chap 9. gene discovery. dnarna cdna protein est (expressed seq. tag)
TRANSCRIPT
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Chap 9. Gene Discovery
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DNA RNA
cDNA
protein
EST (Expressed Seq. Tag)
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Gene Discovery
A major application of bioinformatics Matching known patterns of genes A gene
Promoter + 5’ UTR + Protein coding sequence + 3’ UTR
Coding sequence starts with ATG, stops with TAG,TGA or TAA Coding sequence is called an open reading
frame (ORF)
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Gene Structure
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ORF (Open Reading Frame): DNA can encode six Proteins
5’ CAT CAA 5’ ATC AAC 5’ TCA ACT
5’ GTG GGT 5’ TGG GTA 5’ GGG TAG
5’ CATCAACTACAACTCCAAAGACACCCTTACACATCAACAAACCTACCCAC 3’3’ GTAGTTGATGTTGAGGTTTCTGTGGGAATGTGTAGTTGTTTGGATGGGTG 5’
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Transcription
Gene sequence is copied from one strand Sense strand = mRNA sequence
Antisense strand is used to generate mRNA sequence
5’CGCTATAGCGTTTCAT 3’ -- antisense, template strand 3’GCGATATCGCAAAGTA 5’ – sense, coding strand
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sense
Template, anti-sense
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Transcription initiation Double-helix DNA strands are separated in the gene coding
region Which enzyme detects the beginning of a gene ?
RNA Polymerase (multi-subunit enzyme that synthesize RNA) binds to promoter RNA polymerase I – 28S, 5.8S and 18S rRNA
genes RNA polymerase II – coding genes, snRNA RNA polymerase III – tRNA, 5S rRNA, snoRNA
Other enzymes General (Basal) Transcription Factor (GTF)
TFIIA, TFIIB, TFIID TFIID – recognize promoter sequence
http://www.youtube.com/watch?v=MkUgkDLp2iE
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Promoter in E.coli
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Transcription initiation in E.coli
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Transcription initiation in eukaryotes Promoter consists of
-25 or TATA box(TATAWAW; W=A, T) And Inr (initiator) seq. (YYCARR: Y=C,T; R=A,G)
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Transcription initiation in eukaryotes
Initial contact is made by general transcription factor (GTF) TFIID, which consists of TATA-binding protein (TBP) and at least 12 TBP-associated factors (TAF)
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Transcription Start Site (TSS) www.cs.uml.edu/~Kim/580/review_polII_11_Kadonaga.p
df TSS – the first base copied to mRNA Core promoter – region around a TSS Conventionally, core promoter has
TA box at -30 bp of a Inr (Initiator) Transcription Factor (TF) bind to TATA box, Inr
sequence, and other sites; bend DNA 90 degree; recruite general TF
CpG islands: 300-3000 bp of C & G in 40% of promoters
More recently, TATA box only in 10-20% or promoters
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Core Promoter Elements
IIB Recognition Element (BRE) (SSRCGCC) BREu (BREd) suppresses (enhances) transcription
TATA box – TATAWAAR (metazoans) W (A,T); R (A,G-Purine); Y (T,C – Pyrimidine)
Inr – YYANWYY (A+1) DPE (downstream Core Promoter Element) MTE (Motif Ten Element)
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Focused/Dispersed TSS
Focused (Sharp) TSS Distinct TSS site Usually TATA box in sharp TSS Primarily in tissue-specific expressions
Dispersed (Broad) TSS Multiple weak start sites in 50-100 nt A few Inr or Inr-like seq in the neighborhood Generally associated with ubiquitously expressed
genes Thought to be related to CpG islands
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How to recognize the end of transcription ? Terminator seq. stalls polymerase
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Splicing Alternative splicing to produce mRNA
Splicesome – a collection of snRNA
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Function of Introns www.cs.uml.edu/~kim/580/review_intron.pdf When inserted into protomer, boost expression
level First introns are long Alternative exons are flanked by long introns But, association between intron length and expression
breadth in human is not found Removal of 2nd intron of human beta-globin gene reduces
the efficiency of 3’-end formation RNA pol II elongation rate – 3.8kb/min
Introns may serve as time delays between activation of a gene
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Annotation: How do I get from this…
>mouse_ear_cress_1080 AGGCTTGTAAAAGTGATTAAAACTGTGACATTTACTCTAAGAGAAGTAACCTGTTTGATGCATTTCCCTAATATACCGGTGTGGAAAAGTGTAGGTATCTGTACTCAGCTGAAATGGTGGACGATTTTGAAGAAGATGAACTCTCATTGACTGAAAGCGGGTTGAAGAGTGAAGATGGCGTTATTATCGAGATGAATGTCTCCTGGATGCTTTTATTATCATGTTTGGGAATTTACCAAGGGAGAGGTATCAGAATCTATCTTAGAAGGTTACATTTAGCTCAAGCTTGCATCAACATCTTTACTTAGAGCTCTACGGGTTTTAGTGTGTTTGAAGTTTCTTAACTCCTAGTATAATTAGAATCTTCTGCAGCAGACTTTAGAGTTTTGGGATGTAGAGCTAACCAGAGTCGGTTTGTTTAAACTAGAATCTTTTTATGTAGCAGACTTGTTCAGTACCTGAATACCAGTTTTAAATTACCGTCAGATGTTGATCTTGTTGGTAATAATGGAGAAACGGAAGAATAATTAGACGAAACAAACTCTTTAAGAACGTATCTTTCAGTTTTCCATCACAAATTTTCTTACAAGCTACAAAAATCGAACTATATATAACTGAACCGAATTTAAACCGGAGGGAGGGTTTGACTTTGGTCAATCACATTTCCAATGATACCGTCGTTTGGTTTGGGGAAGCCTCGTCGTACAAATACGACGTCGTTTAAGGAAAGCCCTCCTTAACCCCAGTTATAAGCTCAAAGTTGTACTTGACCTTTTTAAAGAAGCACGAAACGAAAAACCCTAAAATTCCCAAGCAGAGAAAGAGAGACAGAGCAAGTACAGATTTCAACTAGCTCAAGATGATCATCCCTGTTCGTTGCTTTACTTGTGGAAAGGTTGATATTTTCCCCTTCGCTTTGGTCTTATTTAGGGTTTTACTCCGTCTTTATAGGGTTTTAGTTACTCCAAATTTGGCTAAGAAGAGATCTTTACTCTCTGTATTTGACACGAATGTTTTTAATCGGTTGGATACATGTTGGGTCGATTAGAGAAATAAAGTATTGAGCTTTACTAAGCTTTCACCTTGTGATTGGTTTAGGTGATTGGAAACAAATGGGATCAGTATCTTGATCTTCTCCAGCTCGACTACACTGAAGGGTAAGCTTACAATGATTCTCACTTCTTGCTGCTCTAATCATCATACTTTGTGTCAAAAAGAGAGTAATTGCTTTGCGTTTTAGAGAAATTAGCCCAGATTTCGTATTGGGTCTGTGAAGTTTCATATTAGCTAACACACTTCTCTAATTGATAACAGAAGCTATAAAATAGATTTGCTGATGAAGGAGTTAGCTTTTTATAATCTTCTGTGTTTGTGTTTTACTGTCTGTGTCATTGGAAGAGACTATGTCCTGCCTATATAATCTCTATGTGCCTATCTAGATTTTCTATACAATTGATATTTGA
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…to this?
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Meaning?
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Comparative Tools (Database searches)
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What do we know about genes? Expressed (Transcribed)
Transcriptional start & termination sites (TXSS, TXTS) Transcription artifacts (cDNA & ESTs (Expressed
Sequence Tags)) Regulated
Promoters (TATAAA) Transcription Factor Binding Sites CpG
Meaningful (Translated) 3n basepairs Codon usage Translational start & stop/termination codons (TLSS,
TLTS) Translation artifacts (proteins)
Spliced Splice sites (GT-AG)
Derived (Homology: Paralogy/Orthology) Search for known genes, proteins (BLAST)
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How might this knowledge help to find genes?
Predict genes Look for potential starts and stops. Connect them into open reading frames (ORFs). Filter for “correct’ length & codon usage.
Search databases Known genes: UniGene Known proteins: UniProt
Use transcript evidence cDNA ESTs (Expressed Sequence Tags) proteins
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Exon ExonIntron
Pre-m
RN
A
5’ Splice Site
3’ Splice Site
Reddy, S.N. Annu. Rev. Plant Biol. 2007 58:267-94Of 1588 examined predicted splice sites in Arabidopsis
1470 sites (93%) followed the canonical GT…AG
consensus. (Plant (2004) 39, 877–885)
Canonical splice sites
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The primary transcript of a gene is spliced into different mRNAs leading to multiple proteins
generated from the same gene.
- Contributes to protein diversity.- Can occur in any part of the transcript including
UTRs..- Can alter start codons, stop codons, reading
frame, CDS, UTRs.- May alter stability-life, translation (time, location,
duration), protein sequence, or both.
Alternative Splicing
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One gene, one enzyme
One gene, one polypetide
One gene, one set of transcripts (> 0)
The dogmas – they are a~changing…
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Alternative splicing in metazoans (Animalia)
• Alternative splicing well characterized in animals.• As many as 96% of human genes may have multiple splice forms.• Functional significance of alternative spicing still poorly understood.
Alternative splicing in animals. Nature Genetics Research 36; 2004
Bridging the gap between genome and transcriptome Nucleic Acids Research 32, 2004.
Splice statistics for human genes
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RuBisCo alternative splicing one of first plant examples:
“The data presented here demonstrate the existence of alternative splicing in plant systems, but the physiological significance of synthesizing two forms of rubisco activase remains unclear. However, this process may have important implications in photosynthesis. If these polypeptides were functionally equivalent enzymes in the chloroplast, there would be no need for the production of both….”
Alternative splicing in plants
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Biological significance of AS in plants…includes:
- regulation of flowering;- resistance to diseases;- enzyme activity (timing, duration, turn-over time, location).
Most genome databases give alternatively spliced plant gene variants
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Example: Jasmonate signaling in Arabidopsis
- Plant hormone; affects cell division, growth, reproduction and responses to insects, pathogens, and abiotic stress factors.
- Jasmonate Signaling Repressor Protein JAZ 10 splice variants JAZ 10.1, JAZ 10.3 and JAZ 10.4 differ in susceptibility to degradation.
- Phenotypic consequences include male sterility and altered root growth.
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Example: Jasmonate signaling in Arabidopsis
- Alternative splice sites C’ and D’ lead to different splice variants
- JAZ10.3: premature stop codon in D exon, intact JAS domain
- JAZ10.4: truncated C exon, protein lacks JAS domain- JAZ 10 encoded by At5G13220
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AS in different Reading Frames
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Gene Prediction
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Gene Prediction Methods
Intrinsic or template methods (ab initio) Search by signal
Signals (Short, functional DNA elements involved in gene spec)
Four basic signals defining coding exons Translation start site, 5’ (donor), 3’ (acceptor),
stop site Search by content
Extrinsic or look-up methods Homology-based
Compare sequence of interest against known coding sequences
Comparative gene prediction Compare sequence of interest against anonymous
sequences
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Gene Prediction Methods
Sequence-based Search for ORFs, and consensus sequences
Alignment-based Search for orthologous genes of other organisms
Search for strong conservation of a genome region Content-based
Search for patterns such as nucleotide or codon frequency, characteristic of coding sequences
Probabilistic Prediction algorithsm
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Typical Computational Steps in Gene Prediction Identify and score suitable splice sites and start/stop signals
along the query sequence Predict candidate exons as detected by these signals Score exons as a function of signals and coding stats
Factor in the quality of alignment between the query and known coding sequences
Assemble a subset of these exon candidates into a predicted gene structure Assemble to maximizes a particular scoring function
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Prediction and Scoring of Exons Protein coding regions have characteristic
compositional bias e.g., A triplet pattern in coding region
Hexamer frequency method with 5th order Markov models widely used Likelihood of a particular base at a given position is
dependent on five preceding bases
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From Exons to RNA
Assembly of several Exons to a gene Combinatorially difficult Can use dynamic programming GRAIL (Gene Recognition and Anslysis Internet
Link), FGENESH, GENEID HMM (Hidden Markov Model)
GENSCAN Sequence Similarity-Based Gene Prediction
GENEWISE
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How Well Do Predictions Work ?
Sensitivity (Sn) = TP / (TP+FN) Specificity (Sp) = TP / (TP+FP) Correlation coefficient (CC)
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Accuracy of Gene Finding Programs
• Sanja Rogic, Alan K. Mackworth, and Francis B.F. Ouellette (2001) Genome Research 11
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Promoter Analysis
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Annotation Cheat Sheet
• Open existing project or generate new (Red square)
• Run RepeatMasker
• Generate evidence (Predictions, BLAST searches)
• Synthesize evidence into gene models (Apollo)
• Browse results locally and in context (Phytozome)
• Conduct functional analysis (link from Browser)
• Prospect for gene family (Yellow Line from Browser)
A. DNA Subway
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• Select region that holds biological gene evidence
• Optimize work space and zoom to region (View tab)
• Expand all tiers (Tiers tab)
• Drag evidence item(s) onto workspace (mouse)
• Edit to match biol. evidence (right-click item for tools)
• Record what was done in Annotation Info Editor
• Assess necessity to build alternative model(s)
• Upload model(s) to DNA Subway (File tab)
B. Apollo