Pattern Recognition of DNA Sequences using Automata with application to Species Distinction

Darwin wasn\u27t just provocative in saying that we descend from the apes—he didn\u27t go far enough, we are apes in every way, from our long arms and tailless bodies to our habits and temperament. said Frans de Waal, a primate scientist at Emory University in Atlanta, Georgia. 1.3 million Species have been named and analyzed by scientists. This project focuses on capturing various nucleotide sequences of various species and determining the similarity and differences between them. Finite state automata have been used to accomplish this. The automata for a DNA genome is created using Alergia algorithm and is used as the foundation for comparing it to the other species DNA sequences

Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines

BACKGROUND: Predicting the subcellular localization of proteins is important for determining the function of proteins. Previous works focused on predicting protein localization in Gram-negative bacteria obtained good results. However, these methods had relatively low accuracies for the localization of extracellular proteins. This paper studies ways to improve the accuracy for predicting extracellular localization in Gram-negative bacteria. RESULTS: We have developed a system for predicting the subcellular localization of proteins for Gram-negative bacteria based on amino acid subalphabets and a combination of multiple support vector machines. The recall of the extracellular site and overall recall of our predictor reach 86.0% and 89.8%, respectively, in 5-fold cross-validation. To the best of our knowledge, these are the most accurate results for predicting subcellular localization in Gram-negative bacteria. CONCLUSION: Clustering 20 amino acids into a few groups by the proposed greedy algorithm provides a new way to extract features from protein sequences to cover more adjacent amino acids and hence reduce the dimensionality of the input vector of protein features. It was observed that a good amino acid grouping leads to an increase in prediction performance. Furthermore, a proper choice of a subset of complementary support vector machines constructed by different features of proteins maximizes the prediction accuracy

DNA Sequence Representation by Use of Statistical Finite Automata

This project defines and intends to solve the problem of representing information carried by DNA sequences in terms of amino acids, through application of the theory of finite automata. Sequences can be compared against each other to find existing patterns, if any, which may include important genetic information. Comparison can state whether the DNA sequences belong to the same, related or entirely different species in the ‘Tree of Life’ (phylogeny). This is achieved by using extended and statistical finite automata. In order to solve this problem, the concepts of automata and their extension, i.e. Alergia algorithm have been used. In this specific case, we have used the chemical property - polarity of amino acids to analyze the DNA sequences

PATTERN DISCOVERY IN DNA USING STOCHASTIC AUTOMATA

We consider the problem of identifying similarities between different species of DNA. To do this we infer a stochastic finite automata from a given training data and compare it with a test data. The training and test data consist of DNA sequence of different species. Our method first identifies sentences in DNA. To identify sentences we read DNA sequence one character at a time, 3 characters form a codon and codons form proteins (also known as amino acid chains).Each amino acid in proteins belongs to a group. In total we have 5 groups’ polar, non-polar, acidic, basic and stop codons. A protein always starts with a start codon ATG that belongs to the group polar and ends with one of the stop codons that belongs to the group stop codon. After identifying sentences our method converts it into a symbolic representation of strings where each number represents the group to which an amino acid belongs to. We then generate a PTA tree and merge equivalent states to produce a Stochastic Finite Automata for a DNA. In addition to producing SFA, we apply secondary storage to handle huge DNA sequences. We also explain some concepts that are necessary to understand our paper

Classification of proteins using sequential and structural features

Classification of proteins is an important process in many areas of bioinformatics research. In this thesis, we devised three different strategies to classify proteins with high accuracy that may have implications for function and attribute annotation. First, protein families were classified into different functional subtypes using a classification-via-clustering approach by using relative complexity measure with reduced amino acid alphabets (RAAA). The devised procedure does not require multiple alignment of sequences and produce high classification accuracies. Second, different fixed-length motif and RAAA combinations were used as features to represent proteins from different thermostability classes. A T-test based dimensionality reduction scheme was applied to reduce the number of features and those features were used to develop support vector machine classifiers. The devised procedure produced better results with less number of features than purely using native protein alphabet. Third, a non-homologous protein structure dataset containing hyperthermophilic, thermophilic, and mesophilic proteins was assembled de novo. Comprehensive statistical analyses of the dataset were carried out to highlight novel features correlated with increased thermostability and machine learning approaches were used to discriminate the proteins. For the first time, our results strongly indicate that combined sequential and structural features are better predictors of protein thermostability than purely sequential or structural features. Furthermore, the discrimination capability of machine learning models strongly depends on RAAAs