24,192 research outputs found
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
Edit Distance: Sketching, Streaming and Document Exchange
We show that in the document exchange problem, where Alice holds and Bob holds , Alice can send Bob a message of
size bits such that Bob can recover using the
message and his input if the edit distance between and is no more
than , and output "error" otherwise. Both the encoding and decoding can be
done in time . This result significantly
improves the previous communication bounds under polynomial encoding/decoding
time. We also show that in the referee model, where Alice and Bob hold and
respectively, they can compute sketches of and of sizes
bits (the encoding), and send to the referee, who can
then compute the edit distance between and together with all the edit
operations if the edit distance is no more than , and output "error"
otherwise (the decoding). To the best of our knowledge, this is the first
result for sketching edit distance using bits.
Moreover, the encoding phase of our sketching algorithm can be performed by
scanning the input string in one pass. Thus our sketching algorithm also
implies the first streaming algorithm for computing edit distance and all the
edits exactly using bits of space.Comment: Full version of an article to be presented at the 57th Annual IEEE
Symposium on Foundations of Computer Science (FOCS 2016
Suffix Tree of Alignment: An Efficient Index for Similar Data
We consider an index data structure for similar strings. The generalized
suffix tree can be a solution for this. The generalized suffix tree of two
strings and is a compacted trie representing all suffixes in and
. It has leaves and can be constructed in time.
However, if the two strings are similar, the generalized suffix tree is not
efficient because it does not exploit the similarity which is usually
represented as an alignment of and .
In this paper we propose a space/time-efficient suffix tree of alignment
which wisely exploits the similarity in an alignment. Our suffix tree for an
alignment of and has leaves where is the sum of
the lengths of all parts of different from and is the sum of the
lengths of some common parts of and . We did not compromise the pattern
search to reduce the space. Our suffix tree can be searched for a pattern
in time where is the number of occurrences of in and
. We also present an efficient algorithm to construct the suffix tree of
alignment. When the suffix tree is constructed from scratch, the algorithm
requires time where is the sum of the lengths
of other common substrings of and . When the suffix tree of is
already given, it requires time.Comment: 12 page
Accelerating exhaustive pairwise metagenomic comparisons
In this manuscript, we present an optimized and parallel version of our previous work IMSAME, an exhaustive gapped aligner for the pairwise and accurate comparison of metagenomes. Parallelization strategies are applied to take advantage of modern multiprocessor architectures. In addition, sequential optimizations in CPU time and memory consumption are provided. These algorithmic and computational enhancements enable IMSAME to calculate near optimal alignments which are used to directly assess similarity between metagenomes without requiring reference databases. We show that the overall efficiency of the parallel implementation is superior to 80% while retaining scalability as the number of parallel cores used increases. Moreover, we also show thats equential optimizations yield up to 8x speedup for scenarios with larger data.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec
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
Sparse Long Blocks and the Micro-Structure of the Longest Common Subsequences
Consider two random strings having the same length and generated by an iid
sequence taking its values uniformly in a fixed finite alphabet. Artificially
place a long constant block into one of the strings, where a constant block is
a contiguous substring consisting only of one type of symbol. The long block
replaces a segment of equal size and its length is smaller than the length of
the strings, but larger than its square-root. We show that for sufficiently
long strings the optimal alignment corresponding to a Longest Common
Subsequence (LCS) treats the inserted block very differently depending on the
size of the alphabet. For two-letter alphabets, the long constant block gets
mainly aligned with the same symbol from the other string, while for three or
more letters the opposite is true and the block gets mainly aligned with gaps.
We further provide simulation results on the proportion of gaps in blocks of
various lengths. In our simulations, the blocks are "regular blocks" in an iid
sequence, and are not artificially inserted. Nonetheless, we observe for these
natural blocks a phenomenon similar to the one shown in case of
artificially-inserted blocks: with two letters, the long blocks get aligned
with a smaller proportion of gaps; for three or more letters, the opposite is
true.
It thus appears that the microscopic nature of two-letter optimal alignments
and three-letter optimal alignments are entirely different from each other.Comment: To appear: Journal of Statistical Physic
JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition
This paper proposes a novel algorithm to reassemble an arbitrarily shredded
image to its original status. Existing reassembly pipelines commonly consist of
a local matching stage and a global compositions stage. In the local stage, a
key challenge in fragment reassembly is to reliably compute and identify
correct pairwise matching, for which most existing algorithms use handcrafted
features, and hence, cannot reliably handle complicated puzzles. We build a
deep convolutional neural network to detect the compatibility of a pairwise
stitching, and use it to prune computed pairwise matches. To improve the
network efficiency and accuracy, we transfer the calculation of CNN to the
stitching region and apply a boost training strategy. In the global composition
stage, we modify the commonly adopted greedy edge selection strategies to two
new loop closure based searching algorithms. Extensive experiments show that
our algorithm significantly outperforms existing methods on solving various
puzzles, especially those challenging ones with many fragment pieces
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