24,296 research outputs found
A methodology for determining amino-acid substitution matrices from set covers
We introduce a new methodology for the determination of amino-acid
substitution matrices for use in the alignment of proteins. The new methodology
is based on a pre-existing set cover on the set of residues and on the
undirected graph that describes residue exchangeability given the set cover.
For fixed functional forms indicating how to obtain edge weights from the set
cover and, after that, substitution-matrix elements from weighted distances on
the graph, the resulting substitution matrix can be checked for performance
against some known set of reference alignments and for given gap costs. Finding
the appropriate functional forms and gap costs can then be formulated as an
optimization problem that seeks to maximize the performance of the substitution
matrix on the reference alignment set. We give computational results on the
BAliBASE suite using a genetic algorithm for optimization. Our results indicate
that it is possible to obtain substitution matrices whose performance is either
comparable to or surpasses that of several others, depending on the particular
scenario under consideration
Aligning Multiple Sequences with Genetic Algorithm
The alignment of biological sequences is a crucial
tool in molecular biology and genome analysis. It helps to build
a phylogenetic tree of related DNA sequences and also to predict
the function and structure of unknown protein sequences by
aligning with other sequences whose function and structure is
already known. However, finding an optimal multiple sequence
alignment takes time and space exponential with the length or
number of sequences increases. Genetic Algorithms (GAs) are
strategies of random searching that optimize an objective
function which is a measure of alignment quality (distance) and
has the ability for exploratory search through the solution space
and exploitation of current results
Higher accuracy protein Multiple Sequence Alignment by Stochastic Algorithm
Multiple Sequence Alignment gives insight into evolutionary, structural and functional relationships among the proteins. Here, a novel Protein Alignment by Stochastic Algorithm (PASA) is developed. Evolutionary operators of a genetic algorithm, namely, mutation and selection are utilized in combining the output of two most important sequence alignment programs and then developing an optimized new algorithm. Efficiency of protein alignments is evaluated in terms of Total Column score which is equal to the number of correctly aligned columns between a test alignment and the reference alignment divided by the total number of columns in the reference alignment. The PASA optimizer achieves, on an average, significant better alignment over the well known individual bioinformatics tools. This PASA is statistically the most accurate protein alignment method today. It can have potential applications in drug discovery processes in the biotechnology industry
Scaling Laws and Similarity Detection in Sequence Alignment with Gaps
We study the problem of similarity detection by sequence alignment with gaps,
using a recently established theoretical framework based on the morphology of
alignment paths. Alignments of sequences without mutual correlations are found
to have scale-invariant statistics. This is the basis for a scaling theory of
alignments of correlated sequences. Using a simple Markov model of evolution,
we generate sequences with well-defined mutual correlations and quantify the
fidelity of an alignment in an unambiguous way. The scaling theory predicts the
dependence of the fidelity on the alignment parameters and on the statistical
evolution parameters characterizing the sequence correlations. Specific
criteria for the optimal choice of alignment parameters emerge from this
theory. The results are verified by extensive numerical simulations.Comment: 25 pages, 11 figure
Characterization of chromatin accessibility with a transposome hypersensitive sites sequencing (THS-seq) assay.
Chromatin accessibility captures in vivo protein-chromosome binding status, and is considered an informative proxy for protein-DNA interactions. DNase I and Tn5 transposase assays require thousands to millions of fresh cells for comprehensive chromatin mapping. Applying Tn5 tagmentation to hundreds of cells results in sparse chromatin maps. We present a transposome hypersensitive sites sequencing assay for highly sensitive characterization of chromatin accessibility. Linear amplification of accessible DNA ends with in vitro transcription, coupled with an engineered Tn5 super-mutant, demonstrates improved sensitivity on limited input materials, and accessibility of small regions near distal enhancers, compared with ATAC-seq
Parametric Inference for Biological Sequence Analysis
One of the major successes in computational biology has been the unification,
using the graphical model formalism, of a multitude of algorithms for
annotating and comparing biological sequences. Graphical models that have been
applied towards these problems include hidden Markov models for annotation,
tree models for phylogenetics, and pair hidden Markov models for alignment. A
single algorithm, the sum-product algorithm, solves many of the inference
problems associated with different statistical models. This paper introduces
the \emph{polytope propagation algorithm} for computing the Newton polytope of
an observation from a graphical model. This algorithm is a geometric version of
the sum-product algorithm and is used to analyze the parametric behavior of
maximum a posteriori inference calculations for graphical models.Comment: 15 pages, 4 figures. See also companion paper "Tropical Geometry of
Statistical Models" (q-bio.QM/0311009
Towards Reliable Automatic Protein Structure Alignment
A variety of methods have been proposed for structure similarity calculation,
which are called structure alignment or superposition. One major shortcoming in
current structure alignment algorithms is in their inherent design, which is
based on local structure similarity. In this work, we propose a method to
incorporate global information in obtaining optimal alignments and
superpositions. Our method, when applied to optimizing the TM-score and the GDT
score, produces significantly better results than current state-of-the-art
protein structure alignment tools. Specifically, if the highest TM-score found
by TMalign is lower than (0.6) and the highest TM-score found by one of the
tested methods is higher than (0.5), there is a probability of (42%) that
TMalign failed to find TM-scores higher than (0.5), while the same probability
is reduced to (2%) if our method is used. This could significantly improve the
accuracy of fold detection if the cutoff TM-score of (0.5) is used.
In addition, existing structure alignment algorithms focus on structure
similarity alone and simply ignore other important similarities, such as
sequence similarity. Our approach has the capacity to incorporate multiple
similarities into the scoring function. Results show that sequence similarity
aids in finding high quality protein structure alignments that are more
consistent with eye-examined alignments in HOMSTRAD. Even when structure
similarity itself fails to find alignments with any consistency with
eye-examined alignments, our method remains capable of finding alignments
highly similar to, or even identical to, eye-examined alignments.Comment: Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013
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