27 research outputs found
MAVID multiple alignment server
MAVID is a multiple alignment program suitable for many large genomic regions. The MAVID web server allows biomedical researchers to quickly obtain multiple alignments for genomic sequences and to subsequently analyse the alignments for conserved regions. MAVID has been successfully used for the alignment of closely related species such as primates and also for the alignment of more distant organisms such as human and fugu. The server is fast, capable of aligning hundreds of kilobases in less than a minute. The multiple alignment is used to build a phylogenetic tree for the sequences, which is subsequently used as a basis for identifying conserved regions in the alignment. The server can be accessed at http://baboon.math.berkeley.edu/mavid/
MAVID: Constrained ancestral alignment of multiple sequences
We describe a new global multiple alignment program capable of aligning a
large number of genomic regions. Our progressive alignment approach
incorporates the following ideas: maximum-likelihood inference of ancestral
sequences, automatic guide-tree construction, protein based anchoring of
ab-initio gene predictions, and constraints derived from a global homology map
of the sequences. We have implemented these ideas in the MAVID program, which
is able to accurately align multiple genomic regions up to megabases long.
MAVID is able to effectively align divergent sequences, as well as incomplete
unfinished sequences. We demonstrate the capabilities of the program on the
benchmark CFTR region which consists of 1.8Mb of human sequence and 20
orthologous regions in marsupials, birds, fish, and mammals. Finally, we
describe two large MAVID alignments: an alignment of all the available HIV
genomes and a multiple alignment of the entire human, mouse and rat genomes
The ENCODE Project at UC Santa Cruz
The goal of the Encyclopedia Of DNA Elements (ENCODE) Project is to identify all functional elements in the human genome. The pilot phase is for comparison of existing methods and for the development of new methods to rigorously analyze a defined 1% of the human genome sequence. Experimental datasets are focused on the origin of replication, DNase I hypersensitivity, chromatin immunoprecipitation, promoter function, gene structure, pseudogenes, non-protein-coding RNAs, transcribed RNAs, multiple sequence alignment and evolutionarily constrained elements. The ENCODE project at UCSC website () is the primary portal for the sequence-based data produced as part of the ENCODE project. In the pilot phase of the project, over 30 labs provided experimental results for a total of 56 browser tracks supported by 385 database tables. The site provides researchers with a number of tools that allow them to visualize and analyze the data as well as download data for local analyses. This paper describes the portal to the data, highlights the data that has been made available, and presents the tools that have been developed within the ENCODE project. Access to the data and types of interactive analysis that are possible are illustrated through supplemental examples
Parametric Alignment of Drosophila Genomes
The classic algorithms of Needleman--Wunsch and Smith--Waterman find a
maximum a posteriori probability alignment for a pair hidden Markov model
(PHMM). In order to process large genomes that have undergone complex genome
rearrangements, almost all existing whole genome alignment methods apply fast
heuristics to divide genomes into small pieces which are suitable for
Needleman--Wunsch alignment. In these alignment methods, it is standard
practice to fix the parameters and to produce a single alignment for subsequent
analysis by biologists.
Our main result is the construction of a whole genome parametric alignment of
Drosophila melanogaster and Drosophila pseudoobscura. Parametric alignment
resolves the issue of robustness to changes in parameters by finding all
optimal alignments for all possible parameters in a PHMM. Our alignment draws
on existing heuristics for dividing whole genomes into small pieces for
alignment, and it relies on advances we have made in computing convex polytopes
that allow us to parametrically align non-coding regions using biologically
realistic models. We demonstrate the utility of our parametric alignment for
biological inference by showing that cis-regulatory elements are more conserved
between Drosophila melanogaster and Drosophila pseudoobscura than previously
thought. We also show how whole genome parametric alignment can be used to
quantitatively assess the dependence of branch length estimates on alignment
parameters.
The alignment polytopes, software, and supplementary material can be
downloaded at http://bio.math.berkeley.edu/parametric/.Comment: 19 pages, 3 figure
A novel approach to identifying regulatory motifs in distantly related genomes
Although proven successful in the identification of regulatory motifs, phylogenetic footprinting methods still show some shortcomings. To assess these difficulties, most apparent when applying phylogenetic footprinting to distantly related organisms, we developed a two-step procedure that combines the advantages of sequence alignment and motif detection approaches. The results on well-studied benchmark datasets indicate that the presented method outperforms other methods when the sequences become either too long or too heterogeneous in size
Sequence alignment, mutual information, and dissimilarity measures for constructing phylogenies
Existing sequence alignment algorithms use heuristic scoring schemes which
cannot be used as objective distance metrics. Therefore one relies on measures
like the p- or log-det distances, or makes explicit, and often simplistic,
assumptions about sequence evolution. Information theory provides an
alternative, in the form of mutual information (MI) which is, in principle, an
objective and model independent similarity measure. MI can be estimated by
concatenating and zipping sequences, yielding thereby the "normalized
compression distance". So far this has produced promising results, but with
uncontrolled errors. We describe a simple approach to get robust estimates of
MI from global pairwise alignments. Using standard alignment algorithms, this
gives for animal mitochondrial DNA estimates that are strikingly close to
estimates obtained from the alignment free methods mentioned above. Our main
result uses algorithmic (Kolmogorov) information theory, but we show that
similar results can also be obtained from Shannon theory. Due to the fact that
it is not additive, normalized compression distance is not an optimal metric
for phylogenetics, but we propose a simple modification that overcomes the
issue of additivity. We test several versions of our MI based distance measures
on a large number of randomly chosen quartets and demonstrate that they all
perform better than traditional measures like the Kimura or log-det (resp.
paralinear) distances. Even a simplified version based on single letter Shannon
entropies, which can be easily incorporated in existing software packages, gave
superior results throughout the entire animal kingdom. But we see the main
virtue of our approach in a more general way. For example, it can also help to
judge the relative merits of different alignment algorithms, by estimating the
significance of specific alignments.Comment: 19 pages + 16 pages of supplementary materia
Functional Evolution of a cis-Regulatory Module
Lack of knowledge about how regulatory regions evolve in relation to their structure–function may limit the utility of comparative sequence analysis in deciphering cis-regulatory sequences. To address this we applied reverse genetics to carry out a functional genetic complementation analysis of a eukaryotic cis-regulatory module—the even-skipped stripe 2 enhancer—from four Drosophila species. The evolution of this enhancer is non-clock-like, with important functional differences between closely related species and functional convergence between distantly related species. Functional divergence is attributable to differences in activation levels rather than spatiotemporal control of gene expression. Our findings have implications for understanding enhancer structure–function, mechanisms of speciation and computational identification of regulatory modules