Vol 13, Issue 1, 73-80, January 2003
LETTER
Strategies and Tools for Whole-Genome Alignments
Olivier Couronne1,3,
Alexander Poliakov1,
Nicolas Bray1,
Tigran Ishkhanov1,
Dmitriy Ryaboy1,
Edward Rubin1,
Lior Pachter2,3 and
Inna Dubchak1
1Lawrence Berkeley National Laboratory, Berkeley,
California 94720, USA; 2Department of Mathematics,
University of California at Berkeley,
Berkeley, California 94720, USA
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ABSTRACT
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The availability of the assembled mouse genome makes possible, for
the first time, an alignment and comparison of two large vertebrate
genomes. We investigated different strategies of alignment for the
subsequent analysis of conservation of genomes that are effective for
assemblies of different quality. These strategies were applied to the
comparison of the working draft of the human genome with the Mouse
Genome Sequencing Consortium assembly, as well as other intermediate
mouse assemblies. Our methods are fast and the resulting alignments
exhibit a high degree of sensitivity, covering more than 90% of known
coding exons in the human genome. We obtained such coverage while
preserving specificity. With a view towards the end user, we developed
a suite of tools and Web sites for automatically aligning and
subsequently browsing and working with whole-genome comparisons. We
describe the use of these tools to identify conserved non-coding
regions between the human and mouse genomes, some of which have not
been identified by other methods.
The expectation behind the sequencing of the mouse
genome is to gain a deeper understanding of the human genome through
comparative analysis. Comparative genomic studies of selected regions
have already resulted in interesting biological discoveries; from many
examples we mention here the discovery of new genes (Dehal et al. 2001 ;
Pennacchio et al. 2001) and the identification of conserved non-coding
sequences with regulatory functions (Hardison et al. 1997; Oeltjen et
al. 1997; Hardison 2000; Loots et al. 2000; Krivan and Wasserman 2001).
These comparative genomic studies were based on sequence alignments and
were successful because the evolutionary distance between mouse and
human appears to be small enough so that genes and other functional
elements have been conserved in both sequence (Hardison et al. 1997;
Batzoglou et al. 2000) and function (Huxley 1997). On the other hand,
sufficient time has elapsed so that nonfunctional sequence has diverged
enough to yield a good "signal to noise" ratio.
Alignments of whole genomes have been undertaken for complete genomic
sequences of various bacterial species (Tatusov et al. 1997; Delcher et
al. 1999; Florea et al. 2000), and that was feasible due to
the small genomic size of these organisms (up to 4 Mb). The recently
published Fugu genome (Aparicio et al. 2002) was aligned to the human
genome using the BLAST program (Aetshul et al. 1990), but the
complexity of the problem was mitigated by the small size of the Fugu
genome and its relatively simple repeat structure. A similar local
alignment approach was applied to the mouse genome by the BLASTZ group
(Schwartz et al. 2003).
Aligning large vertebrate genomes that are structurally complex poses a
variety of problems not encountered on smaller scales. Such genomes are
rich in repetitive elements ( 50% in the human genome, International
Human Genome Sequencing Consortium 2001; Venter et al. 2001) and
contain multiple segmental duplications (the human genome seems likely
to contain about 5% segmental duplication, with most of this sequence
in large blocks greater than 10 kb; Bailey et al. 2002). The sizes of
the sequences is perhaps the biggest hurdle, since many alignment
algorithms were designed for comparing single proteins and are
extremely inefficient when processing large genomic intervals (Miller
2001). The complexity of vertebrate genomes also increases the
difficulty of identifying true orthologous DNA segments in alignments.
Taking into account that there are sometimes near perfect matches
between paralogous DNA regions, it is necessary to statistically assess
the identification of the most likely orthologous DNA segments, while
minimizing the rate of misaligned regions.
In this paper we describe our strategies and results for the human and
mouse genomes. We integrated both local and global alignment programs,
and our study provides the first quantitative analysis of how such
strategies perform in tandem. The resulting implementation allows rapid
and specific whole-genome alignments and comparisons.
The ultimate goal of genome alignment is to facilitate biological
discovery, and with this in mind we also integrated in the
computational system a variety of browsing and analysis tools. We
present visualization tools for browsing the alignments, as well as a
Web server that allows users to align their own sequences against
completed genome assemblies.
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METHODS
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Algorithms
Finding the orthologous regions between two species computationally
is a nontrivial task that has never been explored on a whole-genome
scale for vertebrate genomes.
Local alignment tools find many high-scoring matching segments, in
particular the orthologous segments, but in addition they identify many
paralogous relationships, or even false-positive alignments resulting
from simple sequence repeats and other sequence artifacts (Chen et al.
2001). BLAST was successfully utilized in the study by Gibbs and
coauthors (Chen et al. 2001) where high-quality rat WGS reads (covering
7.5% of the rat genome) were compared with the GoldenPath human genome
assembly. Those authors investigated statistical significance of BLAST
search results and parameters, but they did not focus on finding
‘true’ orthologs and were mostly interested in higher sensitivity of
alignment and completeness of coverage of coding exons. When compared
with the human assembly, more than 47.3% of all aligned reads produced
between two and 12 hits (which correspond to medium represented
elements), and 7.6% had more than 12 hits (likely containing
repetitive elements).
Unlike local alignment, global alignment methods require aligned
features to be conserved in both order and orientation, and are
therefore appropriate for aligning orthologous regions in the domain
where this assumption applies. But whole-genome rearrangements,
duplications, inversions, and other evolutionary events restrict the
resolution at which the order and orientation assumption of global
alignment applies. In the case of the human and mouse genomes, it
appears that this assumption applies, on average, to regions less than
eight megabases in length (Mural et al. 2002).
Our strategy is to use a fast local alignment method to find anchors on
the base genome to identify regions of possible homology for a query
sequence. The key element is then to be able to postprocess these
anchors in order to delimit a region of homology where the order and
orientation seem conserved. These regions are then globally aligned. In
the work presented here, we used BLAT (Kent 2002) to find anchors and
AVID (version 2.0 at http://bio.math.berkeley.edu/avid; Bray et al.
2003) to generate global alignments (see Fig.
1 for an overview of the pipeline and how they were
combined). BLAT was designed for cDNA/DNA alignment and first used in
Intronerator (Kent and Zahler 2000). BLAT is not optimized for
cross-species alignments (Kent 2002), but we chose this program because
our tests demonstrated that it performed very well as an anchoring tool
in our computational scheme. It is also about 500 times faster than
other existing alignment tools.
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Figure 1. General scheme of the pipeline. The pipeline processes individual
contigs, supercontigs, or long fragments of assemblies.
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Heuristic for Selecting Candidate Regions for Global Aligning (Postprocessing of Anchors)
For each sequence, BLAT matches are sorted by score, and regions of
possible homology are selected around the strongest matches which serve
as anchors. All BLAT hits at most L bases apart are grouped
together (here L is the length of the region being aligned). For groups
smaller than L/4, the regions were then extended out by min(50k,
L/2-–G) where G is the length of the group. For groups with G greater
than L/4, the regions were extended out by min(50k,L/4). The groups
obtained are compared and the ones with less than 30% of the score of
the best group are rejected at this stage (see Fig.
2). Various parameters for these heuristics
were explored in order to obtain a method that would work for different
sizes of sequences.
This heuristic may identify multiple regions of possible homology of
different size and score in the base genome. These regions are proposed
as candidates to the alignment program. The score obtained by the
global alignment is used to make the decision about which alignments to
report or to reject.
Strategies for Different Types of Analyzed Sequence
Different sequencing strategies, coupled with the various assembly
methods used to build contigs and scaffolds, result in genomic sequence
at different stages of completion and of different quality. There is a
significant number of BAC-size finished contigs particularly suitable
for higher-quality comparative analysis (Mardis et al. 2002), whereas
whole-genome shotgun-generated assemblies result in shorter contigs and
scaffolds. We developed different strategies for aligning sequences at
different stages of completion by taking into account all available
information, such as the scaffold or the map information. Table
1 summarizes the computational schemes we
developed for different types of sequences.
In the case of finished BACs or individual sequences submitted by the
user through the pipeline interface, no other information is available
and we use a ‘contig’ scheme where mapping is based solely on the
found anchors, followed by the global alignment stage with its scoring.
When we align an assembly with the scaffold information available,
anchors obtained for different contigs in a scaffold are analyzed
together to select candidate positions. We map the whole scaffold, but
have the flexibility to reorient and reorder each of the contigs in the
scaffold at the alignment stage if necessary. The algorithm also allows
us to break the scaffold by selecting more than one candidate region,
so that some of the contigs can be aligned to a different place. These
last two features allow our alignment to be tolerant to scaffold
assembly errors.
For an advanced assembly, scaffold information is very reliable. In
MGSCv3, the quality of assembly was high enough that contigs and
scaffolds were mapped to the mouse chromosomes (Waterston et al.
2002). For such cases we chopped the mouse chromosomes into large
sections before aligning them. The chromosomes were chopped around the
contig boundaries in order not to split them. We tested different sizes
and found that fragments averaging 250 kbp in length give us the best
sensitivity.
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RESULTS
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Here we present the results of alignment of the Mouse Genome
Sequencing Consortium assembly MGSCv3 with the June 2002 Human Genome
freeze (NCBI build 30). Alignments on this freeze as well as the
December 2001 freeze are available at http://pipeline.lbl.gov. The
human genome sequence was soft-masked, so that repeats were not
considered at the anchoring level, although the global alignments
generated at later stages do extend into repeats.
Sensitivity
For the final alignment we calculated the level of coverage on known
coding and non-coding functional features of the human genome (Table
2). The alignments were scored according to
the procedure described in the paper on the mouse genome (Waterston et
al. 2002). Three different evolutionary models were selected for
scoring the alignments. For coding regions, a high stringency and high
penalty for indels was chosen. In order to assess performance on
less-conserved regulatory regions, we also applied less stringent
filters. The overall coverage was computed, as well as the coverage of
the RefSeq exons, upstream (100, 200, and 500 bp) and downstream (200
bp) regions, and UTR.
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Table 2. Percentage of Base Pairs Covered for Known Coding and Non-Coding
Functional Features of the
Human Genome
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About 2% of aligned base pairs of the human genome were covered by
more than one mouse sequence fragment. Figure
3 shows an example of a chromosome 3
location where several copies of the mouse pseudogene of Laminin B
receptor (LAMR1) from different chromosomes was aligned (laminin B
receptor has multiple pseudogenes,
http://www.ncbi.nlm.nih.gov/LocusLink/).
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Figure 3. Location at chr3:38787874–38793594 on the human genome, June 2002
(hg12/ncbi30) where LAMR1 gene is covered by the alignments of
sequences from different mouse chromosomes.
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Our alignment showed more than one million regions conserved at
higher than 70% conservation over the 100-bp level. These features
cover about 217 million base pairs. Only 61.6% of them are covered by
at least one base pair of a BLAT hit. This means that about two-fifths
of the conserved features are found only at the global alignment stage.
This result is critical because it proves that a local aligner such as
BLAT, set up with parameters for which its sensitivity is not optimal,
but its speed is, can nevertheless be used as an anchoring system
because the global alignment retrieves many additional conserved
regions outside the anchors (Fig. 4). The
amount of conserved non-coding sequence was extraordinarily high. At
least 5.82% of the bases in the genome are conserved at the
70%/100-bp threshold but do not overlap annotated RefSeq, mRNA,
Genscan predictions, or ESTs. Our scheme has the flexibility to align a
query sequence to multiple regions in the genome. Among the conserved
features (70% over 100 bp), 6.6% of the total conserved sequences
came from secondary hits. These conserved regions may arise from
genomic rearrangements or duplications.
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Figure 4. The global alignment of the mouse finished sequence NT_002570 against
the region found by BLAT anchors revealed conserved coding and
non-coding elements not found by the BLAT program. The anchoring scheme
is sensitive enough to provide the global alignment with the correct
homology candidate. The location found for this mouse finished contig
on the human genome, June 2002 (hg12/ncbi30) is
chr20:42974590–42993423.
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Specificity
Measuring the specificity, that is, how much alignments are correct,
is considerably more difficult than measuring the sensitivity. To test
the specificity of our method, we aligned a "random" mouse genome
obtained by reversing (not complementing) the mouse sequences (as
proposed by A. Smit, pers. comm.). Figure 5
presents the ratio of the number of nucleotides on each human
chromosome covered by alignments of the random mouse sequence and the
number of nucleotides covered by the real one for each chromosome.
Alignments were filtered out at different thresholds. For most of the
chromosomes, this ratio is below 0.0005, meaning that less than 0.05%
of the mouse versus human alignments can be accounted for by random
sequence alignments even at low thresholds. This number is higher for
certain chromosomes, especially short ones, partly because of numerical
instability caused by the very small amount of alignment obtained on
these chromosomes.
Another test to estimate specificity is to measure the total coverage
of the human chromosome 20 by alignments of sequences from all mouse
chromosomes with the exception of chromosome 2. The human chromosome 20
being entirely syntenic with the mouse chromosome 2, we should expect
to have, for example, only a few percentage of nonsyntenic coverage
coming from pseudogenes. We found a coverage of only 5.6% for exons,
with the tight filter, and 0.43% for upstream 100, with the medium
filter (Table 3). It is interesting to note
that most of these are covered more than once.
An interesting example is the case of the Apolipoprotein(a) region. The
expressed gene is confined to a subset of primates, as most mammals
lack apo(a). Only hedgehogs produce an apo(a)-like protein (Lawn et al.
1997). Figure 6 shows the coverage in this
region by the mouse sequence utilizing two methods: BLASTZ (Schwartz et
al. 2003) and the method presented here. Our method is the
only one to predict that apo(a) has no homology in the mouse, as has
been shown experimentally. This example is interesting because it
uniquely demonstrates the importance of specificity.
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Figure 6. Apolipoprotein(a) region. The expressed gene is confined to a subset of
primates, as most mammals lack apo(a). Shown are the coverage in this
region by the mouse sequence utilizing Blastz and that obtained by the
method presented here. Our method is the only one to predict that
apo(a) has no homology in the mouse, as has been shown
experimentally.
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We set up a database of conserved elements obtained by three different
groups using different methods of genome alignment (local and global)
and the same conservation cutoff (available at
http://pipeline.lbl.gov/cgi-bin/cnc). The most interesting result of
comparing the three different sets of conserved non-coding sequences is
that the sets overlap by no more than 80%. This suggests that a
combination of strategies and methods could lead to a better overall
whole-genome alignment; this is similar to the situation that has been
observed in gene finding (Rogic et al. 2002). An analysis of
conservation was performed, and every conserved region was classified
as coding, noncoding, intronic, repetitive element, or UTR based on
annotations associated with the human genome assembly. The alignments
of the human and mouse sequences have revealed a significant number of
conserved coding and non-coding elements. In addition to deciphering
the coding component of the genome, the discovery of conserved
non-coding sequences (CNCs) for their potential role in gene regulation
is of particular interest. The identification of all such regions is
complicated by the high level of conservation between as yet
un-annotated coding regions (which can be viewed as non-coding false
positives) and the variation in underlying mutation rates throughout
the genome. As in the mouse genome paper (Waterston et al. 2002), we
believe the annotation of the genome is not missing vast numbers of
genes, which suggests that most of the CNC bases identified do not code
for proteins.
Conserved sequences for the whole genome were calculated by identifying
all regions at least 100-bp long conserved at greater than 70%
identity. In many cases, this scheme has allowed for retrieving
important regulatory sequences (Henkel et al. 1992; Loots et al. 2000).
Alternatively, more sophisticated methods for retrieving
"significant" conserved non-coding regions can be selected by the
user, such as regions identified by scoring with evolutionary
model-based matrices (Waterston et al. 2002).
Implementation
Database and Software
The pipeline was built on a MySQL database platform selected for its
compatibility with major sources of annotation data such as Ensembl
(Hubbard et al. 2002) and the UCSC Genome Browser
(http://genome.ucsc.edu; Kent et al. 2002). The tables contain all the
input sequences (format, draft, or finished), and all the data
generated by the pipeline, repeat regions, anchors, alignments, and
regions of high conservation (both coding and noncoding). The pipeline
software consists of a combination of Perl, C, and Java programs. It
includes a scheduler that gets control data from the database, builds a
queue of jobs, and dispatches them to the computation nodes of the
cluster for execution, and the main program that processes individual
sequences. A Perl library acts as an interface between the database and
the above programs. The use of a separate library allows the programs
to function independently of the database schema. The library also
improves on the standard Perl MySQL database interface package by
providing auto-reconnect functionality and improved error handling.
One of the main features of the pipeline is its modular design, which
allows us to be relatively independent of the specific choice of
integrated programs; with slight modifications of input and output
scripts, other alignment and visualization tools can substitute for the
ones mentioned above. The code source is available at
http://pipeline.lbl.gov/downloads.shtml.
Performance
The whole alignment of the mouse and the human genomes presented
here took 17 h on a cluster of 16 2.2GHz Pentium IV CPUs (20 CPU d).
For comparison, the Blastz alignment took an order of magnitude longer
time (Waterston et al. 2002; Supp. Mat.). Our generated alignments
represent 7.5GB of data, stored in binary format in a MySQL database,
available for download in AXT format at
http://pipeline.lbl.gov/downloads.shtml.
Data Presentation
Two schemes of data presentation on the whole genome scale are
available to the user: the VISTA Genome Browser and the Text browser,
both synchronized with the pipeline database. They can be accessed at
the gateway site http://pipeline.lbl.gov.
VISTA Genome Browser is a Java applet for interactively visualizing
results of comparative sequence analysis in a VISTA format (Mayor et
al. 2000) on the scale of whole chromosomes. It has a number of
options, such as zoom, extraction of a region to be displayed,
user-defined parameters for conservation level, and options for
selecting sequence elements to study (Fig.
7). VISTA Genome Browser is realized as a
dynamic Web-interface synchronized with the central MySQL.
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Figure 7. Results of an on-line submission of a draft unannotated platypus
sequence to the genome alignment Web server. The gene has been
correctly identified. Note the general lack of conservation in
non-coding regions, except for a few highly conserved islands. The
submission was done directly with the GenBank accession number
(AC130185) and was completed in less than 30 sec.
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The Text browser is the most direct front end to the central MySQL
database. It allows a user to examine detailed information about each
mouse sequence aligned to the selected region on the human genome. For
each aligned region, the exact location of alignments on the human and
mouse genomes, the sequences, alignments, coordinates of conserved
regions, and much other information is easily retrieved.
The pipeline annotation of conserved regions is DAS-compatible
(Distributed Annotation System, Dowell et al. 2001; Fumoto et al. 2002)
and can be viewed through the Ensembl browser at http://www.ensembl.org
(step-by-step instructions for viewing the data are available at
http://pipeline.lbl.gov/das.shtml).
Web-Based Server To Align and Compare User-Submitted Sequences With a Base Genome
As described above, we developed alignment methods for sequences of
different quality and length against the whole-genome assembly. Our
computational system is open for user queries through a Web interface
accessible from http://pipeline.lbl.gov. Comparative analysis can be
done against the base human or mouse genomes. This server accepts
sequences in either finished or draft format. Contigs in draft
sequences are ordered and oriented according to their alignment with
the base genome (Fig. 7). The server also accepts GenBank accession
numbers and connects automatically to GenBank to retrieve the sequence.
The user is provided with detailed results of the comparative analysis,
including the alignments, VISTA pictures, and the ability to
interactively navigate the Vista Genome Browser. A typical query
sequence of up to a few hundred kilobases is processed in seconds.
Based on current usage (5000 requests/month), we determined that the
average query size is 150 kb.
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DISCUSSION
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As we mentioned above, an alignment of the whole human and mouse
genomes represents an algorithmic challenge and yet holds the promise
of significant biological understanding. We expect that the methodology
for aligning the human and mouse genomes will change over time,
eventually leading to a "true" alignment of the genomes which
correctly identifies orthologous relationships between genes and
nucleotides, and in which parologous genes and duplications within
genomes are correctly handled. It seems to us that a dramatic
improvement in the alignment of the human and mouse genomes will be
possible with more genomes available.
Significant initial results from the alignment of the human and mouse
genomes are that coding regions are highly conserved as expected, but
an additional large portion of the genome (roughly as much sequence as
is coding) is highly conserved with unknown function. This conserved
sequence is arguably not coding and cannot all be explained by neutral
evolution (Waterston et al. 2002). It is interesting to note that
comparisons of three species (Dubchak et al. 2000) show that many
human-mouse conserved regions are also present in the dog, suggesting
that they may indeed be functional. Unfortunately, current methods for
predicting transcription factor binding sites and other regulatory
elements are not accurate enough to classify the conserved regions
(Fickett and Wasserman 2000).
Our studies of alignment efficiency with respect to different contig
sizes should be useful for dynamic alignment tools that rapidly align
query sequences to genomes, and for devising strategies for combined
local and global alignment. It is important to note that we
specifically designed our method in such a way that anchor selection is
a stand-alone module, so that different methods can be used without
difficulty. For example, it is possible that other whole-genome local
alignment methods such as PatternHunter (Ma et al. 2002) or BLASTZ
(http://bio.cse.psu.edu/) could also be very effective at selecting
anchors. We plan to test different local and global programs, and novel
methods for combining them to optimize performance and accuracy of our
comparative analysis scheme. Unfortunately, it still remains an open
problem to devise accurate criteria for judging the accuracy of an
alignment. The sensitivity is not that difficult to measure (one can,
for example, check to see how many exons were aligned), but the
specificity (a measure of how much incorrect alignment there is) is
considerably harder to estimate, as we have discussed.
The alignments described here are currently being used by a number of
projects, including a comparative-based annotation of genes in the
human and mouse genomes (Pachter et al. 2002) and a study of the
rearrangement history of the genomes (P. Pevzner, unpubl.). These
projects will in turn lead to better alignments, and eventually, in
conjunction with more genomes, a more complete understanding of genome
evolution.
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WEB SITE REFERENCES
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http://pipeline.lbl.gov; comparative analysis pipeline gateway at
Lawrence Berkeley National laboratory.
http://pipeline.lbl.gov/cgi-bin/cnc; database of conserved sequences
from LBNL, PSU, and UCSC.
http://bio.math.berkeley.edu/avid; Avid Web site.
http://genome.ucsc.edu/; UCSC Web site from which the human genome
assemblies used in this study where downloaded.
http://www.ncbi.nlm.nih.gov/LocusLink/; NCBI Locus Link.
http://pipeline.lbl.gov/downloads.shtml; Download page for whole-
genome alignments and other materials.
http://www.ensembl.org; The ENSEMBL project Web site.
http://pipeline.lbl.gov/das.shtnl; Alignment DAS server.
http://bio.cse.psu.edu; BLASTZ.
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Acknowledgements
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We thank the Mouse Genome Sequencing Consortium for the opportunity
to work with the mouse genome during the sequencing phases and in the
subsequent analysis phase. The analysis group, comprising many
individuals and teams from around the world, was particularly helpful
not only in providing crucial suggestions and advice as the project
unfolded, but also in contributing many independent ideas. Special
thanks go to Jim Kent, who coordinated the alignment efforts of the
mouse sequencing consortium analysis group and designed the filtering
methods for calculating alignment coverage. Thanks also to the Penn
State Group (Laura Elnitsky, Ross Hardison, Webb Miller, Scott
Schwartz, and others) and the PatternHunter Group (Ming Li, Mike Zody,
and others), who developed different alignment strategies which we
compared. We thank Ivan Ovcharenko for initiating the project and
developing the prototype. We also thank Serafim Batzoglou for his help
with generating simulated reads and assemblies for our test sets. The
project was partially supported by a Program for Genomic Applications
grant from the National Heart Lung and Blood Institute. This work was
supported by the Director, Office of Science, of the U.S. Department of
Energy under Contract No. DE-AC03-76SF00098.
The publication costs of this article were defrayed in part by payment
of page charges. This article must therefore be hereby marked
"advertisement" in accordance with 18 USC section 1734 solely to
indicate this fact.
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Footnotes
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3 Corresponding author. 
E-MAIL ocouronne{at}lbl.gov; FAX (510) 486-5717.
E-MAIL
lpachter{at}math.berkeley.edu.
Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.762503.
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Received September 4, 2002;
accepted in revised format November 6, 2002.
13:73-80 © by 2003 Cold Spring Harbor Laboratory Press ISSN 1088-9051/03 $5.00
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