Sequence Databases

1
Sequence Databases

• As DNA and protein sequences accumulate, they are
  deposited in public databases.
• One of the most popular of these is GenBank,
  which stores all types of different sequences in
  plain text files, where different features are
  separated by blank spaces and special symbols.
• Each file can be downloaded directly. Sample
  file
• LOCUS name of locusSOURCE source organism of
  DNAFEATURES information about sequence by base
  positionORIGIN 1 gaattcgata aatctctggg
  ttattgtgac gtttataatg acgttaggca 51
  atatcattct atcattaagc

2
Relational Databases

• A more structured way to store data is by using
  relational databases. A database is made up of a
  set of tables which stores data in a
  non-redundant manner to facilitate data updates.
• student uin firstname lastname 123456789 Alice
  Smith 333445555 Tom Wang
• major uin department 123456789 BICH 12345678
  9 CPSC 333445555 BICH

3
Query Languages

• Instead of allowing users to look at the contents
  of a database directly, access is through the use
  of a query language over a connection to the
  database.
• SQL is one of the most popular query languages.
  The following is a sample SQL query
• select firstname, lastname from student,
  major where student.uin major.uin and
  major.department BICH
• This approach imposes a structure to the data and
  allows updates to be seen instantaneously.

4
Graphical User Interface

• Fortunately, there is no need to learn a query
  language in order to be able to access a
  relational database.
• Modern database software packages provide
  graphical user interfaces developed on top of the
  query language level, so that users dont need to
  know anything about query languages.
• Relational databases are very popular within
  biotech companies and web interfaces are becoming
  standard.

5
Comparing Sequences

• Mutation in DNA sequences is modeled by three
  basic operations insertion of a nucleotide,
  deletion of a nucleotide and substitution of a
  nucleotide.
• Mutation is tolerated to a certain extent before
  the function of a DNA sequence is lost or
  changed. Thus, similarity between sequences
  reflects similarity in function.
• As DNA sequences of different organisms
  accumulate, comparing a newly discovered sequence
  to known sequences helps to identify the function
  of the new sequence.

6
Pairwise Alignment

• Given two (DNA or protein) sequences, an
  alignment puts the two strings against each other
  so that similar parts are aligned together.
• For example, given two strings ATCTCGAT and
  TGCATAT, one alignment is
  AT-C-TCGCT -TGCAT--ATwhere -
  denotes a gap introduced to pad non-similar
  parts.
• Given a scoring scheme, the pairwise alignment
  problem is the problem of finding the best
  alignment with optimal score. A special form of
  alignment called local alignment allows
  alignments to not cover entire sequences.

7
Finding Optimal Alignment

• Dynamic programming techniques are used to find
  the optimal alignment (which use a
  divide-and-conquer approach to express the
  original problem as smaller subproblems).
• These algorithms for finding optimal alignment
  takes time proportional to the product of the
  length of input sequences.
• In large scale comparisons, heuristics are
  commonly employed to find good alignment (which
  is not necessarily optimal) between two sequences.

8
Database Search

• As the amount of sequenced DNA increases, it is
  likely that a new DNA sequence has similarity to
  at least one of the known sequences.
• New DNA sequences are annotated as much as
  possible and collected in sequence databases such
  as GENBANK.
• When a new DNA sequence is obtained, the first
  logical step is to try to search for similar
  sequences in a database of known sequences.
• If similar sequences are found from database
  search, sequence similarity would imply
  similarity in function.

9
Filtration Idea

• Given a database of sequences s1, , sn and a
  sequence s, the first step is to determine which
  of the pairs (s, si) are likely to be similar.
• There are conflicting objectives
• Dont want to lose real similarities.
• Want to cut down the number of possible
  similarities.
• The simplest way to perform such a filtration is
  to consider a k-mer appearing in s and check if
  it appears in each of si.

s1
s2
s
s3
s4
10
Seed-Extension Approach

• The next step is to extend the short similarities
  (seeds) we found in the filtration step to see if
  the gaps between the short similar areas can be
  filled.
• All hits with strong similarity found are sorted
  in decreasing order of score.
• It is possible to compute the optimal alignment
  in detail for a limited subset of high scoring
  hits.

11
BLAST

• BLAST is one of the most popular programs people
  use to perform database search.
• There are various variations of the basic
  programs to compare DNA against DNA, DNA against
  protein, protein against protein, etc.
• In addition to employing the filtration-extension
  approach, BLAST employs an additional set of
  heuristics to optimize its performance.

12
BLAST Seed Selection

• BLAST uses different seed lengths for different
  types of sequences
• use words of length 11 for DNA sequences.
• use words of length 3 for protein sequences.
• perform a translation from DNA to protein for
  comparisons involving DNA and proteins. All
  three reading frames are investigated.
• For protein sequences, in addition to look for
  exact matches, BLAST also looks for approximate
  matches with score above a preset threshold.

13
BLAST Alignment Extension

• BLAST tries to extend an alignment from the
  matching words in both directions, and continue
  the extension as long as the score continues to
  increase. If this results in a gap between two
  matching words being filled, the extension is
  successful.

s



si
14
Multiple Alignment

• It is frequently very difficult to reveal weak
  similarities between two sequences.
• These similarities can often be captured by
  comparing multiple sequences.
• Multiple alignment generalizes the alignment idea
  to handle many sequences.
• AT-C-TCGAT
  -TGCAT--AT ATCCA-CGCT

15
ClustalW

• ClustalW is a very popular software for multiple
  alignment.
• It uses the progressive alignment idea treat
  each sequence as an alignment initially and
  iteratively select the next two most similar
  alignments to be combined into a bigger alignment
  by pairwise alignment techniques.
• The order to combine the alignments is determined
  by following an evolutionary tree generated on
  the fly between the sequences.

?

16
TCoffee

• When computing an initial pairwise alignment,
  TCoffee incorporates consistency information from
  other sequences to improve its consistency within
  the final multiple alignment
• It accepts different kinds of pairwise alignments
  as input, and constructs a weighted library to
  represent the pairwise relationships.
• For each sequence pair A and B, it extends the
  original library by trying to align A and B
  through each sequence C in the input set and
  adding consistency information to the (A,B) pair.
• TCoffee is much more accurate than ClustalW.

17
Gene Prediction

• In eukaryotes, genes are made up of exons and
  introns. Introns are cut out of the RNA and
  exons are concatenated together to form the mRNA
  before translation into a protein.
• The gene prediction problem is the following
  problem Given a genomic sequence, locate the
  exon-intron boundaries.

exon
exon
exon
intron
intron
gene (RNA)
mRNA
18
GENSCAN

• GENSCAN is one of the most popular software
  programs used for gene structure prediction.
• It integrates multiple types of information in a
  hidden markov model (HMM), including promoter
  signals, splicing signals, length distributions
  for exons and introns, poly-A transcriptional end
  signal, etc.
• A HMM consists of a set of states and transition
  probabilities between the states. Each state
  represents a modeled feature.
• Although the structure of the HMM is
  pre-determined, it is necessary to provide
  estimates of other parameters (such as transition
  probabilities) via a set of training examples.

19
Similarity-based Approaches

• As the amount of sequenced DNA increases, it is
  likely that a new (unspliced) genomic sequence
  has similarity to a known cDNA or protein.
• BLAST can be used to find similar sequences from
  the database of known sequences. These BLAST
  hits reveal only the most significant local
  alignments between the genomic sequence and a
  similar sequence, which usually represent one or
  few exons.
• The GenomeScan software integrates this
  information with the HMM model of GENSCAN.

20
Regulation of Gene Expression

• Gene regulatory proteins bind to specific places
  (regulatory sites) on DNA. These sites are
  usually close to the gene.

off
gene
site
regulatory protein
on
gene
site
21
Finding Regulatory Sites

• Identify a set of genes believed to be controlled
  by the same regulatory mechanism (co-regulated
  genes).
• Extract regulatory regions of the genes (usually
  upstream sequences) to form a sample of
  sequences.
• Find some way to identify conserved patterns
  shared by these sequences, resulting in a list of
  potential regulatory sites.
• The problem of finding patterns shared by a given
  sample of sequences is called the motif finding
  problem.

22
Motif Finding Problem
sample
gene
site
gene
site
gene
site
gene
site
gene
site
23
MEME

• MEME is one of the most popular programs for
  motif finding. It finds a motif, represented by
  a set of ungapped patterns, which is the least
  likely to appear by chance.
• It uses the expectation-maximization (EM)
  approach first obtain an initial motif (which
  may not be very good), then iteratively obtain a
  better motif with the following two steps
• Expectation compute the statistical composition
  of the current motif and find the probability of
  finding the site at each position in each
  sequence.
• Maximization These probabilities are used to
  update the statistical composition.

24
Finding Regulatory Sites by Alignment

• Recall that one way to find regulatory sites is
  to construct samples of upstream regions of
  co-regulated genes and use motif finding
  algorithms to look for shared patterns.
• These samples of co-regulated genes are
  constructed from diverse sources. Regulatory
  sites can reside on either strand and they are
  not at a fixed distance from the transcriptional
  start site.
• For these samples, alignment algorithms are not
  useful. However, alignment approaches can be
  very useful when we specifically consider
  co-regulated genes which are very close in
  evolutionary distance.

25
What to Look For

• There are frequently more than one regulatory
  site for a gene. When sufficiently close
  upstream regions of co-regulated genes are
  aligned, we expect the sites to be short, well
  conserved, in the same order and on the same
  strand. These short blocks of highly conserved
  positions are separated by less conserved
  regions.
• Although evolutionarily close genes should be
  used, they should not be so close that the entire
  upstream region is highly conserved, which does
  not give much information.

26
Using Closely Related Yeast Species

• Align upstream sequences from a few Saccharomyces
  species to try to identify regulatory elements
• Many alignments of S.mikatae and S.bayanus
  promoter regions with S.cerevisiae show short
  runs of conserved sequences, which predicts
  regulatory sites.
• The two species S.cariocanus and S.paradoxus are
  too closely related to S.cerevisiae. Not much
  information is revealed in their alignments.

27
Inferring Evolution

• Using estimates on the evolutionary distance
  between multiple organisms, an evolutionary tree
  can be constructed predicting the evolutionary
  relationships.
• It is possible that conflicting trees are
  produced from different data sources. We are
  much more confident that a tree is correct when
  the same tree is produced from various data
  sources.

mouse
rat
human
28
Evolutionary Distance Estimates

• Estimates of evolutionary distance
• Each multiple alignment gives distance estimates
  between species. To obtain more accurate
  estimates, a large set of multiple alignments
  including different regions or genes should be
  utilized collectively.

29
Using Evolutionary Information in Alignment

• Progressive multiple alignment algorithms often
  construct an evolutionary tree on the fly to
  determine the order in which smaller alignments
  are merged to form bigger ones.
• If the sequences to be aligned are from different
  species, it is also possible to utilize a
  pre-determined evolutionary tree.
• Since an evolutionary tree can be constructed
  given a multiple alignment, it is possible to
  iteratively improve both the evolutionary tree
  and the multiple alignment from crude initial
  estimates.

30
Using Evolutionary Information in Motif Finding

• The motif finding problem is to find a pattern
  shared by a given set of sequences. In the case
  when the sequences are from different species,
  evolutionary information can be utilized to get
  more accurate results.
• Given an evolutionary tree of the represented
  species, a better way to score a motif is to
  compute the total number of mutations along the
  branches of the tree. It has been shown that
  this approach produces better motifs.