BMS 5525 Bioregulation
Mon. April 21, 2008
A Whirlwind
Bioinformatics Survey
. . . some taste of theory, and
a few practicalities
Steve Thompson
Florida State University School of
Computational Science (SCS)
To begin,
some terminology —
What is bioinformatics,
genomics, proteomics,
sequence analysis,
computational molecular
biology . . . ?
My definitions, lots of overlap
Biocomputing and computational biology are synonyms and
describe the use of computers and computational techniques
to analyze any type of a biological system, from individual
molecules to organisms to overall ecology.
Bioinformatics describes using computational techniques to
access, analyze, and interpret the biological information in
any type of biological database.
Sequence analysis is the study of molecular sequence data for
the purpose of inferring the function, interactions, evolution,
and perhaps structure of biological molecules.
Genomics analyzes the context of genes or complete genomes
(the total DNA content of an organism) within the same and/or
across different genomes.
Proteomics is the subdivision of genomics concerned with
analyzing the complete protein complement, i.e. the proteome,
of organisms, both within and between different organisms.
And one way to think about it —
the Reverse Biochemistry Analogy
Biochemists no longer have to begin a research
project by isolating and purifying massive amounts
of a protein from its native organism in order to
characterize a particular gene product. Rather,
now scientists can amplify a section of some
genome based on its similarity to other genomes,
sequence that piece of DNA and, using sequence
analysis tools, infer all sorts of functional,
regulatory, evolutionary, and, perhaps, structural
insight into that stretch of DNA!
The computer and molecular databases are a
necessary, integral part of this entire process.
The exponential growth of molecular
sequence databases & cpu power
Year
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
BasePairs
680338
2274029
3368765
5204420
9615371
15514776
23800000
34762585
49179285
71947426
101008486
157152442
217102462
384939485
651972984
1160300687
2008761784
3841163011
11101066288
15849921438
28507990166
36553368485
44575745176
56037734462
69019290705
83874179730
Sequences
606
2427
4175
5700
9978
14584
20579
28791
39533
55627
78608
143492
215273
555694
1021211
1765847
2837897
4864570
10106023
14976310
22318883
30968418
40604319
52016762
64893747
80388382
Doubling time about a year and half!
http://www.ncbi.nlm.nih.gov/Genbank/genbankstats.html
Sequence database growth, continued
The International Human Genome Sequencing
Consortium announced the completion of the "Working
Draft" of the human genome in June 2000;
independently that same month, the private company
Celera Genomics announced that it had completed the
first “Assembly” of the human genome. The classic
articles were published mid-February 2001 in the
journals Science and Nature.
Genome projects keep the data coming at an incredible
rate. Currently around 50 Archaea, 600 Bacteria, and
20 Eukaryote complete genomes, and 200 Eukaryote
assemblies are represented, not counting the almost
3,000 virus and viroid genomes available.
Some neat stuff from the human genome papers
Homo sapiens, aren’t nearly as special as we once
thought. Of the 3.2 billion base pairs in our DNA:
Traditional gene number estimates were often in the
100,000 range; turns out we’ve only got about twice
as many as a fruit fly, between 25’ and 30,000!
The protein coding region of the genome is only about
1% or so, a bunch of the remainder is ‘jumping,’
‘selfish DNA,’ sometimes called ‘junk,’ much of which
may be involved in regulation and control.
Some 100-200 genes were transferred from an
ancestral bacterial genome to an ancestral
vertebrate genome!
(Later shown to be false by more extensive analyses, and
to be due to gene loss not transfer.)
NCBI’s
Entrez
Let’s start with sequence databases
Sequence databases are an organized way to store exponentially
accumulating sequence data. An ‘alphabet soup’ of three major
organizations maintain them. They largely ‘mirror’ one another and
share accession codes, but NOT proper identifier names:
North America: the National Center for Biotechnology Information
(NCBI), a division of the National Library of Medicine (NLM), at the
National Institute of Health (NIH), maintains the GenBank (& WGS)
nucleotide, GenPept amino acid, and RefSeq genome,
transcriptome, and proteome databases.
Europe: the European Molecular Biology Laboratory (EMBL), the
European Bioinformatics Institute (EBI), and the Swiss Institute of
Bioinformatics (SIB) all help maintain the EMBL nucleotide
sequence database, and the UNIPROT (SWISS-PROT + TrEMBL)
amino acid sequence database (with USA PIR/NBRF support also).
Asia: The National Institute of Genetics (NIG) supports the Center
for Information Biology’s (CIG) DNA Data Bank of Japan (DDBJ).
A little history
The first well recognized sequence database was Dr.
Margaret Dayhoff’s hardbound Atlas of Protein
Sequence and Structure begun in the mid-sixties.
That became PIR. DDBJ began in 1984, GenBank
in 1982, and EMBL in 1980. They are all attempts at
establishing an organized, reliable, comprehensive,
and openly available library of genetic sequences.
Sequence databases have long-since outgrown a
hardbound atlas that you can pull off of a library shelf.
They have become gargantuan and have evolved
through many, many changes.
What are sequence databases like?
Just what are primary sequences?
(Central Dogma: DNA —> RNA —> protein)
Primary refers to one dimension — all of the ‘symbol’ information written in
sequential order necessary to specify a particular biological molecular
entity, be it polypeptide or nucleotide.
The symbols are the one letter codes for all of the biological nitrogenous
bases and amino acid residues and their ambiguity codes. Biological
carbohydrates, lipids, and structural and functional information are not
sequence data. Not even DNA CDS protein translations in a DNA
database are sequence data!
However, much of this feature and bibliographic type information is
available in the reference documentation sections associated with
primary sequences in the databases.
Software is required to successfully interact with these databases, and
access is most easily handled through various software packages and
interfaces, on the World Wide Web or otherwise.
Sequence database organization
Nucleic acid sequence databases are split into subdivisions based
on taxonomy and data type. TrEMBL sequences are merged into
SWISS-PROT as they receive increased levels of annotation. Both
together comprise UNIPROT. GenPept has minimal annotation.
Nucleic Acid DB’s
GenBank/EMBL/DDBJ
all Taxonomic
categories +
WGS, HTC & HTG +
STS, EST, & GSS,
a.k.a. “Tags”
Amino Acid DB’s
UNIPROT =
SWISS-PROT +
TrEMBL (with
help from PIR)
Genpept
Parts and problems
Important elements associated with each sequence entry:
Name: LOCUS, ENTRY, ID, all are unique identifiers.
Definition: a.k.a. title, a brief textual sequence description.
Accession Number: a constant data identifier.
Source and taxonomy information; complete literature
references; comments and keywords;
and the all important FEATURE table!
A summary or checksum line, and the sequence itself.
However:
Each major database as well as each major suite of software
tools has its own distinct format requirements. Changes over
the years are a huge hassle. Standards are argued, e.g. XML,
but unfortunately, until all biologists and computer scientists
worldwide agree on one standard, and all software is (re)written
to that standard, neither of which is likely to happen very
quickly, if ever, format issues will remain one of the most
confusing and troubling aspects of working with sequence data.
Specialized format conversion tools expedite the chore, but
becoming familiar with some of the common formats helps a lot.
More format complications
Indels and missing
data symbols (i.e.
gaps) designation
discrepancy
headaches —
., -, ~, ?, N, or X
. . . . . Help!
Specialized ‘sequence’ -type databases
Databases that contain special types of sequence
information, such as patterns, motifs, and profiles.
These include: REBASE, EPD, PROSITE, BLOCKS,
ProDom, Pfam . . . .
Databases that contain multiple sequence entries
aligned, e.g. PopSet, RDP and ALN.
Databases that contain families of sequences ordered
functionally, structurally, or phylogenetically, e.g.
iProClass and HOVERGEN.
Databases of species specific sequences, e.g. the HIV
Database and the Giardia lamblia Genome Project.
And on and on . . . . See Amos Bairoch’s excellent links
page: http://us.expasy.org/alinks.html.
What about other types of biological databases?
Three-dimensional structure databases
The Protein Data Bank and Rutgers Nucleic Acid Database.
See Molecules to Go at http://molbio.info.nih.gov/cgi-bin/pdb/.
These databases contain all of the 3D atomic coordinate data
necessary to define the tertiary shape of a particular biological
molecule. The data is usually experimentally derived, either by Xray crystallography or by NMR, sometimes it’s hypothetical.
Secondary structure boundaries, sequence data, source,
resolution,and references are given in the annotation.
These databases enable the technique of homology modeling to
actually work pretty well given your sequence is similar enough to
solved structures (see the automated Swiss-Model server at
http://swissmodel.expasy.org/SWISS-MODEL.html).
Molecular visualization and/or modeling software is required to
interact with the data. It has little meaning on its own.
And still other types of bioinfo’ databases
Consider these ‘non-molecular’ but they often link to molecules:
Reference Databases (all w/ pointers to sequences): e.g.
LocusLink/Gene — integrated knowledge base
OMIM — Online Mendelian Inheritance in Man
PubMed/MedLine — over 11 million citations from more
than 4 thousand bio/medical scientific journals.
Phylogenetic Tree Databases: e.g. the Tree of Life.
Metabolic Pathway Databases: e.g. WIT (What Is There),
Japan’s GenomeNet KEGG (the Kyoto Encyclopedia of
Genes and Genomes), and the human Reactome.
Population studies data — which strains, where, etc.
And then databases that many biocomputing people don’t even
usually consider: e.g. GIS/GPS/remote sensing data, medical
records, census counts, mortality and birth rates . . . .
OK, but what can you do with new genomic
sequence data without annotation?
Easy —
restriction digests and associated mapping.
Harder —
fragment and genome assembly; such as packages
from the University of Washington’s Genome Center
(http://www.genome.washington.edu/),
Phred/Phrap/Consed (http://www.phrap.org/), and
The Institute for Genomic Research’s
(http://www.tigr.org/) Lucy and Assembler programs.
Very hard — gene finding and sequence annotation. What’s it
all mean! This is a primary focus of genomics
research, and we’ll use it as a theme today.
Easy—
forward translation to peptides after genes (and all
intron/exon strructure) are identified.
Hard again — genome scale comparisons and analyses. The BIG
picture — structure, function, regulation, evolution.
Therefore, genome characterization:
Recognizing coding sequences
Three general solutions to the gene finding problem:
1) all genes have particular regulatory signals near them,
2) all genes by definition contain specific code patterns,
3) and many genes have already been characterized in other
organisms so we can infer function and location by homology if
our new sequence is similar enough to an existing sequence.
All of these principles can be used to help locate the position of
genes in DNA, and are often known as “searching by signal,”
“searching by content,” and “homology inference”
respectively.
We’ll illustrate these concepts in the rest of today’s
lecture — and, hopefully, you’ll learn about much of
bioinformatics in the process!
So, given some biological sequence data,
what more can we learn about its evolution,
structure, function, mechanism and
regulation in life?
Enter pairwise alignment,
similarity searching,
significance, and
homology.
First, just what is homology and
similarity — are they the same?
Don’t confuse homology with similarity:
there is a huge difference! Similarity is a
statistic that describes how much two
(sub)sequences are alike according to
some set scoring criteria. It can be
normalized to ascertain statistical
significance, but it’s still just a number.
Homology, in contrast and by definition
implies an evolutionary relationship — more than just
everything evolving from the same primordial ‘ooze.’
Reconstruct the phylogeny of the organisms or genes of
interest to demonstrate homology. Better yet, show
experimental evidence — structural, morphological,
genetic, and/or fossil — that corroborates your claim.
There is no such thing as percent homology; something
is either homologous or it is not. Walter Fitch said
“homology is like pregnancy — you can’t be 45%
pregnant, just like something can’t be 45% homologous.
You either are or you are not.” Highly significant
similarity can argue for homology, but not the inverse.
OK, so how can we see if two
sequences are similar? First, to
introduce the concept, a graphical
method . . .
One way — dot matrices.
Provide a ‘Gestalt’ of all possible alignments
between two sequences.
To begin — very simple 0, 1 (match, nomatch)
identity scoring function.
Put a dot wherever symbols match.
Identities and insertion/deletion events (indels)
identified (zero:one match score matrix, no window).
Noise due to random composition effects contributes to confusion. To ‘clean up’
the plot consider a filtered windowing approach. A dot is placed at the middle of
a window if some ‘stringency’ is met within that defined window size. Then the
window is shifted one position and the entire process is repeated (zero:one
match score, window of size three and a stringency level of two out of three).
Exact alignment — but how can we ‘see’ the
correspondence of individual residues?
We can compare one molecule against another by
aligning them. However, a ‘brute force’ approach just
won’t work. Even without considering the introduction of
gaps, the computation required to compare all possible
alignments between two sequences requires time
proportional to the product of the lengths of the two
sequences. Therefore, if the two sequences are
approximately the same length (N), this is a N2 problem.
To include gaps, we would have to repeat the
calculation 2N times to examine the possibility of gaps
at each possible position within the sequences, now a
N4N problem. There’s no way! We need an algorithm.
But . . .
Just what the heck is an algorithm?
Merriam-Webster’s says: “A rule
of procedure for solving a
problem [often mathematical]
that frequently involves repetition
of an operation.”
So, you could write an algorithm
for tying your shoe! It’s just a set
of explicit instructions for doing
some routine task.
Enter the Dynamic Programming Algorithm!
Computer scientists figured it out long ago; Needleman and Wunsch
applied it to the alignment of the full lengths of two sequences in
1970. An optimal alignment is defined as an arrangement of two
sequences, 1 of length i and 2 of length j, such that:
1)
2)
3)
you maximize the number of matching symbols between 1 and 2;
you minimize the number of indels within 1 and 2; and
you minimize the number of mismatched symbols between 1 and 2.
Therefore, the actual solution can be represented by:
Sij = sij + max
Si-1
max
2 <
max
2 <
j-1
Si-x j-1 + wx-1
x < i
Si-1 j-y + wy-1
y < I
or
or
Where Sij is the score for the alignment ending at i in sequence 1 and j
in sequence 2,
sij is the score for aligning i with j,
wx is the score for making a x long gap in sequence 1,
wy is the score for making a y long gap in sequence 2,
allowing gaps to be any length in either sequence.
An oversimplified path matrix example
total penalty = gap opening penalty {zero here} + ([length of gap][gap extension penalty {one here}])
Optimum Alignments
There may be more than one best path through the matrix (and
optimum doesn’t guarantee biologically correct). Starting at the top
and working down, then tracing back, the two best trace-back routes
define the following two alignments:
cTATAtAagg
| ||||| and
cg.TAtAaT.
cTATAtAagg
|||||
.cgTAtAaT.
With the example’s scoring scheme these alignments have a score
of 5, the highest bottom-right score in the trace-back path graph,
and the sum of six matches minus one interior gap. This is the
number optimized by the algorithm, not any type of a similarity or
identity percentage, here 75% and 62% respectively! Software will
report only one optimal solution.
This was a Needleman Wunsch global solution. Smith Waterman
style local solutions use negative numbers in the match matrix and
pick the best diagonal within the overall graph.
What about proteins — conservative replacements and similarity as
opposed to identity. The nitrogenous bases are either the same or
they’re not, but amino acids can be similar, genetically, evolutionarily,
and structurally! The BLOSUM62 table (Henikoff and Henikoff, 1992)
A
B
C
D
E
F
G
H
I
K
L
M
N
P
Q
R
S
T
V
W
X
Y
Z
A
4
-2
0
-2
-1
-2
0
-2
-1
-1
-1
-1
-2
-1
-1
-1
1
0
0
-3
-1
-2
-1
B
-2
6
-3
6
2
-3
-1
-1
-3
-1
-4
-3
1
-1
0
-2
0
-1
-3
-4
-1
-3
2
C
0
-3
9
-3
-4
-2
-3
-3
-1
-3
-1
-1
-3
-3
-3
-3
-1
-1
-1
-2
-1
-2
-4
D
-2
6
-3
6
2
-3
-1
-1
-3
-1
-4
-3
1
-1
0
-2
0
-1
-3
-4
-1
-3
2
E
-1
2
-4
2
5
-3
-2
0
-3
1
-3
-2
0
-1
2
0
0
-1
-2
-3
-1
-2
5
F
-2
-3
-2
-3
-3
6
-3
-1
0
-3
0
0
-3
-4
-3
-3
-2
-2
-1
1
-1
3
-3
G
0
-1
-3
-1
-2
-3
6
-2
-4
-2
-4
-3
0
-2
-2
-2
0
-2
-3
-2
-1
-3
-2
H
-2
-1
-3
-1
0
-1
-2
8
-3
-1
-3
-2
1
-2
0
0
-1
-2
-3
-2
-1
2
0
I
-1
-3
-1
-3
-3
0
-4
-3
4
-3
2
1
-3
-3
-3
-3
-2
-1
3
-3
-1
-1
-3
K
-1
-1
-3
-1
1
-3
-2
-1
-3
5
-2
-1
0
-1
1
2
0
-1
-2
-3
-1
-2
1
L
-1
-4
-1
-4
-3
0
-4
-3
2
-2
4
2
-3
-3
-2
-2
-2
-1
1
-2
-1
-1
-3
M
-1
-3
-1
-3
-2
0
-3
-2
1
-1
2
5
-2
-2
0
-1
-1
-1
1
-1
-1
-1
-2
N
-2
1
-3
1
0
-3
0
1
-3
0
-3
-2
6
-2
0
0
1
0
-3
-4
-1
-2
0
P
-1
-1
-3
-1
-1
-4
-2
-2
-3
-1
-3
-2
-2
7
-1
-2
-1
-1
-2
-4
-1
-3
-1
Q
-1
0
-3
0
2
-3
-2
0
-3
1
-2
0
0
-1
5
1
0
-1
-2
-2
-1
-1
2
R
-1
-2
-3
-2
0
-3
-2
0
-3
2
-2
-1
0
-2
1
5
-1
-1
-3
-3
-1
-2
0
S
1
0
-1
0
0
-2
0
-1
-2
0
-2
-1
1
-1
0
-1
4
1
-2
-3
-1
-2
0
T
0
-1
-1
-1
-1
-2
-2
-2
-1
-1
-1
-1
0
-1
-1
-1
1
5
0
-2
-1
-2
-1
V
0
-3
-1
-3
-2
-1
-3
-3
3
-2
1
1
-3
-2
-2
-3
-2
0
4
-3
-1
-1
-2
W
-3
-4
-2
-4
-3
1
-2
-2
-3
-3
-2
-1
-4
-4
-2
-3
-3
-2
-3
11
-1
2
-3
X
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
Y
-2
-3
-2
-3
-2
3
-3
2
-1
-2
-1
-1
-2
-3
-1
-2
-2
-2
-1
2
-1
7
-2
Z
-1
2
-4
2
5
-3
-2
0
-3
1
-3
-2
0
-1
2
0
0
-1
-2
-3
-1
-2
5
Identity values range from 4 to 11, some similarities are as high as 3, and negative values for those
substitutions that rarely occur go as low as –4. The most conserved residue is tryptophan with a
score of 11; cysteine is next with a score of 9; both proline and tyrosine get scores of 7 for identity.
We can imagine screening databases for sequences
similar to ours using these concepts of dynamic
programming and substitution scoring matrices and
some yet to be described algorithmic tricks. But what do
database searches tell us; what can we gain from them?
Why even bother? Inference
through homology is a fundamental
principle of biology!
When a sequence is found to fall into a preexisting family
we may be able recognize genes, and infer function,
regulation, mechanism, evolution, and perhaps even
structure, based on homology with its neighbors.
Independent of all that, what is a
‘good’ alignment?
So, first — significance:
when is any alignment worth
anything biologically?
An old statistics trick — Monte Carlo simulations:
Z score = [ ( actual score ) - ( mean of randomized scores ) ]
( standard deviation of randomized score distribution )
The Normal distribution
Many Z scores measure the distance from the mean
using this simplistic Monte Carlo model assuming a
Gaussian distribution, a.k.a. the Normal distribution
(http://mathworld.wolfram.com/NormalDistribution.html),
in spite of the fact that ‘sequence-space’ actually
follows what is know as the ‘Extreme Value distribution.’
However, the Monte Carlo method does approximate
significance estimates fairly well.
0:==
< 20 650
0:
0
22
0:=
3
24
8:*
26 22
28 98 87:*
30 289 528:*
32 1714 2042:===*
34 5585 5539:=========*
36 12495 11375:==================*==
38 21957 18799:===============================*=====
40 28875 26223:===========================================*====
42 34153 32054:=====================================================*===
44 35427 35359:==========================================================*
46 36219 36014:===========================================================*
48 33699 34479:======================================================== *
50 30727 31462:=================================================== *
52 27288 27661:=============================================*
54 22538 23627:====================================== *
56 18055 19736:============================== *
58 14617 16203:========================= *
60 12595 13125:=====================*
62 10563 10522:=================*
64 8626 8368:=============*=
66 6426 6614:==========*
68 4770 5203:========*
70 4017 4077:======*
72 2920 3186:=====*
74 2448 2484:====*
76 1696 1933:===*
78 1178 1503:==*
80 935 1167:=*
82 722 893:=*
84 454 707:=*
86 438 547:*
88 322 423:*
90 257 328:*
92 175 253:*
94 210 196:*
96 102 152:*
98 63 117:*
100 58 91:*
102 40 70:*
104 30 54:*
106 17 42:*
108 14 33:*
110 14 25:*
112 12 20:*
9 15:*
114
6 12:*
116
9:*
8
118
7:*=
>120 1030
‘Sequence-space’ (Huh, what’s that?)
actually follows the ‘Extreme Value distribution’
(http://mathworld.wolfram.com/ExtremeValueDistribution.html).
Based on this known statistical
distribution, and robust
statistical methodology, a
realistic Expectation function,
the E Value, can be calculated
from database searches.
The ‘take-home’ message is . . .
The Expectation Value!
The higher the E value is, the more probable that the
observed match is due to chance in a search of the
same size database, and the lower its Z score will be,
i.e. is NOT significant. Therefore, the smaller the E
value, i.e. the closer it is to zero, the more significant it
is and the higher its Z score will be! The E value is the
number that really matters. In other words, in order to
assess whether a given alignment constitutes evidence
for homology, it helps to know how strong an alignment
can be expected from chance alone.
Rules of thumb for a protein search
The Z score represents the number of standard deviations some
particular alignment is from a distribution of random alignments
(often the Normal distribution).
They very roughly correspond to the listed E Values (based on
the Extreme Value distribution) for a typical protein sequence
similarity search through a database with ~250,000 protein entries.
On to the searches
How can you search the databases for similar
sequences, if pairwise alignments take N2
time?! Significance and heuristics . . .
Database searching programs use the two concepts of
dynamic programming and substitution scoring
matrices; however, dynamic programming takes far too
long when used against most sequence databases with
a ‘normal’ computer. Remember how big the
databases are!
Therefore, the programs use tricks to make things
happen faster. These tricks fall into two main
categories, that of hashing, and that of
approximation.
Corn beef hash? Huh . . .
Hashing is the process of breaking your sequence into
small ‘words’ or ‘k-tuples’ (think all chopped up, just like
corn beef hash) of a set size and creating a ‘look-up’
table with those words keyed to position numbers.
Computers can deal with numbers way faster than they
can deal with strings of letters, and this preprocessing
step happens very quickly.
Then when any of the word positions match part of an
entry in the database, that match, the ‘offset,’ is saved.
In general, hashing reduces the complexity of the search
problem from N2 for dynamic programming to N, the
length of all the sequences in the database.
OK. Heuristics . . . What’s that?
Approximation techniques are collectively known as ‘heuristics.’
Webster’s defines heuristic as “serving to guide, discover, or
reveal; . . . but unproved or incapable of proof.”
In database similarity searching techniques the heuristic usually
restricts the necessary search space by calculating some sort of a
statistic that allows the program to decide whether further scrutiny
of a particular match should be pursued. This statistic may miss
things depending on the parameters set — that’s what makes it
heuristic. ‘Worthwhile’ results at the end are compiled and the
longest alignment within the program’s restrictions is created.
The exact implementation varies between the different programs,
but the basic idea follows in most all of them.
Two predominant versions exist: BLAST and Fast
Both return local alignments, and are not a single program, but
rather a family of programs with implementations designed to
compare a sequence to a database every which way.
These include:
1) a DNA sequence against a DNA database (not recommended unless
forced to do so because you are dealing with a non-translated region of
the genome — DNA is just too darn noisy, only identity & four bases!),
2) a translated (where the translation is done ‘on-the-fly’ in all six frames)
version of a DNA sequence against a translated (‘on-the-fly’ six-frame)
version of the DNA database (not available in the Fast package),
3) a translated (‘on-the-fly’ six-frame) version of a DNA sequence against a
protein database,
4) a protein sequence against a translated (‘on-the-fly’ six-frame) version
of a DNA database,
5) or a protein sequence against a protein database.
Translated comparisons allow penalty-free frame shifts.
The BLAST and Fast programs — some generalities
BLAST — Basic Local Alignment
Search Tool, developed at NCBI.
FastA — and its family of relatives,
developed by Bill Pearson at the
University of Virginia.
1) Normally NOT a good idea
to use for DNA against
DNA searches w/o
translation (not optimized);
2) Pre-filters repeat and “low
complexity” sequence
regions;
4) Can find more than one
region of gapped similarity;
5) Very fast heuristic and
parallel implementation;
6) Restricted to precompiled,
specially formatted
databases;
1) Works well for DNA
against DNA searches
(within limits of possible
sensitivity);
2) Can find only one gapped
region of similarity;
3) Relatively slow, should
often be run in the
background;
4) Does not require specially
prepared, preformatted
databases.
The algorithms, very briefly
BLAST:
Two word hits on the
same diagonal above
some similarity
threshold triggers
ungapped extension until
the score isn’t improved
enough above another
threshold:
the HSP.
Initiate gapped extensions
using dynamic programming for
those HSP’s above a third
threshold up to the point where
the score starts to drop below a
fourth threshold: yields
alignment.
Find all ungapped exact
word hits; maximize the
ten best continuous
regions’ scores: init1.
Fast:
Combine nonoverlapping init
regions on different
diagonals:
initn.
Use dynamic
programming ‘in a
band’ for all regions
with initn scores
better than some
threshold: opt score.
What’s the deal with DNA versus protein for
searches and alignment?
All database similarity searching and sequence alignment,
regardless of the algorithm used, is far more sensitive at the amino
acid level than at the DNA level. This is because proteins have
twenty match criteria versus DNA’s four, and those four DNA
bases can generally only be identical, not similar, to each other;
and many DNA base changes (especially third position changes)
do not change the encoded protein.
All of these factors drastically increase the ‘noise’ level of a DNA
against DNA search, and give protein searches a much greater
‘look-back’ time, at least doubling it.
Therefore, whenever dealing with coding sequence, it is always
prudent to search at the protein level!
What can we do with the significant results
of database searching — multiple sequence
alignment & analysis — why even bother?
More data yields stronger analyses — as
long as it is done carefully!
Mosaic ideas and evolutionary ‘importance.’
Applications:
Probe, primer, and motif design;
Graphical illustrations;
Comparative ‘homology’ inference;
Molecular evolutionary analysis.
All right — how do you do it?
Dynamic programming’s complexity
increases exponentially with the number of
sequences being compared:
N-dimensional matrix . . . .
complexity=[sequence length]number of sequences
i.e. complexity is O(en)
Multiple Sequence Dynamic Programming
Therefore, the most
common implementation,
pairwise, progressive
dynamic programming,
restricts the solution to the
neighborhood of only two
sequences at a time.
All sequences are
compared, pairwise, and
then each is aligned to its
most similar partner or
group of partners. Each
group of partners is then
aligned to finish the
complete multiple
sequence alignment.
Web resources for pairwise,
progressive multiple alignment
in the USA, include the Baylor College of
Medicine’s Search Launcher —
http://searchlauncher.bcm.tmc.edu/
However, problems with large datasets and
huge multiple alignments make doing multiple
sequence alignment on the Web impractical
after your dataset has reached a certain size.
You’ll know it when you’re there!
So, what else is available?
Stand-alone ClustalW is available for all
operating systems; its graphical user interface
ClustalX, makes running it very easy.
And dedicated biocomputing server suites, like
the GCG Wisconsin Package, which includes
PileUp and ClustalW and the SeqLab graphical
user interface, are another powerful solution.
Furthermore, newer software such as TCoffee,
MUSCLE, ProbCons, POA, MAFFT, etc. add
various tweaks and tricks to make the entire
process more accurate and/or faster.
Reliability and the
Comparative Approach
explicit homologous correspondence;
manual adjustments based on
knowledge,
especially structural, regulatory, and
functional sites.
Therefore, editors like SeqLab and
structure based databses like
the Ribosomal Database Project:
http://rdp.cme.msu.edu/index.jsp
Structural & Functional correspondence in
the Wisconsin Package’s SeqLab
As with pairwise methods, work
with proteins! If at all possible
Twenty match symbols versus four, plus
similarity! Way better signal to noise.
Also guarantees no indels are placed
within codons. So translate, then align.
Nucleotide sequences will only reliably
align if they are very similar to each
other. And they will require extensive
hand editing and careful consideration.
Beware of aligning apples and
oranges [and grapefruit]!
Receptor versus
activator, on ad
nauseam;
parologue versus
orthologue;
genomic versus cDNA;
mature versus
precursor.
Mask out uncertain areas
Complications —
Order dependence.
Not that big of a deal.
Substitution matrices and gap penalties.
A very big deal!
Regional ‘realignment’ becomes
incredibly important, especially with
sequences that have areas of high and
low similarity
Homology inference is especially
powerful for finding genes and
functional and regulatory
domains within them!
The information within a multiple sequence
alignment can dramatically point to
evolutionarily constrained elements in the
sequences. Furthermore, often functions can
experimentally be ascribed to them.
Therefore, we can search for those elements
in unknown sequences to attempt to identify
the unknown’s function. How does this work?
The consensus and motifs
HMG
box
Conserved
regions in
alignments can
be visualized
with a sliding
window
approach and
appear as
peaks.
Refer to the peak
seen here in a
SRY/SOX
alignment.
The HMG box DNA binding
domain of SRY/SOX
A consensus isn’t
necessarily the
biologically
“correct”
combination.
A simple
consensus
throws much
information away!
Therefore, motif
consensus
KRPMNAFMVYXKXXRRKIXXXXPXXHNXEISKRLGXXWKXLXXXEKXPYIXEAXR
definition.
PROSITE, a simple fast approach
The trick is to define a motif such that it minimizes false positives
and maximizes true positives — it needs to be just discriminatory
enough. Development is largely empirical; a pattern is made, tested
against the database, then refined, over and over, although when
experimental evidence is available, it is always incorporated. This
is known as motif definition and Amos Bairoch, has done it a bunch!
His database of catalogued structural, regulatory, and enzymatic
consensus patterns or ‘signatures’ is the PROSITE Database of
protein families and domains and contains 1,510 documentation
entries that describe 2,877 different patterns, rules, and
profiles/matrices (Release 20.77, Feb. 26, 2008). Pattern
descriptions for these characteristic local sequence areas are
variously and confusingly known as motifs, templates, signatures,
patterns, and even fingerprints.
The HMG box —
Defined as:
[FI]-S-[KR]-K-C-x[EK]-R-W-K-T-M.
A one-dimensional
‘regular-expression’
of a conserved site.
QuickTime™ and a
Graphics decompressor
are needed to see this picture.
Not necessarily
biologically
meaningful though,
and motifs are
limited in their ability
to discriminate a
residue’s
‘importance.’
And we can use this approach in signal searching for
locating transcription and translation affecter sites.
For example, compiling lots of known sequence data
provides these regular expressions:
Start Sites:
Prokaryote promoter ‘Pribnow Box,’ TTGACwx{15,21}TAtAaT;
Eukaryote transcription factor site database, Ghosh’s TFD;
Shine-Dalgarno site, (AGG,GAG,GGA)x{6,9}ATG, in prokaryotes;
Kozak eukaryote start consensus, cc(A,g)ccAUGg;
AUG start codon in about 90% of genomes, exceptions in some
prokaryotes and organelles.
End Sites:
‘Nonsense’ chain terminating, stop codons — UAA, UAG, UGA;
Eukaryote terminator consensus, YGTGTTYY;
Eukaryote poly(A) adenylation signal AAUAAA;
but exceptions in some ciliated protists and due to eukaryote suppresser
tRNAs.
However — remember the problems inherent to consensus approaches!
So, lets look at another strategy —
locating transcription and translation affecter sites
with two-dimensional weight matrices. Looking at
multiple sequence alignments of splice sites yields:
Exon/Intron Junctions
Donor Site
Acceptor Site
Exon Intron Exon
A64G73G100T100A62A68G84T63 . . . 6Py74-87NC65A100G100N
The splice cut sites occur before a 100% GT
consensus at the donor site and after a 100%
AG consensus at the acceptor site, but a
simple consensus is not informative enough.
We need to use those percentages somehow.
To do that we need to include ‘all’ of the
information from the multiple sequence
alignment, or of some region within the
alignment, in a description that doesn’t
throw anything away!
Enter — two-dimensional techniques
for homology searching — the PSSM (position
specific site matrix) and the ‘profile’ algorithms,
including PsiBLAST, MEME, and HMMer . . .
How do these work?
We’ll begin with the signal searching example —
locating transcription and translation affecter sites.
PSSMs describe the probability at each base position to be
either A, C, U (same as T), or G, in percentages.
The Donor Matrix.
CONSENSUS from:
Donor Splice site sequences
from Stephen Mount NAR 10(2) 459;472 figure 1 page 460
Exon
%G
%A
%U
%C
20
30
20
30
9
40
7
44
cutsite
11
64
13
11
74
9
12
6
100
0
0
0
Intron
0
0
100
0
29
61
7
2
12
67
11
9
84
9
5
2
9
16
63
12
18
39
22
20
CONSENSUS sequence to a certainty level of 75 percent.
VMWKGTRRGWHH
The matrix begins four bases ahead of the splice site!
20
24
27
28
More signal searching examples—
locating transcription and translation affecter sites.
PSSMs, continued
The Acceptor Matrix.
CONSENSUS of: Acceptor.Dat. IVS Acceptor Splice Site Sequences
from Stephen Mount NAR 10(2); 459-472 figure 1 page 460
Intron
cutsite
Exon
%G
15
22
10
10
10
6
7
9
7
5
5
24
1
0
100
52
24
19
%A
15
10
10
15
6
15
11
19
12
3
10
25
4
100
0
22
17
20
%T
52
44
50
54
60
49
48
45
45
57
58
30
31
0
0
8
37
29
%C
18
25
30
21
24
30
34
28
36
35
27
21
64
0
0
18
22
32
to
a
CONSENSUS
position:
sequence
certainty
level
of
75.0
percent
at
BBYHYYYHYYYDYAGVBH
The matrix begins fifteen bases upstream of the splice site!
each
More signal searching examples—
locating transcription and translation affecter sites.
PSSMs, continued
The CCAAT site — occurs around 75 base pairs upstream of the start point
of eukaryotic transcription, may be involved in the initial binding of RNA
polymerase II.
Base freguencies according to Philipp Bucher (1990) J. Mol. Biol. 212:563-578.
Preferred region:
%G
%A
%U
%C
7
32
30
31
25
18
27
30
motif within -212 to -57.
14
14
45
27
40
58
1
1
57
29
11
3
1
0
1
99
Optimized cut-off value:
0
0
0 100
1
0
99
0
12
68
15
5
9
10
82
0
87.2%.
34
13
2
51
30
66
1
3
CONSENSUS sequence to a certainty level of 68 percent at each position:
HBYRRCCAATSR
More signal searching examples—
locating transcription and translation affecter sites.
PSSMs, continued
The TATA site (aka “Hogness” box) — a conserved A-T rich
sequence found about 25 base pairs upstream of the start point of
eukaryotic transcription, may be involved in positioning RNA
polymerase II for correct initiation and binds Transcription Factor IID.
Base freguencies according to Philipp Bucher (1990) J. Mol. Biol. 212:563-578.
Preferred region:
center between -36 and -20.
%G 39 5 1 1 1 0 5 11 40
%A 16 4 90 1 91 69 93 57 40
%U 8 79 9 96 8 31 2 31 8
%C 37 12 0 3 0 0 1 1 11
Optimized cut-off value:
39 33 33 33 36
14 21 21 21 17
12 8 13 16 19
35 38 33 30 28
79%.
36
20
18
26
CONSENSUS sequence to a certainty level of 61 percent at each position:
STATAWAWRSSSSSS
More signal searching examples—
locating transcription and translation affecter sites.
PSSMs, continued
The GC box — may relate to the binding of transcription
factor Sp1.
Base freguencies according to Philipp Bucher (1990) J. Mol. Biol. 212:563-578.
Preferred region:
%G
%A
%U
%C
18
37
30
15
41
35
12
11
motif within -164 to +1.
56 75 100
18 24
0
23 0
0
2
0
0
99
1
0
0
Optimized cut-off value:
88%.
0 82 81 62 70 13 19 40
20 17 0 29 8 0 7 15
18 1 18 9 15 27 42 37
62 0 1 0 6 61 31 9
CONSENSUS sequence to a certainty level of 67 percent at each position:
WRKGGGHGGRGBYK
More signal searching examples—
locating transcription and translation affecter sites.
PSSMs, continued
The cap signal — a structure at the 5’ end of eukaryotic mRNA introduced after
transcription by linking the 5’ end of a guanine nucleotide to the terminal base of the
mRNA and methylating at least the additional guanine; the structure is
7MeG5’ppp5’Np.
Base freguencies according to Philipp Bucher (1990) J. Mol. Biol. 212:563-578.
Preferred region:
%G
%A
%U
%C
23
16
45
16
center between 1 and +5. Optimized cut-off value:
0
0
0
100
0
95
5
0
38
9
26
27
0
25
43
31
15
22
24
39
24
15
33
28
81.4%.
18
17
33
32
CONSENSUS sequence to a certainty level of 63 percent at each position:
KCABHYBY
More signal searching examples—
locating transcription and translation affecter sites.
PSSMs, continued
The eukaryotic terminator weight matrix.
Base frequencies according to McLauchlan et al.
(1985) N.A.R. 13:1347-1368.
Found in about 2/3's of all eukaryotic gene sequences.
%G
%A
%U
%C
19
13
51
17
81
9
9
1
9
3
89
0
94
3
3
0
14
4
79
3
10
0
61
29
11
11
56
21
19
13
47
21
CONSENSUS sequence to a certainty level of 68 percent at each position:
BGTGTBYY
And to extend the 2D
PSSM concept even
further . . .
Michael Gribskov envisioned special weight
matrices in which conserved areas of the
alignment receive the most importance,
variable regions hardly matter, and gaps are
variably weighted depending where they are!
These are often called “profiles.”
The basic idea is to tabulate how often every possible character occurs at each
position, scale conserved positions up, variable positions down, and store the
whole thing in a matrix. With protein data it’ll be twenty residues wide, with
nucleic acids four bases wide, by the length of your pattern either way.
A small piece of a profile —
Cons A
B
C
D
E
F
G
H
I
K
L
M
N
P
Q
R
S
T
-3
-41
-8
-6
-84
-4
-42
-78
-7
-78
-43
38
-43
-6
-38
135
-7 -146
-52
-50 -139
223
-92
-43
-5
-53
55
91
31
-20
2
-25
-56
8
37
140 -141
275
S
45
K
-49
R
-28
L
-66 -279
-41
-68
-57
45 -145 -102
-16
-69 -279 -209
-61
-77
-38
-2 -278 -210
-31
W
X
Y
40
-71 -123
-28
-81
-6 -163 100 100
-4
-49
-92 -146
-44
-95
48 -199 100 100
-22
-14
-26
-33
-49
-10 -123 100 100
137 -210 -209 -141 -141 -138
-69
-80
71 -142 -108
Z
*
Gap Len
-71 -209 -281 100 100
G
6
-63 -185
-62 -118 -187
360 -124 -246 -122 -246 -185
K
2
-14
-75
-19
37
-76
-47
-23
-72
48
-58
-36
-13
-39
20
27
8
-21
-51
-87
-27
-53
30 -123 100 100
R
-22
-39
-66
-41
2
-55
-70
-33
-34
7
-14
14
-27
-54
14
20
-17
-25
-29
-74
-31
-48
4 -120 100 100
W -300 -400 -200 -400 -300
K
-42
L
-6
14 -105
-41
-47
-25
-48
2 -122 -183 -129 -108 -186 -119 -252 100 100
100 -200 -200 -300 -300 -200 -100 -400 -400 -200 -300 -300 -200 -300 1100 -188
24 -100
-30
-4 -123 -121 -123
V
-46
-58
-54
4 -106
-49
-15
200 -300 -400 100 100
116
-79
-43
38
-47
30
59
4
-31
-82 -109
-31
-59
25 -142 100 100
-25
7
4
-16
-59
-21
-33
-7
-12
-14
-34
-49
-30 -122 100 100
-80
The greatest conservation is the invariant tryptophan. It’s the only residue absolutely
conserved — it gets the highest score, 1100! The -400 scores are from substituting that
tryptophan with an aspartate, asparagine, or proline. In the BLOSUM series tryptophan
has the highest identity score of any residue, and the most negative substitution scores
include those from tryptophan to aspartate, asparagine, and proline, times the highest
conservation in the region, equals the most negative scores in the profile.
Some profile variations
As powerful as ‘traditional’
Gribskov style profiles are, they
require a lot of time and skill to
prepare and validate, and they
are heuristics based. Excess
subjectivity and a lack of formal
statistical rigor contribute as
drawbacks. Sean Eddy
developed the HMMer package,
which uses Hidden Markov
modeling, with a formal
probabilistic basis and consistent
gap insertion theory, to build and
manipulate HMMer profiles and
profile databases, to search
sequences against HMMer
profile databases and visa versa,
and to easily create multiple
sequence alignments using
HMMer profiles as a ‘seed.’
QuickTime™ and a
TIFF (LZW) decompres sor
are needed to see this picture.
Profile variations, continued
Bailey and Elkan’s Expectation Maximization (MEME) uses Bayesian
probabilities and unsupervised learning to find, de novo, unknown
conserved motifs among a group of unaligned, ungapped sequences.
The motifs do not have to be in congruent order among the different
sequences; i.e. it has the power to discover ‘unalignable’ motifs between
sequences. This characteristic differentiates MEME from the other profile
building techniques. It can be particularly effective in discovering
regulatory elements in common between co-regulated genes.
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
What about those content approaches —
strategies for finding coding regions based on
the content of the DNA itself
Searching by content utilizes the fact that genes necessarily have
many implicit biological constraints imposed on their genetic code.
This induces certain periodicities and patterns to produce distinctly
unique coding sequences; non-coding stretches do not exhibit this
type of periodic compositional bias. These principles can help
discriminate structural genes in two ways:
1) based on the local “non-randomness” of a stretch, and
2) based on the known codon usage of a particular life form.
The first, the non-randomness test, does not tell us anything about
the particular strand or reading frame; however, it does not require
a previously built codon usage table. The second approach is
based on the fact that different organisms use different
frequencies of codons to code for particular amino acids. This
does require a codon usage table built up from known translations;
however, it also tells us the strand and reading frame for the gene
products as opposed to the former.
“Non-randomness” content approaches —
Rely solely on the base compositional bias of every third position base
but does not tell us anything about the particular strand or reading frame;
however, it does not require a previously built codon usage table.
The plot is divided into three regions: top and bottom areas predict coding and
noncoding regions, respectively, the middle area claims no statistical significance.
Codon usage content approaches —
Genomes use synonymous codons unequally sorted phylogenetically.
Requires a codon usage table built up from known translations; however, it
also tells us the strand and reading frame for the gene products.
Each forward reading frame indicates a red codon preference curve and a
blue third position GC bias curve.
Web servers for gene finding
Many servers are available for help with gene finding
analyses. Most combine many of the previousmethods
but they consolidate the information and often combine
signal and content methods with homology inference in
order to ascertain exon locations. Many use powerful
neural net or artificial intelligence approaches to assist
in this difficult ‘decision’ process.
A wonderful bibliography on computational methods for
gene recognition has been compiled by Wentian Li
(http://www.nslij-genetics.org/gene/),
and the Baylor College of Medicine’s Gene Feature
Search (http://searchlauncher.bcm.tmc.edu/seqsearch/gene-search.html) is another nice portal to
several gene finding tools.
Genome scale analyses — map browsers try
to tie it all together —
Genetic linkage mapping databases for most large
genome projects— H. sapiens, Mus, Drosophila, C.
elegans, Saccharomyces, Arabidopsis, E. coli . . .
. . . usually link to other databases within the context
of a genome browser or map viewer.
Examples include: NCBI’s Map Viewer
(http://www.ncbi.nlm.nih.gov/mapview/), the
Ensemble Project (http://www.ensembl.org/), the
UCSC Genome Browser at
(http://genome.ucsc.edu/), and the Lawrence
Livermore National Laboratory ECR Browser
(http://www.dcode.org/).
NCBI’s Map Viewer
(http://www.ncbi.nlm.nih.gov/mapview/) —
Sanger Center for BioInformatics Ensembl project (http://www.ensembl.org/) —
Qui ck Ti me ™ an d a
TIFF (LZW) de co mpre ss or
a re ne ed ed to s ee th i s pi c tu re.
University of California, Santa Cruz Genome Browser (http://genome.ucsc.edu/)
—
Qui ck Ti me ™ an d a
TIFF (LZW) de co mpre ss or
a re ne ed ed to s ee th i s pi c tu re.
The comparative approach and noncoding elements
The colinearity of orthologous markers between chromosomes is called
synteny. Genome map browsers’ prebuilt syntenic segment alignments
illustrate conservation across taxa. Segments conserved through evolution
that do not code for structure are often inferred to relate to regulation.
These are sometimes CNEs for conserved noncoding elements, and can
be seen across vast phylogenetic distances.
CNEs from human to fish —
CNEs in common
Number
Total length (kb)
Average length (bp)
Maximum length (bp)
Mean identity (range) (%)
Human/shark
Human/Fugu
Human/zebrafish
4782
1003
210
937
83 (71-98)
2107
379
180
982
83 (71-98)
2838
530
187
880
84 (73-99)
Venkatesh, et al. Science 22 December 2006: Vol. 314. no. 5807, p. 1892
In particular, it suggested that in mammals there is at least twice as much
conserved genomic DNA as there is protein coding DNA! So, if you accept
~1.5% (≤30,000 genes) as the amount of protein coding DNA in the human
genome, around 3% of the remaining falls into this CNE class of regulatory
elements (≥60,000 CNEs).
CNEs evolved in Vertebrates very early on —
McEwen, et al. Genome Res. 2006 Apr;16(4):451-65. Epub 2006 Mar 13
Function through phylogenomics —
Beware of gene duplication with
subsequent divergence. Information
about the evolutionary relationships
among genes can be used to predict
the functions of uncharacterized
genes. Two hypothetical scenarios are
presented and the path of trying to
infer the function of two
uncharacterized genes in each case is
traced. (A) A gene family has
undergone a gene duplication that was
accompanied by functional divergence.
(B) Gene function has changed in one
lineage. The thin branches in the
evolutionary trees correspond to the
gene phylogeny and the thick gray
branches in A (bottom) correspond to
the phylogeny of the species in which
the duplicate genes evolve in parallel
(as paralogs). Different colors (and
symbols) represent different gene
functions; gray (with hatching)
represents either unknown or
unpredictable functions.
Jonathan A. Eisen Genome
Res. 1998; 8: 163-167
And the sort of genomics that lots of
folks like to think about —
DNA microarrays,
SAGE, as well as
yeast two-hybrid
assays, phage
display, 2D gels,
and mass spec’
tech’s, are all at the
interface between
bioinformatics and
wet-lab techniques
to help gain
understanding of
biological function.
I do not have a
good working
knowledge of these
systems.
Conclusions —
There’s a bewildering assortment of bioinformatics databases and ways to
access and manipulate the information within them. The key is to learn
how to use the data and the methods in the most efficient manner! The
better you understand the chemical, physical, and biological systems
involved, the better your chance of success in analyzing them. Certain
strategies are inherently more appropriate to others in certain
circumstances. Making these types of subjective, discriminatory decisions
is one of the most important ‘take-home’ messages I can offer!
Gunnar von Heijne in his old but incredibly readable treatise, Sequence
Analysis in Molecular Biology; Treasure Trove or Trivial Pursuit (1987),
provides a very appropriate conclusion:
“Think about what you’re doing; use your knowledge of the molecular
system involved to guide both your interpretation of results and your
direction of inquiry; use as much information as possible; and do not
blindly accept everything the computer offers you.”
“. . . if any lesson is to be drawn . . . it surely is that to be able to make
a useful contribution one must first and foremost be a biologist, and
only second a theoretician . . . . We have to develop better algorithms,
we have to find ways to cope with the massive amounts of data, and
above all we have to become better biologists. But that’s all it takes.”
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