scholarly journals Contextual Multiple Sequence Alignment

2005 ◽  
Vol 2005 (2) ◽  
pp. 124-131 ◽  
Author(s):  
Anna Gambin ◽  
Rafał Otto
Keyword(s):  
Pairwise Alignment ◽  
Multiple Alignment ◽  
Biological Data ◽  
Context Dependency ◽  
Alignment Algorithm ◽  
Orphan Genes ◽  
Multiple Sequence ◽  
New Family ◽  
Substitution Matrices ◽  
Global And Local

In a recently proposed contextual alignment model, efficient algorithms exist for global and local pairwise alignment of protein sequences. Preliminary results obtained for biological data are very promising. Our main motivation was to adopt the idea of context dependency to the multiple-alignment setting. To this aim the relaxation of the model was developed (we call this new modelaveraged contextual alignment) and a new family of amino acids substitution matrices are constructed. In this paper we present a contextual multiple-alignment algorithm and report the outcomes of experiments performed for the BAliBASE test set. The contextual approach turned out to give much better results for the set of sequences containing orphan genes.

Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques

2020 ◽  
pp. 565-579 ◽  
Author(s):  
Mohamed Issa ◽  
Aboul Ella Hassanien
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Trees ◽  
Pairwise Alignment ◽  
Accurate Method ◽  
Alignment Algorithm ◽  
Bacterial Foraging Optimization ◽  
Multiple Sequence ◽  
Speed Up ◽  
Dna Fragment Assembly

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques

2017 ◽  
pp. 409-423 ◽  
Author(s):  
Mohamed Issa ◽  
Aboul Ella Hassanien
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Trees ◽  
Pairwise Alignment ◽  
Accurate Method ◽  
Alignment Algorithm ◽  
Bacterial Foraging Optimization ◽  
Multiple Sequence ◽  
Speed Up ◽  
Dna Fragment Assembly

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

scholarly journals End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman

2021 ◽  
Author(s):  
Samantha Petti ◽  
Nicholas Bhattacharya ◽  
Roshan Rao ◽  
Justas Dauparas ◽  
Neil Thomas ◽  
...  
Keyword(s):  
Random Field ◽  
Structure Prediction ◽  
Pairwise Alignment ◽  
Learning System ◽  
Alignment Algorithm ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Multiple Sequence Alignments ◽  
End To End

Multiple Sequence Alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for. Here, we implement a smooth and differentiable version of the Smith-Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF mildly improves contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold2 and maximizing the predicted confidence metric, we can learn MSAs that improve structure predictions over the initial MSAs. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment.

MULTIPLE ALIGNMENT OF PROMOTER SEQUENCES FROM HUMAN GENOM

2020 ◽  
pp. 256-257
Keyword(s):  
Human Genome ◽  
Multiple Alignment ◽  
Alignment Algorithm ◽  
Promoter Sequences ◽  
Multiple Alignments

A new multiple alignment algorithm has been developed. With its help, the alignment of promoter sequences from the human genome is calculated. Based on the calculated multiple alignments, 17 classes of promoter sequences were created.

scholarly journals Plant Community Assembly in Invaded Recipient Californian Grasslands and Putative Donor Grasslands in Spain

Diversity ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 193
Author(s):  
Javier Galán Díaz ◽  
Enrique de la Riva ◽  
Ingrid Parker ◽  
María Leiva ◽  
Rubén Bernardo-Madrid ◽  
...  
Keyword(s):  
Community Structure ◽  
Exotic Species ◽  
Community Assembly ◽  
Model System ◽  
Context Dependency ◽  
Novel Ecosystems ◽  
Pest Species ◽  
Mediterranean Regions ◽  
Global And Local ◽  
Plant Community Assembly

The introduction of exotic species to new regions offers opportunities to test fundamental questions in ecology, such as the context-dependency of community structure and assembly. Annual grasslands provide a model system of a major unidirectional introduction of plant species from Europe to North America. We compared the community structure of grasslands in two Mediterranean regions by surveying plots in Spain and in California with similar environmental and management conditions. All species found in Spanish grasslands were native to Spain, and over half of them (74 of 139 species) are known to have colonized California. In contrast, in California, over half of the species (52 of 95 species) were exotic species, all of them native to Spain. Nineteen species were found in multiple plots in both regions (i.e., shared species). The abundance of shared species in California was either similar to (13 species) or greater than (6 species) in Spain. In California, plants considered pests were more likely than non-pest species to have higher abundance. Co-occurring shared species tended to maintain their relative abundance in native and introduced communities, which indicates that pools of exotic species might assemble similarly at home and away. These findings provide interesting insights into community assembly in novel ecosystems. They also highlight an example of startling global and local floristic homogenization.

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