

GR

RCACC

G

UCCCR U Y

C

GAC

C

GRA

GU Y

AA

GC

YG

G C Y R

GUA C U

R R YGGG

GACY

YUGGG

AAY

RGGY

GYYGY

R

5'


Uses for a motif DB (I): Improving a RNA alignments andstructure prediction constraints

Y

R C G R C

C A U A

C R

G R

RC

A C C

G

U C

C C R U Y

C

G A C C

G R A G

U Y A A G C

Y G

G C Y R

G U A C U

R R Y G G G G A C Y

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

G Y Y G Y

R

5'

Original Rfammodel

Sarcin-ricinloop

GNRAtetraloop

Refined Rfammodel

Y

R C G G C

C A U A

C R

G R

R C A C C

G

U C C C R U Y

C G A C C

G A A G

U Y A A G C

Y G

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

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

Y Y G Y

R

5'


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

I The Lysine Riboswitch

R

RGUAGAG

GY

GCRRYYA

AGUA

YUYG

RRR

R

Y C R R UG

AR R R Y

GA A R GGR R R R U Y G C

CGA A

RY

R

RR

Y Y

YY

Y

GYU G

G

YR

YR

Y

G A A

RR

RY

RR

RA

CU

GU C A Y R R

Y

YRUGGA

GGC

UAYY

5´

GAGY

R

RC RGA

5´

RG

RYR A

CY

5´

GR

A

5´

UR

5´

32% K-turn11% U-turn

13% T-loop 21% GNRATetraloop

P1

P2

P3

P4

P5


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

I The Lysine Riboswitch

Pasteurella multocida

Bacillus sp.

Escherichia coli

Staphylococcus epidermidis

Lactococcus lactis subsp. lactis

Staphylococcus haemolyticusStaphylococcus saprophyticus

Actinobacillus pleuropneumoniaeHaemophilus ducreyi

Bacillus halodurans

Vibrio splendidus

Bacillus subtilis

Serrat ia proteamaculans

Listeria monocytogenes

Vibrio sp.

Vibrio vulnificus

Lactobacil lus plantarum

Mannheimia succiniciproducens

Bacillus cereus

Klebsiella pneumoniae

Lactococcus lactis subsp. cremoris

Listeria innocua

Haemophilus influenzae

Vibrio parahaemolyticus

Clostridium botulinumThermoanaerobacter tengcongensis

Lactobacillus brevis

Thermoanaerobacter sp.

Bacillus licheniformis

Pectobacterium atrosepticum

Mannheimia haemolyt ica

Yersinia frederiksenii

Staphylococcus aureus

Haemophilus somnus

Listeria welshimeri

Proteobacteria

Firmicutes

Pasteurell.

Vibrionales

Enterobac.

Clostridia

Lactobacil.

Bacillales

P2 stem

RYGGAGY

R RR

RC R

RGA

R

R

5´

Kink-turn

R

RGC

GU

R ARA

GCY

5´

U-turn

P4 stem

Y

GA

A

R

5´

GNRA tetrloop

R

UR R R

Y

5´

T-loop

P2 P4


Uses for a motif DB (III): Functionally characterising novelRNAs

Y

Y U

R R

U

Y Y

Y A G

R A

R Y C R C A Y

G G A Y G Y G R Y A G

R

Y G

G C C A Y G G A

Y G G C R Y R U

R R

C G G Y Y

C G U Y

R

R

Y

R

A R

Y Y Y

R

R5'

YRG G A

R

5´

Csr-Rsm binding motif

I TwoAYGGAY sRNA: 216 homologuesfound in Human gut metagenomesequences, γ-Proteobacteria andClostridiales species

I Weinberg Z et al. (2010)”Comparative genomics reveals 104candidate structured RNAs frombacteria, archaea and theirmetagenomes”. Genome Biol.


Csr-Rsm background

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


Uses for a motif DB (IV): Identifying and ranking ncRNAsof interest in transcriptome results

0 50 100 150 200

0.00

0.03

0.06

Distribution of motif scores on S. typhi ncRNAs

Sum of motif scores (bits)

Den

sity

Known RNAsRNA−seq ncRNAsShuffled

0 50 100 150 200

0.0

0.4

0.8

CDF of motif scores on S. typhi ncRNAs


CD

F K−S.test(Known RNA > Shuffled) => P=2.11e−37K−S.test(RNA−seq RNA > Shuffled) => P=1.47e−09


Uses for a motif DB (IV): Identifying and ranking ncRNAsof interest in transcriptome results

0 500 1000 1500 2000

0.00

000.

0010

Distribution of motif scores on S. typhi ncRNAs alignments


Den

sity

Known RNAsRNA−seq ncRNAsShuffled

0 500 1000 1500 2000

0.0

0.4

0.8

CDF of motif scores on S. typhi ncRNAs alignments


CD

F

K−S.test(Known RNA > Shuffled) => P=8.15e−06K−S.test(RNA−seq RNA > Shuffled) => P=0.53


80 highest scoring matches between RMfam & Rfam

T−loopTerminator

Csr

−Rsm

Dom

ain−

VGN

RAtandem−GA

k−turn

SRP_S

UN

CG

U−t

urn

twist_up

sarcin−ricin

●

●

●●

●

●

●

●

●●

●

●

tmRNAcyano_tmRNAtRNA−SectRNA

mascRNA−menRNA

RNaseP_bact_a

suhBHis_

leade

r

L10_

lead

er

L20_

lead

er

Phe

_lea

der

Thr_

lead

er

rimP

QrrIRE

S_A

ptho

CsrB

PrrB

_Rsm

Z

TwoAY

GG

AYU6

Intron_gpII

RNaseP_arch

RNaseP_bact_b

GEMM_RNA_motif

alpha_tmRNAIntron_gpI

group−II−D1D4−7group−II−D1D4−6group−II−D1D4−5

group−II−D1D4−3


MOCO_RNA_motif

manA

Clostridiales−1

RtT

serC

T−box

SNORA73SA

M

Arch

aea_

SRP

Bac

teria

_sm

all_

SR

PB

acte

ria_l

arge

_SR

PF

ungi

_SR

PM

etaz

oa_S

RP

Plant_S

RP

wcaG

U3

C4

HIV

_FETerm

ite−leu5S_rRNA

SSU_rRNA_bacteria

SSU_rRNA_archaea

SSU_rRNA_eukarya

Cobalamin

FMN

Entero_5_CRE


●

●

●

●

●

●

●

●

●●

●●

●●●●●●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

● ● ● ● ● ●●

●●

●

●

●

●

●

●

●

●


What about the highly expressed ncRNAs?

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

AAGCYCGG

RAGGC

CAA GGYCGA

CC

GGAA G A

GAA

GGYUCGAYCUCCCGAY

CCGRGC

YCC

CA G C A C G G R

C CG G

Y A CC C A CG C G

GAGCR

GCCGCR A Y

AA

RGGCARAAGYGU U

GYGGGCCUGCGURAU

U C G R AR

YRRAC GGY

CGACGRCC C UU

UGGGUG

GGG

YU

GCRRCCRRA

RG

UCGYR

R

CGCCGAG

GCCA

CCCA CGC

AACCCRY

YGCACGCUUGG

UAACC

GRG

UCCGUGCURGCGGGCGGYGA

YC

RUY

GGR

CGCCGCCCGCU

UYRYUR

5'

RYGGAGY

R RR

RC R

RGA

R

R

5´

Possible K-turn

RRARR Y

YUUU

U U U Y5´

Possible Rho-independent terminator






N_gon_RUF7

Y YGCUG

RCCRCYGCY

UUUA

U C CCUUU

UGGCRGUGCAGC

A A G U A AGACGU

UUUC

C CGCAAUG

UAUU G

RCCAUUCAUACU

U

C CCUU

RGUAUGRAUGGUUU

UU

UUURYR

R

R A A A U G C C G U C U G A A YG

UUCAGACGGCAUUURU

5´

C_dif_RUF9

UCAGAAGGC

UU

GYUUUCUGA

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

UC

C C CA

AAGU AAGAAU

A U Y A C Y Y U U RGAURRGC

AGCYYAUC

U U U U U U U A A A5´

C_dif_RUF11

UCAGAAGGC

UU

GYUUUCUGA

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

UC

C C CA

AAGU AAGAAU

A U Y A C Y Y U U RGAURRGC

AGCYYAUC

U U U U U U U U A A R5´

M_tub_RUF3


Low Seq. G+C

RNA RNAcode RNAz M otifs complexity sRNA (gen.)

M .tub. RUF3 No RNA (prob=1.00) Terminator,k-turn No 59% (66%)

N .gon. RUF7 No RNA (prob=0.94) Terminator No 43% (52%)

C.dif. RUF9 ? (P=0.044) RNA (prob=1.00) Terminator No 30% (29%)

C.dif. RUF11 ? (P=0.079) RNA (prob=1.00) Terminator Yes (23/ 170nts) 26% (29%)



I Are they antisense to any 5‘ UTRs

0 5 10 15 20 25 30

0.0

0.4

0.8

RNAup RNA−RNA energies between sRNAs and 5' UTRs

negenergy (−kcal/mol)

Fre

q.M.tub3 (native)M.tub3 (shuffled)N.gon7 (native)N.gon7 (shuffled)

C.dif9 (native)C.dif9 (shuffled)C.dif11 (native)C.dif11 (shuffled)

0 5 10 15 20 25 30

0.00

0.10

Difference between native and shuffled CDFs


∆CD

F

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




I Are they antisense to any 3‘ UTRs

0 5 10 15 20

0.0

0.4

0.8

RNAup RNA−RNA energies between sRNAs and 3' UTRs


Fre

q.M.tub3 (native)M.tub3 (shuffled)N.gon7 (native)N.gon7 (shuffled)

C.dif9 (native)C.dif9 (shuffled)C.dif11 (native)C.dif11 (shuffled)

0 5 10 15 20

0.00

0.10

Difference between native and shuffled CDFs


∆CD

F

K−S.test(M.tub3 RNA ~ Shuffled) => P=5.13e−09

K−S.test(N.gon7 RNA ~ Shuffled) => P=8.21e−06




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 SalmonellaTyphi 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 fromhttps://github.com/ppgardne/RMfam

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


https://github.com/ppgardne/RMfam

Thanks!

I Lars Barquist, AlexBateman & Rob Knight

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


RNA motifs

ANYA

R

AU

A

GA

U YA

C

AU

U5´

GNRA

Y

GA

A

R

5´

UNCG

CU

U CG

G

5´

T-loop

R

UR R R

Y

5´

U-turn

R

RGC

GU

R ARA

GCY

5´

k-turn-1

RYGGAGY

R RR

RC R

RGA

R

R

5´

k-turn-2

CGAAGYY

R

Y

Y RR

GG

GRUGGAG

5´

sarcin-ricin-1

RACCYRRYGAAC

UGA

AR C

AUCU

CA GUAR

YRG AGG

A R A A5´

sarcin-ricin-2

Y

Y

AGUA

G Y RA

G

GAA

R

5´

twist_up

CCGA

YCC

CAU C

CG

AA

CUCGG

5´

UAA_GAN

RY

GR

YAA

Y

C

RY

A Y

YA

G

RG

AA

YC

5´

Csr-Rsm

R

CAG G A

G

Y5´

C-loop

GA

CAC U

G

Y

R

Y R Y R RR

R

CAR

U

Y

5´

domain-V

RAGC

R

C

GR

A

GY A

YG

YYRGUUY

5´

terminator1

AAAAAGCYRYY R

RYGGYUUUUU

U Y U Y5´

terminator2

RRARR Y

YUUU

U U U Y5´

TRIT

R Y Y Y Y

GCGAGCAGACGCARAA

CRCCCRR

Y

R

R

YGGGYG

UUYUGCGUCUGCUCGC

R R R R5´

R R A R G G R G R R Y A U G R R5´

R R G G R R R Y A U G A R R5´

A A G G R R A U G R5´

T−loopTerminator

Csr

−Rsm

Dom

ain−

VGN

RAtandem−GA

k−turn

SRP_S

UN

CG

U−t

urn

twist_up

sarcin−ricin

●

●

●●

●

●

●

●

●●

●

●

tmRNAcyano_tmRNAtRNA−SectRNA

mascRNA−menRNA

RNaseP_bact_a

suhBHis_

leade

r

L10_

lead

er

L20_

lead

er

Phe

_lea

der

Thr_

lead

er

rimP

QrrIRE

S_A

ptho

CsrB

PrrB

_Rsm

Z

TwoAY

GG

AYU6

Intron_gpII

RNaseP_arch

RNaseP_bact_b

GEMM_RNA_motif

alpha_tmRNAIntron_gpI

group−II−D1D4−7group−II−D1D4−6group−II−D1D4−5



MOCO_RNA_motif

manA

Clostridiales−1

RtT

serC

T−box

SNORA73SA

M

Arch

aea_

SRP

Bac

teria

_sm

all_

SR

PB

acte

ria_l

arge

_SR

PF

ungi

_SR

PM

etaz

oa_S

RP

Plant_S

RP

wcaG

U3

C4

HIV

_FETerm

ite−leu5S_rRNA

SSU_rRNA_bacteria

SSU_rRNA_archaea

SSU_rRNA_eukarya

Cobalamin

FMN

Entero_5_CRE


●

●

●

●

●

●

●

●

●●

●●

●●●●●●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

● ● ● ● ● ●●

●●

●

●

●

●

●

●

●

●


RNA classification

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


benasque rna 2012: rna motifs

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