118,784 research outputs found
Statistical method of context evaluation for biological sequence similarity
Within this paper we are proposing and testing a new strategy for detection and measurement of similarity between sequences of proteins. Our approach has its roots in computational linguistics and the related techniques for quantifying and comparing content in strings of characters. The pairwise comparison of proteins relies on the content regularities expected to uniquely characterize each sequence. These regularities are captured by n-gram based modelling techniques and exploited by cross-entropy related measures. In this new attempt to incorporate theoretical ideas from computational linguistics into the field of bioinformatics, we experimented using two implementations having always as ultimate goal the development of practical, computationally efficient algorithms for expressing protein similarity. The experimental analysis reported herein provides evidence for the usefulness of the proposed approach and motivates the further development of linguistics-related tools as a means of analysing biological sequences.IFIP International Conference on Artificial Intelligence in Theory and Practice - Integration of AI with other TechnologiesRed de Universidades con Carreras en Informática (RedUNCI
Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance
We introduce a new measure of distance between languages based on word
embedding, called word embedding language divergence (WELD). WELD is defined as
divergence between unified similarity distribution of words between languages.
Using such a measure, we perform language comparison for fifty natural
languages and twelve genetic languages. Our natural language dataset is a
collection of sentence-aligned parallel corpora from bible translations for
fifty languages spanning a variety of language families. Although we use
parallel corpora, which guarantees having the same content in all languages,
interestingly in many cases languages within the same family cluster together.
In addition to natural languages, we perform language comparison for the coding
regions in the genomes of 12 different organisms (4 plants, 6 animals, and two
human subjects). Our result confirms a significant high-level difference in the
genetic language model of humans/animals versus plants. The proposed method is
a step toward defining a quantitative measure of similarity between languages,
with applications in languages classification, genre identification, dialect
identification, and evaluation of translations
Bootstrapping Lexical Choice via Multiple-Sequence Alignment
An important component of any generation system is the mapping dictionary, a
lexicon of elementary semantic expressions and corresponding natural language
realizations. Typically, labor-intensive knowledge-based methods are used to
construct the dictionary. We instead propose to acquire it automatically via a
novel multiple-pass algorithm employing multiple-sequence alignment, a
technique commonly used in bioinformatics. Crucially, our method leverages
latent information contained in multi-parallel corpora -- datasets that supply
several verbalizations of the corresponding semantics rather than just one.
We used our techniques to generate natural language versions of
computer-generated mathematical proofs, with good results on both a
per-component and overall-output basis. For example, in evaluations involving a
dozen human judges, our system produced output whose readability and
faithfulness to the semantic input rivaled that of a traditional generation
system.Comment: 8 pages; to appear in the proceedings of EMNLP-200
Who Watches the Watchmen? An Appraisal of Benchmarks for Multiple Sequence Alignment
Multiple sequence alignment (MSA) is a fundamental and ubiquitous technique
in bioinformatics used to infer related residues among biological sequences.
Thus alignment accuracy is crucial to a vast range of analyses, often in ways
difficult to assess in those analyses. To compare the performance of different
aligners and help detect systematic errors in alignments, a number of
benchmarking strategies have been pursued. Here we present an overview of the
main strategies--based on simulation, consistency, protein structure, and
phylogeny--and discuss their different advantages and associated risks. We
outline a set of desirable characteristics for effective benchmarking, and
evaluate each strategy in light of them. We conclude that there is currently no
universally applicable means of benchmarking MSA, and that developers and users
of alignment tools should base their choice of benchmark depending on the
context of application--with a keen awareness of the assumptions underlying
each benchmarking strategy.Comment: Revie
Fast search of sequences with complex symbol correlations using profile context-sensitive HMMS and pre-screening filters
Recently, profile context-sensitive HMMs (profile-csHMMs) have been proposed which are very effective in modeling the common patterns and motifs in related symbol sequences. Profile-csHMMs are capable of representing long-range correlations between distant symbols, even when these correlations are entangled in a complicated
manner. This makes profile-csHMMs an useful tool in computational biology, especially in modeling noncoding RNAs (ncRNAs) and finding new ncRNA genes. However, a profile-csHMM based search is quite slow, hence not practical for searching a large database. In this paper, we propose a practical scheme for making the search speed significantly faster without any degradation in the
prediction accuracy. The proposed method utilizes a pre-screening filter based on a profile-HMM, which filters out most sequences that will not be predicted as a match by the original profile-csHMM. Experimental results show that the proposed approach can make the search speed eighty times faster
Pair HMM based gap statistics for re-evaluation of indels in alignments with affine gap penalties: Extended Version
Although computationally aligning sequence is a crucial step in the vast
majority of comparative genomics studies our understanding of alignment biases
still needs to be improved. To infer true structural or homologous regions
computational alignments need further evaluation. It has been shown that the
accuracy of aligned positions can drop substantially in particular around gaps.
Here we focus on re-evaluation of score-based alignments with affine gap
penalty costs. We exploit their relationships with pair hidden Markov models
and develop efficient algorithms by which to identify gaps which are
significant in terms of length and multiplicity. We evaluate our statistics
with respect to the well-established structural alignments from SABmark and
find that indel reliability substantially increases with their significance in
particular in worst-case twilight zone alignments. This points out that our
statistics can reliably complement other methods which mostly focus on the
reliability of match positions.Comment: 17 pages, 7 figure
Pairwise alignment incorporating dipeptide covariation
Motivation: Standard algorithms for pairwise protein sequence alignment make
the simplifying assumption that amino acid substitutions at neighboring sites
are uncorrelated. This assumption allows implementation of fast algorithms for
pairwise sequence alignment, but it ignores information that could conceivably
increase the power of remote homolog detection. We examine the validity of this
assumption by constructing extended substitution matrixes that encapsulate the
observed correlations between neighboring sites, by developing an efficient and
rigorous algorithm for pairwise protein sequence alignment that incorporates
these local substitution correlations, and by assessing the ability of this
algorithm to detect remote homologies. Results: Our analysis indicates that
local correlations between substitutions are not strong on the average.
Furthermore, incorporating local substitution correlations into pairwise
alignment did not lead to a statistically significant improvement in remote
homology detection. Therefore, the standard assumption that individual residues
within protein sequences evolve independently of neighboring positions appears
to be an efficient and appropriate approximation
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