Analyses of deep mammalian sequence
alignments and constraint predictions
for 1% of the human genome
Elliott H. Margulies,2,7,8,21Gregory M. Cooper,2,3,9George Asimenos,2,10
Daryl J. Thomas,2,11,12Colin N. Dewey,2,4,13Adam Siepel,5,12Ewan Birney,14
Damian Keefe,14Ariel S. Schwartz,13Minmei Hou,15James Taylor,15
Sergey Nikolaev,16Juan I. Montoya-Burgos,17Ari Löytynoja,14Simon Whelan,6,14
Fabio Pardi,14Tim Massingham,14James B. Brown,18Peter Bickel,19Ian Holmes,20
James C. Mullikin,8,21Abel Ureta-Vidal,14Benedict Paten,14Eric A. Stone,9
Kate R. Rosenbloom,12W. James Kent,11,12NISC Comparative Sequencing
Program,1,8,21Baylor College of Medicine Human Genome Sequencing Center,1
Washington University Genome Sequencing Center,1Broad Institute,1UCSC Genome
Browser Team,1British Columbia Cancer Agency Genome Sciences Center,1
Stylianos E. Antonarakis,16Serafim Batzoglou,10Nick Goldman,14Ross Hardison,22
David Haussler,11,12,24Webb Miller,22Lior Pachter,24Eric D. Green,8,21and
A key component of the ongoing ENCODE project involves rigorous comparative sequence analyses for the initially
targeted 1% of the human genome. Here, we present orthologous sequence generation, alignment, and evolutionary
constraint analyses of 23 mammalian species for all ENCODE targets. Alignments were generated using four
different methods; comparisons of these methods reveal large-scale consistency but substantial differences in terms of
small genomic rearrangements, sensitivity (sequence coverage), and specificity (alignment accuracy). We describe the
quantitative and qualitative trade-offs concomitant with alignment method choice and the levels of technical error
that need to be accounted for in applications that require multisequence alignments. Using the generated alignments,
we identified constrained regions using three different methods. While the different constraint-detecting methods are
in general agreement, there are important discrepancies relating to both the underlying alignments and the specific
algorithms. However, by integrating the results across the alignments and constraint-detecting methods, we produced
constraint annotations that were found to be robust based on multiple independent measures. Analyses of these
annotations illustrate that most classes of experimentally annotated functional elements are enriched for constrained
sequences; however, large portions of each class (with the exception of protein-coding sequences) do not overlap
constrained regions. The latter elements might not be under primary sequence constraint, might not be constrained
across all mammals, or might have expendable molecular functions. Conversely, 40% of the constrained sequences
do not overlap any of the functional elements that have been experimentally identified. Together, these findings
demonstrate and quantify how many genomic functional elements await basic molecular characterization.
[Supplemental material is available online at www.genome.org.]
The identification of sequences under evolutionary constraint is
a powerful approach for inferring the locations of functional el-
ements in a genome; mutations that affect bases with sequence-
specific functionality will often be deleterious to the organism
and be eliminated by purifying selection (Kimura 1983). This
paradigm can be leveraged to identify both protein-coding and
noncoding functions, and represents one of the best computa-
tional methods available for annotating genomic sites that are
likely to be of functional, phenotypic importance (Nobrega and
Pennacchio 2004). Indeed, leveraging evolutionary constraints is
a cornerstone approach of modern genomics, motivating many
1A list of participants and affiliations appears at the end of this
2These authors contributed equally to this work.
Present addresses:3Department of Genome Sciences, University of
Washington School of Medicine, Seattle, WA 98195, USA;4Depart-
ment of Biostatistics and Medical Informatics, University of Wiscon-
sin–Madison, Madison, WI 53706, USA;5Department of Biological
Statistics and Computational Biology, Cornell University, Ithaca, NY
14853, USA;6Faculty of Life Sciences, The University of Manchester,
Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK.
E-mail email@example.com; fax (301) 480-3520.
Article is online at http://www.genome.org/cgi/doi/10.1101/gr.6034307.
Freely available online through the Genome Research Open Access option.
760 Genome Research
17:760–774 ©2007 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/07; www.genome.org
vertebrate genome-sequencing efforts (Collins et al. 2003; Mar-
gulies et al. 2005b) as well as similar projects involving model
organism taxa (Cliften et al. 2003; Kellis et al. 2003; Stein et al.
2003; Davis and White 2004).
The ENCODE Project Consortium set an ambitious goal of
identifying all functional elements in the human genome, in-
cluding regulators of gene expression, chromatin structural com-
ponents, and sites of protein–DNA interaction (The ENCODE
Project Consortium 2004). In its pilot phase, ENCODE targeted
44 individual genomic regions (see http://genome.ucsc.edu/
ENCODE/regions.html for details on the target selection process)
that total roughly 30 Mb (∼1% of the human genome) for func-
tional annotation. A major component of this effort has been to
generate a large resource of multispecies sequence data ortholo-
gous to these human genomic regions. The rationale for a major
comparative genomics component of ENCODE includes the fol-
● Comparative sequence analyses reveal evolutionary con-
straint, and this is complementary to experimental assays be-
cause it is agnostic to any specific function. Furthermore, the
experimental assays used to date by ENCODE only investigate
a subset of potential functions and mostly emphasize the use
of cell culture systems, which are limited in their ability to
detect functional processes unique to the development, physi-
ology, and anatomy of an organism.
● Significant technical challenges regarding the alignment and
analysis of deep mammalian genome sequence data sets re-
main unsolved and reduce the efficacy of comparative analy-
ses. Systematic evaluation and comparison of the best compu-
tational tools, which requires such a large comparative genom-
ics data set, would be a valuable contribution to future efforts.
● Until now, no synchronized effort between evolutionarily
deep comparative sequence analyses and comprehensive iden-
tification of broad classes of functional elements has been pur-
sued. The selected ENCODE regions of the human genome
provide such a test bed for exploring the relationship between
evolutionary sequence constraint and sequence function in a
Here, we report the comparative sequence analyses per-
formed for the pilot phase of the ENCODE project. This has in-
cluded the generation and analysis of roughly 500 Mb of com-
parative sequence data. Emphasis was deliberately placed on
the mammalian phylogenetic scope, which currently corre-
sponds to the most effective combination of capturing human
evolutionarily constrained elements at reasonable cost (Cooper
and Sidow 2003). This will guide future analyses that can exploit
the large number of mammals whose whole genomes are being
Through the use of several alignment methods and ap-
proaches for identifying constrained sequences, we generate con-
straint annotations at several degrees of statistical confidence.
We perform a variety of systematic, quantitative comparisons to
assess the results described here, which have been generated by
the best available computational tools for generating and ana-
lyzing multisequence alignments of mammalian genomic DNA.
Discrepancies in results point to significant challenges that re-
main to be met in multisequence alignment and constraint de-
tection. However, despite the analytical uncertainties we iden-
tify, we demonstrate that our constraint annotations achieve rea-
sonable levels of sensitivity and specificity using multiple
measures of validation, and we subsequently compare our con-
straint annotations with the experimentally defined annotations
of functional elements generated by The ENCODE Project Con-
sortium. These results lead to important conclusions relevant to
future large-scale comparative genomic analyses and efforts to
comprehensively identify functional elements in the human ge-
Results and Discussion
Comparative sequence data
We generated and/or obtained sequences orthologous to the 44
ENCODE regions (The ENCODE Project Consortium 2004) from
28 vertebrates (Fig. 1; Supplemental Table S1). For 14 mammals,
a total of 206 Mb of sequence was obtained from mapped bacte-
rial artificial chromosomes (BACs) and finished to “comparative-
grade” standards (Blakesley et al. 2004) specifically for these stud-
ies; for another 14 species, a total of 340 Mb of sequence was
obtained from genome-wide sequencing efforts at varying levels
of completeness and quality (Aparicio et al. 2002; International
Mouse Genome Sequencing Consortium 2002; International
Chicken Genome Sequencing Consortium 2004; International
Human Genome Sequencing Consortium 2004; Jaillon et al.
2004; Rat Genome Sequencing Project Consortium 2004; Chim-
panzee Sequencing and Analysis Consortium 2005; Lindblad-
Toh et al. 2005; Margulies et al. 2005b) (see also Methods and
Generation of multisequence alignments
For each human base in the ENCODE regions, we aimed to iden-
tify an orthologous genomic position in every other species. To-
ward that end, we generated four sets of multisequence align-
ments, and refer to each by the name of the principal program
used—namely, MAVID (Bray and Pachter 2004), MLAGAN
(Brudno et al. 2003), TBA (Blanchette et al. 2004), and the re-
cently developed PECAN (B. Paten and B.E. Pecan, in prep.). The
multisequence alignments are represented using the human se-
quence as a reference coordinate system in which non-human
sequences are manipulated to be in a “humanized” order and
orientation; as such, two nucleotides in a non-human sequence
need not be in the same orientation in which they natively re-
side (Fig. 2). All human bases are present in the resulting align-
ments, and have at most one aligned nucleotide from each other
species. Thus, duplications in non-human lineages were resolved
so that a single orthologous copy is aligned; in contrast, non-
human bases may be aligned more than once if they are ortholo-
gous to multiple human positions as a result of a duplication in
the human genome (note that MAVID alignments enforced a
strict one-to-one orthology; see below). Although all human
bases are present in the final alignments, positions in the non-
human sequences may have been omitted. For example, se-
quence corresponding to large species-specific insertions or
human deletions might have been removed due to a lack of
orthology with the human sequence. It is thus important to
keep in mind that these alignments were built in a “pipeline”
fashion, in which nucleotide-level global alignment is only one
Equally important as generating alignments is defining met-
rics for alignment quality. Unlike protein alignments, where
ENCODE multispecies genomic sequence comparisons
structural information can be used to generate reference align-
ments (Van Walle et al. 2005), or the prediction of transcription
factor-binding sites, where experimental data can be used to
define bound and unbound sites (Tompa et al. 2005), no such
“gold standards” exist for genomic sequence alignments. The
challenge is to define measurements for alignment specificity
(i.e., fraction of orthology predictions that are correct) and
sensitivity (i.e., fraction of all truly orthologous relationships
that are correctly predicted). Since multisequence alignments
are used to generate and test evolutionary hypotheses, mea-
surements of alignment quality should be tied to the quality of
the evolutionary inferences derived
from them; subsequently, we compare
the sets of alignments in this manner. It
is worth noting that in many instances,
the “true” evolutionary history of a par-
ticular nucleotide or region is unknown
(and perhaps unknowable), and in many
of the concomitant comparisons no de-
finitive assessment of “better” or
“worse” can be generated. Whenever
such assertions can be made (such as
with respect to alignment coverage of
protein-coding sequences as a measure
of sensitivity), however, we attempt to
Alignment comparison—Region level
The alignments allow inferences to be
made about large-scale evolutionary
events that have shaped the ENCODE
loci in these mammalian genomes. For
59.2% of ENCODE region–species pairs,
a single segment in the query species ge-
nome was predicted to contain sequence
orthologous to sequence in the human
region, indicating that these regions
have been largely undisturbed through-
out mammalian evolution. However,
many small-scale rearrangements were
detected (“conserved synteny” of a large
genomic region does not imply colinear-
ity of all nucleotides within that region).
The number of rearrangement break-
points within a given region was highly
dependent on the size of alignment
blocks considered. Figure 3A summarizes
the number of rearrangement break-
points determined by MLAGAN/Shuffle-
LAGAN, TBA/BlastZ, and MAVID/
Mercator as the minimum block size was
varied for five species (see Methods).
Blocks of length <100 base pairs (bp)
were found to cause the vast majority of
the breakpoints, consistent with both
higher probabilities of occurrence and
an increase in the probability of spurious
alignments. Mercator/MAVID predicted
very few small-scale rearrangements,
while MLAGAN predicted the largest
number, particularly with respect to
cow. However, the three methods were
largely in agreement for rearranged blocks longer than 100 bp.
Blocks of at least 1 kb numbered from 70 in marmoset to 101 in
rat, as determined in the MLAGAN/Shuffle-LAGAN alignments.
For these blocks, the median block lengths were roughly 300 kb
and 14 kb, respectively.
The TBA and MLAGAN alignments allowed multiple human
positions to be aligned to a single position in a query species. In
such cases, the alignment states that both human positions are
orthologous to the query position, and are paralogous to each
other as a result of a duplication event in the human lineage
since its last common ancestor with the query species. These
lengths illustrate the relationships among the analyzed species’ sequences. Analytical support for the
represented tree is provided elsewhere (Nikolaev et al. 2007). The numbers next to each species name
indicate the amount of sequence (in Mb) examined in this study (some species have >30 Mb of
sequence either as a result of lineage-specific expansions of these regions or the resolution with which
orthologous sequences can be identified before alignment) (see Supplemental Material for additional
details); (red numbers) BAC-derived sequence sequenced to “comparative grade” (see Methods); (blue
numbers) sequence obtained from whole-genome sequencing efforts; and (black numbers) finished
human sequence. Blue and green branches distinguish mammalian from non-mammalian sequences,
Phylogenetic tree relating the set of analyzed species. The depicted topology and branch
quence (middle) is aligned to two other species’ sequences (top and bottom). In the final alignment
(right), nucleotides from the other species need not have retained their original order and orientation;
they may, for example, have been subjected to inversions (top blue) or duplications (bottom green).
Non-human duplications need to be resolved (top magenta), so that each position in the human
sequence is aligned to at most one position in any other species’ sequence.
“Human-centric” approach for constructing multisequence alignments. The human se-
Margulies et al.
positions are said to be “inparalogous,” a relationship that de-
pends on a query species (Sonnhammer and Koonin 2002). Fig-
ure 3B shows the fraction of ENCODE human positions that were
determined to be inparalogous relative to each query species. For
16 of 22 query species that had sequence for all ENCODE regions,
MLAGAN predicted more such positions than TBA. For six spe-
cies, MLAGAN predicted more than twice as many positions as
TBA. The fraction of inparalogous positions varied greatly over
the different ENCODE regions. For example, >30% of positions in
region ENr233 were predicted to be human-specific duplicates
relative to marmoset by both aligners, compared to <5% of po-
sitions in region ENm004 relative to all species.
Alignment comparison—Nucleotide level
We also sought to compare our alignments at the nucleotide
level, as this is the level at which many downstream applications
operate. We find that the level of agreement between alignments
varies significantly between species, with agreement much
higher when comparing alignments of primates versus those of
more distant species (Supplemental Table S2). In general, agree-
ment between the different alignments is influenced signifi-
cantly by the total coverage; for example, MAVID aligns 27.4% of
human bases to an armadillo nucleotide, versus 42.4%, 41.2%,
and 40.1% for MLAGAN, PECAN, and TBA, respectively; and thus
the maximum possible agreement be-
tween all the alignments is 27.4%. We
find that 17.5% of all human nucleo-
tides are aligned to the same armadillo
nucleotide by all four alignments, and
66.1% of all human bases are identically
aligned if we consider gapped columns
(i.e., columns in which a human nucleo-
tide is predicted not to have an ortholo-
gous nucleotide in the armadillo se-
quence). We conclude that there are
substantial variations between the
nucleotide-level orthology predictions
made by the four alignments, although a
significant majority of all human
nucleotides are aligned identically be-
tween human and a given non-human
An important use of multisequence
alignments is to characterize rates of
nucleotide substitution in predomi-
nantly neutral DNA. Such estimates are
not only important to understand ge-
nome evolution, but may also illustrate
differences between alignments at the
nucleotide level. We therefore estimated
rates of evolution in ancestral repeats
(AR) in our alignments (Supplemental
Table S3; also see Methods). Eutherian
ARs are fragments of mobile elements
believed to have inserted into the com-
mon ancestor of all placental mammals
and been retained since then. Assuming
these elements are largely not func-
tional, they are free to evolve in the ab-
sence of selection (with notable excep-
tions: Nekrutenko and Li 2001; Jordan et
al. 2003; Silva et al. 2003; Cooper et al.
2005; Kamal et al. 2006) and thus constitute a good model for
neutral evolution in mammalian genomics (International Mouse
Genome Sequencing Consortium 2002; Ellegren et al. 2003;
Hardison et al. 2003; Rat Genome Sequencing Project Consor-
tium 2004; Yang et al. 2004). First, we note that rates of evolution
in ARs are similar to, but higher than, rates estimated from four-
fold degenerate sites within proteins (average increases of 2%–
13%, depending on the alignment and region). This may indicate
weak purifying selection on synonymous sites (e.g., Kimchi-
Sarfaty et al. 2007; Komar 2007), but may also result from an
increased proportion of errors in alignment of ARs, which are
more difficult to align. We observe considerable variation both
between genomic regions and between alignments. Regional rate
variation has been well documented for mammalian genomes
(International Mouse Genome Sequencing Consortium 2002; Rat
Genome Sequencing Project Consortium 2004), and we find
similar results here, with a standard deviation (averaged over the
four alignments) of 0.15 substitutions per site (∼3.7% of the neu-
tral rate) among the 44 ENCODE loci. Furthermore, while this
regional variation is highly correlated among the alignment sets
(average pairwise R2of ∼0.62), we find that the standard devia-
tion between the four alignments in a given locus is 0.2 substi-
tutions per site, roughly similar to the level resulting from re-
gional variation. Thus, while relative rate fluctuations between
ment breakpoints in the ENCODE regions as a function of minimum block size, determined by three
alignment methods. For each species, the average number of breakpoints over all regions (Y-axis) was
calculated for all minimum block sizes (in base pairs; X-axis). The species shown are chimp (dark blue),
baboon (brown), mouse (green), dog (orange), and cow (light blue). For each minimum block size, the
number of breakpoints in a given region was determined after removing blocks in order of increasing
size and joining consistent blocks until no block had size less than the minimum (see Methods). (B)
Duplicated human nucleotide positions in the ENCODE regions. The fraction of ENCODE positions that
are inparalogous to one another relative to a given species is plotted for each species, as determined
by TBA (yellow) and MLAGAN (green). Colobus Monkey, Dusky Titi, Mouse Lemur, and Owl Monkey
are not shown because sequence from these species was only obtained for one region (ENm001).
Rearrangements and duplications inferred by the alignments. (A) Number of rearrange-
ENCODE multispecies genomic sequence comparisons
regions are correlated with legitimate fluctuations in local rates
of nucleotide substitution, interpretation of absolute rates of
nucleotide substitution for any given region must be done cau-
tiously, with appropriate accommodation of technical error for
any downstream application that requires such estimates. The
“true” neutral rate for any given region of the human genome is
thus only estimable given some nontrivial technical uncertainty.
Assessing alignment coverage
As a surrogate for sensitivity, we determined the coverage of an-
notated protein-coding sequences in each of our alignments.
Since coding exons are regions of the human genome that
are largely ancient and likely to be shared among all of the lin-
eages analyzed here, they represent a set of nucleotides heavily
enriched for “true positive” (i.e., actually orthologous) positions.
We expect that alignment “coverage,” defined by the number
of human coding bases aligning to a given non-human species,
will be highly correlated with alignment sensitivity. Note
that the simple existence of an alignment does not imply that
an alignment is correct (“correctness” is addressed below), but
we assume that sensitivity will be proportional to the total
amount of aligned sequence. We find that coverage of coding
exons varies considerably among the different alignments,
especially when analyzing alignments between humans and
more distant species (i.e., non-primates). When counting the
number of coding exons with at least one base pair aligned to
a base in the mouse genome, for example, coverage ranges
from 55% in MAVID to 72% in MLAGAN (Fig. 4, top panel),
with TBA and PECAN showing intermediate values. Alterna-
tively, when looking at only those coding exons that are fully
covered (i.e., no gaps), these values range from 29% in MAVID to
38% in PECAN (Fig. 4, middle panel). PECAN and MLAGAN ex-
hibit the highest values by these measures and are similar for
However, quantifying rates of evolution in neutral DNA is
dependent on our ability to align orthologous regions that are
more dissimilar than typical coding exons. ARs provide a more
realistic measure in this regard. To develop a sensitivity measure
on the basis of AR alignments, we first independently identified
repeats in each aligned species’ sequence using RepeatMasker
(http://www.repeatmasker.org). Then, for each alignment, we
quantified the number of human AR bases (filtered from the
RepeatMasker output as previously described, Margulies et al.
2003) that are aligned to a base within an element of the same
class and family in each of the non-human sequences. As above
for coding sequence, in principle, these alignments are not nec-
essarily correct. However, it is reasonable to assume that the total
amount of aligned mobile element fragments (classified as “an-
cestral” within humans and independently identified to be of the
same class and family in the non-human sequence) is propor-
tional to actual sensitivity. As for coding exons, we find consid-
erable variability between the alignments. In this case, however,
PECAN alignments are clearly the most sensitive. For example,
>47% of the ∼5.8 million AR bases in the human are aligned to a
dog nucleotide by PECAN, while only 24% are aligned by MAVID
(Fig. 4, bottom panel). PECAN has an average coverage increase
of 2.4%, 3.8%, and 12.5% over MLAGAN, TBA, and MAVID, re-
spectively. Keeping in mind that there are ∼5.8 million AR bases
in the human ENCODE regions, we find that there are substantial
differences in sensitivity to neutrally evolving DNA among these
Assessing alignment correctness
We also sought to estimate the specificity of our alignments,
since the simple presence of an alignment does not imply cor-
rectness. Because we do not know with certainty what should
and should not align, we used two alternate measures as surro-
gates for alignment specificity. The first approach uses our
knowledge of mobile element fragments to measure “false-
positive” alignments; since Alu element activity is phylogeneti-
cally restricted to primates, alignment of human Alu elements to
any non-primate mammalian sequence is a false orthology pre-
diction. Furthermore, since Alus are abundant in the human ge-
nome and are also SINEs, they can potentially generate many
similar matches between human and even distantly related
mammalian species. In this regard, they are a direct and stringent
measurement of incorrect orthology predictions. On the basis of
the ∼3.8 million Alu bases in the ENCODE targets, we observe
that TBA is the most “specific” aligner (Fig. 5, top panel), fol-
lowed by PECAN, MAVID, and MLAGAN, with an average de-
crease in Alu exclusion rate of 1.3%, 3.0%, and 3.5%, respec-
tively. As above for the AR analysis, we note that while these
numbers appear small, they are substantial, with a 1% difference
For a representative group of mammalian species (X-axis), the fraction of
human coding exons covered by at least 1 base (top panel) or completely
covered (i.e., no gaps, middle panel) are shown for the MAVID (blue), TBA
(yellow), MLAGAN (green), and PECAN (red) alignments. For the same
set of species, we also show the percentage of all human “ancestral
repeat” bases (out of a total of ∼5.8 million) that are aligned to a nucleo-
tide within a mobile element of the same class and family. Note that
absolute coverage levels should be interpreted cautiously, as they reflect
both phylogenetic signal (i.e., insertions and deletions of DNA between
human and the query species) and sequence completeness.
Alignment coverage of coding exons and ancestral repeats.
Margulies et al.
amounting to nearly 40,000 human nucleotides that are differ-
entially (and incorrectly in this case) aligned.
The second measure exploits our knowledge of coding se-
quence, where we expect that correct alignments will exhibit
periodicity in the pattern of inferred nucleotide substitutions due
to the enrichment of synonymous sites at codon third positions.
Thus, we quantified the levels of periodicity in the coding exon
alignments as a proxy measure for their nucleotide-level speci-
ficity (see Methods). Furthermore, to eliminate those coding ex-
ons that are missing in a particular species or not periodic (i.e.,
due to a false prediction or too few synonymous changes, as
often occurs between human and chimp sequence), we only in-
clude those coding exons that exhibit periodicity in at least one
alignment and some level of nucleotide coverage in all align-
ments. These specificity measures are therefore not confounded
by differences in coverage (see above) or false coding exons. TBA
and PECAN exhibit the highest levels of codon periodicity (and
thus inferred specificity), with TBA being on average slightly
higher (1.4%) than PECAN (Fig. 5, bottom panel). In contrast,
MLAGAN is moderately weaker than TBA (average decrease of
4.4%), while the MAVID alignments have the lowest levels of
periodicity (average decrease from TBA of ∼21.3%).
Explaining alignment discrepancies
We observed substantial differences between the four align-
ments; determining the sources for these differences is difficult,
but a few conclusions can be drawn. For example, MAVID’s lower
coverage estimates likely result from the strict one-to-one orthol-
ogy requirement, which eliminates human-specific duplications.
The discrepancy in coverage between MAVID and the other
aligners that is due to this restriction can be upper-bounded by
the amount of inparalogous human sequence, as predicted by
the other aligners. Up to 4% of human bases in the ENCODE
regions were predicted to be inparalogous, depending on the
query species (Fig. 3B). These bases represent up to roughly 10%
of those covered by the aligners. Furthermore, some of the ran-
domly picked ENCODE regions have very low gene content,
which may affect the sensitivity of Mercator’s (the region-level
orthology prediction algorithm used by MAVID; see Methods)
primarily exon-based orthology detection process. On the other
hand, MLAGAN and PECAN coverage estimates generally appear
higher. The Shuffle-LAGAN “humanization” step is somewhat
lenient and rearranges the original sequences with a rather coarse
resolution; nonorthologous pieces may be kept if they fall be-
tween long stretches of orthologous sequences, for example, and
rearrangement boundaries are generally approximate. The re-
duced specificity seen for MLAGAN may result from this leniency
in combination with the fact that MLAGAN preserves all of the
input sequence in its output, resulting in alignments that aggres-
sively span nonorthologous regions. Conversely, PECAN, which
uses the same Shuffle-LAGAN humanization step but showed
higher specificity levels than the MLAGAN alignments, does not
force an alignment along the entire input. In addition, PECAN
uses “consistency,” which has been shown to give marked im-
provements in protein alignments (Notredame et al. 2000; Do et
al. 2005) but is a novel addition to genomic sequence align-
ments. The TBA alignments generally have the highest specific-
ity, and are the most effective at ignoring highly similar, but
nonorthologous, alignments resulting from Alu elements. Since
the blocks produced by TBA emerge from local alignments, they
usually have tight boundaries and are fairly compact, and can
avoid the long insertions that are harder to dismiss by the three
global alignment techniques.
Identification and measurement of constraint
in the ENCODE regions
Our multisequence alignments covered more of the human ge-
nome at greater evolutionary depth than previous studies, which
have either used whole-genome sequences from only a few spe-
cies (International Mouse Genome Sequencing Consortium
2002; Cooper et al. 2004; Rat Genome Sequencing Project
Consortium 2004; Lindblad-Toh et al. 2005; Siepel et al. 2005)
or included many species’ sequences but were limited to single
loci <2 Mb in size (Boffelli et al. 2003; Margulies et al. 2003;
Cooper et al. 2005). These alignments thus provided a unique
opportunity to systematically identify constrained sequences
for a large segment of the human genome. Evolutionary con-
straint was detected using three distinct methods: phastCons,
which uses a phylo-HMM (Siepel et al. 2005); GERP, which ex-
ploits single-site maximum likelihood rate estimation (Cooper
et al. 2005); and binCons, which quantifies pairwise similarities
using a binomial distribution from sliding windows (Margulies
et al. 2003). Details for each of these methods are provided in
their respective citations, and additional details about the use
of each algorithm are also available at the UCSC Genome
Browser (http://genome.ucsc.edu; Kent et al. 2002; Karolchik et
al. 2003) and in the Methods section. Each method analyzed the
same human-referenced multisequence alignments (performed
separately with three of the alignments; note that PECAN align-
ments were not included because of its recent development) to
generate scores and element predictions across all ENCODE re-
gions. We further equalized constraint detection thresholds us-
periodicity of substitutions in coding exons. For a group of non-primate
mammals, the fraction of human Alu bases (out of a total of ∼3.8 million)
that are not aligned (i.e., gapped) is shown (top panel). A score of 1
would correlate with complete exclusion of all Alus, as would be the case
in alignments with no false-positive orthology predictions. We also show
the fraction of human coding exons that show a triplet periodicity in
substitutions in the pairwise alignment between human and each query
species (see Methods). Note that this is purely a relative measure, since
we exclude exons that are completely gapped in at least one alignment,
or fail to show periodicity in at least one alignment.
Alignment “correctness” as measured by Alu exclusion and
ENCODE multispecies genomic sequence comparisons
ing an empirically generated, standardized “null” alignment for
each ENCODE-region alignment. In all cases, sequences deemed
as constrained are significant at a “relative” false-positive rate of
<5% (see Methods).
In addition to nine independent sets of constrained se-
quences (three methods analyzing three alignments), we also
generated constraint annotations that integrate these data. Three
annotation sets emerged from this integration: a “loose” set, de-
fined by the union of all bases predicted as constrained for any
method on any alignment; a “moderate” set, defined by the
union of all bases predicted as constrained for at least two meth-
ods on at least two alignments; and a “strict” set, defined by the
intersection of all three methods on all three alignments. Overall,
the loose, moderate, and strict data sets identify 11.8%, 4.9%,
and 2.4% of the ENCODE regions, respectively. We observe con-
siderable variation among the different regions in the total frac-
tion of constrained sequence, likely reflecting the genomic diver-
sity of the 44 ENCODE regions and the biology encoded therein
(Fig. 6). We find that both sensitivity and the level of error rise,
as expected, from the strict to loose sets. For example, treating
coding exons as a set of true positives, the strict set has a sensi-
tivity of 44%, which increases to 69% and 88% (measured per
nucleotide) in the moderate and loose sets, respectively. Con-
versely, using mammalian ancestral repeats as a surrogate of neu-
trally evolving sequences (i.e., “true negatives”) (International
Mouse Genome Sequencing Consortium 2002; Margulies et al.
2003), we estimate that the false-discovery rate increases from
the strict to moderate to loose sets as 0.1%, 0.5%, and 4.8%
(measured per nucleotide) of constrained bases, respectively. This
likely indicates a decrease in specificity concomitant with the
increase in sensitivity in the three sets.
Explaining constraint prediction discrepancies
While our false-discovery rate standardization accounts for a sig-
nificant fraction of aligner-specific behaviors in neutrally evolv-
ing DNA (see Methods), alignment discrepancies are clearly con-
tributors to differences in constraint predictions. Even within an
alignment, however, we observe that the methods used for in-
ference of constraint make distinct predictions, with approxi-
mately one-third of the predicted constrained bases being dis-
crepantly predicted by at least one method (Table 1). Manual
analyses reveal that one of the most informative classes of such
differences reveals a dichotomy between the high-resolution,
phylogenetic methods (phastCons, GERP) and the more heuristic
binCons approach, which uses a 25-bp sliding window. While
binCons is incapable of detecting many of the smaller elements
identified by the phylogenetic approaches (phastCons and GERP
elements have median sizes of 15–20 bases), we found that bin-
Cons is less sensitive to spurious alignments resulting from short
regions of high similarity between distant species. Another im-
portant difference arises from the handling of regions of the
alignment that exhibit low neutral diversity, such as might be
seen in an alignment of only a handful of primate sequences.
While GERP explicitly ignored columns with <0.5 substitutions
per neutral site, phastCons and binCons did not and may occa-
sionally annotate constrained sequences within these regions
(which of statistical necessity are generally long elements that
therefore inflate the level of disagreement between methods). We
also note that a major fraction of the discrepancies among the
nine annotation sets results from the precise definition of con-
strained sequence boundaries rather than the presence of con-
straint per se; ∼80% of constrained sequence regions (as opposed
to nucleotides) overlap by at least one base in the intersection of
all nine annotations, in contrast with 60%–70% of all nucleo-
tides (analogous to the distinction in element overlaps made in
Supplemental Fig. S1).
Comparative analyses of ENCODE experimental annotations
Elsewhere we report on the extent of correlation between the
moderate set of constrained sequences and each class of experi-
CODE region (Y-axis), the percentage of nucleotides found to be under
evolutionary constraint in the strict (red), moderate (blue), and loose sets
(yellow) is shown (X-axis). The 44 regions are ranked from top to bottom
by the fraction of bases in the moderate (green) annotations. For all the
manually picked regions, their biological significance is noted in paren-
Constrained bases in each ENCODE region. For each EN-
alignment/constraint method combination
Density of constraint predictions for each
MAVIDMLAGANTBAIntersect 2 of 3
2 of 3
The first three columns and rows indicate each alignment and constraint
method, respectively. Also reported are the densities for the intersection
of all methods (Intersect) and regions identified in two out of three meth-
ods (2 of 3).
Margulies et al.
766 Genome Research
mentally annotated element (The ENCODE Project Consortium
2007). We noted that 40% of the moderate constrained sequence
represents protein-coding exons and their associated untrans-
lated regions, and an additional 20% of the constrained sequence
overlaps other experimentally identified functional regions, leav-
ing 40% of the constrained sequence without any ENCODE-
generated experimental annotation.
Constrained sequences not overlapping experimental annotations
Two independent lines of evidence suggest a functional role for
these remaining constrained sequences, despite a lack of experi-
mental annotation. First, these sequences are not enriched for
weakly constrained bases (Fig. 7), as would be expected if our
analyses yielded too many false-positive results (i.e., neutral se-
quence falsely identified as constrained). In fact, the region of
greatest evolutionary constraint (based on length and per-
position alignment score, residing within an intron of FOXP2) as
well as 16 of the top 50 constrained sequences do not overlap an
experimental annotation (Supplemental Table S2). Second,
analyses of human polymorphisms show that constrained se-
quences (both the annotations specifically described here and
others in general) correlate with reduced heterozygosity and de-
rived allele frequencies, indicative of recent purifying selection in
humans (Drake et al. 2006; The ENCODE Project Consortium
2007). Thus, constrained sequences are neither mutational cold
spots nor do they appear to have lost function recently in human
It is also unlikely that the unannotated constrained se-
quences primarily encode unknown proteins, as we observe little
overlap with predictions of coding potential analyzed from mul-
tisequence alignments (Siepel and Haussler 2004a; see Supple-
mental Material). These sequences therefore likely reflect func-
tional elements that were not detected by the assays used to date
by the ENCODE project. For example, functional elements in-
volved in embryonic development might have escaped detection
due to an emphasis on using cells grown in tissue culture. Indeed,
recent experiments show that many highly constrained pan-
vertebrate sequences are developmental enhancers (Nobrega et
al. 2003; Woolfe et al. 2005; Pennacchio et al. 2006), with func-
tions that are perhaps only detectable in the context of the de-
veloping organism. In addition, while the array of functions ex-
amined by ENCODE is broad, certain known classes (e.g., en-
hancer and silencer elements) have only been assayed indirectly
(e.g., by DNase I hypersensitivity or DNA–protein binding) or not
at all. Finally, it is almost certain that as-yet-unknown types of
function are conferred by some of the unannotated constrained
We thus conclude that many (at least 40%; this number is
likely to be larger given the limited resolution for some experi-
mental assays) constrained sequences have received no pur-
ported functional annotation to date, despite considerable ex-
perimental effort by the ENCODE project. Indeed, we show that
there are many regions of the human genome that likely have
functions critical to mammalian biology but that have not been
detected by the experimental assays employed thus far.
Assessing evolutionary constraints on experimentally annotated sequences
While the association of constrained sequence and genome func-
tion is well established (Hardison 2000), the converse relation-
ship—i.e., the extent to which the sequences of functional ele-
ments are under evolutionary constraint—has not been explored
in detail. Elsewhere, we examine the overlap between con-
strained sequences and each class of experimental annotation
(The ENCODE Project Consortium 2007). We noted that most
experimentally identified elements showed a significant level of
overlap with constrained sequences, but there was a wide varia-
tion in the amount of that overlap. While coding exons appeared
to have the majority of their bases constrained, noncoding func-
tional elements overlap considerably less (although still statisti-
cally significant), with some subclasses failing to exhibit a non-
random level of overlap. Since the experimental assays employed
by the ENCODE project to date appear to be generally reliable
and have tolerably low false-positive rates (The ENCODE Project
Consortium 2007), we explored a number of explanations for the
relative paucity of constraint within experimentally annotated
noncoding elements. We note that these are not mutually exclu-
First, some fraction of bases within experimentally anno-
tated sequence is unlikely to be part of the corresponding func-
tional elements because of resolution limitations of the experi-
mental assay. An experimentally annotated element may there-
fore be a mixture of functional and nonfunctional sequence, and
thus contain significant amounts of unconstrained sequence.
Elsewhere, we showed that most such annotated elements have
several “islands” of constrained sequences within them, with
many experimentally annotated elements overlapping con-
strained sequence more significantly at the annotation level than
at the base level (The ENCODE Project Consortium 2007; also see
Supplemental Fig. S1). For example, while non-protein-coding
transcripts of unknown function (TUFs) (see Supplemental Box
S1) exhibit relatively weak evidence for evolutionary constraint
on average over all of their bases (Supplemental Fig. S1, yellow
bars, column 4), they are significantly enriched for annotations
that overlap at least some amount of constrained sequence
(Supplemental Fig. S1, blue bars, column 4).
To test the possibility that this “island effect” could result
from the relatively low resolution of the experimental methods
each block of constrained sequence, a score based on the log-likelihood
of observing such a sequence under a model of constrained versus neutral
evolution was computed using the phastOdds program (Siepel et al.
2005). These values were divided by the length of each block to compute
a normalized per-base log-likelihood that reflects constraint intensity (X-
axis). These values were plotted as a frequency histogram (Y-axis) for the
blocks of constrained sequences that do (yellow) or do not (blue) overlap
an experimental annotation. The distributions largely overlap (green),
even at the extreme positive end in which highly constrained sequences
reside. For comparison, the distribution for ancestral repeat sequences is
shown as a representation of largely neutral DNA.
Annotated versus unannotated constrained sequences. For
ENCODE multispecies genomic sequence comparisons
used to establish these annotations, we asked whether the over-
lap between constrained sequences and experimentally anno-
tated elements could be improved by “trimming” the latter from
either end, leveraging the hypothesis that the functional subre-
gions would, on average, be toward the center of these annotated
regions. We find that this is, indeed, the case for certain experi-
mental annotations (Fig. 8; Supplemental Fig. S2), particularly so
for assays that detect protein–DNA binding of sequence-specific
transcription factors. Thus, it is plausible that the functional por-
tion of these experimentally annotated elements may be only a
handful of bases long and correspond more closely to the con-
strained sequence than the extent of the experimental annota-
tion suggests. This is in contrast to annotations with precise bor-
ders (such as UTRs), where it is clear that only portions of the
functional element are evolutionarily constrained (Fig. 8).
Second, analyses of evolutionary constraint fail to detect
functional constraint that is not reflected in primary sequence
conservation (e.g., Ludwig et al. 2000). For example, we note that
60% of the detected transcriptional promoters fail to overlap any
constrained sequence whatsoever. Promoters can be detected
with several orthogonal and highly reliable assays, and their lo-
cations are often conserved between humans and mice (Trinklein
et al. 2004). At the very least, the core promoter of ∼50 bases
within the majority of these annotations must be functional se-
quence, yet in many cases it is not under detectable evolutionary
constraint, suggesting that characteristics other than primary se-
quence are important for conferring function.
A third possibility that could explain these unconstrained
experimental annotations is that they are only functional within
a subset of the mammalian phylogeny, such as primates. This
explanation is consistent with the identification of purifying se-
lection against human polymorphisms even after excluding pan-
mammalian constrained sequences (The ENCODE Project Con-
sortium 2007). By definition, these elements are either com-
pletely absent or have evolved swiftly in some lineages,
significantly reducing the chance that we would identify them as
being under constraint (Stone et al. 2005). To address the possi-
bility of primate-specific constraint (other patterns of constraint
gains and losses are also possible, see Supplemental Material), we
used a novel algorithm to detect lineage-specific constrained se-
quences (Siepel et al. 2006). Although our power to detect pri-
mate-specific constrained sequences is relatively weak, especially
if they are short or have become constrained very recently, we
found 94 such sequences (median length 164 bases; range 69–
615 bases), some of which are quite striking (Supplemental Figs.
S3 and S4). These results suggest that, while most constrained
sequences are shared among mammals, there are some that are
specific to primates, and these sequences account for a small
portion of the apparently unconstrained experimental annota-
tions. As more primate sequence data become available, our
power to detect such regions in the genome will improve.
Fourth, it is conceivable that there are genomic regions that
reproducibly appear to be “functional” by an experimental assay
(e.g., transcription-factor-binding sites or RNA transcription
units) that are of no consequence to the organism, and thus are
“invisible” to natural selection. Such elements might exist in the
genome at a steady-state frequency dictated by the sequence
specificity of the function and the rate of neutral turnover of
genomic sequence throughout evolution. Short and degenerate
elements, for example, could emerge often in a large genome and
be quite abundant, while larger and more complex elements
would be rare. This is consistent with our observation that many
perimental annotations. We quantified the ratio of “observed” to “ran-
domized” overlaps between constrained sequences and experimental an-
notations (see Supplemental Box S1), after adding and subtracting a
given number of bases to the ends of each experimentally identified
annotation. Randomized data sets were generated by randomizing the
start positions of features within each ENCODE target, preserving the
length distribution of each feature set and any target-specific regional
effects. (A) This analysis is illustrated for a hypothetical set of annotations.
(Orange bars) The positions of constrained sequences; trimmed (blue
bars), observed (green bars), and expanded (red) experimental annota-
tions. (Vertical gray bars) Regions of overlap between constrained se-
quences and experimental annotations. A table summarizing the overlaps
among the different scenarios is provided below the diagram. For this
hypothetical example, note how the ratio of overlap between the ob-
served and randomized data sets increases as the experimental annota-
tions are trimmed, indicating an enrichment of constrained sequence in
the trimmed annotations. (B) This analysis for several experimentally
identified elements is plotted, where the X-axis indicates the amount of
trimmed (negative) or expanded (positive) sequence on each element,
and the Y-axis indicates the ratio of observed-to-randomized overlap
(scale varies between plots). Note that CDSs exhibit a slight enrichment
after deletion of a small number of bases at either end, but are very similar
to what is expected given the theoretically optimal self–self overlap
(“Constrained Sequence”), where we know that trimming should not
increase specificity. For many annotations (e.g., “TUFs” and “5?-UTRs”)
(see Supplemental Box S1), such enrichment quickly drops off as the
annotations are expanded or trimmed. However, some annotations, such
as “FAIRE Sites” and “Sequence-Specific Factors,” exhibit a clear improve-
ment in overlap after trimming substantial amounts of sequence from
either end (250 and 500 bases for “FAIRE Sites” and “Sequence-Specific
Factors,” respectively). Similar plots for all experimental annotations are
available as Supplemental Figure S4.
Significance of constrained sequence overlapping various ex-
Margulies et al.
annotated sites of protein–DNA interaction, in many cases
thought to be dictated (at least in part) by short and degenerate
motifs, do not overlap any constrained sequence, while nearly all
coding exons, which would emerge at random extremely rarely,
are under constraint. Thus, it is plausible that many biochemi-
cally functional but biologically inert elements exist in the hu-
man genome and provide evolutionary potential from which
new functions may arise.
It is interesting to note that a sizable fraction of each class of
experimental annotation is not evolutionarily constrained by the
methods used here. If the corresponding elements are, indeed,
important for human biology, then it becomes important to es-
tablish how their function is encoded in the absence of evolu-
tionary constraint at the primary sequence level (The ENCODE
Project Consortium 2007). Alternatively, if some of the annota-
tions reflect functional elements that are of no consequence to
the organism, then our definition of biological function will re-
quire refinement not unlike the expansion of our understanding
of evolution that came about with the development of the neu-
tral theory (Kimura 1983).
Comparative analyses necessitating accurate alignments of mul-
tiple, large genomic sequences are now crucial parts of many
biological analyses. Here, we describe one of the largest compara-
tive genomic challenges documented, generating and analyzing
alignments of 30 Mb of human sequence to 27 other vertebrate
species. This field remains an active area of research and devel-
opment, as the four prominent alignment tools that we have
used show significant levels of discrepancies. It is impossible at
the moment to make definitive qualitative statements concern-
ing the alignment tools, as there are distinct trade-offs in their
behaviors; for example, alignments produced by MLAGAN ex-
hibit global increases in alignment coverage when compared to
TBA and MAVID, but this includes increases in incorrect align-
ments (Figs. 4, 5). PECAN may be achieving a better compromise
in this regard, with better specificity than MLAGAN but similar
levels of sensitivity. Thus, these alignments offer distinct speci-
ficity/sensitivity trade-offs that are reflected in changes in the
inferred rates of both indel and substitution events (Supplemen-
tal Table S3). Other factors may also influence alignment choice,
such as the basic modeling assumption used concerning the
types of orthology that are to be predicted (Dewey and Pachter
2005). Additionally, PECAN is at the moment only a global
aligner and therefore incapable of handling rearranged se-
quences. Thus, choice of alignment method and goals depends
on many factors and ultimately should be dictated by the down-
stream application employed. Furthermore, all downstream ap-
plications should be cognizant of such technical discrepancies,
and account for uncertainty whenever resulting parameters, such
as rates of nucleotide substitution in neutral sites, are utilized.
Similar qualitative caveats can be made with respect to inferring
the locations of evolutionarily constrained sequences in the hu-
man genome. For example, one trade-off that we identified is
that the sliding-window approach employed by binCons, while
being less sensitive to many of the smaller elements that phast-
Cons and GERP identify with confidence, is less prone to anno-
tate alignment artifacts resulting from isolated and short but
highly similar sequence matches between humans and distantly
related species. We find that there is significant room for im-
provement in the computational analyses of diverse mammalian
sequences. A particularly pertinent area will be the standardiza-
tion of benchmarks and, perhaps more importantly, concepts
and definitions for both multisequence alignments and analyses
of constrained sequences.
However, despite this uncertainty, we show that compara-
tive sequence analyses are a critical component of efforts to sys-
tematically identify and characterize functional elements in the
human genome. Our observation that 40% of all constrained
sequences fail to overlap any ENCODE experimental annotation
suggests that future efforts aimed at the comprehensive identifi-
cation of genomic functional elements require a more diverse
array of experimental approaches, and also lends support to in-
corporating medium-to-high-throughput model-organism ex-
perimentation. We also demonstrate that constraint analyses can
be used to refine experimental annotations made with relatively
low-resolution methods, and that such efforts can likely guide
future experimental and computational analyses of these experi-
mental data. Our studies have thus yielded both an important
resource for comparative genomics and biological insights to
guide future functional analyses of the entire human genome.
Alignments and other annotations generated and used for the
studies reported here are available at http://genome.ucsc.edu/
ENCODE (click on the “Downloads” link in the blue column
along the left side of the page). They are also displayed in the
UCSC Genome Browser under the “ENCODE Comparative Ge-
nomics” set of tracks. PECAN alignments are available at http://
tar.bz2. All experimental annotations were obtained from pub-
licly available ENCODE project data (The ENCODE Project Con-
sortium 2007); a bulk download of these data is available at
ENCODE genomic sequence data
The ENCODE regions represent a mix of manually and randomly
selected targets, with details available at http://genome.ucsc.edu/
ENCODE/regions.html (Thomas et al. 2006). In addition to the
NISC BAC-based comparative grade sequence data generated spe-
cifically for this project, orthologous regions of the following
whole-genome assemblies were used: chicken (CGSC_Feb._2004,
galGal2); chimpanzee (NCBI_Build_1_v1, panTro1); dog (Broad-
_Institute_v._1.0, canFam1); Fugu (IMCB/JGI, fr1); macaque
(BCM, rheMac1); monodelphis (Broad_Institute, monDom1);
mouse (NCBI_Build_33, mm6); rat (Baylor_HGSC_v3.1, rn3); te-
traodon (Genoscope_V7, tetNig1); Xenopus (JGI, xenTro1); and
zebrafish (Sanger_Zv4, danRer2). For non-human vertebrate spe-
cies with genome-wide assemblies, the identification of ortholo-
gous regions (i.e., large genomic intervals in each non-human
sequence that are orthologous to each ENCODE target) was done
with the liftOver program (Kent et al. 2003) and the Mercator
program (Dewey 2006). These predictions were merged to pro-
duce a comprehensive sequence data set, which was then Repeat-
Masked. All analyses presented here use a sequence freeze dated
September 2005 (labeled as SEP-2005).
The Threaded Blockset Aligner (TBA) (Blanchette et al. 2004) was
used to generate multisequence alignments as follows. First, com-
ENCODE multispecies genomic sequence comparisons
binatorial pairwise alignments were generated with BLASTZ
(Schwartz et al. 2003) using the following command-line param-
eters: Y=3400 H=2000. For mammalian-sequence comparisons, we
additionally added B=2 C=0. For all other comparisons (except
tetraodon and Fugu, which were treated as a mammalian com-
parison), we instead used the HoxD55 alternate scoring matrix
(Margulies et al. 2005a). Pairwise alignments that included the
human reference sequence were permitted to include sequence
from the other species to align to more than one position. The
pairwise sequence alignments, along with a generally accepted
tree topology (Murphy et al. 2001; Thomas et al. 2003; Margulies
et al. 2005a), were used to generate the multisequence alignment,
which was then projected onto the human sequence to remove
alignment blocks that did not contain the human reference se-
MLAGAN alignments were produced by a pipeline specifically
designed for ENCODE. First, WU-BLAST (W. Gish, 1996–2004;
http://blast.wustl.edu) was used to find local similarities (an-
chors) between the human sequence and the sequence of every
other species. Then, Shuffle-LAGAN was used to calculate the
highest-scoring human-monotonic chain of these local similari-
ties (according to a scoring scheme that penalized evolutionary
rearrangements) and (with the help of a utility called SuperMap)
produce a map of orthologous segments in increasing human
coordinates. This map was used to “undo” the genomic rearrange-
ments of the other sequence and convert it to a form that was
directly alignable to the human sequence. The new humanized
sequences, together with the human sequence, were then multi-
ply aligned using MLAGAN. The resulting alignments were sub-
sequently refined using MUSCLE (Edgar 2004), which processed
small nonoverlapping alignment windows and realigned them in
an iterative fashion, keeping the refined alignment if it had a
better sum-of-pairs score than the original. Finally, a pairwise re-
finement round was performed, during which the pieces that had
very low identity (in the induced pairwise alignments between
human and each species) were removed from the alignment.
One set of alignments was created by a combination of Mercator
(Dewey 2006), an orthology mapping program, and MAVID
(Bray and Pachter 2004), a multiple global alignment program.
For each ENCODE region, Mercator was first used to determine a
small-scale collinear orthology map: sets of orthologous and col-
linear segments within the sequences given for that region. These
sets of segments were determined in a symmetric fashion, with-
out the use of the human sequence as a reference, and included
sets that contained segments from only a subset of the input
species. The orthology maps determined by Mercator were one to
one, and thus had the property that a sequence position in any
species was present in at most one segment set. Given the orthol-
ogy maps, MAVID was then used to produce nucleotide-level
multisequence alignments of each segment set. Only those seg-
ment sets that contained human sequence were retained for
downstream analyses. Several programs were used to generate the
input for Mercator. First, GENSCAN (Burge and Karlin 1997) was
used to predict coding exons in all of the input sequences. The
amino acid sequences corresponding to the coding exons were
then compared to each other in an all-versus-all fashion with
BLAT (Kent 2002). In order to detect noncoding rearrangements
in the input sequences, MUMmer (Kurtz et al. 2004) was run to
detect exact matches of length at least 20 bases between all pairs
of genomes. The output of MUMmer was processed to produce a
set of noncoding and nonoverlapping landmarks in each of the
genomes. Mercator was then run with both coding and noncod-
ing landmarks to determine an orthology map for each ENCODE
region, as well as a set of alignment constraints within the seg-
ment sets based on matched landmarks. Nucleotide-level mul-
tisequence alignments of each segment set that obeyed the align-
ment constraints were constructed by MAVID. As part of its pro-
gressive multisequence alignment strategy, MAVID utilized a
phylogenetic tree of the species with branch lengths determined
from fourfold degenerate sites in all ENCODE regions.
PECAN is a global alignment algorithm that has similarities with
the Probcons (Do et al. 2005) and T-Coffee (Notredame et al.
2000) programs, but is adapted to deal with arbitrarily long se-
quences by a process of “sequence progressive” iteration (B. Paten
and B.E. Pecan, in prep.). Sequences were first reordered in ref-
erence to the human sequence using Shuffle-LAGAN (see above).
PECAN alignments were generated by running the program in
three stages. First, the primate sequences were aligned, followed
by the alignment of the placental mammals, and finally the more
distant species were added. As PECAN can currently only align
sequences, it was necessary to convert the intermediate products
of the alignments (first the primate, then the placental mammal)
to consensus ancestral sequences, for which we used Felsenstein’s
algorithm (Felsenstein 1981). We avoided the issue of ancestral
insertions and deletions by computing the consensus sequence
based on the human sequence. Thus, all and only the bases pres-
ent in the human sequence were included. This human-centric
approach is sensible in light of ENCODE’s overall goals, the prob-
lems of partial sequence coverage in non-human species (which
may be incorrectly inferred as gaps), and the general limited
availability of algorithm implementations for accurately comput-
ing insertion and deletion histories. Prior to alignment, some
training of PECAN’s pair hidden Markov models was performed
using rearranged sequences from a subset of the ENCODE re-
gions. Alignments have not been post-processed and largely rep-
resent the default parameters of the program (v0.6).
Inferring rearrangement events
For all ENCODE alignments, a pairwise alignment between hu-
man and each other species was extracted. The pairwise align-
ments were converted into a “threaded block set” format (Blan-
chette et al. 2004), where each block was required to be un-
gapped. Blocks that were species-specific or duplicated in human
were removed, and neighboring collinear blocks were merged.
For a given minimum block size, blocks were removed from the
block set in order of increasing size, with adjacent collinear
blocks merged after each removal stage, until all blocks had size
greater than or equal to the minimum. The number of break-
points was simply the number blocks remaining minus one. The
number and length of blocks in a given alignment were calcu-
lated based on the blocks removed from the alignment in the
process described, and not on all blocks present in the initial
Estimating rates of evolution at neutral sites
We first generated a tree on the basis of aligned fourfold degen-
erate sites within coding exons (taken from the longest transcript
if there was more than one at a given locus). For any given non-
human sequence, sites that fell within gaps or that were no
longer synonymous (because of changes in the first two bases)
were treated as missing data. Substitution rates were estimated by
maximum likelihood with the PHAST package (Siepel and Haus-
Margulies et al.
sler 2004b) and the XRATE package (Klosterman et al. 2006). A
generally accepted tree topology for the analyzed species was
used. The most general reversible substitution model (REV) was
used, and no molecular clock was assumed. Further details are
available as Supplemental Material.
Assessment of periodicity in coding exons
The periodicity assessment considers the mutation pattern be-
tween human and each non-human, “informant” species. We
expect the pattern of mutations to be 3-periodic as a result of
degeneracy in the third base of many codons. The assessment
determines, for each CDS in the test set and for each species in
the alignment, whether the alignment for the species, when
paired with human, exhibits evidence of a 3-periodic substitu-
tion pattern either over the whole length of the CDS or in at least
one 48-bp window. Evidence of periodicity is taken to be a
“hi_spi” value of 3 or above. The “hi_spi” value is calculated as
the ratio of the number of substitutions in frame “2,” divided by
the number of substitutions in frame “0,” where frame “2” is
identified as the frame with the highest number of substitutions.
If the denominator is zero, it is changed to 1. The analyses are
referenced to human annotations, thus gaps in the human se-
quence were removed from both species before the substitution
counts were made. Because closely related species and some
highly conserved genes have low levels of synonymous substitu-
tion, it is not possible to detect periodicity in all exons, and this
will vary from species to species. Therefore, for each species, we
count how many of the exons exhibit periodicity in at least one
alignment method (n) and divide the raw counts by n to give the
percent figures displayed in Figure 5.
Identification of constrained sequence
PhastCons parses a multisequence alignment into constrained
and nonconstrained regions using a phylo-HMM. The phylo-
HMM has two states, one for constrained regions and one for
nonconstrained regions, and these states are associated with
identical phylogenetic models, except that the branch lengths of
the constrained phylogeny are scaled by a factor ? (0 < ? < 1).
Constrained elements are predicted using the Viterbi algorithm.
The predictions depend on several parameters, including the
scaling parameter ?, two parameters ? and ? that define the state-
transition probabilities, and the parameters of the shared phylo-
genetic model (branch lengths and substitution rate matrix). We
used a parameter estimation procedure slightly different from the
one described in Siepel et al. (2005). Briefly, the nonconstrained
model was estimated separately, from fourfold degenerate sites in
coding regions (using the REV substitution model and the phy-
loFit program) (Siepel and Haussler 2004b), and other parameter
estimates were conditioned on this model. We allowed phast-
Cons to estimate the scaling parameter ? by maximum likeli-
hood, and adjusted the tuning parameters ? and ? to achieve the
desired false discovery rate (see below).
Genomic evolutionary rate profiling (GERP) was run as described
(Cooper et al. 2005). Briefly, each position of the human-
projected multisequence alignment was evaluated indepen-
dently, with a resulting estimate of both the observed (obtained
with maximum likelihood under an HKY 85 model of nucleotide
substitution) and expected (on the basis of a neutral tree; see
above) substitution count obtained. All gapped species were
eliminated from consideration at each column. Subsequently,
each group of consecutive columns (with each column corre-
sponding to one human nucleotide) in which the observed
counts are smaller than the expected counts were identified as
candidate constrained elements, with a merging tolerance of one
unconstrained base. These candidates are summed according to
the total deviation between observed and expected counts, with
those meeting a certain threshold (using the target/alignment
null model defined below) retained as legitimately constrained
The binomial-based conservation approach was used essentially
as described (Margulies et al. 2003, 2004). Briefly, the amount of
sequence conservation is calculated for each overlapping 25-base
window, where each species’ contribution is weighted by its cor-
responding neutral rate (as calculated above). In this fashion,
more diverged sequences contribute more to the overall conser-
vation score than do less diverged sequences. This is computed
with a cumulative binomial distribution, with the neutral rate of
each species representing the null distribution. For the calcula-
tions reported here, the exact amount of constrained sequence
predicted by this method was tuned to the mean amount of
predicted sequence by GERP and phastCons.
Standardizing false-discovery rates
Given the diversity of methodologies employed, we sought to
simplify and standardize parameter choices among the methods
as much as possible. The most crucial parameter is a threshold for
differentiating regions that are truly constrained (i.e., subject to
purifying selection) from those that appear constrained by
chance. While ideally such a measure would use a set of true
positives and true negatives, such elements are unavailable. Cod-
ing exons are generally true positives, for example, but are well
known to be a nonrepresentative minority of the total space of
constrained sequence. On the other hand, ancestral repeats are
generally thought to evolve neutrally, but have been previously
shown to include a nontrivial amount of constrained DNA (Silva
et al. 2003; Cooper et al. 2005; Kamal et al. 2006). We therefore
adopted an empirical approach to measure and standardize false-
discovery rates that can also effectively cope with both region
and alignment variation in the underlying neutral rates, similar
to a previously described method (Cooper et al. 2005). For each
ENCODE-region alignment, we generated a bootstrapped null or
“neutral” alignment of 1 Mb in length. Specificity thresholds
were then defined on the basis of the number of “constrained”
bases identified in these bootstrapped alignments (false posi-
tives). Thresholds were set such that the number of false positives
amounted to 5% of the total number of constrained bases iden-
tified in the true alignment (for example, if 50,000 bases are
annotated to be constrained in the real alignment, 2500 would
be annotated in the bootstrapped alignment).
Statistical evaluation of overlaps
We quantified the overlap between constrained sequences and
each class of experimentally identified element at both the
nucleotide and regional levels (Fig. 6); this same method was
used elsewhere (The ENCODE Project Consortium 2007). We
used an implementation of the Block Bootstrap (Künsch 1989) to
model the variance in randomly expected levels of overlap. This
empirical method agrees well with analytical variance computa-
tions (achievable for the nucleotide-level overlaps, but not for
region-level overlaps), and also accounts for the intrinsic biases
against repetitive sequence observed in both the constraint and
experimental annotations (see the Supplemental Material). All
ENCODE multispecies genomic sequence comparisons
confidence intervals shown for the overlap statistics are at 99.8%
Note added in proof
Recent reports resolve an early node (Murphy et al. 2007) and an
internal node (Nishihara et al. 2006) of the boreoeutherian tree
differently than shown in Figure 1. However, it is unlikely that
these differences in tree topology will have a significant impact
on the conclusions drawn here.
Complete list of authors
NISC Comparative Sequencing Program
Gerard G. Bouffard,8,21Xiaobin Guan,21Nancy F. Hansen,21Jac-
quelyn R. Idol,8Valerie V.B. Maduro,8Baishali Maskeri,21Jen-
nifer C. McDowell,21Morgan Park,21Pamela J. Thomas,21Alice
C. Young,21and Robert W. Blakesley8,21
Baylor College of Medicine Human Genome
Donna M. Muzny,26Erica Sodergren,26David A. Wheeler,26
Kim C. Worley,26Huaiyang Jiang,26George M. Weinstock,26and
Richard A. Gibbs26
Washington University Genome Sequencing Center
Tina Graves,27Robert Fulton,27Elaine R. Mardis,27and Richard
Michele Clamp,28James Cuff,28Sante Gnerre,28David B. Jaffe,28
Jean L. Chang,28Kerstin Lindblad-Toh,28and Eric S. Lander28,29
UCSC Genome Browser Team
Angie Hinrichs,12Heather Trumbower,12Hiram Clawson,12Ann
Zweig,12Robert M. Kuhn,12Galt Barber,12Rachel Harte,12and
British Columbia Cancer Agency Genome Sciences Center
Matthew A. Field,30Richard A. Moore,30Carrie A. Mathewson,30
Jacqueline E. Schein,30and Marco A. Marra30
We thank F. Collins for critical review of the manuscript; all
other ENCODE analysis subgroups for their camaraderie and col-
laboration; P. Good, E. Feingold, and L. Liefer for ENCODE Con-
sortium guidance and administrative assistance; the Wellcome
Trust Sanger Institute, the Max Planck Institute for Developmen-
tal Biology, and The Netherlands Institute for Developmental
Biology for providing a draft zebrafish genome sequence prior to
publication; the DOE Joint Genome Institute for providing a
draft Xenopus sequence prior to publication; G. Schuler for mak-
ing ENCODE comparative sequence data available at NCBI; D.
Church for coordinating the identification of finished mouse se-
quence orthologous to ENCODE regions; and the anonymous
reviewers of this manuscript for their constructive comments on
previous drafts. This research was supported in part by the Intra-
mural Research Program of the National Human Genome Re-
search Institute, National Institutes of Health (E.H.M., J.C.M.,
and E.D.G.). G.M.C. was a Howard Hughes Medical Institute pre-
doctoral Fellow. G.A. is a Bio-X Graduate Student Fellow. D.J.T. is
supported by NIH 1 P41 HG02371-05. C.N.D. is supported in part
by NIH HG003150. M.H., J.T., and W.M. are supported in part by
R01:HG002238. T.M. was supported by BBSRC grant 721/
BEP17055. I.H. was funded in part by NIH/NHGRI grant
1R01GM076705-01. S.E.A., S.N., and J.I.M. thank the “Vital IT”
computational platform and are supported by grants from NIH
ENCODE, Swiss National Science Foundation, European Union,
and the ChildCare Foundation. L.P. is supported in part by
R01:HG02632 and U01:HG003150. N.G. was supported in part
by the Wellcome Trust. D.H. and A. Sidow are supported by funds
from NHGRI. A. Siepel was supported by the UCBREP GREAT
fellowship (University of California Biotechnology Research and
Education Program Graduate Research and Education in Adaptive
Aparicio, S., Chapman, J., Stupka, E., Putnam, N., Chia, J.-M., Dehal, P.,
Christoffels, A., Rash, S., Hoon, S., Smit, A., et al. 2002.
Whole-genome shotgun assembly and analysis of the genome of
Fugu rubripes. Science 297: 1301–1310.
Blakesley, R.W., Hansen, N.F., Mullikin, J.C., Thomas, P.J., McDowell,
J.C., Maskeri, B., Young, A.C., Benjamin, B., Brooks, S.Y., Coleman,
B.I., et al. 2004. An intermediate grade of finished genomic sequence
suitable for comparative analyses. Genome Res. 14: 2235–2244.
Blanchette, M., Kent, W.J., Riemer, C., Elnitski, L., Smit, A.F.A., Roskin,
K.M., Baertsch, R., Rosenbloom, K., Clawson, H., Green, E.D., et al.
2004. Aligning multiple genomic sequences with the threaded
8Genome Technology Branch, National Human Genome Research Institute,
National Institutes of Health, Bethesda, MD 20892, USA.
9Department of Genetics, Stanford University, Stanford, CA 94305, USA.
10Department of Computer Science, Stanford University, Stanford, CA 94305,
11Department of Biomolecular Engineering, University of California, Santa
Cruz, CA 95064, USA.
12Center for Biomolecular Science and Engineering, University of California,
Santa Cruz, CA 95064, USA.
13Department of Electrical Engineering and Computer Sciences, University of
California, Berkeley, CA 94720, USA.
14European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinx-
ton CB10 1SA, UK.
15Department of Computer Science and Engineering, The Pennsylvania State
University, University Park, PA 16802, USA.
16Department of Genetic Medicine and Development, University of Geneva
Medical School, Geneva, Switzerland.
17Department of Zoology and Animal Biology, Faculty of Sciences, University
of Geneva, Geneva, Switzerland.
18Department of Applied Science & Technology, University of California,
Berkeley, CA 94720, USA.
19Department of Statistics, University of California, Berkeley, CA 94720, USA.
20Department of Bioengineering, University of California, Berkeley, CA 94720-
21NIH Intramural Sequencing Center, National Human Genome Research In-
stitute, National Institutes of Health, Bethesda, MD 20892, USA.
22Center for Comparative Genomics and Bioinformatics, Huck Institutes for
Life Sciences, Penn State University, University Park, PA 16802, USA.
23Howard Hughes Medical Institute, University of California, Santa Cruz, CA
24Department of Mathematics, University of California, Berkeley, CA 94720,
25Department of Pathology, Stanford University, Stanford, CA 94305, USA.
26Human Genome Sequencing Center and Department of Molecular and Hu-
man Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
27Genome Sequencing Center, Washington University School of Medicine,
Campus Box 8501, 4444 Forest Park Avenue, St. Louis, MO 63108, USA.
28Broad Institute of Harvard and MIT, 320 Charles Street, Cambridge, MA
29Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cam-
bridge, MA 02142, USA.
30Canada’s Michael Smith Genome Sciences Centre, BC Cancer Research Cen-
tre, BC Cancer Agency, Vancouver, British Columbia V5Z 4S6, Canada.
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