Computational Genomics
description
Transcript of Computational Genomics
Computational Genomics
Izabela MakalowskaJuly 15, 2006
The main task in modern biology is to find out how this…
TGCATCGATCGTAGCTAGCTAGCGCATGCTAGCTAGCTAGCTAGCTACGATGCATCGTGCATCGATCGATGCATGCTAGCTAGCTAGCTAGCATGCTAGCTAGCTAGCTATTGGCGCTAGCTAGCATGCATGCATGCATCGATGCATCGATTATAAGCGCGATGACGTCAGCGCGCGCATTATGCCGCGGCATGCTGCGCACACACAGTACTATAGCATTAGTAAAAAGGCCGCGTATATTTTACACGATAGTGCGGCGCGGCGCGTAGCTAGTGCTAGCTAGTCTCCGGTTACACAGGTAGCTAGCTAGCTGCTAGCTAGCTGCTGCATGCATGCATTAGTAGCTAGTGTAGCTAGCTAGCATGCTGCTAGCATGCAGCATGCATCGGGCGCGATGCTGCTAGCGCTGCTAGCTAGCTAGCTAGCTAGGCGCTAATTATTTATTTTGGGGGGTTAAAAAAAAAAATTTCGCTGCTTATACCCCCCCCCACATGATGATCGTTAGTAGCTACTAGCTCTCATCGCGCGGGGGGATGCTTAGCGTGGTGTGTGTGTGTGGTGTGTGTGGTCCTATAATTAGTGCATCGGCGCATCGATGGCTAGTCGATCGATCGATTTTATATATCTAAAGACCCCATCTCTCTCTCTTTTCCCTTCTCTCGCTAGCGGGCGGTACGATTTACC
…becomes this
DNA sequence contains all information but we need to decipher it.
TGCATCGATCGTAGCTAGCTAGCGCATGCTAGCTAGCTAGCTAGCTACGATGCATCGTGCATCGATCGATGCATGCTAGCTAGCTAGCTAGCATGCTAGCTAGCTAGCTATTGGCGCTAGCTAGCATGCATGCATGCATCGATGCATCGATTATAAGCGCGATGACGTCAGCGCGCGCATTATGCCGCGGCATGCTGCGCACACACAGTACTATAGCATTAGTAAAAAGGCCGCGTATATTTTACACGATAGTGCGGCGCGGCGCGTAGCTAGTGCTAGCTAGTCTCCGGTTACACAGGTAGCTAGCTAGCTGCTAGCTAGCTGCTGCATGCATGCATTAGTAGCTAGTGTAGCTAGCTAGCATGCTGCTAGCATGCAGCATGCATCGGGCGCGATGCTGCTAGCGCTGCTAGCTAGCTAGCTAGCTAGGCGCTAATTATTTATTTTGGGGGGTTAAAAAAAAAAATTTCGCTGCTTATACCCCCCCCCACATGATGATCGTTAGTAGCTACTAGCTCTCATCGCGCGGGGGGATGCTTAGCGTGGTGTGTGTGTGTGGTGTGTGTGGTCCTATAATTAGTGCATCGGCGCATCGATGGCTAGTCGATCGATCGATTTTATATATCTAAAGACCCCATCTCTCTCTCTTTTCCCTTCTCTCGCTAGCGGGCGGTACGATTTACC
Program for life
DNA in our cells store information in a way that is very similar to the way computers do.
Instead of being a binary memory, where everything is either 0 or 1, DNA is a 4 letter alphabet: A, C, G, T
Using computer metaphor we can say that:
Plant cell do not look like a mouse cell because their “programs” are different Liver cells work differently than lung cells because of different input to the
program Children look like parents because their program is a “revision” of parents
program Many diseases are caused by “bugs” in program:
Tay-Sach’s disease: A simple mistake in one line of codeHuntington’s disease: A “line” of code gets repeated a bunch of times
by accident Different ways to solve the same problem:
Plants: photosynthesis = turn light into sugar Animals: eat plant or other animals
What exactly are we looking for in the DNA sequence? Genes
Protein coding RNA genes Retrogenes
Regulatory elements Promotors Enhancers siRNA
Repetitive elements LINES SINES Simple repeats
Are genes just protein or RNA coding elements?
Makorin1-p1 is a non-coding pseudogene of Makorin1. Makorin1-p1 regulates the expression of its related coding gene. It acts by stabilizing the Makorin1 gene by blocking of a cis-acting RNA decay element within the 5’ region of Makorin1.
Are repeats just a junk DNA?
Translation of mRNA containing Alu-cassette results in soluble form of the protein
Caras, I.W., Davitz, M.A., Rhee, L., Weddell, G., Martin Jr., D.W., Namba, T., Sugimoto, Y., Negishi, M., Irie, A., Ushikubi, F., Kaki-Nussenzweig,V., Cloning of decay-accelerating factor suggests novel use of splicing to generate two proteins. Nature. 1987 Feb 5-11;325(6104):545-9.
Getting all genes The most direct way to identify a gene is to document the
transcription of a fragment of the genome - EST sequencing Requires less sequencing since it is focused on coding
sequence only Small rate of false positives, although even 10% of EST
sequences could be artifacts Genes with very restricted expression may newer be
discovered In most cases gives only partial sequences
Genome sequencing Access to entire genome, allows to learn more about
genome organization Regulatory elements Only small percentage of the genome codes for genes Hard to identify less typical genes High rate of false positives
Cell or tissue
Isolate mRNA andreverse transcribe into cDNA
Clone cDNA into a vectorto make a cDNA library
cDNA
vectorPick individual clones
Sequence the5' and 3' endsof cDNA inserts
5' EST
3' EST
Analyze
Constructing EST
Problems with EST data
Contamination Low quality – the error rates are high in individual
ESTs Highly redundant, for highly expressed genes we
can have hundreds of ESTs representing a single gene
The databases are skewed for sequences near 3’ end of mRNA
For most ESTs there is no indication as to the gene from which it was derived
Overlapping genes Splice variants
An example of a good chromatogram showing well-resolved peaks and no ambiguities
This is a region of a chromatogram fairly far along the sequence where some bases in runs of 2 or more are no longer visible as single peaks. Many peaks are beginning to broaden and smear into one another, interpretation of the peaks has become more difficult, and the basecalling software has begun to use 'N's. This is a region of a chromatogram where the traces have become too ambiguous for accurate basecalling. While some parts of this region of the chromatogram can be useful for linking to existing sequences following manual editing, it should not be considered accurate.
Chromatogram
Sequence quality screening
ABI sequencing software contains a program for quality screening
PHRED - reads DNA sequence data, calls bases, and writes the base calls and quality values to output files
Quality file
Phred quality scores
Phred quality score Probability that the base is called wrong
Accuracy of the base call
10 1 in 10 90%
20 1 in 100 99%
30 1 in 1,000 99.9%
40 1 in 10,000 99.99%
50 1 in 100,000 99.999%
Contamination
Vectors - DNA/cDNAs from the biological source organism/organelle are usually inserted into a cloning vector so that they can be cloned, propagated, and manipulated. Sequencing of such constructs frequently produces raw sequences that include segments derived from vector.
Adapters, linkers, and PCR primers - Various oligonucleotides can be attached to the DNA/RNA under investigation as part of the cloning or amplification process.
Impurities in the DNA/RNA - Nucleic acid preparations may contain DNA/RNA from sources other than the intended one.
nucleic acids from an organelle mRNA/DNA present in a reagent used in the isolation, purification, or
cloning procedures nucleic acids from other organisms present in the material from which the
DNA/RNA was isolated other DNAs/RNAs used in the laboratory (e.g., from accidental mixing of
samples or cross contamination from dirty pipettes, tips, tubes, or equipment)
Consequences of contamination
Time and effort wasted on meaningless analyses
Erroneous conclusions drawn about the biological significance of the sequence
Misassembly of sequence contigs and false clustering of Expressed Sequence Tags (ESTs)
Why contamination is causing problems
Gene A Gene B
Assembly
Contigs
VecScreeVecScreenn
http://www.ncbi.nlm.nih.gov/VecScreen/VecScreen_docs.html
Cross_match
Cross_match is a general purpose application for comparing any two DNA sequence sets. For example, it can be used to compare a set of reads to a set of vector sequences and produce vector-masked versions of the reads. It is slower but more sensitive than BLAST.
GCACGCACAACCAGACCATGCTCGGACGACCCGCTGTACATCGGCCTGCGGCAGAGGCGCGTGCGCGGCGCCGCGTACGACGAGTTCGTCGACGAGTTCATGCAGGCGGTCGTCAAGCGCTTCGGGCAGAACTGCCTCATACAGTTCGAGGACTTCGCCAACGCGAACGCGTTCCGCCTGCTCGAGAAGTACCGCGGCAGGTACTGCACGTTCAACGACGACCTCCAGGGCACGGCGGCGGTGGCGGTGGCCGGGCTGCTCGCGTCGCTGCGCATCACCGGCAAGCGGCTCTCCGACAACGTGTTCGTGTTCCAGGGAGCCGGCGAGGCATCTCTGGGTATCGCCGAGCTGTGCGTGATGGCGATGAAGAACGAGGGTACATCGGACGCCGATGCCCGCTGCAAGATTTGGATGGTGGACTCCAAGGGTCTCATCGTGAAGAACCGTCCTGAAGGTGGACTGAACGAACACAAGGAGAAGTTTGCCCAGAACTGCTCCCCCATTCGGACACTTGCCGAAGTTATAAATGTTGCTAAGCCTTCTGTACTGATTGGCXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXGCTCGACGGCGGAAGGAAAA
Qscreen
Sequencing data management and quality checking system Quality screening Relational database for sequence data
management and archive Easy data access via web interface Easy data sharing Sequence and trace view Project statistics Password protected
Qscreen: project and user management
Gene discovery strategy
Cluster and assemble EST sequences to lower redundancy and to increase the length of transcripts
Find coding regions and reading frame Use deducted protein to search
databases and assign function to the gene
Cluster and assemble EST - resources Assembly tools:
TGICL http://www.tigr.org/tdb/tgi/software/ Cap 3 http://genome.cs.mtu.edu/sas.html Phrap http://www.phrap.org/
EST clusters databases UniGene http://www.ncbi.nlm.nih.gov/ TGI http://www.tigr.org/
EST analysis pipeline SMAPi http://smapi.cbio.psu.edu
Two stage process: Clustering ESTs based on the similarity and clone ID
Assembling ESTs
Assembling inside each cluster
Important parameters
Criteria too stringent = many ESTs will not be assembled and genes will stay fragmented
Criteria too loose = ESTs from genes from the same family will be assembled into one gene
Length and similarity level of overlapping fragments
Length of overhanging fragments
Contig quality
ATGTCTCTNTCACTGA TCTGTCCC-CAGTCACGATCGANATGTCTCTGTCNCTNAGTCACGATCGAN
ATGTCTCTNTCACTGA TCTGTCCC-CAGTCACGATCGANATGTCTCGGTCAC-CAGTCACGATCGATATGTCTCGGTCAC-CAGTCACGATCGATTTGTCTGGGTCAC-CTCC GGTGGC-CAGTCACGATNGANATGTCTCGGTCAC-CAGTCACGATCGAT
ATGTCTCTGTCNCTNAGTCACGATCGANATGTCTCGGTCAC-CAGTCACGATCGAT
One gene one cluster?
5’ESTs 3’ESTs
Joined based on similarity Joined based on similarityJoined based on clone ID
One cluster one gene?
Cap3
Use of forward-reverse constraints to correct assembly errors and link contigs.
Use of base quality values in alignment of sequence reads.
Automatic clipping of 5' and 3' poor regions of reads.
Generation of assembly results in ‘ace’ file format for Consed.
Input files
CAP3 takes as input a file of sequence reads in FASTA format. CAP3 takes two optional files: a file of quality values in FASTA format and a file of forward-reverse constraints.
The file of quality values must be named "xyz.qual", and the file of forward-reverse constraints must be named "xyz.con", where "xyz" is the name of the sequence file. CAP3 uses the same format of a quality file as Phrap.
Web interfacehttp://bio.ifom-firc.it/ASSEMBLY/assemble.html
Output files
Clustering – uses modified megablast program to cluster sequences together
Assembly – CAP3 is used to assemble sequences inside each cluster
TGICL – TIGR Gene Indices clustering tool
TGICL – TIGR Gene Indices clustering tool Sequences need to be cleaned before using
TGICL (Lucy, UniVEc, SeqClean) mRNA sequences may be used as ‘seeds’ for
clustering. Caution: partial mRNAs mislabeled as complete can prevent cluster extension beyond the seed.
Difficulty with highly expressed genes that have several thousand ESTs in a single cluster (assembly program may run out of memory)
Phrap part of the Phred/Phrap/Consed program for assembling shotgun DNA sequence data. allows use of the entire read and not just the trimmed high
quality part uses a combination of user-supplied and internally computed
data quality information to improve assembly accuracy constructs the contig sequence as a mosaic of the highest
quality read segments rather than a consensus provides extensive assembly information to assist in trouble-
shooting assembly problems handles large datasets It is strongly recommended that phrap be used in conjunction
with the base calls and base quality values produced by the basecaller, phred; and with the sequence editor/assembly viewer, consed.
Phrap output in Consed
Apple EST assembly
183, 732 ESTsPhrap:
24,199 contigs; 9,765 singletonsCAP3
19,791 contigs; 18,927 singletonsTGICL
22,481 contigs; 28,279 singletons
NCBI UniGene
Clustering at NCBI
EST must have at least 100bp after removing contaminants The overlap between similar ESTs must be at least 70 bp Similarity between overlapping area must be at least 96%
over the 70% of overlapping region
>70% of overlap with at least 96% of similarity
>70bp
>100bp
>100bp
www.tigr.org
TIGR Gene Indices
What are TIGR gene indices?
Clustered and assembled ESTs and mRNA sequences
Each gene and each splice variant is represented by a single consensus sequence
Provides ORF annotation, genome mapping, expression profiles, domain annotation, unique oligomers
>40bp of overlap with at least 95% of similarity
<30bp <30bp
TGI annotations
TGI sequence annotations
BLAST search
SMAPi – Sequence Management and Analysis Pipeline
Online access to data Easy sequence management Custom as well as automated
annotation Sharing among researchers Integrated usage of various
analysis tools (BLAST, Phred/Phrap.)
Supported by grant from Pennsylvania Greenhouse for Life Sciences
The Parasitic Plant Genome Project Comparative
genomics of related parasitic genera
Host-parasite lateral transfer
NCBI ORF finder
Mapping contigs to the genome Splign http://www.ncbi.nlm.nih.gov/sutils/splign/
Genome sequencing
Access to entire genome Regulatory elements Only small percentage of the genome codes for
genes Hard to identify less typical genes High rate of false positives
TGCATCGATCGTAGCTAGCTAGCGCATGCTAGCTAGCTAGCTAGCTACGATGCATCGTGCATCGATCGATGCATGCTAGCTAGCTAGCTAGCATGCTAGCTAGCTAGCTATTGGCGCTAGCTAGCATGCATGCATGCATCGATGCATCGATTATAAGCGCGATGACGTCAGCGCGCGCATTATGCCGCGGCATGCTGCGCACACACAGTACTATAGCATTAGTAAAAAGGCCGCGTATATTTTACACGATAGTGCGGCGCGGCGCGTAGCTAGTGCTAGCTAGTCTCCGGTTACACAGGTAGCTAGCTAGCTGCTAGCTAGCTGCTGCATGCATGCATTAGTAGCTAGTGTAGCTAGCTAGCATGCTGCTAGCATGCAGCATGCATCGGGCGCGATGCTGCTAGCGCTGCTAGCTAGCTAGCTAGCTAGGCGCTAATTATTTATTTTGGGGGGTTAAAAAAAAAAATTTCGCTGCTTATACCCCCCCCCACATGATGATCGTTAGTAGCTACTAGCTCTCATCGCGCGGGGGGATGCTTAGCGTGGTGTGTGTGTGTGGTGTGTGTGGTCCTATAATTAGTGCATCGGCGCATCGATGGCTAGTCGATCGATCGATTTTATATATCTAAAGACCCCATCTCTCTCTCTTTTCCCTTCTCTCGCTAGCGGGCGGTACGATTTACC
Genome sequencing strategies
Page-by-page sequencing strategy
Sequence = determining the letters of each word on each piece of paper
Assembly = fitting the words back together in the correct order
All-at-once sequencing strategy
Find small pieces of paper
Decipher the words on each fragment
Look for overlaps to assemble
Gene identification methods
Molecular techniques Very laborious Time consuming Expensive Low rate of false positives
Computational methods Fast Relatively low cost High rate of false positives Poor performance on less typical genes
Before we start analysis…
We have to: Check sequences quality Remove contamination Assembly sequence reads into longer
contigs Close gaps (in perfect situation)
ComparativeSite-BasedContent-Based
Genomic Sequence
Bulk properties ofsequence:
Open reading frameCodon usageRepeat periodicityCompositional complexity
Absolute properties ofsequence:
Consensus sequencesDonor and acceptor splice sites
Transcription factor binding sites
Polyadenylation signalsStart codonStop codon
Inferences based on sequence homology:
Protein sequence with similarity to translated product of query
Similarity to EST sequences
Conserved regions between species (comparative genomics)
Model based
Gene finding strategies
Gene finding methods classificationSimilarity based predictors: make use of similarity to already known genes and proteins coded by these genes as well as expression data including sequences from cDNAs and data from hybridization experiments (tiling arrays for example)
Dual- and multi-genome predictors: rely on the fact that functional regions of a genome sequence are more conserved during evolution
Model based predictors: use a single genome sequence and exon/intron structure is predicted based on absolute and bulk properties of the sequence
http://pipmaker.bx.psu.edu/pipmaker/
Comparative genomics - MultiPipmaker
Model based methods
We take advantage of what we already learned about gene structures and features of coding sequences. Based on this knowledge we can build theoretical model, develop an algorithm to search for important features, train it on known data and use to search for coding sequences in anonymous genomic fragments
Pattern recognition and matching
The ability of a program to compare novel and known patterns and determine the degree of similarity forms the basis of sequence analysis including gene identification. In similarity based methods we search the genome directly for nucleotide or amino acid pattern observed in one or more already known genes; in comparative genomics we look for similar sequence pattern in two or more genomes, and in method based prediction we look for patterns in sequence composition and signals.
One of the major challenges associated with using pattern matching is in that, in most cases, we need to identify patterns that are ‘similar’ to a target pattern, but the concept of similarity isn’t well defined from programmatic and biological sense. Also, only already known pattern may be used for searches, therefore genes with unusual patterns may not be discovered using these methods.
Coding regions in Prokaryotes
INTERGENIC REGION START CODING
SEQUENCE STOP
Genetic code
Gene Model in Prokaryotes
A 1GT 32
START
CODON
AGT
AAT
GAT
STOP
Open Reading Frames
+3
+2
+1
-3
-2
-1
Properties of the sequence
We can check if sequence in particular ORF has some other features which could tell us if this is a putative coding sequence or the ORF is false positive. We can look at the sequence content and compare it with known coding sequence and non-coding sequence and check to which of these two the ORF sequence is more similar to.
Markov chain
A Markov chain describes at successive times the states of a system. At these times the system may have changed from the state it was in the moment before to another or stayed in the same state. The changes of state are called transitions. The Markov property means the system is memoryless, i.e. it does not "remember" the states it was in before, just "knows" its present state, and hence bases its "decision" to which future state it will transit purely on the present, not considering the past.
Examples of Markov chain
A game of Monopoly, snakes and ladders or any other game whose moves are determined entirely by dice is a Markov chain. This is in contrast to card games such as poker or blackjack, where the cards represent a 'memory' of the past moves. To see the difference, consider the probability for a certain event in the game. In the above mentioned dice games, the only thing that matters is the current state of the board. The next state of the board depends on the current state, and the next roll of the dice. It doesn't depend on how things got to their current state. In a game such as poker or blackjack, a player can gain an advantage by remembering which hands have already been played (and hence which cards are no longer in the deck), so the next state (or hand) of the game is not independent of the past states.
Markov chain – weather model
The matrix P represents the weather model in which a sunny day is 90% likely to be followed by another sunny day, and a rainy day is 50% likely to be followed by another rainy day.
0.9 0.1
0.5 0.5
P =
0.1
0.5
0.9 0.5
Markov chain – sequence properties
A T
GC
How can we do this?CGATCGATCGATCGGCCGATGCTGATGAGTCTCCTACGTCTCGTAGCTCGCGTCGT
ATATCGCGATCGCATCGCGTACGCGATCGCTATCTCGCATCGCGTACTGCAT
CGATCGATCGATCGGCCGATGCTGATGAGTCTCCTACGTCTCGTAGCTCGCGTCGTATATCGCGATCGCATCGCGTACGCGATCGCATCTCGCATCGCGTACTGCAT
CGATCGATCGATCGGCCGATGCTGATGAGTCTCCTACGTCTCGTAGCTCGCGTCGTATATCGCGATCGCATCGCGTACGCGATCGCATCTCGCATCGCGTACTGCAT
CGATCGATCGATCGGCCGATGCTGATGAGTCTCCTACGTCTCGTAGCTCGCGTCGTATATCGCGATCGCATCGCGTACGCGATCGCATCTCGCATCGCGTACTGCAT
20 G7 GA1 GG5 GT7 GC
7/201/205/207/20
For non-coding sequence we assume that probability of each is equal. The more ‘popular’ in coding sequence transition, the higher probability the sequence is coding
Markov Chains
GCGCTAGCGCCGATCATCTACTCG
GCGCTAGCGCCGATCATCTACTCG
GCGCTAGCGCCGATCATCTACTCG }GCGCTAGCGCCGATCATCTACTCG
GCGCTAGCGCCGATCATCTACTCG }GCGCTAGCGCCGATCATCTACTCG
GCGCTAGCGCCGATCATCTACTCG }
first order
second order
fifth order
How far can we go?
Order of our model will have influence on specificity and sensitivity of our program. Too short sequences may not be specific enough and program may return a lot of false positives. Long chains may be to specific and our program will not be sensitive enough and will return a lot of false negatives.
Probability matrix
first order Markov Model - matrix of 16 probabilities
p(A/A),p(A/T),p(A/C),p(A/G)p(T/A),p(T/T),p(T/C),p(T/G)p(C/A),p(C/T),p(C/C),p(C/G)p(G/A),p(G/T),p(G/C),p(G/G)
41+1= 4 =162
42+1= 4 =643
43+1 4= 4 =256
4K+1
Probabilities in different reading frames
GCGCTAGCGCCGATCATCTACTCG
GCG CTA GCG CCG ATC ATC TAC TCG
G CGC TAG CGC CGA TCA TCT ACT CG
Does G more often follow GC when it is in third
position in codon or if it is in second or firstposition?
GC GCT AGC GCC GAT CAT CTA CTC G
Number of probabilities
41+1= 4 = 162
41+1= 4 = 3x16 = 482
Estimating coding potential
LP (S) = log P (S)P (S)
i
0
To estimate if the sequence is coding we have to calculate the probability that the sequence is coding and the probability the sequence is non-coding. Next we calculate the logarithm of the ratio of these two probability values
If the calculated value is > 0 the likelihood that the sequence is coding is higher than the sequence is not coding. If the value is < 0 there is higher likelihood that sequence is not coding.
Markov Models - probabilities
S=AGGACG
P(S) =f(A,1)F(G,A)F(G,G)F(A,G)F(C,A)F(G,C)1 11 22 3
P(S) = 0.27 x 0.19 x 0.27 x 0.24 x 0.21 x 0.12 = 0.00008377
P (S)P (S)
i
0
LP(S) = log
P(S) = 0.25 x 0.25 x 0.25 x 0.25 x 0.25 x 0.25 =0.0002441
LP(S) = log(0.00008377/0.0002441) = -0.4644
Calculating LP
P (S)P (S)
i
0
LP(S) = log
LP(S) = log + log + log + log + log + log 0.270.25
0.190.25
0.270.25
0.240.25
0.210.25
0.120.25
LP(S) = log 1.08 + log 0.76 + log 1.08 + log 0.96 + log 0.84 + log 0.48
LP(S) = 0.0334 + (-0.1191) +0.0334 + (-0.0177) + (-0.0757) + (-0.3187)
LP(S) = -0.4644
GLIMMER
Gene finding program for ProkaryotesSaltzberg et. al, 1998For prediction uses:
StartStopSequence compositionInterpolated Markov Models
The GLIMMER system
Part 1 – Program is trained for given data set (species) Part 2 – Program identifies putative genes in the
genomic sequence Identify all ORFs longer than a thresholdScore each ORF in each reading frame and select
these which gets highest scores in correct reading frame
Score overlapping genes in each frame separately to see which frame score the highest
Running the program
First run build-imm on a set of sequences to make the Markov models (long ORFs from the same or closely related species)build-imm train.seqbuild-imm train.seq
Then run GLIMMER to find genes in your sequenceglimmer your.seq train.seq <options>glimmer your.seq train.seq <options>
GLIMMER options
-g set minimum gene length -o set minimum overlap -p set minimum overlap percentage+r/-r independent probability score
ON/OFF -t set threshold score for calling as
gene
GLIMMER outputMinimum gene length = 180Minimum overlap length = 30Minimum overlap percent = 10.0%Threshold score = 90Use independent scores = TrueUse first start codon = True
Orf Gene Lengths Gene -- Frame Scores - Indep ID# Fr Start Start End Orf Gene Score F1 F2 F3 R1 R2 R3 Score F2 302 305 616 315 312 0 _ 0 _ 99 _ _ 0 1 R1 660 633 220 441 414 99 _ _ _ 99 _ _ 0 F2 620 650 901 282 252 0 _ 0 _ _ _ 99 0 2 R3 1114 1105 638 477 468 99 _ _ _ _ _ 99 0 F3 1119 1140 1466 348 327 0 _ _ 0 _ _ 99 0 3 R3 2026 1999 1118 909 882 99 _ _ _ _ _ 99 0 4 F3 1815 1830 2054 240 225 99 _ _ 99 _ _ _ 0 *** Overlaps #3 by 170 Overlap Region Scores: _ _ 0 _ _ 99 0 Reject #4 5 R2 2600 2597 1935 666 663 99 _ _ _ _ 99 _ 0 *** Overlaps #4 by 120 Overlap Region Scores: _ _ 0 _ 99 _ 0 6 F1 2710 2719 3399 690 681 99 99 _ _ _ _ _ 0 R3 4153 4153 3962 192 192 0 99 _ _ _ _ 0 0 7 F1 3403 3403 4230 828 828 99 99 _ _ _ _ _ 0 R2 4700 4679 4455 246 225 0 _ _ _ _ 0 _ 99
R2 68906 68897 68670 237 228 13 _ _ _ _ 13 _ 86 8 R1 101574 101544 101296 279 249 96 _ _ _ 96 _ _ 3 R3 193228 193204 193022 207 183 56 _ _ _ _ _ 56 43
List of putative genes
Putative Genes:
1 633 220 2 1105 638 3 1999 1118 5 2597 1935 6 2719 3399 7 3403 4230... 39 38472 38741 [Shorter 40 80 74] 40 38662 39450 [Bad Overlap 39 80 25]... 482 464206 464424 [Shadowed by 483]... 636 616213 615965 [Delay by 33 637 50 0]
Output description
39 38472 38741 [Shorter 40 80 74]
40 38662 39450 [Bad Overlap 39 80 25]
[Bad Overlap a b c] means that gene number a overlapped this one and was shorter but scored higher on the overlap region. b is the length of the overlap region and c is the score of *this* gene on the overlap region. There should be a [Shorter ...] notation with gene a giving its score.
[Shorter a b c] means that gene number a overlapped this one and was longer but scored lower on the overlap region. b is the length of the overlap region and c is the score of *this* gene on the overlap region. There should be a [Bad overlap ...] notation with gene a giving its score.
Output description - 2
482 464206 464424 [Shadowed by 483]
... 636 616213 615965 [Delay by 33 637 50 0]
[Shadowed by a] means that this gene was completed contained as part of gene a 's region, but in another frame.
[Delay by a b c d] means that this gene was tentatively rejected because of an overlap with gene b , but if the start codon is postponed by a positions, then this would be a valid gene. The start position reported for this gene includes the delay. c is the length of the overlap region that caused the rejection and d is the score in this gene's frame on that overlap region.
Prokaryotic vs. Eukaryotic Genes Prokaryotes
small genomes high gene density no introns (or splicing) no RNA processing similar promoters terminators important overlapping genes
Eukaryotes large genomes low gene density introns (splicing) RNA processing heterogeneous promoters terminators not important polyadenylation
Coding regions in Prokaryotes
INTERGENIC REGION START CODING
SEQUENCE STOP
From DNA to protein
Gene structure
Pseudogenes and repetitive elements
Complicated gene structures
Overlapping genes
Eukaryotic gene structure
5’ 3’
DNAExon1 Exon2 Exon3 Exon4
Intron1 Intron2 Intron3
polyA signalPyrimidinetract
Branchpoint
CTGAC
Splice siteCAG
Splice siteGGTGAG
TranslationInitiationATG
Stop codonTAG/TGA/TAA
PromoterTATA
Ex1 In1 Ex2 Ex2 In2 Ex3 In3 Ex4 In4 Ex5 Ex5
5’ UTR 3’ UTR
G T AG
E0 E1 E2
I0 I1 I2
Einit Eterm
Single exon gene
5’ UTR 3’ UTR
Poly A
Signal
promoter
Intergenic region
Markov Models
Searching for coding sequences using Markov chain
In this case we do not want check if given sequence fragment is coding or not but we rather want to identify coding fragments in a long sequence. In most cases this is done by calculating statistics in overlapping windows.
Windows coding potentials are used to create profiles. This example shows a profile for a sequence analyzed using 120bp windows with 10 bp distance between them.
AGTACGATATTAGCGGCAATCGTATGACTACGTCTTGCTACGTCTTCTCTCGTCTGCTCTAG
Codon usage
Arg AGG 12.09 0.22Arg AGA 11.73 0.21Ser AGT 10.18 0.14Ser AGC 18.54 0.25 Lys AAG 33.79 0.60Lys AAA 22.32 0.40Asn AAT 16.43 0.44Asn AAC 21.30 0.56 Met ATG 21.86 1.00Ile ATA 6.05 0.14Ile ATT 15.03 0.35Ile ATC 22.47 0.52 Thr ACG 6.80 0.12Thr ACA 15.04 0.27Thr ACT 13.24 0.23Thr ACC 21.52 0.38
Trp TGG 14.74 1.00End TGA 2.64 0.61Cys TGT 9.99 0.42Cys TGC 13.86 0.58 End TAG 0.73 0.17End TAA 0.95 0.22Tyr TAT 11.80 0.42Tyr TAC 16.48 0.58 Leu TTG 11.43 0.12Leu TTA 5.55 0.06Phe TTT 15.36 0.43Phe TTC 20.72 0.57 Ser TCG 4.38 0.06Ser TCA 10.96 0.15Ser TCT 13.51 0.18Ser TCC 17.37 0.23
Gly GGG 17.08 0.23Gly GGA 19.31 0.26Gly GGT 13.66 0.18Gly GGC 24.94 0.33 Glu GAG 38.82 0.59Glu GAA 27.51 0.41Asp GAT 21.45 0.44Asp GAC 27.06 0.56 Val GTG 28.60 0.48Val GTA 6.09 0.10Val GTT 10.30 0.17Val GTC 15.01 0.25 Ala GCG 7.27 0.10Ala GCA 15.50 0.22Ala GCT 20.23 0.28Ala GCC 28.43 0.40
Arg CGG 10.40 0.19Arg CGA 5.63 0.10Arg CGT 5.16 0.09Arg CGC 10.82 0.19 Gln CAG 32.95 0.73Gln CAA 11.94 0.27His CAT 9.56 0.41His CAC 14.00 0.59 Leu CTG 39.93 0.43Leu CTA 6.42 0.07Leu CTT 11.24 0.12Leu CTC 19.14 0.20 Pro CCG 7.02 0.11Pro CCA 17.11 0.27Pro CCT 18.03 0.29Pro CCC 20.51 0.33
Codon usage
S=AGGACGGGATCADNA sequence can be divided into noneoverlapping codons in three reading framesC=C1C2...Cm
AGG ACG GGA TCAA GGA CGG GAT CAAG GAC GGG ATC A
C =AGG11
C =GGA21 C =GGG3
2
Probability that sequence is coding
Probability that sequence is coding is equal probability that sequence of codons is coding. Assuming independence between adjacent codons the probabilty that sequence is coding will be equal to the product of codon frequencies.
P(C) = F(C1)F(C2)..F(Cm)
AGG ACG GGA TCAA GGA CGG GAT CAAG GAC GGG ATC A
P(C) = F(AGG)F(ACG) = 0.022 x 0.038 =0.000836
0.022
0.038
Probability that sequence is non-coding
If the sequence is non-coding the codon frequency will be random and each codon will be equally prbable. In this case frequency for each codon will be 0.0156. This is because we have 64 codons and each of them is equally possible.
Therefore probability that the sequence is non-coding will be:
P(C) = F(AGG)F(AGC) = 0.0156 x 0.0156 = 0.000244
Log-likelihood ratio
LP (S) = log P (S)P (S)
i
0
LP(S) = log 1.4102+log 2.4358 = 0.1493 + 0.3866 = 0.53 > 0
Rule based methodMinimal length ORF
Splicing sites
Putative exon
Codon usage
Neural networkMinimal length ORF
Splicing sites
Codon usage
Przypuszczalnyekson
Putative exon
GRAIL
Gene recognition and Analysis Internet Link Uberbacher and Mural, 1991 First gene prediction program GRAIL1
Neural network recognizing coding potential within fixed-size window (100 bp)
Evaluates coding potential without looking for additional features (e.g. splice junctions, start and stop codons)
GRAIL2 Variable size of windows Incorporated genomic context information (splice sites, start and
stop, polyadenylation signals) GRAILEXP http://compbio.ornl.gov/grailexp/
GRAILEXP
MZEF
M. Zhang 1997 Predicts exons only, does not build gene
structure Uses ‘quadratic discriminant analysis” Variable measures:
Exon length Intron-exon transition Branch site scores
http://rulai.cshl.org/tools/genefinder/
MZEF
FGENES Solovyev et al., 1994 Predicts internal exons Linear discriminant analysis
Donor and acceptor splice sites Putative coding regions Intronic regions both 5’ and 3’ to the putative
exon Passes results to a dynamic programming
algorithm to come up with coherent gene model
http://www.softberry.com/berry.phtml
FGENESH
FGENESH
GENSCAN
Burge and Karlin Search for general and specific compositional
properties of distinct functional units in eukaryotic genes
General fifth-order markov model of coding regions Analyze both DNA strands Sequences may contain multiple and/or partial
genes http://genes.mit.edu/GENSCAN
GENSCAN options
OrganismvertebrateMaizeArabidopsis
Outputpredicted peptides onlypredicted CDS and peptides
Suboptimal exon cutoff1.000.500.25...0.01
GENSCAN output
Prom = promoterInit = Initial exonIntr = Internal exonTerm = Terminal exonSngl = Single exon genePlyA = poly-A signal
P-range Accuracy0.00-0.50 29.8%0.50-0.75 54.1%0.75-0.90 74.8%0.95-0.99 92.4%0.99-1.00 97.7%
Graphical output
Are predictions the same?
GRAILEXP
MZEF
FGENESH
GenScan
TP TPFP FN TN
TP - true positive
FP - false positive
FN - false negative
TN - true negative
Evaluation statistic
Sensitivity Fraction of actual coding regions that are correctly predicted as coding, ranging from 0 to 1
Sn = TP/(TP+FN)
Specificity Fraction of the prediction that is actually correct, ranging from 0 to 1
Sp = TP/(TP+FP)
Correlation Combined measure of sensitivity and specificity, ranging from -1 (always wrong) to +1 (always right)
CC = TP x TN + FPx FN
(PP)(PN)(AP)(AN)
20 not annotated human BAC clones3 finished17 unfinished
Genes that had at least two exons, each predicted by at least two programs the overlap of the predicted exons did not have to be perfectsimilarity to ESTs or known genes was used as supporting evidence but was not required
40 genes (number of exons 2-11)Six single exons predicted by three or four programsThree two-exon genes predicted by one program only but strongly supported by similarities to EST sequences
12 genes were eliminated from further studies as they contained repetitive elements and were most likely false positives
Total: 37 putative transcripts
Experimental validation of predicted genes
predicted exons specificity sensitivity
MZEF 34 0.51 0.56
GRAIL 11 0.48 0.19
GENSCAN 52 0.46 0.91
FGENES 45 0.37 0.75
37 genes were tested, 16 of them (43%) were confirmed.At the exon level there were 159 exons and 58 (36%) were found to be real
Prediction programs performance
Gene structure and alternative splicing
12 genes containing large fragments of repetitive elements were eliminated from experimental validation. However some proteins are partially coded by Alu or MER (Makalowski 1994, 2000)Gene gm121 contains MER at 5' end of cDNA and this is experimentally confirmed gene.Gene gm124 was eliminated from our experimental validation based on the presence of repetitive element. Further analysis showed that this gene is a part of transcript FLJ23129, GenBank accession AK026782
Repetitive elements
50% of mammalian genome are repeats:DNA transposonsretrotransposonsLINEsSINEstandem repeats
masking before similarity search - helps avoid getting similarities caused by the presence of repetitive elements, not because of sequences homology
predicted gene with repetitive elements are less likely to be real, although sometimes repeats are true parts of coding sequence
Repetitive elements
Searches for Alu, MIR, LINE, LTR and other repeats by comparison to sequences in RepBase library
RepBase is a database of repetitive DNA sequence elements found in a variety of eukaryotic organisms including primates, rodents, cow, dog, chicken, fugu, drosophila, arabidopsis, rice
Accepts local databases with repetitive elementsRepetitive elements are masked by replacing nucleotides with string of letters N
Masking can be limited to certain types of repeats only
RepeatMasker
RepeatMaskerRepeatMasker
http://www.repeatmasker.org/
RepeatMasker outputs
Functional annotation- Gene Ontology Gene Ontology: A controlled vocabulary to describe gene
products - proteins and RNA - in any organism. Gene is described in three categories:
Cellular component: where a gene
product acts
Molecular function: activities or “jobs”
of a gene product
Biological process: a commonly
recognized series of events
Annotation source
ISS Inferred from Sequence/Structural SimilarityIDA Inferred from Direct AssayIPI Inferred from Physical InteractionTAS Traceable Author StatementNAS Non-traceable Author StatementIMP Inferred from Mutant PhenotypeIGI Inferred from Genetic InteractionIEP Inferred from Expression PatternIC Inferred by CuratorND No Data available
IEA Inferred from electronic annotation
Gene annotation
Annotating genes
Model organism: look at curated databases
Annotating novel genes
Similarity search against curated and well annotated database: functional annotations deducted from close and significant match
Functional domains identification: mapping domains and terms to Gene Ontology
Annotating novel genes
Similarity search against curated and well annotated database: functional annotations deducted from close and significant match
Identification of functional domains: mapping domain to GO term
Regulatory sequences
Difficult to identify using computer programs Most regulatory elements are still not
identified They are usually very short: 6-10 base pairs They may differ in one or more places
Finding regulatory elements
Phylogenetic footprinting – comparative genomics used to identify conserved non-coding sequences (MultiPipMaker)
Phylogenetic shadowing – conceptually similar to phylogenetic footprinting, with the difference that closely related species are compared (eShadow)
Gibbs sampling – random iterations of the promoter regions alignments to identify blocks of conserved residues (Gibbs Motif Sampler)
Hidden Markov Models – searching for known regulatory elements using probability matrices (Cister)
Phylogenetic shadowing
Gibbs sampling
ACGGTACGTTGG
GGTACGTAGGAC
TGGCTACCTTGG
CGGTACGTAGGT
******* **
Building model from existing alignment
A HMM model for a DNA motif alignments, The transitions are shown with arrows whose thickness indicate their probability. In each state, the histogram shows the probabilities of the four bases.
ACA - - - ATG TCA ACT ATCACA C - - AGCAGA - - - ATCACC G - - ATC Transition probabilities
Output Probabilities
insertion
Detects cis-elements clusters by using Hidden Markov Detects cis-elements clusters by using Hidden Markov ModelModel
For each element uses separate matrix with frequencies of For each element uses separate matrix with frequencies of each nucleotide in each position; user can input matrix for each nucleotide in each position; user can input matrix for elements not included in the basic optionelements not included in the basic option
User can specify:User can specify:ƒ distance between neighboring cis-elements within a distance between neighboring cis-elements within a
clusterclusterƒ number of cis-elements in the clusternumber of cis-elements in the clusterƒ distance between clustersdistance between clustersƒ half-width of the sliding windowhalf-width of the sliding window
Cister
NA AML-1aXXDE runt-factor AML-1XXBF T02256; AML1a; Species: human, Homo sapiens.XXP0 A C G T01 5 1 2 49 T02 2 2 52 1 G03 4 14 1 38 T04 0 0 57 0 G05 1 0 55 1 G06 1 4 0 52 T
TGTGGTTGCGGTTGTGGTAGTGGTTGTGGC
Sequences of experimentaly identified elements are alignedand frequencies in each position are calculated
Frequency matrix
http://zlab.bu.edu/~mfrith/cister.shtml
Cister