A machine learning based framework to identify and classify long terminal repeat retrotransposons

Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within a genome, increasing genome size and contributing to genetic diversity within and across species. Accurate identification and classification of TEs present in a genome is an important step towards understanding their effects on genes and their role in genome evolution. We introduce TE-LEARNER, a framework based on machine learning that automatically identifies TEs in a given genome and assigns a classification to them. We present an implementation of our framework towards LTR retrotransposons, a particular type of TEs characterized by having long terminal repeats (LTRs) at their boundaries. We evaluate the predictive performance of our framework on the well-annotated genomes of Drosophila melanogaster and Arabidopsis thaliana and we compare our results for three LTR retrotransposon superfamilies with the results of three widely used methods for TE identification or classification: REPEATMASKER, CENSOR and LTRDIGEST. In contrast to these methods, TE-LEARNER is the first to incorporate machine learning techniques, outperforming these methods in terms of predictive performance , while able to learn models and make predictions efficiently. Moreover, we show that our method was able to identify TEs that none of the above method could find, and we investigated TE-LEARNER'S predictions which did not correspond to an official annotation. It turns out that many of these predictions are in fact strongly homologous to a known TE

On the hierarchical classification of G Protein-Coupled Receptors

Motivation: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. Results: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases

Evaluation of machine learning approaches for prediction of protein coding genes in prokaryotic DNA sequences

According to the National Human Genome Research Institute the amount of genomic data generated on a yearly basis is constantly increasing. This rapid growth in genomic data has led to a subsequent surge in the demand for efficient analysis and handling of said data. Gene prediction involves identifying the areas of a DNA sequence that code for proteins, also called protein coding genes. This task falls within the scope of bioinformatics, and there has been surprisingly little development in this field of study, over the past years. Despite there being sufficient state-of-the-art gene prediction tools, there is still room for improvement in terms of efficiency and accuracy. Advances made within the field of gene prediction can, among other things, aid the medical and pharmaceutical industry, as well as impact environmental and anthropological research. Machine learning techniques such as the Random Forest classifiers and Artificial Neural Networks (ANN) have proved successful at the task of gene prediction. In this thesis one deep learning model and two other machine learning models were tested. The first model implemented was the established Random Forest classifier. When it comes to the use of ensemble methods, such as the Random Forest classifier, feature engineering is critical for the success of such models. The exploration of different feature selection and extraction techniques underpinned its relevance. It also showed that feature importance varies greatly among genomes, and revealed possibilities that can be further explored in future work. The second model tested was the ensemble method Extreme Gradient Boosting (XGBoost), which served as a good competitor to the Random Forest classifier. Finally, a Recurrent Neural Network (RNN) was implemented. RNNs are known to be good with handling sequential data, therefore it seemed like a good candidate for gene prediction. The 15 prokaryotic genomes used to train the models were extracted from the NCBI genome database. Each model was tasked with classifying sub-sequences of the genomes, called open reading frames (ORFs), as either protein coding ORFs, or non-coding ORFs. One challenge when preparing these datasets was that the number of protein coding ORFs was very small compared to the number of non-coding ORFs. Another problem encountered in the dataset was that protein coding ORFs in general are longer than non-coding ORFs, which can bias the models to simply classify long ORFs as protein coding, and short ORFs as non-coding. For these reasons, two datasets for each genome were created, taking each imbalance into account. The models were trained, tuned and tested on both datasets for all genomes, and a combination of genomes. The models were evaluated with regard to accuracy, precision and recall. The results show that all three methods have potential and attained somewhat similar performance scores. Despite the fact that both time and data were limited during model development, they still yielded promising results. Considering there are several parameters that have not yet been tuned in all models, many possibilities for further research remain. The fact that a relatively simple RNN architecture performed so well, and has no requirement for feature engineering, shows great promise for further applications in gene prediction, and possibly other fields in bioinformatics.M-D

Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure