RNA Structure Function

10/31/05 D Dobbs ISU - BCB 444/544X: RNA Structure & Function 8 Promoter prediction: Eukaryotes vs prokaryotes Promoter prediction is easier in microbial genomes Why?. 10/31/05 D Dobbs I

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10/31/05 D Dobbs ISU - BCB 444/544X: RNA St

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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 II

Plant Steroid Hormone Signal Transduction

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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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Prediction

Mon Review - promoter prediction

RNA structure & function

Wed RNA structure prediction

2' & 3' structure prediction miRNA & target prediction RNA function prediction?

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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

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Promoter prediction: Eukaryotes vs prokaryotes

Promoter prediction is easier in microbial genomes

Why? Highly conserved

Simpler gene structuresMore sequenced genomes!

(for comparative approaches)

Methods? Previously, again mostly HMM-based

Now: similarity-based comparative methods

because 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 genes

But: "regulatory" regions are much less conserved than coding regions

well-• Locate ORFs

• Identify TSS ( T ranscription S tart S ite)

• Use promoter prediction program s

• Analyze motifs, etc in sequence (TRANSFAC)

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Promoter Prediction: Steps & Strategies

• One of biggest problems is determining exact TSS!

Not very many full-length cDNAs!

• Good starting point? (human & vertebrate genes)

Use FirstEF

found within UCSC Genome Browser

or submit to FirstEF web server

Fig 5.10

Baxevanis &

Ouellette 2005

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• 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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Solutions to problem of too many FP predictions?

• Take sequence context/biology into account

• Eukaryotes: clusters of TFBSs are common

• Prokaryotes: knowledge of σ 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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• Assumption: common functionality can be deduced from

sequence conservation

• Alignments of co-regulated genes should highlight

elements involved in regulation

Careful: 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!

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Examples of promoter prediction/characterization software

Lab: used MATCH, MatInspector

TRANSFAC MEME & MAST BLAST, etc.

Others?

FIRST EF

Dragon 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 5.14

Baxevanis &

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 online

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New Today:

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• Levels of organization

• Bonds & energetics

(more about this on Wed)

• RNA types & functions

• Genomic information storage/transfer

• Structural

• Catalytic

• Regulatory

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• Transfer of genetic information

• mRNA = "coding RNA" - encodes proteins

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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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• 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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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>

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Thanks to Chris Burge, MIT

for following slides

Slightly modified from:

Gene Regulation and MicroRNAs

Session introduction presented at

ISMB 2005, Detroit, MI

Chris Burge cburge@MIT.EDU

C Burge 2005

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C Burge 2005

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C Burge 2005

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Steps in Transcription

Emerson Cell 2002

C 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

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

ESE/ESS = Exonic Splicing Enhancers/Silencers ISE/ISS = Intronic Splicing Enhancers/Silencers

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”

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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and/or mRNA degradation

mRNA cleavage, degradation

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

Tiêu đề	RNA Structure and Function
Trường học	Iowa State University
Chuyên ngành	Bioinformatics
Thể loại	Lecture
Năm xuất bản	2005
Thành phố	Ames

Định dạng
Số trang	45
Dung lượng	1,81 MB