Dataset Information

An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.

ABSTRACT: Antifreeze proteins (AFPs) play a pivotal role in the antifreeze effect of overwintering organisms. They have a wide range of applications in numerous fields, such as improving the production of crops and the quality of frozen foods. Accurate identification of AFPs may provide important clues to decipher the underlying mechanisms of AFPs in ice-binding and to facilitate the selection of the most appropriate AFPs for several applications. Based on an ensemble learning technique, this study proposes an AFP identification system called AFP-Ensemble. In this system, random forest classifiers are trained by different training subsets and then aggregated into a consensus classifier by majority voting. The resulting predictor yields a sensitivity of 0.892, a specificity of 0.940, an accuracy of 0.938 and a balanced accuracy of 0.916 on an independent dataset, which are far better than the results obtained by previous methods. These results reveal that AFP-Ensemble is an effective and promising predictor for large-scale determination of AFPs. The detailed feature analysis in this study may give useful insights into the molecular mechanisms of AFP-ice interactions and provide guidance for the related experimental validation. A web server has been designed to implement the proposed method.

SUBMITTER: Yang R

PROVIDER: S-EPMC4613249 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.

Yang Runtao R Zhang Chengjin C Gao Rui R Zhang Lina L

International journal of molecular sciences 20150907 9

Antifreeze proteins (AFPs) play a pivotal role in the antifreeze effect of overwintering organisms. They have a wide range of applications in numerous fields, such as improving the production of crops and the quality of frozen foods. Accurate identification of AFPs may provide important clues to decipher the underlying mechanisms of AFPs in ice-binding and to facilitate the selection of the most appropriate AFPs for several applications. Based on an ensemble learning technique, this study propos ...[more]

PMID: 26370959

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Project description:BACKGROUND: Highly repetitive sequences are the bane of genome sequence assembly, and the short read lengths produced by current next generation sequencing technologies further exacerbates this obstacle. An adopted practice is to exclude repetitive sequences in genome data assembly, as the majority of repeats lack protein-coding genes. However, this could result in the exclusion of important genotypes in newly sequenced non-model species. The absence of the antifreeze glycoproteins (AFGP) gene family in the recently sequenced Atlantic cod genome serves as an example. RESULTS: The Atlantic cod (Gadus morhua) genome was assembled entirely from Roche 454 short reads, demonstrating the feasibility of this approach. However, a well-known major adaptive trait, the AFGP, essential for survival in frigid Arctic marine habitats was absent in the annotated genome. To assess whether this resulted from population difference, we performed Southern blot analysis of genomic DNA from multiple individuals from the North East Arctic cod population that the sequenced cod belonged, and verified that the AFGP genotype is indeed present. We searched the raw assemblies of the Atlantic cod using our G. morhua AFGP gene, and located partial AFGP coding sequences in two sequence scaffolds. We found these two scaffolds constitute a partial genomic AFGP locus through comparative sequence analyses with our newly assembled genomic AFGP locus of the related polar cod, Boreogadus saida. By examining the sequence assembly and annotation methodologies used for the Atlantic cod genome, we deduced the primary cause of the absence of the AFGP gene family from the annotated genome was the removal of all repetitive Roche 454 short reads before sequence assembly, which would exclude most of the highly repetitive AFGP coding sequences. Secondarily, the model teleost genomes used in projection annotation of the Atlantic cod genome have no antifreeze trait, perpetuating the unawareness that the AFGP gene family is missing. CONCLUSIONS: We recovered some of the missing AFGP coding sequences and reconstructed a partial AFGP locus in the Atlantic cod genome, bringing to light that not all repetitive sequences lack protein coding information. Also, reliance on genomes of model organisms as reference for annotating protein-coding gene content of a newly sequenced non-model species could lead to omission of novel genetic traits.

Project description:BACKGROUND:In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to advances on discerning biological roles for long non-protein coding RNAs in animal systems, this RNA class in plants is largely understudied. With comparatively few validated plant long non-coding RNAs, research on this potentially critical class of RNA is hindered by a lack of appropriate prediction tools and databases. Supervised learning models trained on data sets of mostly non-validated, non-coding transcripts have been previously used to identify this enigmatic RNA class with applications largely focused on animal systems. Our approach uses a training set comprised only of empirically validated long non-protein coding RNAs from plant, animal, and viral sources to predict and rank candidate long non-protein coding gene products for future functional validation. RESULTS:Individual stochastic gradient boosting and random forest classifiers trained on only empirically validated long non-protein coding RNAs were constructed. In order to use the strengths of multiple classifiers, we combined multiple models into a single stacking meta-learner. This ensemble approach benefits from the diversity of several learners to effectively identify putative plant long non-coding RNAs from transcript sequence features. When the predicted genes identified by the ensemble classifier were compared to those listed in GreeNC, an established plant long non-coding RNA database, overlap for predicted genes from Arabidopsis thaliana, Oryza sativa and Eutrema salsugineum ranged from 51 to 83% with the highest agreement in Eutrema salsugineum. Most of the highest ranking predictions from Arabidopsis thaliana were annotated as potential natural antisense genes, pseudogenes, transposable elements, or simply computationally predicted hypothetical protein. Due to the nature of this tool, the model can be updated as new long non-protein coding transcripts are identified and functionally verified. CONCLUSIONS:This ensemble classifier is an accurate tool that can be used to rank long non-protein coding RNA predictions for use in conjunction with gene expression studies. Selection of plant transcripts with a high potential for regulatory roles as long non-protein coding RNAs will advance research in the elucidation of long non-protein coding RNA function.

Dataset Information

An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.

Publications

An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.

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