1. Dilvan Moreira (based on Prof. André Carvalho presentation)
Sequence Analyzes
Dilvan Moreira (based on Prof. André Carvalho presentation)

2. Reading
Introduction to Computational Genomics: A Case Studies Approach
Chapter1

3. Introduction Cells Molecular Biology Probabilistic Sequence Models
Multinomiais Models
Markov Models
Genome Annotation
Data Base
André de Carvalho - ICMC/USP
1/27/2018

4. Cells

5. Cells Cell Basic unity of all living being
Compartment involved by membrane, filled with aqueous solution
May have organelles with specific functions
Mitochondria: energy generation
Golgi Complex: accumulation of secretions
Among others
André de Carvalho - ICMC/USP
27/01/2018

6. Cells
Cell Doctrines
All living beings are made of cells and its products
Cells have structure and function
All the cells emerge from pre existing cells
One cell can made copies of itself by replication and division
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27/01/2018

7. Cells Depending on the number of cells, an organism is classified as:
Unicellular (bacteria, protozoa)
Pluricellular (worms, mammals)
According to the presence of a nucleus in the cells, an organism can be classified as:
Eukaryote: has a nucleus defined by membrane
Prokaryote: do not has a nucleus
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27/01/2018

8. Cells
The fact that an organism is a prokaryote does not mean it is unicellular
The majority lives as a unicellular organism
Although some species group in a “bunch”, chains or other organization forms of multicellular structures
Many unicellular organisms are eukaryotes
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1/27/2018

9. An animal cell Nuclei: DNA and RNA.
Rough Endoplasmic Reticulum (ER): produces proteins
Smooth ER: produce lipides
Golgi Complex: cellular digestion as a basic function
Mitochondria: Produces energy. It has its own DNA and auto duplication capability.
1/27/2018
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10. Cells All the cells of the same organism have the same genes
Not all the cells have the same organelles in equal proportions
Cells vary in form and function
Normally, the form is related to function
Specific cell function and form are defined by its expressed genes
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1/27/2018

11. Cells
The chemical processes that occur in a cell are basically the same
For all the cell types and organisms
Even though those cells present different forms and functions
The DNA replication in a bacteria is similar to the DNA replication in a mammal
It makes scientific advances easier
Allowing experiments made with basal living to be used to infer results for other beings
André de Carvalho - ICMC/USP
1/27/2018

12. Cytology X Molecular Biology
Science that studies the cell (fixed)
Studies the cellular organization, types, functioning, division mechanism, etc
With science advances, it is possible to analyze living cells (in vivo)
Molecular level
Originated the term: Molecular Biology
André de Carvalho - ICMC/USP
1/27/2018

13. DNA Deoxyribonucleic Acid May have single or double-stranded
Double-stranded DNA
two strands twisted around each other to form a double helix
The long polymer compacts itself in a chromosome
The DNA is composed by four different nucleotides (bases)
Adenine, Cytosine, Guanine and Thymine (Uracil on RNA)
The double-strand is caused by base pairing
André de Carvalho - ICMC/USP
1/27/2018

14. DNA
The DNA strands are kept together by links that connect each nucleotide of one strand to its complement in the other strand
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1/27/2018

15. DNA
The DNA is always read from the 5‘ end to the 3‘ end in the transcriptional process
5’ ATTTAGGCC 3’
3’ TAAATCCGG 5’
27/01/2018
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16. DNA
5’ end
In one end, there is the first nucleotide. It has a phosphate group C5 projecting out.
3’end
In the other end, there is the last nucleotide added to the DNA strand. It is the only one that still has the component C3–OH.
27/01/2018
André de Carvalho - ICMC/USP

17. Molecular Biology
The genome is the set of all DNA from a cell (organism)
Including the genes
Genes carry the necessary information to produce the required proteins of an organism
The proteins determine
Organism’s appearance
How the body metabolizes food or defend itself from infections
Sometimes, the organism behavior
André de Carvalho - ICMC/USP
1/27/2018

18. Fraction of yeast genome
CCACACCACACCCACACACCCACACACCACACCACACACCACACCACACCCACACACACACATCCTAACACTACCCTAACACAGCCCTAATCTAACCCTGGCCAACCTGTCTCTCAACTTACCCTCCATTACCCTGCCTCCACTCGTTACCCTGTCCCATTCAACCATACCACTCCGAACCACCATCCATCCCTCTACTTACTACCACTCACCCACCGTTACCCTCCAATTACCCATATCCAACCCACTGCCACTTACCCTACCATTACCCTACCATCCACCATGACCTACTCACCATACTGTTCTTCTACCCACCATATTGAAACGCTAACAAATGATCGTAAATAACACACACGTGCTTACCCTACCACTTTATACCACCACCACATGCCATACTCACCCTCACTTGTATACTGATTTTACGTACGCACACGGATGCTACAGTATATACCATCTCAAACTTACCCTACTCTCAGATTCCACTTCACTCCATGGCCCATCTCTCACTGAATCAGTACCAAATGCACTCACATCATTATGCACGGCACTTGCCTCAGCGGTCTATACCCTGTGCCATTTACCCATAACGCCCATCATTATCCACATTTTGATATCTATATCTCATTCGGCGGTCCCAAATATTGTATAACTGCCCTTAATACATACGTTATACCACTTTTGCACCATATACTTACCACTCCATTTATATACACTTATGTCAATATTACAGAAAAATCCCCACAAAAATCACCTAAACATAAAAATATTCTACTTTTCAACAATAATACATAAACATATTGGCTTGTGGTAGCAACACTATCATGGTATCACTAACGTAAAAGTTCCTCAATATTGCAATTTGCTTGAACGGATGCTATTTCAGAATATTTCGTACTTACACAGGCCATACATTAGAATAATATGTCACATCACTGTCGTAACACTCTTTATTCACCGAGCAATAATACGGTAGTGGCTCAAACTCATGCGGGTGCTATGATACAATTATATCTTATTTCCATTCCCATATGCTAACCGCAATATCCTAAAAGCATAACTGATGCATCTTTAATCTTGTATGTGACACTACTCATACGAAGGGACTATATCTAGTCAAGACGATACTGTGATAGGTACGTTATTTAATAGGATCTATAACGAAATGTCAAATAATTTTACGGTAATATAACTTATCAGCGGCGTATACTAAAACGGACGTTACGATATTGTCTCACTTCATCTTACCACCCTCTATCTTATTGCTGATAGAACACTAACCCCTCAGCTTTATTTCTAGTTACAGTTACACAAAAAACTATGCCAACCCAGAAATCTTGATATTTTACGTGTCAAAAAATGAGGGTCTCTAAATGAGAGTTTGGTACCATGACTTGTAACTCGCACTGCCCTGATCTGCAATCTTGTTCTTAGAAGTGACGCATATTCTATACGGCCCGACGCGACGCGCCAAAAAATGAAAAACGAAGCAGCGACTCATTTTTATTTAAGGACAAAGGTTGCGAAGCCGCACATTTCCAATTTCATTGTTGTTTATTGGACATACACTGTTAGCTTTATTACCGTCCACGTTTTTTCTACAATAGTGTAGAAGTTTCTTTCTTATGTTCATCGTATTCATAAAATGCTTCACGAACACCGTCATTGATCAAATAGGTCTATAATATTAATATACATTTATATAATCTACGGTATTTATATCATCAAAAAAAAGTAGTTTTTTTATTTTATTTTGTTCGTTAATTTTCAATTTCTATGGAAACCCGTTCGTAAAATTGGCGTTTGTCTCTAGTTTGCGATAGTGTAGATACCGTCCTTGGATAGAGCACTGGAGATGGCTGGCTTTAATCTGCTGGAGTACCATGGAACACCGGTGATCATTCTGGTCACTTGGTCTGGAGCAATACCGGTCAACATGGTGGTGAAGTCACCGTAGTTGAAAACGGCTTCAGCAACTTCGACTGGGTAGGTTTCAGTTGGGTGGGCGGCTTGGAACATGTAGTATTGGGCTAAGTGAGCTCTGATATCAGAGACGTAGACACCCAATTCCACCAAGTTGACTCTTTCGTCAGATTGAGCTAGAGTGGTGGTTGCAGAAGCAGTAGCAGCGATGGCAGCGACACCAGCGGCGATTGAAGTTAATTTGACCATTGTATTTGTTTTGTTTGTTAGTGCTGATATAAGCTTAACAGGAAAGGAAAGAATAAAGACATATTCTCAAAGGCATATAGTTGAAGCAGCTCTATTTATACCCATTCCCTCATGGGTTGTTGCTATTTAAACGATCGCTGACTGGCACCAGTTCCTCATCAAATATTCTCTATATCTCATCTTTCACACAATCTCATTATCTCTATGGAGATGCTCTTGTTTCTGAACGAATCATAAATCTTTCATAGGTTTCGTATGTGGAGTACTGTTTTATGGCGCTTATGTGTATTCGTATGCGCAGAATGTGGGAATGCCAATTATAGGGGTGCCGAGGTGCCTTATAAAACCCTTTTCTGTGCCTGTGACATTTCCTTTTTCGGTCAAAAAGAATATCCGAATTTTAGATTTGGACCCTCGTACAGAAGCTTATTGTCTAAGCCTGAATTCAGTCTGCTTTAAACGGCTTCCGCGGAGGAAATATTTCCATCTCTTGAATTCGTACAACATTAAACGTGTGTTGGGAGTCGTATACTGTTAGGGTCTGTAAACTTGTGAACTCTCGGCAAATGCCTTGGTGCAATTACGTAATTTTAGCCGCTGAGAAGCGGATGGTAATGAGACAAGTTGATATCAAACAGATACATATTTAAAAGAGGGTACCGCTAATTTAGCAGGGCAGTATTATTGTAGTTTGATATGTACGGCTAACTGAACCTAAGTAGGGATATGAGAGTAAGAACGTTCGGCTACTCTTCTTTCTAAGTGGGATTTTTCTTAATCCTTGGATTCTTAAAAGGTTATTAAAGTTCCGCACAAAGAACGCTTGGAAATCGCATTCATCAAAGAACAACTCTTCGTTTTCCAAACAATCTTCCCGAAAAAGTAGCCGTTCATTTCCCTTCCGATTTCATTCCTAGACTGCCAAATTTTTCTTGCTCATTTATAATGATTGATAAGAATTGTATTTGTGTCCCATTCTCGTAGATAAAATTCTTGGATGTTAAAAAATTATTATTTTCTTCATAAAGAAGCTTTCAAGATATAAGATACGAAATAGGGGTTGATAATTGCATGACAGTAGCTTTAGATCAAAAAGGAAAGCATGGAGGGAAACAGTAAACAGTGAAAATTCTCTTGAGAACCAAAGTAAACCTTCATTGAAGAGCTTCCTTAAAAAATTTAGAATCTCCCATGTCAACGGGTTTCCATACCTCCCCAGCATCATACATCTTTTTTCAAAGAAACTTCAAATGCCTCTTTTATGCAAGGGGCAAAATCCTGAAATGACTTAAACTTAGCAGTTTCGTCTTTTTTCAAAGAGAATGGTTGAAGAAGAATTGTTTTGGACGCTTATTGACAATCTGTTGCATTGATAAAGTACCTACTATCCCAGACTATATTTGTATACAAGTACAAAATTAGGTTTGTTGAAACAACTTTCCGATCATTGGTGCCCGTATCTGATGTTTTTTTAGTAATTTCTTTGTAAATACAGGGAGTTGTTTCGAAAGCTTATGAGAAAAATACATGAATGACAGGTAAAAATATTGGCTCGAAAAAGAGGACAAAAAGAGAAATCATAAATGAGTAAACCCACTTGCTGGACATTATCCAGTAAAGGCTTGGTAGTAACCATAATATTACCCAGGTACGAAACGCTAAGAACCTTGAAAGACTCATAAAACTTCCAGGTTAAGCTATTTTTGAAAATATTCTGAGGTAAAAGCCATTAAGGTCCAGATAACCAAGGGACAATAAACCTATGCTTTTCTTGTCTTCAATTTCAGTATCTTTCCATTTTGATAATGAGCATGTGATCCGGAAAGCTACTTTATGATGTTTCAAGGCCTGAAGTTTGAATATTTATGTAGTTCAACATCAAATGTGTCTATTTTGTGATGAGGCAACCGTCGACAACCTTATTATCGAAAAAGAACAACAAGTTCACATGCTTGTTACTCTCTATAACTAGAGAGTACTTTTTTTGGAAGCAAGTAAGAATAAGTCAATTTCTACTTACCTCATTAGGGAAAAATTTAATAGCAGTTGTTATAACGACAAATACAGGCCCTAAAAAATTCACTGTATTCAATGGTCTACGAATCGTCAATCGCTTGCGGTTATGGCACGAAGAACAATGCAATAGCTCTTACAAGCCACTACATGACAAGCAACTCATAATTTAA
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27/01/2018

19. Molecular Biology Haploid Cells: 1 set of chromosomes
Diploid Cells: 2 sets of chromosomes (pairs)
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1/27/2018

20. Molecular Biology Genes Subsequences of DNA
Found in the chromosome
They are the mold of protein or RNA production
Between the genes there segments called are non- coding regions
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1/27/2018

21. Not all the DNA code genes?
3200 x 106
Human
House fly
13601
180 x 106
Drosophila melanogaster
worm
19099
95.5 x 106
C. elegans
yeast
5885
12.1 x 106
Saccharomyces cerevisiae
4406
E. coli
Ear infection
1738
Hemophilus influenzae
Pneumonia
680
816394
Mycoplasma pneumoniae
subcelular organell 37
16569
Human mitochondrion
E.coli virus 10
5386
ФX-174
Descrição
Genes
Num. de pb
Organismo
Humans
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27/01/2018

22. Non-coding DNA It is not part of protein/RNA synthesis
It was considered genetic “junk”
It binds to a DNA strand
One of its functions: blocking the transcriptional process
The bound gene is not read
It avoids the expression of the associated protein
Inhibition of genes may prevent tumor cells growth
Researchers were able to bind genes not related to tumor growth
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1/27/2018

23. Molecular Biology
Scientists identified the gene related to breast cancer (SATB1)
Paper published on Nature (March, 2008)
Healthy organism: organizer of other genes
Cancerous organism: growth of tumors, controlling around other genes
Gang leader, gang, mob
Active role on formation of other cancer focus (metastasis)
Most common cause of death in ill patients
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1/27/2018

24. Cells of breast cancer with a defective gene
Bioinformatics
Experiments in rats:
After the gene’s inactivation, the tumor cells exacerbated proliferation ends
Cancer looses aggressiveness potential
Allows more accurate and early diagnosis
Cells of breast cancer with a defective gene
1/27/2018
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25. Molecular Biology Proteins
Define structure, function and regulatory mechanism of cells
Example of regulatory mechanisms: control of cellular cycle, genic transcription
Linear sequences
20 different amino acid combinations
Three consecutive nucleotide (codon) form an amino acid
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1/27/2018

26. Size of Genomes Prokaryotes Viral Eukaryotes Organelles
0.5 to 12 megabases - MB - ( bp)
Viral
5 to 50 kilobases - KB - (1.000 bp)
Eukaryotes
8 megabases to 670 gigabases - GB- ( bp)
High amount of repetitive DNA
Organelles
Majority of eukaryote also have a genome out of nuclei
Probably rests of prokaryotes that lived in symbiosis
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1/27/2018

27. André de Carvalho - ICMC/USP
Virus X Bacteria
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Bacteria
Unicellular, prokaryotes
Free living
May be found isolated or in colonies
Generally have a circular genome single- stranded
Virus
Smaller than bacteria
Mandatory parasites
Single or double- stranded
Basically made of proteins
Reproduce by invasion and control of auto replication cellular apparatus
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28. Probabilistic Models of Sequences
The majority of computational genomic studies uses statistical methods
Ex.: find structures of interest in sequences of millions bp
The majority of the sequence does not have relevant information
Need to obtain probabilistic models of DNA sequences
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1/27/2018

29. Probabilistic Models of Sequences
Abstraction of the 3D molecule to a symbol sequence (linear)
Alphabet {A, C, T, G}
Allows the use of powerful mathematic tools
Lack of care with information of the tridimensional structure
André de Carvalho - ICMC/USP
1/27/2018

30. Probabilistic Models of Sequences
Definition1.1
A DNA sequence s is a finite string of the alphabet N = {A, C, T, G} of nucleotides
Genome is the set of all DNA sequences of an organism or organelle
It allow us the use of statistical models of:
Sequence evolution, sequence similarities, etc
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1/27/2018

31. Probabilistic Models of Sequences
Definition 1.2
The sequence elements s are denoted by
s = s1,s2, ..., sn,
where each si represents one element
Given a set of indices K, it is possible to concatenate elements of s in its original order
s(K) = si,sj, sk if K = {i, j, k}
It is also possible to use K = [i, j] = (i:j)
Specific symbol may be denoted by k = {i}, si = s(i)
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1/27/2018

32. Exercise Given a DNA sequence s = ATATGTCGTGCA, find: s{7} = s(2:6) =
André de Carvalho - ICMC/USP
1/27/2018

33. Probabilistic Models of Sequences:
Probabilistic Models of Sequences
Almost all probabilistic methods of sequence analysis can be grouped into two categories:
Multinomial Models
Markov Models
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1/27/2018

34. Multinomial Models Simpler models
Assume a probability distribution p on the alphabet
Nucleotides are independent and identically distributed (i.i.d.) along the sequence
Ex.: For the DNA sequence
p = (pa, pb, pc, pd), where Px = p(si = x)
Independent of I position
Pa + pb + pc + pd = 1 (normalization restriction)
Define equal probabilities or based on the frequency of each nucleotide
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1/27/2018

35. Multinomial Models
It is not expected that DNA sequences are truly random
Model validity can be tested with real sequences
Estimate frequency symbols in the sequence regions
Test of independence violations checking correlations between neighbor individuals
Regions where changes occur and there is interest in the correlations
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1/27/2018

36. Markov Models Provide more complex model of DNA sequences
Probability of observing a symbol depends on the previous symbol in the sequence
Last symbol – order 1
Last two – order 2
No previous – order 0 (multinomial)
Can shape local co-relations between nucleotides
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1/27/2018

37. Markov Models Transition Matrix PCA for De = A C G T
0.99
0.99
0.002 A C
for
0.002 A C G T
0.99
0.002
0.006
0.006
0.006
0.002 G T De
0.002
0.99
0.99
Equal Probabilities = multinomial
= A C G T
Probabilities of each initial state
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27/01/2018

38. Markov Models Transition matrix entry are defined by:
pxy = p(si+1 = y/ si = x)
p(s) = p(s1 s2 ... sn)
p(s) = p(s1) p(s2) ... p(sn) - order 0
p(s) = p(sn/sn-1) p(sn-1/sn-2) ... p(s2/s1) (s1) - order 1
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1/27/2018

39. Basic statistics of H. influenza (1.830.138 bp)
Genome Annotation
Simple statistics may describe important characteristics
Base Number Frequency A C G T
Basic statistics of H. influenza ( bp)
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27/01/2018

40. Genome Annotation Base composition
Bases frequencies are different in genomes of different organisms
Frequencies may vary in different parts
Violates multinomial model supposition
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1/27/2018

41. Genome Annotation Look only to one of the strands K size window
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42. Genome Annotation Look only to one of the strands K size window
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27/01/2018

43. Genome Annotation GC (C and G) content (frequency)
Most cited measure in papers
C and G (A and T) have similar frequencies
Aggregate frequency GC versus AT (AT = 1–GC)
Organism GC content
H. influenza 38.8
M. turbeculosis 65.8
S. Enteritidis 49.5
GC content for diferent organisms
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27/01/2018

44. Genome Annotation GC content (frequency)
May be used to detect external genetic material in a part of genome
Species may acquire sub sequences from other organisms (ex. virus)
Horizontal genetic transference
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1/27/2018

45. Genome Annotation Point of change analysis
Use this method to detect where the bases distribution (or GC) change
These change regions divide the sequence in more uniform parts
They may help to identify important biological signs
Simpler measure: use threshold
Threshold value definition (like the window size) is a statistical problem
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1/27/2018

46. Genome Annotation
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27/01/2018

47. Genome Annotation k-mer frequency is a motif bias
Other useful measure is the frequency of sequences size 2 and major
Dimers, trimers, k-mers
K-mers are not usual: any word that is in the genome with frequency highest or lowest than expected
Bias in the position or frequency of these words may reveal important information about its function
The number of k-mers is counted by a k-size window covering the sequence
André de Carvalho - ICMC/USP
1/27/2018

48. Genome Annotation k-mer frequency and motif bias
It is also possible to plot the frequency of only some interest k-mers
Ex. Dimers (dinucleotide) AT and CG
There are examples of statistical bias of nucleotides
Ex.: Low frequency of CGs in some organisms
It is easy to see these bias in “genome signatures”
Chaos-Game representation (CGR)
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27/01/2018

49. Genome Signature
CGR represented by colors of observed frequencies of k-mers
The darker, more frequent
2-mers 5-mers 8-mers
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1/27/2018

50. Genome Signature CGR exhibits frequencies of 4k words or strings
The quadrate image is sliced into 4 quadrant q1, one for each nucleotide
A pixel indicating the frequency of all the qq size strings, ending in a given nucleotide, occur in this nucleotide quadrant
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51. Exemple 2-mers C G CC GG A T CA GA CA GA AA TA TT AA TA
Quadrant with the frequencies of all the
words that end with the A nucleotide
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52. Genome Signature Each quadrant q1 is divided into 4 quadrants q2
One q2 for each present nucleotide in the last but one word position
Each q2 is in the same relative position of the same nucleotide in the q1 quadrant
Go on until you have the appropriate number of pixels (4K)
Fill each square with the proportional color to the frequency of the k-mer
C G
A T
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27/01/2018

53. Genome Annotation
The motifs (k-mers) frequency can bring relevant informations
Sequence of frequent nucleotides that may have a biological relevance
Simple statistical analysis may consider the nucleotide frequency
It can find motifs high or low represented
Which helps to decide when the bias is significant or not
Non usual motifs may have biological relevance
Ex.: motifs may frequently be associated with repetitive elements
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1/27/2018

54. Genome Annotation
Look for dimers (dinucleotides) non usual in H. influenza
Frequência
observada
A C G T A C G T
27/01/2018
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55. Important to Distinguish
Pattern matching
Given a motif, find its occurrence in a sequence
Patter discovery
Find interest patterns in a sequence
Useful
Novel
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1/27/2018

56. Genome DataBase
The acquired knowledge in this course will be used in real sequences
DNA and proteins
Stored in DBs available on the internet
It is necessary to know how:
Access
Manipulate
Process
The involved steps are standardized
These data
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27/01/2018

57. Genome DataBase General Specialized
DNA, proteins e carbohydrates, 3-dimension structures , ...
Specialized
EST, STS, SNP, RNA, genomes, protein families, pathways, microarray data, ...)
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27/01/2018

58. Genome DataBase
All published genome sequence have to be available in a public DB
Members of the International Nucleotide Sequence Database Collaboration are the main repositories
Consortia made by 3 big DBs
EMBL (European Molecular Biology Laboratory nucleotide sequence database at EBI, Hinxton, UK)
GenBank (at National Center for Biotechnology information, NCBI, Bethesda, MD, USA)
DDBJ (DNA Data Bank Japan at CIB , Mishima, Japan)
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59. Genome DataBase
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60. GenBank Each sequence Is identified by a unique adhesion number
Includes a quantity of meta data
Data about data or annotation
Ex.: specie of the sequenced organism
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61. Data Format and Annotation
There are several different formats to provide a sequence and its annotation
EMBL, GenBank and DDBJ have their own standard format
There are also formats that are not associated to a DB
Generally to a sequence analyses program though
FASTA
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62. FASTA Format
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63. FASTA Format First line: “>” followed by the annotation
>FOSB_MOUSE Protein fosB. 338 bp
MFQAFPGDYDSGSRCSSSPSAESQYLSSVDSFGSPPTAAASQECAGLGEMPGSFVPTVTA
ITTSQDLQWLVQPTLISSMAQSQGQPLASQPPAVDPYDMPGTSYSTPGLSAYSTGGASGS
GGPSTSTTTSGPVSARPARARPRRPREETLTPEEEEKRRVRRERNKLAAAKCRNRRRELT
DRLQAETDQLEEEKAELESEIAELQKEKERLEFVLVAHKPGCKIPYEEGPGPGPLAEVRD
LPGSTSAKEDGFGWLLPPPPPPPLPFQSSRDAPPNLTASLFTHSEVQVLGDPFPVVSPSY
TSSFVLTCPEVSAFAGAQRTSGSEQPSDPLNSPSLLAL
First line: “>” followed by the annotation
Any format, without breakline
The information about the sequence starts in the following line
Until another symbol “>” to show as the first line character
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64. FASTA Format
Accepted by the majority of the sequence analyses programs
Provided by the majority of the online DBs
Limits the amount of allowed annotation
Other patterns are used to include more meta information
Information about the sequence
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65. GenBank Format An entry has several sections
LOCUS: identifies the sequence
DEFINITION: define the sequence
ACCESSION: only identifies the sequence
Related in publications and used to cross reference to other DBs
SOURCE and ORGANISM: identifiy biological origin of the sequece
REFERENCE: lists articles related to the sequence
ORIGIN: lists all the nucleotides
Among others
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66. GenBank Format
ORIGIN
Sequences are organized in lines content 6 blocks, each of them with 10 bases
Simbol “//” indicates the entry end
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67. GenBank Format
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68. GenBank Format
27/01/2018

69. GenBank Format André de Carvalho - ICMC/USP 27/01/2018
LOCUS SCU bp DNA PLN JUN-1999
DEFINITION Saccharomyces cerevisiae TCP1-beta gene, partial cds, and Axl2p
(AXL2) and Rev7p (REV7) genes, complete cds.
ACCESSION U49845
VERSION U GI:
KEYWORDS .
SOURCE Saccharomyces cerevisiae (baker's yeast)
ORGANISM Saccharomyces cerevisiae
Eukaryota; Fungi; Ascomycota; Saccharomycotina; Saccharomycetes;
Saccharomycetales; Saccharomycetaceae; Saccharomyces.
REFERENCE 1 (bases 1 to 5028)
AUTHORS Torpey,L.E., Gibbs,P.E., Nelson,J. and Lawrence,C.W.
TITLE Cloning and sequence of REV7, a gene whose function is required for
DNA damage-induced mutagenesis in Saccharomyces cerevisiae
JOURNAL Yeast 10 (11), (1994)
MEDLINE
PUBMED
REFERENCE 2 (bases 1 to 5028)
AUTHORS Roemer,T., Madden,K., Chang,J. and Snyder,M.
TITLE Selection of axial growth sites in yeast requires Axl2p, a novel
plasma membrane glycoprotein
JOURNAL Genes Dev. 10 (7), (1996)
MEDLINE
PUBMED
REFERENCE 3 (bases 1 to 5028)
AUTHORS Roemer,T.
TITLE Direct Submission
JOURNAL Submitted (22-FEB-1996) Terry Roemer, Biology, Yale University, New
Haven, CT, USA
FEATURES Location/Qualifiers
source
/organism="Saccharomyces cerevisiae"
/db_xref="taxon:4932"
/chromosome="IX"
gene
/gene="AXL2"
CDS
/note="plasma membrane glycoprotein"
/codon_start=1
/function="required for axial budding pattern of S.
cerevisiae"
/product="Axl2p"
/protein_id="AAA "
/db_xref="GI: "
/translation="MTQLQISLLLTATISLLHLVVATPYEAYPIGKQYPPVARVNESF
TFQISNDTYKSSVDKTAQITYNCFDLPSWLSFDSSSRTFSGEPSSDLLSDANTTLYFN
VILEGTDSADSTSLNNTYQFVVTNRPSISLSSDFNLLALLKNYGYTNGKNALKLDPNE
VFNVTFDRSMFTNEESIVSYYGRSQLYNAPLPNWLFFDSGELKFTGTAPVINSAIAPE
TSYSFVIIATDIEGFSAVEVEFELVIGAHQLTTSIQNSLIINVTDTGNVSYDLPLNYV
YLDDDPISSDKLGSINLLDAPDWVALDNATISGSVPDELLGKNSNPANFSVSIYDTYG
DVIYFNFEVVSTTDLFAISSLPNINATRGEWFSYYFLPSQFTDYVNTNVSLEFTNSSQ
DHDWVKFQSSNLTLAGEVPKNFDKLSLGLKANQGSQSQELYFNIIGMDSKITHSNHSA
NATSTRSSHHSTSTSSYTSSTYTAKISSTSAAATSSAPAALPAANKTSSHNKKAVAIA
CGVAIPLGVILVALICFLIFWRRRRENPDDENLPHAISGPDLNNPANKPNQENATPLN
NPFDDDASSYDDTSIARRLAALNTLKLDNHSATESDISSVDEKRDSLSGMNTYNDQFQ
SQSKEELLAKPPVQPPESPFFDPQNRSSSVYMDSEPAVNKSWRYTGNLSPVSDIVRDS
YGSQKTVDTEKLFDLEAPEKEKRTSRDVTMSSLDPWNSNISPSPVRKSVTPSPYNVTK
HRNRHLQNIQDSQSGKNGITPTTMSTSSSDDFVPVKDGENFCWVHSMEPDRRPSKKRL
VDFSNKSNVNVGQVKDIHGRIPEML
BASE COUNT a c g t
ORIGIN
1 gatcctccat atacaacggt atctccacct caggtttaga tctcaacaac ggaaccattg
61 ccgacatgag acagttaggt atcgtcgaga gttacaagct aaaacgagca gtagtcagct
121 ctgcatctga agccgctgaa gttctactaa gggtggataa catcatccgt gcaagaccaa
181 gaaccgccaa tagacaacat atgtaacata tttaggatat acctcgaaaa taataaaccg
241
André de Carvalho - ICMC/USP
27/01/2018

70. Pattern Alphabet
Sequences in different repositories follow the standard nucleotide alphabet
Include symbols for ambiguous nucleotide
Most common symbols:
A Adenine N any (aNy) base
C Citosine R A or G (puRine)
G Guanine Y C or T (pYrimidine)
T Thymine M A or C (aMino)
André de Carvalho - ICMC/USP
27/01/2018

71. Conclusion Cells Molecular Biology Probabilistic Sequences Models
Multinomial Models
Markov Models
Genome Annotation
Data Base
André de Carvalho - ICMC/USP
1/27/2018

72. Questions?

73. NCBI: National Center for
Biotechnology information
Established in 1988 as a national resource for molecular biology information, NCBI creates public databases, conducts research in computational biology, develops software tools for analyzing genome data, and disseminates biomedical information - all for the better understanding of molecular processes affecting human health and disease.

74. The EMBL Nucleotide Sequence Database (also known as EMBL-Bank) constitutes Europe's primary nucleotide sequence resource. Main sources for DNA and RNA sequences are direct submissions from individual researchers, genome sequencing  projects and patent applications.

75. DDBJ (DNA Data Bank of Japan) began DNA data bank activities in earnest in 1986 at the National Institute of Genetics (NIG). DDBJ has been functioning as the international nucleotide sequence database in collaboration with EBI/EMBL and NCBI/GenBank.

76. Fasta Protein Database Query
Provides sequence similarity searching against nucleotide and protein databases using the Fasta programs.
Fasta can be very specific when identifying long regions of low similarity especially for highly diverged sequences.
You can also conduct sequence similarity searching against complete proteome or genome databases using the Fasta programs.
Download Software