10/31/05
RNA Structure & Function
10/31/05
D Dobbs ISU - BCB 444/544X: 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
Yanhai Yin, GDCB
• BCB Link for Seminar Schedules (updated)
http://www.bcb.iastate.edu/seminars/index.html
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D Dobbs ISU - BCB 444/544X: RNA Structure & Function
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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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D Dobbs ISU - BCB 444/544X: RNA Structure & Function
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RNA Structure & Function
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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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 conserved
Simpler gene structures
More 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 wellconserved 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 FirstEF
found within UCSC Genome Browser
or submit to FirstEF web server
Fig 5.10
Baxevanis &
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 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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Promoter Prediction: Sequence-driven algorithms
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
Table 9.12 http://www.bioinformaticsonline.org/links/ch_09_t_2.html
•
Table in Wasserman & Sandelin Nat Rev Genet article
•
URLs for Baxevanis & Ouellette, Chp 5:
http://proxy.lib.iastate.edu:2103/nrg/journal/v5/n4/full/nrg1315_fs.htm
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=promo
ter
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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RNA structure: 3 levels of organization
Rob Knight
Univ Colorado
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Covalent & non-covalent bonds in RNA
Primary:
Covalent bonds
Secondary/Tertiary
Non-covalent bonds
• H-bonds
(base-pairing)
• Base stacking
Fig 6.2
Baxevanis &
Ouellette 2005
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Base-pairing in RNA
G-C, A-U, G-U ("wobble") & variants
See: IMB Image Library of Biological Molecules
http://www.imbjena.de/ImgLibDoc/nana/IMAGE_NANA.html#sec_element
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Common structural motifs in RNA
Helices
Loops
• Hairpin
• Interior
• Bulge
• Multibranch
Pseudoknots
Fig 6.2
Baxevanis &
Ouellette 2005
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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 functions
Regulatory
Recently discovered important new roles for RNAs
In normal cells:
• in "defense" - esp. in plants
• in normal development
e.g., siRNAs, miRNA
As 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)
t-RNA - transfer
translation (protein synthesis)
<catalytic>
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, 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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Expression of a Typical Eukaryotic Gene
Protein Coding Gene
…
DNA
Transcription
Polyadenylation
intron
exon
primary transcript / pre-mRNA
mRNA
Splicing
Export
Translation
AAAAAAAAA
Degradation
For each of these
processes, there is
a ‘code’
(set of default
recognition rules)
Protein
Folding, Modification,
Transport, Complex
Assembly
Protein Complex
Degradation
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
C Burge 2005
Emerson Cell 2002
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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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Integration of
transcription &
RNA processing
Maniatis & Reed Nature 2002
C Burge 2005
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Early Steps in Pre-mRNA Splicing
• Formation of exon-spanning complex
hnRNP proteins
• Subsequent rearrangement to form
intron-spanning spliceosomes which
catalyze intron excision and exon ligation
Matlin, Clark & Smith Nature Mol Cell Biol 2005
C Burge 2005
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Alternative Splicing
> 50% of human genes
undergo alternative splicing
Matlin, Clark & Smith Nature Mol Cell Biol 2005
Wang (ISU) Genome-wide Comparative Analysis of Alternative
Splicing in Plants
C Burge 2005
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Splicing Regulation
ESE/ESS = Exonic Splicing Enhancers/Silencers
ISE/ISS = Intronic Splicing Enhancers/Silencers
Matlin, Clark & Smith Nature Mol Cell Biol 2005
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
lin-4 precursor
lin-4 RNA
target mRNA
V. Ambros lab
“Translational
repression”
lin-4 RNA
We now know that there are hundreds of microRNA genes
(Ambros, Bartel, Carrington, Ruvkun, Tuschl, others)
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
microRNA pathway
RNAi pathway
MicroRNA primary transcript
Exogenous dsRNA, transposon, etc.
Drosha
precursor
Dicer
Dicer
siRNAs
miRNA
target mRNA
RISC
RISC
“translational repression”
and/or mRNA degradation
C Burge 2005
10/31/05
RISC
mRNA cleavage, degradation
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miRNA Challenges for Computational Biology
• Find the genes encoding microRNAs
• Predict their regulatory targets
Computational Prediction of MicroRNA Genes & Targets
• Integrate miRNAs into gene regulatory pathways &
networks
Need to modify traditional paradigm of
"transcriptional control" by protein-DNA interactions
to include miRNA regulatory mechanisms
C Burge 2005
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