Dataset Information

The GeneOptimizer Algorithm: using a sliding window approach to cope with the vast sequence space in multiparameter DNA sequence optimization.

ABSTRACT: One of the main advantages of de novo gene synthesis is the fact that it frees the researcher from any limitations imposed by the use of natural templates. To make the most out of this opportunity, efficient algorithms are needed to calculate a coding sequence, combining different requirements, such as adapted codon usage or avoidance of restriction sites, in the best possible way. We present an algorithm where a "variation window" covering several amino acid positions slides along the coding sequence. Candidate sequences are built comprising the already optimized part of the complete sequence and all possible combinations of synonymous codons representing the amino acids within the window. The candidate sequences are assessed with a quality function, and the first codon of the best candidates' variation window is fixed. Subsequently the window is shifted by one codon position. As an example of a freely accessible software implementing the algorithm, we present the Mr. Gene web-application. Additionally two experimental applications of the algorithm are shown.

SUBMITTER: Raab D

PROVIDER: S-EPMC2955205 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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The GeneOptimizer Algorithm: using a sliding window approach to cope with the vast sequence space in multiparameter DNA sequence optimization.

Raab David D Graf Marcus M Notka Frank F Schödl Thomas T Wagner Ralf R

Systems and synthetic biology 20100901 3

One of the main advantages of de novo gene synthesis is the fact that it frees the researcher from any limitations imposed by the use of natural templates. To make the most out of this opportunity, efficient algorithms are needed to calculate a coding sequence, combining different requirements, such as adapted codon usage or avoidance of restriction sites, in the best possible way. We present an algorithm where a "variation window" covering several amino acid positions slides along the coding se ...[more]

PMID: 21189842

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Project description:The development of video-based monitoring methods allows for rapid, dynamic and accurate monitoring of individuals or communities, compared to slower traditional methods, with far reaching ecological and evolutionary applications. Large amounts of data are generated using video-based methods, which can be effectively processed using machine learning (ML) algorithms into meaningful ecological information. ML uses user defined classes (e.g. species), derived from a subset (i.e. training data) of video-observed quantitative features (e.g. phenotypic variation), to infer classes in subsequent observations. However, phenotypic variation often changes due to environmental conditions, which may lead to poor classification, if environmentally induced variation in phenotypes is not accounted for. Here we describe a framework for classifying species under changing environmental conditions based on the random forest classification. A sliding window approach was developed that restricts temporal and environmentally conditions to improve the classification. We tested our approach by applying the classification framework to experimental data. The experiment used a set of six ciliate species to monitor changes in community structure and behavior over hundreds of generations, in dozens of species combinations and across a temperature gradient. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, as phenotypic differences driven by environmental change, could be captured by the classifier. Importantly, classification using the random forest algorithm showed comparable success when validated against traditional, slower, manual identification. Our framework allows for reliable classification in dynamic environments, and may help to improve strategies for long-term monitoring of species in changing environments. Our classification pipeline can be applied in fields assessing species community dynamics, such as eco-toxicology, ecology and evolutionary ecology.

Dataset Information

The GeneOptimizer Algorithm: using a sliding window approach to cope with the vast sequence space in multiparameter DNA sequence optimization.

Publications

The GeneOptimizer Algorithm: using a sliding window approach to cope with the vast sequence space in multiparameter DNA sequence optimization.

Similar Datasets

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