The document discusses the history of understanding the human genome from 1998 to present. It describes how early estimates of the number of human genes ranged widely, from 25,000 to over 200,000. The first draft of the human genome was completed in 2001. By 2004, the estimated number of protein-coding genes had dropped to under 25,000. The document also criticizes the ENCODE project's 2012 conclusion that 80% of the human genome is functional, noting issues with their experimental methods and analyses.
Update version of the SMBE/SESBE Lecture on ENCODE & junk DNA (Graur, December 2013)
1. How to Assemble a Human Genome? Mix generous
amounts of Junk DNA and Indifferent DNA, add a Dollop
of Garbage DNA and a Sprinkling of Functional DNA
(Lazarus DNA optional)
Dan Graur
University of Houston
4. In September 2012, 30 papers based on thousands
of data sets were simultaneously published in high
profile journals to describe the major findings from
the ENCODE project.
5. The main finding of the main paper was
picked up by news outlets all over the world.
16. Three problems: (1) If the human genome is indeed devoid
of junk DNA as implied by the ENCODE project, then a
long, undirected evolutionary process, cannot explain the
human genome.
If, on the other hand, organisms are
designed, then all DNA, or as much
as possible, is expected to exhibit
function. If ENCODE is right,
then Evolution is wrong.
17. Three problems: (2) If ENCODE is right, then humans are
the Goldilocks of the living world.
Organism
C-value
Junk
Complexity
Tetraodon fluvialis (pufferfish)
0.35
No
Primitive
Hyla nana (frog)
1.89
No
Primitive
Homo sapiens (human)
3.5
No
Pinnacle of Creation
Extatosoma tiaratum (insect)
8.0
Yes
Primitive
Alium cepa (onion)
16.75
Yes
Primitive
Protopterus aethiopicus (lungfish)
132.83
Yes
Primitive
Paris japonica (canopy plant)
152.20
Yes
Primitive
18. Three problems: (3) If ENCODE-2012 is right, then
ENCODE-2011 is wrong.
80% of t
he huma
n genom
e is func
tional
Evolutio
nary con
s
the frac
tion of t traint indicates
he huma
th
is functi
n genom at
onal is ~
e that
5%.
Nature. 2011. 478:476-482
20. We wrote a critical piece on
ENCODE, and got a very negative
review from Trends in Genetics.
angry?
insane?
“Graur is mad, and not entirely
without cause.”
“It would be good for Trends in
Genetics to publish a reasoned
and dispassionate critical essay
on this topic, preferably by
someone of Graur’s stature, but
not him.”
192 cm
, 6’2”,
115 kg,
254 lb
21. How did ENCODE reach the conclusion that 80%
of the human genome is functional, when the
evidence for selection constraint is ~5%?
• Equating hype with science.
• Wrong experimental systems.
• Inappropriate statistical analyses.
• A peculiar definition of function.
• A peculiar definition of junk.
• A lack of evolutionary perspective.
• A lack of objectivity about the study organism.
• Ignorance of everything that came before
ENCODE.
22. Wrong experimental systems.
A huge chunk of ENCODE data is derived from
HeLa cells and other cancer cells.
Does the HeLa karyotype look human to you?
26. 6 S E P T E M B E R 2 0 1 2
The birth of 80%
“These data enabled us
to assign biochemical
functions for 80% of
the genome…”
26
27. 6 S E P T E M B E R 2 0 1 2
Implication that 80% may be 99%
“The vast majority (80.4%) of the human genome
participates in at least one biochemical RNA- and/or
chromatin-associated event in at least one cell type.
Much of the genome lies close to a regulatory event:
95% of the genome lies within 8 kilobases (kb) of a
DNA–protein interaction..., and 99% is within 1.7 kb
of at least one of the biochemical events measured by
ENCODE.”
27
30. 99% is not enough, 100% is better
ENCODE researcher Ewan Birney tells Ed
Yong that that the 80 percent figure will
increase, possibly reaching 100 percent.
“We don’t really have any large chunks of
redundant DNA,” Birney says. “This
metaphor of junk isn't that useful.”
31. The PR machine at work:
“Virtually all of the DNA passed down from
generation to generation has been kept for a
reason.” An intelligent God, perhaps?
32. 99% disappears and 80% becomes 40%
I [went] back to ENCODE biologist John
Stamatoyannopoulos, who was quoted in the first
wave of news. He said he thought the skeptics
hadn’t fully understood the papers… He did
Faye Flam
admit that the press conference mislead people by
claiming that 80% of our genome was essential
and useful. He puts that number at 40%.
Otherwise he stands by all the ENCODE claims.
38. 20% kills “junk DNA”
•
•
•
•
20% of the genome is functional.
Ergo, 80% must be junk.
Yet, “junk DNA” should be “totally expunged” from the lexicon.
In which universe does Ewan Birney’s logic work?
39. 20% kills “junk DNA”
•
•
•
•
20% of the genome is functional.
Ergo, 80% must be junk.
Yet, “junk DNA” should be “totally expunged” from the lexicon.
In which universe does Ewan Birney’s logic work?
• In a universe in which 20% >> 80%!
40. At the end of 2012, 20%
becomes the favorite
number in Nature
56. THE ORIGIN OF A SPECIES (smart & elegant)
Seiko Astron: Like a smartphone, the Astron is
GPS-enabled, allowing it to determine accurate time
from atomic clocks and automatically update to any
time zone in the world. Unlike a smartphone,
however, it looks nice with a suit, won’t break if
you drop it and uses solar power, so it never needs
to be charged. Darwin would be proud. $2,300
Hemispheres Magazine.
April 2013.
57. Actually, Darwin would not be proud.
Evolution does not produce “smart and elegant”
This is an intelligently designed
Dining Table
This is an evolutionary functional
Dining Table
58. The story of the human genome:
1998 to the present
65. Genebuild last updated/patched:
!May 2012!
!
Total length:
3,287,209,763 bp!
Protein-coding genes:
21,065!
Pseudogenes: !
!
!
15,930!
RNA-specifying genes:
!
12,955!
Genebuild last updated/patched: April 2013!
!
Total length:
3,320,602,130 bp!
Protein-coding genes:
20,774!
Pseudogenes: !
!
!
14,445!
RNA-specifying genes:
!
! 22,493!
65
66. The “end” of the Human Genome Project in 2004
($3.8 Billion) was a big disappointment for
scientists unversed in evolutionary biology
The human genome turned out to be:
• small in size
• sparsely populated with genes
• densely populated with dead genomic parasites
• unoriginal
67. Small
<<
3.5 billion letters in a fourletter alphabet = 7 billion
bits = 0.81 GB (gigabytes)
1 DVD = 8.5 GB
Information content
68. Small
≈
1 DVD = 8.5 GB
3.5 billion letters in a four-letter alphabet =
= 7 billion bits = 0.81 GB (gigabytes)
Information content
69. Sparsely populated with genes.
Organism
Gene Density
(# genes per 1 Mb)
Escherichia coli (bacterium)
911
Saccharomyces cerevisae (yeast)
483
Arabidopsis thaliana (mustard weed)
221
Drosophila melanogaster (fly)
197
Homo sapiens
12
69
70. Densely populated with dead transposable elements
45-67%
+plus at most 0.1% for RNA-specifying genes (non-coding RNA)
+plus at most 0.1% for DNA switches.
72. Unoriginal
0.15% nonsynonymous differences
1.22% synonymous differences
Cost of sequencing your human genome
= ~$25,000. Percent genome recovery
= 90%. Error rate = 1-3%.
I will provide you with your genome
sequence with less error for half the
price (and you can haggle).
Data: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030087
73. Comparing the human genome to other genomes
has given rise to three complexity paradoxes.*
Genomic paradox = A lack of correspondence
between a measure of genome size and the
presumed amount of genetic information
“needed” by the organism (its complexity).
*The paradoxes only exist under
the assumption that humans are
the most complex organisms and
the pinnacle of creation.
74. Defining complexity is difficult
The complexity of a system may
be defined by the minimum
number of independent
characters required to describe it,
where independence is defined as
the ability of the character to
assume any possible character
state independently of any other
character in the system.
74
75. Defining complexity is difficult
Thus the wall on the right is more complex—it has a crack,
75
than the wall on left.
76. However, even if we cannot quantify organismal
complexity very well, in many cases, it is possible
to state unequivocally that A is more complex
than B.
Without doubt,
is more complex than
I
77. However, even if we cannot quantify organismal
complexity very well, in many cases, it is possible
to state unequivocally that A is more complex
than B.
Without doubt,
is more complex than
I
78. K-value paradox: Complexity
does not correlate with
chromosome number.
Homo sapiens
46
Lysandra atlantica
Ophioglossum reticulatum
250
~1260
78
79. C-value paradox: Complexity
does not correlate with
genome size.
3.5 × 109 bp
Homo sapiens
1.5 × 1010 bp
Allium cepa
6.7 × 1011 bp
Amoeba dubia
79
82. Mommy, mommy, a fern has
27 times as many chromosomes
as I do; an amoeba has 200
times more DNA than I do;
and wheat has 5 times more
genes than me.
82
84. What is and what isn’t “junk DNA”
There are known knowns; there are
things we know that we know.
There are known unknowns; that is to
say, there are things that we now know we
don’t know.
But there are also unknown unknowns
—there are things we do not know we don’t
know.”
Donald Rumsfeld
February 12, 2002
86. What is and what isn’t “junk DNA”
Junk DNA is a known known; it is a thing
that we know what it does—it takes space.
Junk DNA is any piece of DNA that has no
function and does not affect fitness. NOT
everything that is not translated or not
transcribed is Junk DNA.
Junk DNA is NOT a known unknown.
Dark DNA is a known unknown.
Dan Graur
June 22, 2013
87. Junk DNA is a consequence of population genetics considerations!
In organisms with
LARGE effective
population sizes, the
strength of natural
selection is relatively
strong.
In organisms with
SMALL effective
population sizes, the
strength of natural
selection is relatively
weak.
88. Junk DNA is a consequence of population genetics considerations!
In organisms with
LARGE effective
population sizes, the
strength of natural
selection is relatively
strong.
In organisms with
SMALL effective
population sizes, the
strength of natural
selection is relatively
weak.
The majority of new
mutations are mildly
deleterious. In humans and
elephants, selection is not
sufficiently strong to
eliminate many such
deleterious mutations.
89. Junk DNA is a consequence of population genetics considerations!
In organisms with
LARGE effective
population sizes, the
strength of natural
selection is relatively
strong.
In organisms with
SMALL effective
population sizes, the
strength of natural
selection is relatively
weak.
Humans and elephants are
expected to accumulate
numerous deleterious
mutations in their
genome.
90. Junk DNA is a consequence of population genetics considerations!
91. 013)
(2
little
Genomic anthropocentrism?
d Doo
Human exceptionalism?
. For
W
“What would we expect for the number of functional
elements (as ENCODE defines them) in genomes much
larger than our own?
If the number [of functional elements] were to stay moreor-less constant, it would seem sensible to consider the rest
of the DNA of larger genomes to be junk.
If on the other hand the number of functional elements
were to rise significantly with genome size, then
organisms with genomes larger than ours should be
more complex phenotypically than we are.”
92. • A peculiar definition of function.
• A peculiar definition of junk.
• A lack of evolutionary perspective.
93. In biology, there are two main concepts
of function:
• A historical concept of function, also
referred to as the “selected effect
function” or “proper function.”
• A non-historical concept of function,
also referred to as the “causal
function.”
94. What is the function of the heart?
The proper function
is to pump blood.
95. What is the function of the heart?
The proper function
is to pump blood.
The causal functions of the heart are to
add 300 grams to the body weight, to
produce sounds, to be encased in the
the pericardium, to partially fill the
mediastinum, to provide an inaccurate
logo for Valentine Day cards, etc.
96. • Evolutionary biologists use the proper
or selected effect function.
• ENCODE used the causal function.
“Operationally, we define a functional element as a
discrete genome segment that encodes a defined
product (for example, protein or non-coding RNA)
or displays a reproducible biochemical signature
(for example, protein binding, or a specific
chromatin structure).”
97. • An example of a function that fits the
ENCODE definition: shoes binding
chewing gum.
“Operationally, we define a
functional element as an
entity that displays a
reproducible signature (for
example, chewing gum
binding.”
98. “By the logic employed by ENCODE, following a
collision between a car and a pedestrian, a car’s
bonnet would be ascribed the 'function' of
projecting a pedestrian many meters and the
pedestrian would have the 'function' of deforming
the car’s bonnet.” Laurence Hurst 2013. BMC Biol. 11:58
99. ENCODE uses a know logical fallacy called
affirming the consequent.
If a functional sequence is transcribed,
then, all transcribed sequences are functional.
Moreover, ENCODE uses the logical fallacy
inconsistently.
100. The ENCODE Project:
74.7% of the genome is transcribed,
56.1% is associated with modified histones,
15.2% is found in open-chromatin areas,
8.5% binds transcription factors,
4.6% consists of methylated CpGs.
The fraction of the genome that is functional
(the Boolean union) is 80.4%.
101. Our additions to ENCODE
74.7% of the genome is transcribed,
56.1% is associated with modified histones,
15.2% is found in open-chromatin areas,
8.5% binds transcription factors,
4.6% consists of methylated CpGs.
84.8% binds histone
100% of the genome is replicated.
The fraction of the genome that is functional is
100%.
103. Inability to deal with randomness
“… nothing is so alien to the human mind
as the idea of randomness.”
John Cohen. 1960. Chance, Skill, and Luck: The Psychology of Guessing
and Gambling. Baltimore,
MD: Penguin Books.
Apophenia /æpɵˈfiːniə/: The experience of seeing
meaningful patterns or connections in random or
meaningless data. A type of mild or incipient
schizophrenia. In statistics, apophenia is known as
Type I error (false positives).
Klaus Conrad. 1958. Die beginnende Schizophrenie. Versuch einer
Gestaltanalyse des Wahns [Incipient Schizophrenia: An Attempt to Analyze
delusion]. Stuttgart: Georg Thieme Verlag.
105. If you search long enough and hard enough for patterns in random texts,
you will find patterns. Especially if you do not employ negative
controls. This pattern, for instance, predicts on the vertical from the
bottom up (in Hebrew) that MITROMNI(TAURA)NSIA, where NSIA
is “president.” The 5 letters in between MITROMNI and NSIA are
random. It also helps that Hebrew has no vowels.
106. The Bible Code employs no negative controls. Someone else did and
they found similar “prophecies” in Moby Dock by Herman Melville.
107. ENCODE has no negative controls.
Mike White provided them and showed in a paper
published in PNAS that random DNA sequences
cause reproducible regulatory effects on the
reporter gene.
Random genetic sequences have as
much or a little a function as the
human genome sequences analyzed
by ENCODE.
109. “Some years ago I noticed that there are two kinds of
rubbish in the world and that most languages have
different words to distinguish them. There is the rubbish
we keep, which is junk, and the rubbish we throw away,
which is garbage. The excess DNA in our genomes is
junk, and it is there because it is harmless, as well as
being useless, and because the molecular
processes generating extra DNA outpace
those getting rid of it.”
Sydney Brenner. 1998. Refuge of spandrels. Current Biology 8:R669.
110. “Were the extra DNA to become disadvantageous, it
would become subject to selection, just as junk that
takes up too much space, or is beginning to smell, is
instantly converted to garbage by one’s wife, that
excellent Darwinian instrument.”
Sydney Brenner. 1998. Refuge of spandrels. Current Biology 8:R669.
111. Graur’s garage: Functional but full of junk
A garage according to ENCODE
A garage in which junk became garbage
115. Sequence-indifferent DNA or
indifferent DNA refers to DNA sites that
are functional, but show no evidence of
selection against point mutations.
Deletion of these sites, however, are
deleterious, and are subject to purifying
selection.
116. Examples of indifferent DNA
are spacers and flanking
elements whose presence is
required but the sequence is not
important. One such case is the
third position of four-fold
redundant codons, which needs
to be present to avoid a
downstream frameshift.
117. Dark DNA refers to the fraction of the genome for
which no good evidence exists as to its evolutionary
impact on fitness.
Dark DNA is an unknown unknown.
The term “dark” is borrowed from the field of
astrophysics.
An astrophysicist (Dr. Or Graur) whose
research deals with dark energy.
Unfortunately, he has no interest in dark
DNA.
118. Interesting Question:
How can one tell if a certain genomic sequence is
functional or not?
Can we make the car on the left less fit for driving?
Can make the car on the right less fit for driving?
121. How do we know if a particular genomic
sequence is functional?
Since most mutations in functional regions are
deleterious and likely to impair the function,
these mutations will tend to be eliminated by
natural selection. Thus, functional regions of
the genome should evolve more slowly, and
therefore be more conserved among species,
than nonfunctional regions.
121
122. Another indicator for the existence of a
genomic function is that losing it has some
consequence for the organism.
Evolution has tested the functionality of every
region of the human genome through
mutation over millions of years of evolution.
122
126. Is it even possible that ENCODE is right?
No! The main reason being that in humans, there is a huge
difference between population size and effective population
size.
Long-term Ne = 10,000
127. Is it even possible that ENCODE is right?
Under such conditions selection is inefficient and most
genetic variation is deleterious. Genomic “perfection” is
unachievable.
Long-term Ne = 10,000
128. Fact 1: It has been known for more than a century that the vast
majority of non-neutral mutations are deleterious (Thomas Morgan
1903).
Fact 2: Mutation rate is evolvable.
These facts have led Alfred Sturtevant
to raise the question “Why does the
mutation rate not become reduced
to zero?” (Sturtevant 1937).
128
129. Motoo Kimura: Mutation rate cannot reach zero,
because of the COST OF FIDELITY.
In other words, the mutation rate in a lineage is a
compromise between the benefits of complete fidelity in
the replication of the genetic material and the cost of
achieving complete fidelity.
The mutation rate
modulation hypot
hesis
129
130. How did ENCODE reach such ridiculous
numbers?
1. It used methodologies encouraging biased
errors in favor of inflating estimates of
functionality.
2. It consistently and excessively favored
sensitivity over specificity.
3. It paid attention to statistical significance,
rather than magnitude of the effect.
131. How did ENCODE reach such ridiculous
numbers?
1. It used methodologies encouraging biased
errors in favor of inflating estimates of
functionality.
2. It consistently and excessively favored
sensitivity over specificity.
3. It paid attention to statistical significance,
rather than magnitude of the effect.
132. Example:
Transcription factors binding sites (TFBS):
So far, almost all known TFBSs range in length from 6
to 14 nucleotides.
The TFBS entries in ENCODE range in size from 457
to 824 nucleotides.
Thus, the estimates of the fraction of the human
genome devoted to transcription factor bindings are
extraordinarily inflated (sometimes by about two
orders of magnitude).
133. How did ENCODE reach such ridiculous
numbers?
1. It used methodologies encouraging biased
errors in favor of inflating estimates of
functionality.
2. It consistently and excessively favored
sensitivity over specificity.
3. It paid attention to statistical significance,
rather than magnitude of the effect.
134.
135. Encode prefers false positives over false
negatives, thus inflating the proportion of
positives.
136. Example:
ENCODE used a probability based alignment tool, and mapped RNA
transcripts onto DNA when the statistical confidence exceeded
90%.
This means that 10% of the correspondences between RNA and
genome are erroneous.
The total number of RNA transcripts in ENCODE is approximately
109 million. The mean transcript length is 564 nucleotides.
Thus, a total of 6 billion nucleotides, or two times the human
genome size, are potentially misplaced (false positives).
137. How did ENCODE reach such ridiculous
numbers?
1. It used methodologies encouraging biased
errors in favor of inflating estimates of
functionality.
2. It consistently and excessively favored
sensitivity over specificity.
3. It paid attention to statistical significance,
rather than magnitude of the effect.
138. “Derived allele frequency spectrum for primate-specific elements, with
variations outside ENCODE elements in black and variations covered
by ENCODE elements in red. The increase in low-frequency alleles
compared to background is indicative of negative selection
occurring in the set of variants annotated by the ENCODE data.”
139. p = 10−37
Magnitude of effect = 0.042%
“Derived allele frequency spectrum for primate-specific elements, with
variations outside ENCODE elements in black and variations covered
by ENCODE elements in red. The increase in low-frequency alleles
compared to background is indicative of negative selection
occurring in the set of variants annotated by the ENCODE data.”
140. Let’s examine the rationale and the
methodology for dealing with the
derived allele frequency spectrum
in primate-specific elements
141. The Why
• If all alleles are neutral, a certain
frequency distribution is expected.
• If some alleles are under negative
selection, an excess of rare derived alleles
is expected.
• This excess is expected to be detectable for
only very short periods of evolutionary
time.
142. The Why
• To deal with very short periods of
evolutionary time, ENCODE decided to use
primate specific sequences.
145. What is missing from the derived allele
frequency spectrum of primate-specific
elements in ENCODE?
Genes!
3,296,458 SNPs that are in annotated
coding regions are not found in the
ENCODE sample.
146. Missing populations and their effect on
estimates of derived alleles and
ancestral alleles.
Three human populations were
available at the time ENCODE was
submitted; ENCODE used only one.
149. Yoruba
Derived allele
Frequency (%)
100
20
0
The ENCODE data includes 2,136
alleles with frequencies of exactly
0. In a miraculous feat of science,
ENCODE was able to determine
the frequencies of nonexistent
alleles.
OUT
152. ENCODE uses only single species from
primates.
There are no derived alleles
153. p = 10−37
Magnitude of effect = 0.042%
“Derived allele frequency spectrum for primate-specific elements, with
variations outside ENCODE elements in black and variations covered
by ENCODE elements in red. The increase in low-frequency alleles
compared to background is indicative of negative selection
occurring in the set of variants annotated by the ENCODE data.”
155. Even supposing that all the 55,000 putative lincRNAs in
this paper are functional and important, then
55,000 × 2000 bp = 110 MB
(less than 4% of the human genome).
Showing that 4% of the genome is functional is “cool,”
but doesn’t bear on the questions of “junk DNA,” which
has to do with the majority of the genome.
156. Conclusion: Badly trained
technicians who do not understand
(1) population genetics, (2) the
concept of effective population size,
(3) random genetic drift, and (4)
the limitations of selection should
be forbidden to even mention
“junk DNA” let alone write papers
on the subject.
157. 6 S E P T E M B E R 2 0 1 2
442 researchers + 288 million dollars.
What have we learned from ENCODE?
157
158. “Data is not information, information is
not knowledge, knowledge is not
wisdom, wisdom is not truth,”
—Robert Royar (1994) paraphrasing Frank
Zappa’s (1979) anadiplosis
159. onion test
The
is a simple reality check for
anyone who thinks they have come up with a
universal function for 80% of the genome, or 100%
of the genome. Whatever the proposed function, ask
yourself this question: Can you explain why onions
need about five times more DNA than humans?”
T. Ryan Gregory
1.5 × 1010 bp
Allium cepa
3.5 × 109 bp
Homo sapiens
159
160. “All science is either physics or
stamp collecting.”
Ernest Rutherford
“ENCODE is stamp collecting.”
Roderic Guigó
“I can think of better uses for
288 million dollars.”
Dan Graur
161. Acknowledgments: The Good Guys
Coauthors: Ricardo Azevedo, Becky Zufall, Nicholas Price, and
Yichen Zheng (UH), and Eran Elhaik (Johns Hopkins).
Reviewers: Giddy Landan (Heirich Heine Universität, Germany),
Michael Lynch (University of Indiana, USA), Naruya Saitou
(National Institute of Genetics, Japan), David Penny (Massey
University, New Zealand), W. Ford Doolittle (Dalhousie University,
Canada + 2 reviewers who think I don’t know who they are.
Editor: Bill Martin (Genome Biology and Evolution)