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10/31/05 D Dobbs ISU - BCB 444/544X: RNA Structure & Fun ction 1 10/31/05 RNA Structure & Function

Transcript of 10/31/05 D Dobbs ISU - BCB 444/544X: RNA Structure & Function1 10/31/05 RNA Structure & Function.

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10/31/05

RNA Structure & Function

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Announcements

Seminar (Mon Oct 31)

12:10 PM IG Faculty Seminar in 101 Ind Ed IIPlant Steroid Hormone Signal TransductionYanhai Yin, GDCB

• BCB Link for Seminar Schedules (updated)

http://www.bcb.iastate.edu/seminars/index.html

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Announcements

BCB 544 Projects - Important Dates:

Nov 2 Wed noon - Project proposals due to David/Drena

Nov 4 Fri 10A - Approvals/responses to students

Dec 2 Fri noon - Written project reports due

Dec 5,7,8,9 class/lab - Oral Presentations (20')

(Dec 15 Thurs = Final Exam)

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RNA Structure & Function Prediction

Mon Review - promoter prediction

RNA structure & function

Wed RNA structure prediction2' & 3' structure prediction

miRNA & target prediction RNA function prediction?

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Reading Assignment (for Mon/Wed)

Mount Bioinformatics• Chp 8 Prediction of RNA Secondary Structure • pp. 327-355• Ck Errata:

http://www.bioinformaticsonline.org/help/errata2.html

Cates (Online) RNA Secondary Structure Prediction Module• http://cnx.rice.edu/content/m11065/latest/

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Review last lecture:

Promoter Prediction

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Promoter Prediction

• Overview of strategies What sequence signals can be used? What other types of information can be used?

• Algorithms a bit more about these in later lectures

• Promoter prediction software • 3 major types• many, many programs!

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Promoter prediction: Eukaryotes vs prokaryotes

Promoter prediction is easier in microbial genomes

Why? Highly conservedSimpler gene structuresMore sequenced genomes!

(for comparative approaches)

Methods? Previously, again mostly HMM-based Now: similarity-based. comparative

methodsbecause so many genomes

available

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Promoter Prediction: Steps & Strategies

Closely related to gene prediction! • Obtain genomic sequence• Use sequence-similarity based comparison

(BLAST, MSA) to find related genesBut: "regulatory" regions are much less well-conserved than coding regions

• Locate ORFs • Identify TSS (Transcription Start Site)• Use promoter prediction programs • Analyze motifs, etc. in sequence (TRANSFAC)

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Promoter Prediction: Steps & Strategies

Identify TSS --if possible?• One of biggest problems is determining exact TSS!

Not very many full-length cDNAs!• Good starting point? (human & vertebrate genes)

Use FirstEFfound within UCSC Genome Browseror submit to FirstEF web server

Fig 5.10Baxevanis & Ouellette 2005

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Promoter prediction strategies

1)Pattern-driven algorithms

2)Sequence-driven algorithms

3)Combined "evidence-based"

BEST RESULTS? Combined, sequential

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Promoter Prediction: Pattern-driven algorithms

• Success depends on availability of collections of annotated binding sites (TRANSFAC & PROMO)

• Tend to produce huge numbers of FPs

• Why? • Binding sites (BS) for specific TFs often variable• Binding sites are short (typically 5-15 bp)• Interactions between TFs (& other proteins)

influence affinity & specificity of TF binding • One binding site often recognized by multiple BFs • Biology is complex: promoters often specific to

organism/cell/stage/environmental condition

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Promoter Prediction: Pattern-driven algorithms

Solutions to problem of too many FP predictions?

• Take sequence context/biology into account • Eukaryotes: clusters of TFBSs are common• Prokaryotes: knowledge of factors helps

• Probability of "real" binding site increases if annotated transcription start site (TSS) nearby • But: What about enhancers? (no TSS nearby!)

& Only a small fraction of TSSs have been experimentally mapped

• Do the wet lab experiments! • But: Promoter-bashing is tedious

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Promoter Prediction: Sequence-driven algorithms

• Assumption: common functionality can be deduced from sequence conservation• Alignments of co-regulated genes should highlight

elements involved in regulationCareful: How determine co-

regulation?• Orthologous genes from difference

species• Genes experimentally determined to be

co-regulated (using microarrays??)• Comparative promoter prediction:

"Phylogenetic footprinting" - more later….

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Problems:• Need sets of co-regulated genes• For comparative (phylogenetic) methods

• Must choose appropriate species• Different genomes evolve at different rates• Classical alignment methods have trouble with translocations, inversions in order of functional

elements• If background conservation of entire region is highly

conserved, comparison is useless• Not enough data (Prokaryotes >>> Eukaryotes)

• Biology is complex: many (most?) regulatory elements are not conserved across species!

Promoter Prediction: Sequence-driven algorithms

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Examples of promoter prediction/characterization software

Lab: used MATCH, MatInspectorTRANSFACMEME & MASTBLAST, etc.

Others?FIRST EFDragon Promoter Finder (these are links in PPTs)

also see Dragon Genome Explorer (has specialized promoter software for GC-rich DNA, finding CpG islands, etc)JASPAR

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Global alignment of human & mouse obese gene promoters (200 bp

upstream from TSS)

Fig 5.14Baxevanis & Ouellette 2005

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Check out optional review & try associated tutorial:

Wasserman WW & Sandelin A (2004) Applied bioinformatics for identification of regulatory elements. Nat Rev Genet 5:276-287

http://proxy.lib.iastate.edu:2103/nrg/journal/v5/n4/full/nrg1315_fs.html

Check this out: http://www.phylofoot.org/NRG_testcases/

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Annotated lists of promoter databases & promoter prediction software

• URLs from Mount Chp 9, available onlineTable 9.12 http://www.bioinformaticsonline.org/links/ch_09_t_2.html

• Table in Wasserman & Sandelin Nat Rev Genet article http://proxy.lib.iastate.edu:2103/nrg/journal/v5/n4/full/nrg1315_fs.htm

• URLs for Baxevanis & Ouellette, Chp 5:http://www.wiley.com/legacy/products/subject/life/bioinformatics/ch05.htm#links

More lists:• http://www.softberry.com/berry.phtml?

topic=index&group=programs&subgroup=promoter• http://bioinformatics.ubc.ca/resources/links_directory/?subcategory_id=104• http://www3.oup.co.uk/nar/database/subcat/1/4/

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New Today:

RNA Structure & Function

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RNA Structure & Function

• RNA structure• Levels of organization• Bonds & energetics

(more about this on Wed)

• RNA types & functions• Genomic information storage/transfer• Structural • Catalytic• Regulatory

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10/31/05 D Dobbs ISU - BCB 444/544X: RNA Structure & Function 22

Rob KnightUniv Colorado

RNA structure: 3 levels of organization

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Fig 6.2Baxevanis & Ouellette 2005

Covalent & non-covalent bonds in RNA

Primary: Covalent bonds

Secondary/Tertiary Non-covalent bonds

• H-bonds (base-pairing)• Base stacking

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G-C, A-U, G-U ("wobble") & variants

Base-pairing in RNA

http://www.imbjena.de/ImgLibDoc/nana/IMAGE_NANA.html#sec_element

See: IMB Image Library of Biological Molecules

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Fig 6.2Baxevanis & Ouellette 2005

Common structural motifs in RNA

Helices

Loops• Hairpin • Interior • Bulge • Multibranch

Pseudoknots

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RNA functions

• Storage/transfer of genetic information

• Structural

• Catalytic

• Regulatory

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RNA functions

Storage/transfer of genetic information

• Genomes • many viruses have RNA genomes

single-stranded (ssRNA)e.g., retroviruses (HIV)

double-stranded (dsRNA)

• Transfer of genetic information • mRNA = "coding RNA" - encodes proteins

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RNA functions

Structural • e.g., rRNA, which is major structural component of ribosomes (Gloria Culver, ISU)

BUT - its role is not just structural, also:

Catalytic RNA in ribosome has peptidyltransferase activity

• Enzymatic activity responsible for peptide bond formation between amino acids in growing peptide chain• Also, many small RNAs are enzymes

"ribozymes" (W Allen Miller, ISU)

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RNA functionsRegulatory

Recently discovered important new roles for RNAs In normal cells:

• in "defense" - esp. in plants• in normal development

e.g., siRNAs, miRNAAs tools:

• for gene therapy or to modify gene expression

• RNAi (used by many at ISU: Diane Bassham,Thomas

Baum, Jeff Essner, Kristen Johansen, Jo Anne Powell-Coffman, Roger Wise, etc.)• RNA aptamers (Marit Nilsen-Hamilton, ISU)

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RNA types & functions Types of RNAs Primary Function(s)

mRNA - messenger translation (protein synthesis) regulatory

rRNA - ribosomal translation (protein synthesis) <catalytic>

t-RNA - transfer translation (protein synthesis)

hnRNA - heterogeneous nuclear

precursors & intermediates of mature mRNAs & other RNAs

scRNA - small cytoplasmic signal recognition particle (SRP)tRNA processing <catalytic>

snRNA - small nuclear snoRNA - small nucleolar

mRNA processing, poly A addition <catalytic>rRNA processing/maturation/methylation

regulatory RNAs (siRNA, miRNA, etc.)

regulation of transcription and translation, other??

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Thanks to Chris Burge, MITfor following slides

Slightly modified from: Gene Regulation and MicroRNAs

Session introduction presented at ISMB 2005, Detroit, MI

Chris Burge [email protected]

C Burge 2005

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Expression of a Typical Eukaryotic Gene

DNA

Transcription

Protein

TranslationmRNA

Splicing

exon intron

AAAAAAAAA

Polyadenylation

Protein Coding Gene

Folding, Modification,Transport, Complex Assembly

Protein Complex

Degradation

Degradation

primary transcript / pre-mRNA

Export

For each of these processes, there is a ‘code’

(set of default recognition rules)

C Burge 2005

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Gene Expression Challenges for Computational Biology

• Understand the ‘code’ for each step in gene expression(set of default recognition rules), e.g., the ‘splicing code’

• Understand the rules for sequence-specific recognition of nucleic acids by protein and ribonucleoprotein (RNP) factors

• Understand the regulatory events that occur at each step and the biological consequences of regulation

Lots of data

Genomes, structures, transcripts, microarrays, ChIP-Chip, etc.

C Burge 2005

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Steps in Transcription

Emerson Cell 2002C Burge 2005

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Sequence-specific Transcription Factors

• typically bind in clusters

» Regulatory modules

Kadonaga Cell 2004

C Burge 2005

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Sequence-specific Transcription Factors

• have modular organization

» Understand DNA-binding specificity

Yan (ISU) A computational method to identify amino acid residues involved in protein-DNA interactions

ATF-2/c-Jun/IRF-3 DNA complex

Panne et al. EMBO J. 2004

C Burge 2005

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Maniatis & Reed Nature 2002

Integration of transcription & RNA processing

C Burge 2005

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Early Steps in Pre-mRNA Splicing

Matlin, Clark & Smith Nature Mol Cell Biol 2005

• Formation of exon-spanning complex

• Subsequent rearrangement to form intron-spanning spliceosomes which catalyze intron excision and exon ligation

hnRNP proteins

C Burge 2005

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Alternative Splicing

Matlin, Clark & Smith Nature Mol Cell Biol 2005

Wang (ISU) Genome-wide Comparative Analysis of Alternative Splicing in Plants

> 50% of human genes

undergo alternative splicing

C Burge 2005

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Splicing Regulation

Matlin, Clark & Smith Nature Mol Cell Biol 2005

ESE/ESS = Exonic Splicing Enhancers/Silencers

ISE/ISS = Intronic Splicing Enhancers/Silencers

C Burge 2005

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Coupling of Splicing & Nonsense-mediated mRNA Decay (NMD)

Maniatis & Reed Nature 2002

C Burge 2005

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C. elegans lin-4 Small Regulatory RNA

We now know that there are hundreds of microRNA genes

(Ambros, Bartel, Carrington, Ruvkun, Tuschl, others)

lin-4 precursor

lin-4 RNA

“Translational repression”

V. Ambros lablin-4 RNA

target mRNA

C Burge 2005

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MicroRNA Biogenesis

N. Kim Nature Rev Mol Cell Biol 2005

C Burge 2005

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miRNA and RNAi pathways

RISC

Dicerprecursor

miRNA siRNAs

Dicer

“translational repression”and/or mRNA degradation

mRNA cleavage, degradation

RNAi pathway

microRNA pathway

MicroRNA primary transcript Exogenous dsRNA, transposon, etc.

target mRNA

Drosha

RISCRISC

C Burge 2005

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miRNA Challenges for Computational Biology

• Find the genes encoding microRNAs

• Predict their regulatory targets

• Integrate miRNAs into gene regulatory pathways & networks

Computational Prediction of MicroRNA Genes & Targets

C Burge 2005

Need to modify traditional paradigm of "transcriptional control" by protein-DNA interactions to include miRNA regulatory mechanisms