bioinformatics topics not covered in this course bmi 730
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Bioinformatics Topics Not Covered in this Course BMI 730. Kun Huang Department of Biomedical Informatics Ohio State University. Non-coding RNA MicroRNA Related Bioinformatics Issues MicroRNA prediction and recognition Second order structure prediction Target prediction - PowerPoint PPT PresentationTRANSCRIPT
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Bioinformatics Topics Not Covered in this Course
BMI 730 Kun Huang
Department of Biomedical InformaticsOhio State University
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Non-coding RNA MicroRNA Related Bioinformatics Issues
MicroRNA prediction and recognition Second order structure prediction Target prediction
Microbial Related Bioinformatics Metagenomics
Other Omics
Other Informatics
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Non-coding RNA• Non-coding DNA
• Junk DNA• Pseudogenes• Retrotransposons - Human Endogenous
Retroviruses (HERVs)• C-value enigma (e.g., Amoeba dubia genome
has more than 670 billion bases; pufferfish genome is 1/10 of human genome)
• Findings from ENCODE – nearly the entire genome is transcribed
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Non-coding RNA (ncRNA)• Any RNA molecule that is not translated into
a protein. • sRNA, npcRNA, nmRNA, snmRNA, fRNA• Also including tRNA, rRNA, snoRNA,
microRNA (miRNA), siRNA, piRNA, long ncRNA (e.g., Xist), shRNA
• Note the difference between siRNA and miRNA
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Non-coding RNA (ncRNA)• RNA-induced silencing complex (RISC)• RNA-induced transcriptional silencing (RITS)
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MicroRNA (miRNA)• Another level of regulation
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a
p
m
1
2
b
E2F1
E2F2
E2F3 Myc
17-5p 17-3p 18a 19a 20a 19b 92-1
c
Myc E2F
mir-17-92
Reviewed by: Coller et al. (2008), PLoS Genet 3(8): e146Figures from Dr. Baltz Agula
MicroRNA (miRNA)
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Non-coding RNA
MicroRNA Related Bioinformatics Issues• Secondary structure prediction• MicroRNA prediction and recognition• Target prediction
Databases
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Secondary structure prediction• Applications
• RNA folding dynamics• ncRNA discovery• Microarray probe validation/comparison
Wang et al. Genome Biology 2004 5:R65
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Secondary structure prediction - Physics-based models
- Minimizing free energy / Dynamical programming / other optimization schemes
- Parameters come from empirical studies of RNA structural energetics (e.g., nearest neighbor interactions in stacking base pairs using synthesized oligonucleotides)
- Restricted from experimental procedure- Scoring models are used- Most ignore sequence dependence of hairpin, bulge,
internal, and multi-branch loop energies- Multi-branch loop energies rely on ad hoc scores- Still top performance- Mfold, ViennaRNA, PKnots, RDfold, etc
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Secondary structure prediction - Probabilistic approach- Stochastic context-free grammars (SCFG) – e.g., QRNA
- Specify grammar rules that induce a joint probability distribution over possible RNA structures and sequences
- Parameter easily learnt without experiments- Parameters may not have physical meanings- Performance inferior to physics-model methods
- Extensions: Conditional log-linear model (CLLM) – e.g., CONTRAfold
- Integrate the learning procedure with energy-based scoring systems
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Secondary structure prediction
CONTRAfold PKnotRG
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Secondary structure prediction - Comparative approach- Single sequence prediction (physics-based, SCFG) have
difficulty in searching all configurations- Structures that have been conserved by evolution are far
more likely to be the functional form
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MicroRNA prediction and discovery- Experimental approach - cloning- MicroRNA array (OSU microarray facility)- Massive sequencing
- Select segments in the range of 20-25nt- Using Solexa/SOLiD sequencer- Map to genome- Enrichment analysis / peak calling- Experimental validation
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MicroRNA prediction and discovery- Bioinformatics / machine learning approach
Wang et al. Genome Biology 2004 5:R65
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MicroRNA prediction and discovery- Bioinformatics / machine learning approach
- Using evolutionary information
Nam, J.-W. et al. Nucl. Acids Res. 2005 33:3570-3581; doi:10.1093/nar/gki668
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MicroRNA prediction and discovery- Bioinformatics / machine learning approach
- Support vector machine / need features
• Features: • Sequence features
• Nucleotide frequency counts• Total G/C content
• Folding features• Pairing propensity• Minimum free energy (MFE)
• Topological features• Packing ratio
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MicroRNA target Prediction- Experimental / bioinformatics approach
- Blast can identify thousands potential targets – how to pin down the real ones?
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MicroRNA target Prediction- Computational / bioinformatics approach
- Mutually exclusive transcription pattern between miRNA and its targets
- Microarray screening- Existing of complementary sequence- Context score – features - Machine learning approaches (e.g., SVM,
regression, etc)
Cell, Volume 136, Issue 2, 215-233, 23 January 2009MicroRNAs: Target Recognition and Regulatory Functions
David P. Bartel
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Non-coding RNA
MicroRNA Related Bioinformatics Issues• Secondary structure prediction• MicroRNA prediction and recognition• Target prediction
Databases
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Databases• MicroRNA.org:
http://www.microrna.org/microrna/getMirnaForm.do• MirBase: http://microrna.sanger.ac.uk• …
Target prediction• MIRDB• TargetScan (http://targetscan.org)• PicTar (http://pictar.bio.nyu.edu)• miRanda (part of Sanger database)• MirTarget • …
Softwares• List at
http://en.wikipedia.org/wiki/List_of_RNA_structure_prediction_software
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Non-coding RNA MicroRNA Related Bioinformatics Issues
MicroRNA prediction and recognition Second order structure prediction Target prediction
Microbial Related Bioinformatics Metagenomics
Other Omics
Other Informatics
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Metagenomics study of genetic material recovered directly from
environmental samples a community of spieces – e.g., microbial from the
stomach of cow Challenges:
Who are there? How many?
16S riRNA – universal primer, highly conserved, used for profiling
forward: AGA GTT TGA TCC TGG CTC AG reverse: ACG GCT ACC TTG TTA CGA CTT
Next generation sequencing – more genes (chicken-and-egg)
Community metabolism – identify metabolic pathways within the community
New challenges: comparative study
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Non-coding RNA MicroRNA Related Bioinformatics Issues
MicroRNA prediction and recognition Second order structure prediction Target prediction
Microbial Related Bioinformatics Metagenomics
Other Omics
Other Informatics