Biological Motivation Gene Finding

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Biological MotivationGene Finding

• Anne R. Haake
• Rhys Price Jones

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Gene Finding

• Why do it?
• Find and annotate all the genes within the large
  volume of DNA sequence data
• how many genes in an organism? homologies?
•  
• Gain understanding of problems in basic science
• e.g. gene regulation-what are the mechanisms
  involved in transcription, splicing, etc?
• Different emphasis in these goals has some effect
  on the design of computational approaches for
  gene finding.

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Gene Finding by Biological Methods

• Extract mRNA
  reverse
• transcribe
  cDNA
• Label cDNA
• Detecting by using
  cDNA probe
• Gene found

DNA library
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Gene Finding by Computational Methods

• Dependent on good experimental data to build
  reliable predictive models
• Various aspects of gene structure/function
  provide information used in gene finding programs

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Figure 12.3

• Figure 12.3

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The Informatics View of Genes

• Genes are character strings embedded in much
  larger strings called the genome
• Genes are composed of ordered elements associated
  with the fundamental genetic processes including
  transcription, splicing, and translation.

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Gene Finding

• Cells recognize genes from DNA sequence
• find genes via their bioprocesses
• Not so easy for us..

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CTAGCAGGGACCCCAGCGCCC
GAGAGACCATGCAGAGGTCGCCTCTGGAAAAGGCCAGCGTTGTCTCCAAA
CTTTTTTTCAGGTGAGAAGGTGGCCAACCGAGCTTCGGAAAGACACGTGC
CCACGAAAGAGGAGGGCGTGTGTATGGGTTGGGTTGGGGTAAAGGAATAA
GCAGTTTTTAAAAAGATGCGCTATCATTCATTGTTTTGAAAGAAAATGTG
GGTATTGTAGAATAAAACAGAAAGCATTAAGAAGAGATGGAAGAATGAAC
TGAAGCTGATTGAATAGAGAGCCACATCTACTTGCAACTGAAAAGTTAGA
ATCTCAAGACTCAAGTACGCTACTATGCACTTGTTTTATTTCATTTTTCT
AAGAAACTAAAAATACTTGTTAATAAGTACCTANGTATGGTTTATTGGTT
TTCCCCCTTCATGCCTTGGACACTTGATTGTCTTCTTGGCACATACAGGT
GCCATGCCTGCATATAGTAAGTGCTCAGAAAACATTTCTTGACTGAATTC
AGCCAACAAAAATTTTGGGGTAGGTAGAAAATATATGCTTAAAGTATTTA
TTGTTATGAGACTGGATATAT...
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G CTAGCAGGGACCCCAGCGCCCGAGAGACCAT
GCAGAGGTCGCCTCTGGAAAAGGCCAGCGTTGTCTCCAAACTTTTTTTCA
GGTGAGAAGGTGGCCAACCGAGCTTCGGAAAGACACGTGCCCACGAAAGA
GGAGGGCGTGTGTATGGGTTGGGTTGGGGTAAAGGAATAAGCAGTTTTTA
AAAAGATGCGCTATCATTCATTGTTTTGAAAGAAAATGTGGGTATTGTAG
AATAAAACAGAAAGCATTAAGAAGAGATGGAAGAATGAACTGAAGCTGAT
TGAATAGAGAGCCACATCTACTTGCAACTGAAAAGTTAGAATCTCAAGAC
TCAAGTACGCTACTATGCACTTGTTTTATTTCATTTTTCTAAGAAACTAA
AAATACTTGTTAATAAGTACCTANGTATGGTTTATTGGTTTTCCCCCTTC
ATGCCTTGGACACTTGATTGTCTTCTTGGCACATACAGGTGCCATGCCTG
CATATAGTAAGTGCTCAGAAAACATTTCTTGACTGAATTCAGCCAACAAA
AATTTTGGGGTAGGTAGAAAATATATGCTTAAAGTATTTATTGTTATGAG
ACTGGATATAT...
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Types of Genes

• Protein coding
• most genes
• RNA genes
• rRNA
• tRNA
• snRNA (small nuclear RNA)
• snoRNA (small nucleolar RNA)

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3 Major Categories of Information used in Gene
Finding Programs

• Signals/features a sequence pattern with
  functional significance e.g. splice donor
  acceptor sites, start and stop codons, promoter
  features such as TATA boxes, TF binding sites,
  CpG islands
• Content/composition -statistical properties of
  coding vs. non-coding regions.
• e.g. codon-bias length of ORFs in prokaryotesGC
  content
• Similarity-compare DNA sequence to known
  sequences in database
• Not only known proteins but also ESTs, cDNAs

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Looking for Protein Coding Genes

• Look for ORF (begins with start codon, ends with
  stop codon, no internal stops!)
• long (usually gt 60-100 aa)
• If homologous to known protein more likely
• Look for basal signals
• Transcription, splicing, translation
• Look for regulatory signals
• Depends on organism
• Prokaryotes vs Eukaryotes
• Vertebrate vs fungi

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Easier problemGene Finding in Bacterial Genomes

• Why?
• Dense Genomes
• Short intergenic regions
• Uninterrupted ORFs
• Conserved signals
• Abundant comparative information
• Complete Genomes available for many

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What do Prokaryotic Genes look like?
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3
Open Reading Frame
Promoter region (maybe)
Ribosome binding site (maybe)
Termination sequence (maybe)
Start codon / Stop Codon
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Prokaryotic Gene Expression
Promoter
Cistron1
Cistron2
CistronN
Terminator
Transcription
RNA Polymerase
mRNA 5
3
1
2
N
SD in polycistronic message
N
N
C
N
C
C
1
2
3
Polypeptides
Slide modified from http//biology.uky.edu/520/Le
cture/lect8/lect8Notes.ppt
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Open Reading Frame (ORF)

• Any stretch of DNA that potentially encodes a
  protein
• The identification of an ORF is the first
  indication that a segment of DNA may be part of a
  functional gene

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Open Reading Frames

• Each grouping of the nucleotides into consecutive
  triplets constitutes a reading frame. There are
  three different reading frames in the 5-gt3
  direction and a further three in the reverse
  direction on the opposite strand.
• A sequence of triplets that contains no stop
  codon is an Open Reading Frame (ORF)

A C G T A A C T G A C T A G G T G A A T
CGT AAC TGA CTA GGT GAA
GTA ACT GAC TAG GTG AAT
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ORFs as gene candidates

• An open reading frame that begins with a start
  codon (usually ATG, GTG or TTG, but this is
  species-dependent)
• Most prokaryotic genes code for proteins that are
  60 or more amino acids in length
• The probability that a random sequence of
  nucleotides of length n has no stop codons is
  (61/64)n
• When n is 50, there is a probability of 92 that
  the random sequence contains a stop codon
• When n is 100, this probability exceeds 99

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Codon Bias

• Genetic code degenerate
• Equivalent triplet codons code for the same amino
  acid
• http//www.pangloss.com/seidel/Protocols/codon.htm
  l
• Codon usage varies
• organism to organism
• gene to gene
• Biological basis
• Avoidance of codons similar to stop
• Preference for codons that correspond to abundant
  tRNAs within the organism

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Codon Bias Gene Differences
GAL4 ADH1 Gly GGG 0.21 0 Gly GGA 0.17 0 Gly
GGT 0.38 0.93 Gly GGC 0.24 0.07
Slide modified from http//biology.uky.edu/520/Le
cture/lect8/lect8Notes.ppt
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Codon BiasOrganism differences

• Yeast Genome arg specified by AGA 48 of time
  (other five equivalent codons 10 each)
• Fruitfly Genome arg specified by CGC 33 of time
  (other five 13 each)
• Complete set of codon usage biases can be found
  at

http//www.kazusa.or.jp/codon/
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GC content

• GC relative to AT is a distinguishing factor of
  bacterial genomes
• Varies dramatically across species
• Serves as a means to identify bacterial species
• For various biological reasons
• Mutational bias of particular DNA polymerases
• DNA repair mechanisms
• horizontal gene transfer (transformation,
  transduction, conjugation)

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GC Content

• GC content may be different in recently acquired
  genes than elsewhere
• This can lead to variations in the frequency of
  codon usage within coding regions
• There may be significant differences in codon
  bias within different genes of a single
  bacteriums genome

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Ribosome Binding Sites

• RBS is also known as a Shine-Dalgarno sequence
  (species-dependent) that should bind well with
  the 3 end of 16S rRNA (part of the ribosome)
• Usually found within 4-18 nucleotides of the
  start codon of a true gene

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Shine-Dalgarno Sequence

• Is a nucleotide sequence (consensus AGGAGG)
  that is present in the 5'-untranslated region of
  prokaryotic mRNAs.
• This sequence serves as a binding site for
  ribosomes and is thought to influence the reading
  frame.
• If a subsequence aligning well with the
  Shine-Dalgarno sequence is found within 4-18
  nucleotides of an ORFs start codon, that
  improves the ORFs candidacy.

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Bacterial Promoter
-35 T82T84G78A65C54A45 (16-18
bp) T80A95T45A60A50T96(A,G) -10
1 Not so simple remember, these are consensus
sequences
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Termination Sequences

• 3-U tail
• Stem/loop
• Inverted repeat immediately preceding the runs of
  uracil
• Termination sequence