Genomics- The Science and Technology Behind the Human Genome Project. Charles R. Cantor, Cassandra L / genomics11-15 / 15

558

RESULTS AND IMPLICATIONS OF LARGE-SCALE DNA SEQUENCING

Sequence alignment by dynamic programming is conveniently viewed by a plot

as

shown in Figure 15.25. The coordinates of the plot are the two sequences, just

as

in the

dot matrix blot discussed earlier. However, what is plotted at each position is not a score

from

the comparison of two sequence windows; instead, it is a cumulative score

for

a

path through the two sequences that represents a particular comparison. In the simple ex-

ample in Figure 15.25, the scoring is just for identities. In a real case, the elements of a

scoring matrix would be used. From the example in Figure 15.25

a it can be seen that a

this final point (Fig. 15.25

b ).

Rigorous dynamic programming is

a very computationally intensive process because

Figure 15.25

A simple example of dynamic programming.

(a) Matrix

used to describe the scores

of various alignments

between two sequences. Note that successive horizontal and

vertical moves

are forbidden, since this introduces a gap on both strands which is equivalent to a diagonal move—

not a gap at all. In

each cell only the route with the highest possible score

is shown. In practice,

each cell can be approached in three different ways—diagonally, horizontally,

and vertically.

(b)

The trace back through the matrix from the highest scoring configuration to find all paths (align-

ments) consistant with that final configuration. Only the path with the highest score is kept.

DYNAMIC PROGRAMMING

559

large number of dynamic

programming comparisons. A number of different

procedures

can be used to speed this up. Some pay the price of a loss of rigor, so no guarantee exists

that the very best alignment will be found.

A very popular program for global database searching has been developed by David

Lipman and his coworkers. This is called FASTA for nucleic acid comparisons, FASTP,

for protein (or translated ORF) comparisons. The test protein

is

broken

into

k-tuple

words. These are searched through the entire database, in looking for exact matches and

scoring them. Some of these best scoring comparisons will form diagonal regions of dot

plots with a high density of exact matches. The 10 best of these regions are rescored with

a more accurate comparison matrix. They are trimmed to remove residues not contribut-

ing to the good score. Then nearby regions are joined, using gap penalties for the in-

evitable gaps required in this process. The resulting sets of comparisons are examined to

find those with the best, provisional fits. Then each of these is examined by dynamic pro-

gramming within a relatively narrow band of 32 residues around the sites of each of the

provisional matches. Even faster algorithms exist, like Lipman’s

BLAST

which

looks

only at short exact fits

but makes a rigorous statistical assessment of their

significance.

This is used to winnow down the vast array of possible comparisons before more rigor-

ous, and time-consuming, methods are employed.

A key point in favor of the dynamic programming method is that for each comparison

cell, as shown in Figure 15.26, one only needs to consider three

immediate

precursors.

This allows the computation to be broken into a set of steps that can be computed in par-

allel. The comparison can proceed down the diagonal of the plot in waves, with each cell

computed

independently

of

what is happening in the other cells along

that

diagonal

(Fig. 15.26). Thus parallel processing computers are well adapted to the dynamic

programming method. The result is an enormous increase in our power

to

do

global

sequence comparisons. Using a parallel architecture, it is possible,

with

the

existing

protein

database, to do

a complete inversion. That is, all sequences

are

compared

with

all sequences by dynamic

programming. The resulting set of scores allows the

database

to be factored into sequence families, and new, unsuspected sets of

families

have

been

found in this way. As an

alternative to parallel processing, specialized

computer

chips

have been designed and built that perform dynamic programming sequences

compar-

isons, but they do this very quickly because each sequence needs to be passed through the

chip only once.

after we have learned much more about

the properties of some of these control regions,

we will be able to do a better job. The true magnitude of this problem, currently, is well

illustrated by the globin gene family where one key, short control element required for

correct tissue-specific gene expression was found (experimentally, and after much effort)

to be located more than 20-kb upstream from the start of transription. Thus finding con-

trol elements is definitely not like shooting fish in a barrel.

DIVERSITY

OF

DNA

SEQUENCES

We cannot be interested in just the DNA sequence of a single individual. All of the intel-

lectual interest

in studying both inherited human diseases and normal human variations

will come from comparisons among the

sequences of different individuals. This is al-

ready done when searching for disease genes. Its ultimate manifestations may be the kind

of large-scale diagnostic DNA sequencing postulated in Chapter 13.

How different are two humans? From recent DNA sequencing projects (hopefully on

representative regions, but there is

no way we can be sure of this) differences between

any two homologous chromosomes occur at a frequency of about 1 in 500. Scaled to the

entire human

genome,

this is 6

10 6 differences between any two chromosomes of a

Figure 15.29

Structure of

a typical upstream regulatory region for

a

eukaryotic gene.

(a)

Transcription factor-binding sequences.

(b) Linear complex

with

transcription factors.

(c) Three-

dimensional structure of transcription factor-DNA complex that generates a binding site for RNA

polymerase.

SOURCES AND

ADDITIONAL READINGS

565

The current population of the earth is around 5

10 9 individuals. Thus,

to record all

of current human diversity would

require a database with

12

10 6 5 10 9 6

10 16 entries. By today’s mass storage standards, this is a large

database. The entire se-

quence of one human haploid genome could be stored on a single CD rom. On this scale,

storage of the catalog of human diversity would require 2

10 7 CD roms, hardly some-

thing one could transport conveniently. However, computer mass storage has been im-

proving at least by 10

4 per decade, and there

is no sign that this rate of increase is slack-

ening. As a result, by the time we have the tools to acquire the complete record of human

diversity, some 15 to 20 years from now, we should be able to store it on whatever is the

equivalent, then, of a few CD roms. Genome analysis is unlikely to be limited now, or in

the future, by computer power.

Why would anyone want to view all of human diversity? There are two basic features

that make this information compellingly interesting. First

is the complexity of a number

of interesting phenotypes that are undoubtedly controlled

by many genes. Included are

such items as personality, affect, and facial features. Given the limited ability for human

experimentation, if we want to dissect some of the genetic

contributions

to

these aspects

of our human characteristics, we will have to be able to compare DNA sequences with a

complex set of observations. The more sequence we have, and the broader the range of

individuals represented, the more likely it seems we should be able to make progress on

problems that today seem totally intractable.

The second motivation for studying the vast expanse of human diversity is a burgeon-

ing field called molecular anthropology. A considerable amount of human prehistory has

left its

traces in our DNA. Within contemporary sequences should be a

record of

who

mated with whom as far back as our species goes. Such a record, if we could untangle it,

would provide information about our past that probably cannot be reconstructed so easily

and accurately in any other way. Included will be information about mass migrations, like

the colonization of the new world, population expansions in prehistory, catastrophes, like

plagues, or major climatic fluctuations. A tool that is useful for such studies is called prin-

ciple component analysis. It looks at DNA or protein sequence variations

across

geo-

graphic regions and attempts to factor out particular variants with localized geographic

distribution, or with gradients of geographic distribution.

Many of the results obtained thus far in molecular anthropology are controversial and

open to alternative explanations. As the body of sequence data that can be fed into such

analyses grows, the robustness of the method and acceptance of its results will probably

increase. Junk DNA may be especially useful for these kinds of analyses. It is much more

variable

between individuals than

coding sequences, and thus the amount of

information

it contains about human prehistory should be all that greater. One final, intriguing source

of information is available from occasional DNA samples found still intact in mummies,

human remains trapped in ice, or other environments that preserve DNA molecules. Such

information will provide benchmarks by which to test the

extrapolations

that otherwise

have to be made from contemporary DNA samples.

SOURCES

AND ADDITIONAL READINGS

Adams, M. D., Kelley, J. M., Gocayne, J. D., Dubnick, M., Polymeropoulos, M. H., et al. 1991.

Complementary DNA sequencing: Expressed sequence tags and human genome project.

Science

252: 1651–1656.