iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components

Meiotic recombination plays an important role in the process of genetic evolution. Previous researches have shown that the recombination rates provide important information about the mechanism of recombination study. However, at present, most methods ignore the hidden correlation and spatial autocorrelation of the DNA sequence. In this study, we proposed a predictor called iRSpot-DTS to identify hot/cold spots based on the benchmark datasets. We proposed a feature extraction method called dinucleotide-based spatial autocorrelation(DSA) which can incorporate the original DNA properties and spatial information of DNA sequence. Then it used t-SNE method to remove the noise which outperformed PCA. Finally, we used SAE softmax classifier to do classification which is based on networks and can get more hidden information of DNA sequence, our iRSpot-DTS achieved remarkable performance. Jackknife cross validation tests were done on two benchmark datasets. We achieved state-of-the-art results with 96.61% overall accuracy(OA), 93.16% Matthews correlation coefficient (MCC) and over 95% in Sn and Sp which are the best in this state.     

iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components

Recombination hotspots in a genome are unevenly distributed. Hotspots are regions in a genome that show higher rates of meiotic recombinations. Computational methods for recombination hotspot prediction often use sophisticated features that are derived from physico-chemical or structure based properties of nucleotides. In this paper, we propose iRSpot-SF that uses sequence based features which are computationally cheap to generate. Four feature groups are used in our method: k-mer composition, gapped k-mer composition, TF-IDF of k-mers and reverse complement k-mer composition. We have used recursive feature elimination to select 17 top features for hotspot prediction. Our analysis shows the superiority of gapped k-mer composition and reverse complement k-mer composition features over others. We have used SVM with RBF kernel as a classification algorithm. We have tested our algorithm on standard benchmark datasets. Compared to other methods iRSpot-SF is able to produce significantly better results in terms of accuracy, Mathew's Correlation Coefficient and sensitivity which are 84.58%, 0.6941 and 84.57%. We have made our method readily available to use as a python based tool and made the datasets and source codes available at: https://github.com/abdlmaruf/iRSpot-SF. An web application is developed based on iRSpot-SF and freely available to use at: http://irspot.pythonanywhere.com/server.html.     

Prediction of MHC class I binding peptides with a new feature encoding technique

The recognition of specific peptides, bound to major histocompatibility complex (MHC) class I molecules, is of particular importance to the robust identification of T-cell epitopes and thus the successful design of protein-based vaccines. Here, we present a new feature amino acid encoding technique termed OEDICHO to predict MHC class I/peptide complexes. In the proposed method, we have combined orthonormal encoding (OE) and the binary representation of selected 10 best physicochemical properties of amino acids derived from Amino Acid Index Database (AAindex). We also have compared our method to current feature encoding techniques. The tests have been carried out on comparatively large Human Leukocyte Antigen (HLA)-A and HLA-B allele peptide binding datasets. Empirical results show that our amino acid encoding scheme leads to better classification performance on a standalone classifier.     

Odintifier - A computational method for identifying insertions of organellar origin from modern and ancient high-throughput sequencing data based on haplotype phasing

Background

Cellular organelles with genomes of their own (e.g. plastids and mitochondria) can pass genetic sequences to other organellar genomes within the cell in many species across the eukaryote phylogeny. The extent of the occurrence of these organellar-derived inserted sequences (odins) is still unknown, but if not accounted for in genomic and phylogenetic studies, they can be a source of error. However, if correctly identified, these inserted sequences can be used for evolutionary and comparative genomic studies. Although such insertions can be detected using various laboratory and bioinformatic strategies, there is currently no straightforward way to apply them as a standard organellar genome assembly on next-generation sequencing data. Furthermore, most current methods for identification of such insertions are unsuitable for use on non-model organisms or ancient DNA datasets.

Results

We present a bioinformatic method that uses phasing algorithms to reconstruct both source and inserted organelle sequences. The method was tested in different shotgun and organellar-enriched DNA high-throughput sequencing (HTS) datasets from ancient and modern samples. Specifically, we used datasets from lions (Panthera leo ssp. and Panthera leo leo) to characterize insertions from mitochondrial origin, and from common grapevine (Vitis vinifera) and bugle (Ajuga reptans) to characterize insertions derived from plastid genomes. Comparison of the results against other available organelle genome assembly methods demonstrated that our new method provides an improvement in the sequence assembly.

Conclusion

Using datasets from a wide range of species and different levels of complexity we showed that our novel bioinformatic method based on phasing algorithms can be used to achieve the next two goals: i) reference-guided assembly of chloroplast/mitochondrial genomes from HTS data and ii) identification and simultaneous assembly of odins. This method represents the first application of haplotype phasing for automatic detection of odins and reference-based organellar genome assembly.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0682-1) contains supplementary material, which is available to authorized users.     

Prediction of protein structural class for low-similarity sequences using support vector machine and PSI-BLAST profile

Knowledge of structural class plays an important role in understanding protein folding patterns. In this study, a simple and powerful computational method, which combines support vector machine with PSI-BLAST profile, is proposed to predict protein structural class for low-similarity sequences. The evolution information encoding in the PSI-BLAST profiles is converted into a series of fixed-length feature vectors by extracting amino acid composition and dipeptide composition from the profiles. The resulting vectors are then fed to a support vector machine classifier for the prediction of protein structural class. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark datasets, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence similarity lower than 40% and 25%, respectively. The overall accuracies attain 70.7% and 72.9% for 1189 and 25PDB datasets, respectively. Comparison of our results with other methods shows that our method is very promising to predict protein structural class particularly for low-similarity datasets and may at least play an important complementary role to existing methods.     

Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC

The emergence of numerous genome projects has made the experimental classification of the protein localization almost impossible due to the exponential increase in the number of protein samples. However, most of the applications are merely developed for single-plex and completely ignored the presence of one protein at two or more locations in a cell. In this regard, few attempts were carried out to target Multi-label protein localizations; consequently, undesirable accuracies are achieved. This paper presents a novel approach, in which a discrete feature extraction method is fused with physicochemical properties of amino acids by using Chou's general form of Pseudo Amino Acid Composition. The technique is tested on two benchmark datasets namely: Gpos-mploc and Virus-mPLoc. The empirical results demonstrated that the proposed method yields better results via two examined classifiers i.e. ML-KNN and Rank-SVM. It is established that the proposed model has improved values in all performance measures considered for the comparison.     

植物内生固氮菌系统发育进化新进展

在植物内生固氮菌系统发育进化关系研究中,常用的方法有形态学与蛋白质水平法、数值分类和自动化鉴定法、化学分类法、分子遗传学方法等。本文简要介绍了常用方法的关键技术,并归纳了它们的优缺点。生物学的研究进入基因组时代后,随着高通量DNA测序技术在微生物学领域应用的迅速发展,全基因组测序被应用到微生物系统发育进化研究中,然而目前并未发现对已测全基因组序列的植物内生固氮菌进行系统总结。本文在对已测序植物内生固氮菌进行归纳的基础上,又详细研究了基于基因组数据的几种具有代表性的新方法(ANI分析法、最大唯一匹配指数法、核心基因组分析、组分矢量法、基因流动性分析),并结合目前系统发育进化研究常用方法,对植物内生固氮菌系统发育进化研究趋势进行总结和展望,旨在使植物内生固氮菌的系统发育进化关系研究在精确度、可靠性等方面有所突破。     

iTIS-PseTNC: A sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition

Translation is a key process for gene expression. Timely identification of the translation initiation site (TIS) is very important for conducting in-depth genome analysis. With the avalanche of genome sequences generated in the postgenomic age, it is highly desirable to develop automated methods for rapidly and effectively identifying TIS. Although some computational methods were proposed in this regard, none of them considered the global or long-range sequence-order effects of DNA, and hence their prediction quality was limited. To count this kind of effects, a new predictor, called “iTIS-PseTNC,” was developed by incorporating the physicochemical properties into the pseudo trinucleotide composition, quite similar to the PseAAC (pseudo amino acid composition) approach widely used in computational proteomics. It was observed by the rigorous cross-validation test on the benchmark dataset that the overall success rate achieved by the new predictor in identifying TIS locations was over 97%. As a web server, iTIS-PseTNC is freely accessible at http://lin.uestc.edu.cn/server/iTIS-PseTNC. To maximize the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web server to obtain the desired results without the need to go through detailed mathematical equations, which are presented in this paper just for the integrity of the new prection method.     

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported enhancers in typical cell lines, are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines. Existing computational methods, capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation, and thus may provide useful information in identifying regulatory elements. Motivated by the aforementioned understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. Benefitting from the well-designed feature extraction mechanism and skip connection strategy, our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, but also has the ability to self-adapt to different sizes of datasets. Besides, with the adoption of auto-encoder, our model is capable of making cross-cell line predictions. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers. The source code and detailed tutorial of DeepCAPE are freely available at https://github.com/ShengquanChen/DeepCAPE.     

A Novel Hybrid Classification Model of Genetic Algorithms,Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher''s discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models.     

iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features

Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly‐SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs, and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique. On standard benchmark datasets, our method is able to significantly outperform existing methods for glycation prediction. A web server for iProtGly‐SS is implemented and publicly available to use: http://brl.uiu.ac.bd/iprotgly-ss/ .     

iDHS-DSAMS: Identifying DNase I hypersensitive sites based on the dinucleotide property matrix and ensemble bagged tree

DNase I hypersensitive site (DHS) is related to DNA regulatory elements, so the understanding of DHS sites is of great significance for biomedical research. However, traditional experiments are not very good at identifying recombinant sites of a large number of emerging DNA sequences by sequencing. Some machine learning methods have been proposed to identify DHS, but most methods ignore spatial autocorrelation of the DNA sequence. In this paper, we proposed a predictor called iDHS-DSAMS to identify DHS based on the benchmark datasets. We develop a feature extraction method called dinucleotide-based spatial autocorrelation (DSA). Then we use Min-Redundancy-Max-Relevance (mRMR) to remove irrelevant and redundant features and a 100-dimensional feature vector is selected. Finally, we utilize ensemble bagged tree as classifier, which is based on the oversampled datasets using SMOTE. Five-fold cross validation tests on two benchmark datasets indicate that the proposed method outperforms its existing counterparts on the individual accuracy (Acc), Matthews correlation coefficient (MCC), sensitivity (Sn) and specificity (Sp).     

X-MATE: a flexible system for mapping short read data

SUMMARY: Accurate and complete mapping of short-read sequencing to a reference genome greatly enhances the discovery of biological results and improves statistical predictions. We recently presented RNA-MATE, a pipeline for the recursive mapping of RNA-Seq datasets. With the rapid increase in genome re-sequencing projects, progression of available mapping software and the evolution of file formats, we now present X-MATE, an updated version of RNA-MATE, capable of mapping both RNA-Seq and DNA datasets and with improved performance, output file formats, configuration files, and flexibility in core mapping software. AVAILABILITY: Executables, source code, junction libraries, test data and results and the user manual are available from http://grimmond.imb.uq.edu.au/X-MATE/.     

Frequency based feature selection method using whale algorithm

Feature selection is the problem of finding the best subset of features which have the most impact in predicting class labels. It is noteworthy that application of feature selection is more valuable in high dimensional datasets. In this paper, a filter feature selection method has been proposed on high dimensional binary medical datasets – Colon, Central Nervous System (CNS), GLI_85, SMK_CAN_187. The proposed method incorporates three sections. First, whale algorithm has been used to discard irrelevant features. Second, the rest of features are ranked based on a frequency based heuristic approach called Mutual Congestion. Third, majority voting has been applied on best feature subsets constructed using forward feature selection with threshold τ = 10. This work provides evidence that Mutual Congestion is solely powerful to predict class labels. Furthermore, applying whale algorithm increases the overall accuracy of Mutual Congestion in most of the cases. The findings also show that the proposed method improves the prediction with selecting the less possible features in comparison with state of the arts.https://github.com/hnematzadeh     

Computer System for Analysis of Molecular Evolution Modes (SAMEM): analysis of molecular evolution modes at deep inner branches of the phylogenetic tree

SAMEM (System for Analysis of Molecular Evolution Modes), a web-based pipeline system for inferring modes of molecular evolution in genes and proteins (http://pixie.bionet.nsc.ru/samem/), is presented. Pipeline 1 performs analyses of protein-coding gene evolution; pipeline 2 performs analyses of protein evolution; pipeline 3 prepares datasets of genes and/or proteins, performs their primary analysis, and builds BLOSUM matrices; pipeline 4 checks if these genes really are protein-coding. Pipeline 1 has an all-new feature, which allows the user to obtain K(R)/K(C) estimates using several different methods. An important feature of pipeline 2 is an original method for analyzing the rates of amino acid substitutions at the branches of a phylogenetic tree. The method is based on Markov modeling and a non-parametric permutation test, which compares expected and observed frequencies of amino acid substitutions, and infers the modes of molecular evolution at deep inner branches.     

nDNA-prot: identification of DNA-binding proteins based on unbalanced classification

Background

DNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. In previous years, several approaches have been developed to improve the identification of DNA-binding proteins. However, the currently available resources are insufficient to accurately identify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy rate and the low identification success of the current methods.

Results

In this paper, we explored the practicality of modelling DNA binding identification and simultaneously employed an ensemble classifier, and a new predictor (nDNA-Prot) was designed. The presented framework is comprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble classifier designated as imDC. Experiments using different datasets showed that our method is more successful than the traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an Area Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test dataset was tested in our method and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot.

Conclusions

Our method can help to accurately identify DNA-binding proteins, and the web server is accessible at http://datamining.xmu.edu.cn/~songli/nDNA. In addition, we also predicted possible DNA-binding protein sequences in all of the sequences from the UniProtKB/Swiss-Prot database.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-298) contains supplementary material, which is available to authorized users.     

A note of feature screening via a rank-based coefficient of correlation

Feature screening is a useful and popular tool to detect informative predictors for ultrahigh-dimensional data before developing statistical analysis or constructing statistical models. While a large body of feature screening procedures has been developed, most methods are restricted to examine either continuous or discrete responses. Moreover, even though many model-free feature screening methods have been proposed, additional assumptions are imposed in those methods to ensure their theoretical results. To address those difficulties and provide simple implementation, in this paper we extend the rank-based coefficient of correlation to develop a feature screening procedure. We show that this new screening criterion is able to deal with continuous and binary responses. Theoretically, the sure screening property is established to justify the proposed method. Simulation studies demonstrate that the predictors with nonlinear and oscillatory trajectories are successfully retained regardless of the distribution of the response. Finally, the proposed method is implemented to analyze two microarray datasets.