DNA sequencing.
• Dideoxy analogs of normal nucleotide triphosphates
(ddNTP) cause premature termination of a growing chain
of nucleotides.
ACAGTCGATTG
ACAddG
ACAGTCddG
ACAGTCGATTddG
• Fragments are separated according to their sizes in gel
electrophoresis. The lengths show the positions of “G” in
the original DNA sequence.
Nucleotides and phosphodiester bond.
Phosphodiester bond
Genomic sequencing.
• Individual chromosomes are broken into 100kb
random fragments.
• This library of fragments is screened to find
overlapping fragments – contigs.
• Unique overlapping clones are chosen for
sequencing.
• Put together overlapping sequenced clones
using computer programs.
Sequencing cDNA libraries.
• mRNA is pooled from the tissues which express genes.
• cDNA libraries are prepared by copying of mRNA with
reverse transcriptase.
• Expressed Sequence Tags (EST) – partial sequences of
expressed genes.
• Comparing translated ESTs to annotated proteins –
annotation of genes.
Gene prediction.
Gene – DNA sequence encoding protein, rRNA,
tRNA …
Gene concept is complicated:
- Introns/exons
- Alternative splicing
- Genes-in-genes
- Multisubunit proteins
Gene structure.
ATG
-35
TER
-10
Promoter sequences
Gene
ATG – start codon; TER (TAA, TAG,TGA) – termination codons
Codon usage tables.
- Each amino acid can be encoded by several codons.
- Each organism has characteristic pattern of codon usage.
Problems arising in gene prediction.
• Distinguishing pseudogenes (not working former
genes) from genes.
• Exon/intron structure in eukaryotes, exon
flanking regions – not very well conserved.
• Exon can be shuffled alternatively – alternative
splicing.
• Genes can overlap each other and occur on
different strands of DNA.
Gene identification
• Homology-based gene prediction
– Similarity Searches (e.g. BLAST, BLAT)
– ESTs
• Ab initio gene prediction
– Prokaryotes
• ORF identification
– Eukaryotes
• Promoter prediction
• PolyA-signal prediction
• Splice site, start/stop-codon predictions
Ab initio gene prediction.
Predictions are based on the observation that gene DNA
sequence is not random:
- Gene-coding sequence has start and stop codons.
- Each species has a characteristic pattern of synonymous
codon usage.
- Non-coding ORFs are very short.
- Gene would correspond to the longest ORF.
These methods look for the characteristic features of genes
and score them high.
Prokaryotic genes – searching for ORFs.
- Small genomes have high gene density
Haemophilus influenza – 85% genic
- No introns
- Operons
One transcript, many genes
- Open reading frames (ORF) –
contiguous set of codons, start with Met-codon, ends with
stop codon.
Example of ORFs.
There are six possible ORFs in each sequence for both directions of
transcription.
Gene preference score – important
indicator of coding region.
Observation: frequencies of codons and codon pairs in coding and noncoding regions are different.
Given a sequence of codons:
and assuming independence, the probability of finding coding region:
The probability of finding sequence “C” in non-coding regions:
The gene preference score:
P(C )
GPS  log(
)
P0 (C )
Classwork I.
Calculate the gene preference score for the
following human DNA sequence:
AGTACA
Ab initio gene prediction methods.
• Grail II – predicts exons, promoters, Poly(A) sites. Neural
network plus dynamic programming.
• GeneParser – predicts the most likely combination of
exons/introns. Dynamic programming.
• GeneMark – mostly for prokaryotes, Hidden Markov
Models.
• GeneScan – Fourier transform of DNA sequence to find
characteristic patterns.
Confirming gene location using EST
libraries.
• Expressed Sequence Tags (ESTs) – sequenced
short segments of cDNA. They are organized in
the database “UniGene”.
• If region matches ESTs with high statistical
significance, then it is a gene or pseudogene.
Gene prediction accuracy.
True positives (TP) – nucleotides, which are
correctly predicted to be within the gene.
Actual positives (AP) – nucleotides, which are
located within the actual gene.
Predicted positives (PP) – nucleotides, which are
predicted in the gene.
Sensitivity = TP / AP
Specificity = TP / PP
Gene prediction accuracy.
GenScan Website
Common difficulties
• First and last exons difficult to annotate because they
contain UTRs.
• Smaller genes are not statistically significant so they are
thrown out.
• Algorithms are trained with sequences from known
genes which biases them against genes about which
nothing is known.
Gene prediction: classwork II.
• Go to http://www.ncbi.nlm.nih.gov/mapview/ and
view all hemoglobin genes of H. sapiens
• Find 6 hemoglobin genes on chromosome 11,
view the DNA sequence of this chromosome
region
• Submit this sequence to GenScan server at
http://genes.mit.edu/GENSCAN.html
Genome analysis.
Genome – the sum of genes and intergenic
sequences of haploid cell.
The value of genome sequences lies in their
annotation
• Annotation – Characterizing genomic features
using computational and experimental methods
• Genes: levels of annotation
– Gene Prediction – Where are genes?
– What do they encode?
– What proteins/pathways involved in?
From Koonin & Galperin
Accuracy of genome annotation.
• In most genomes functional predictions has been made
for majority of genes 54-79%.
• The source of errors in annotation:
- overprediction (those hits which are statistically
significant in the database search are not checked)
- multidomain protein (found the similarity to only one
domain, although the annotation is extended to the
whole protein).
The error of the genome annotation can be as big as 25%.
Sample genomes.
1. There is almost no correlation between the number of genes and
organism’s complexity.
2. There is a correlation between the amount of nonprotein-coding
DNA and complexity.
Species
H.sapiens
Size
Genes
Genes/Mb
3,200Mb
35,000
11
D.melanogaster
137Mb
13.338
97
C.elegans
85.5Mb
18,266
214
A.thaliana
115Mb
25,800
224
S.cerevisiae
15Mb
6,144
410
E.coli
4.6Mb
4,300
934
Human Genome project.
Comparative genomics - comparison of gene
number, gene content and gene location in
genomes..
Campbell & Heyer “Genomics”
Analysis of gene order (synteny).
Genes with a related function are frequently
clustered on the chromosome.
Ex: E.coli genes responsible for synthesis of Trp
are clustered and order is conserved between
different bacterial species.
Operon: set of genes transcribed simultaneously
with the same direction of transcription
Analysis of gene order (synteny).
Koonin & Galperin “Sequence, Evolution, Function”
Analysis of gene order (synteny).
• The order of genes is not very well conserved if
%identity between prokaryotic genomes is <
50%
• The gene neighborhood can be conserved so
that the all neighboring genes belong to the
same functional class.
• Functional prediction based on gene
neighboring.
Classwork III: Comparing microbial
genomes.
• Go to
http://www.ncbi.nlm.nih.gov/genomes/lproks.cgi
• Select Thermus thermophilus genome
• View TaxTable
• What gene clusters do you see which are
common with Archaea?