HDInsight4PSi: Boosting performance of 3D protein structure similarity searching with HDInsight clusters in Microsoft Azure cloud

Sigma70Pred: A highly accurate method for predicting sigma70 promoter in Escherichia coli K-12 strains

Sigma70 factor plays a crucial role in prokaryotes and regulates the transcription of most of the housekeeping genes. One of the major challenges is to predict the sigma70 promoter or sigma70 factor binding site with high precision. In this study, we trained and evaluate our models on a dataset consists of 741 sigma70 promoters and 1,400 non-promoters. We have generated a wide range of features around 8,000, which includes Dinucleotide Auto-Correlation, Dinucleotide Cross-Correlation, Dinucleotide Auto Cross-Correlation, Moran Auto-Correlation, Normalized Moreau-Broto Auto-Correlation, Parallel Correlation Pseudo Tri-Nucleotide Composition, etc. Our SVM based model achieved maximum accuracy 97.38% with AUROC 0.99 on training dataset, using 200 most relevant features. In order to check the robustness of the model, we have tested our model on the independent dataset made by using RegulonDB10.8, which included 1,134 sigma70 and 638 non-promoters, and able to achieve accuracy of 90.41% with AUROC of 0.95. Our model successfully predicted constitutive promoters with accuracy of 81.46% on an independent dataset. We have developed a method, Sigma70Pred, which is available as webserver and standalone packages at https://webs.iiitd.edu.in/raghava/sigma70pred/. The services are freely accessible.

Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies

Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.

Mul-SNO: A novel prediction tool for S-nitrosylation sites based on deep learning methods

Protein s-nitrosylation (SNO is one of the most important post-translational modifications and is formed by the covalent modification of nitric oxide and cysteine residues. Extensive studies have shown that SNO plays a pivotal role in the plant immune response and treating various major human diseases. In recent years, SNO sites have become a hot research topic. Traditional biochemical methods for SNO site identification are time-consuming and costly. In this study, we developed an economical and efficient SNO site prediction tool named Mul-SNO. Mul-SNO ensembled current popular and powerful deep learning model bidirectional long short-term memory (BiLSTM and bidirectional encoder representations from Transformers (BERT . Compared with existing state-of-the-art methods, Mul-SNO obtained better ACC of 0.911 and 0.796 based on 10-fold cross-validation and independent data sets, respectively. The prediction server can be obtained for free at http://lab.malab.cn/~mjq/Mul-SNO/.

pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP

BACKGROUND

Structure comparison can provide useful information to identify functional and evolutionary relationship between proteins. With the dramatic increase of protein structure data in the Protein Data Bank, computation time quickly becomes the bottleneck for large scale structure comparisons. To more efficiently deal with informative multiple structure alignment tasks, we propose pmTM-align, a parallel protein structure alignment approach based on mTM-align/TM-align. pmTM-align contains two stages to handle pairwise structure alignments with Spark and the phylogenetic tree-based multiple structure alignment task on a single computer with OpenMP.

RESULTS

Experiments with the SABmark dataset showed that parallelization along with data structure optimization provided considerable speedup for mTM-align. The Spark-based structure alignments achieved near ideal scalability with large datasets, and the OpenMP-based construction of the phylogenetic tree accelerated the incremental alignment of multiple structures and metrics computation by a factor of about 2-5.

CONCLUSIONS

pmTM-align enables scalable pairwise and multiple structure alignment computing and offers more timely responses for medium to large-sized input data than existing alignment tools such as mTM-align.

Review and comparative analysis of machine learning-based phage virion protein identification methods

Phage virion protein (PVP) identification plays key role in elucidating relationships between phages and hosts. Moreover, PVP identification can facilitate the design of related biochemical entities. Recently, several machine learning approaches have emerged for this purpose and have shown their potential capacities. In this study, the proposed PVP identifiers are systemically reviewed, and the related algorithms and tools are comprehensively analyzed. We summarized the common framework of these PVP identifiers and constructed our own novel identifiers based upon the framework. Furthermore, we focus on a performance comparison of all PVP identifiers by using a training dataset and an independent dataset. Highlighting the pros and cons of these identifiers demonstrates that g-gap DPC (dipeptide composition) features are capable of representing characteristics of PVPs. Moreover, SVM (support vector machine) is proven to be the more effective classifier to distinguish PVPs and non-PVPs.

Processing of big heterogeneous genomic datasets for tertiary analysis of Next Generation Sequencing data

MOTIVATION

We previously proposed a paradigm shift in genomic data management, based on the Genomic Data Model (GDM) for mediating existing data formats and on the GenoMetric Query Language (GMQL) for supporting, at a high level of abstraction, data extraction and the most common data-driven computations required by tertiary data analysis of Next Generation Sequencing datasets. Here, we present a new GMQL-based system with enhanced accessibility, portability, scalability and performance.

RESULTS

The new system has a well-designed modular architecture featuring: (i) an intermediate representation supporting many different implementations (including Spark, Flink and SciDB); (ii) a high-level technology-independent repository abstraction, supporting different repository technologies (e.g., local file system, Hadoop File System, database or others); (iii) several system interfaces, including a user-friendly Web-based interface, a Web Service interface, and a programmatic interface for Python language. Biological use case examples, using public ENCODE, Roadmap Epigenomics and TCGA datasets, demonstrate the relevance of our work.

AVAILABILITY AND IMPLEMENTATION

The GMQL system is freely available for non-commercial use as open source project at: http://www.bioinformatics.deib.polimi.it/GMQLsystem/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters

MOTIVATION

Promoters are short DNA consensus sequences that are localized proximal to the transcription start sites of genes, allowing transcription initiation of particular genes. However, the precise prediction of promoters remains a challenging task because individual promoters often differ from the consensus at one or more positions.

RESULTS

In this study, we present a new multi-layer computational approach, called MULTiPly, for recognizing promoters and their specific types. MULTiPly took into account the sequences themselves, including both local information such as k-tuple nucleotide composition, dinucleotide-based auto covariance and global information of the entire samples based on bi-profile Bayes and k-nearest neighbour feature encodings. Specifically, the F-score feature selection method was applied to identify the best unique type of feature prediction results, in combination with other types of features that were subsequently added to further improve the prediction performance of MULTiPly. Benchmarking experiments on the benchmark dataset and comparisons with five state-of-the-art tools show that MULTiPly can achieve a better prediction performance on 5-fold cross-validation and jackknife tests. Moreover, the superiority of MULTiPly was also validated on a newly constructed independent test dataset. MULTiPly is expected to be used as a useful tool that will facilitate the discovery of both general and specific types of promoters in the post-genomic era.

AVAILABILITY AND IMPLEMENTATION

The MULTiPly webserver and curated datasets are freely available at http://flagshipnt.erc.monash.edu/MULTiPly/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

NCNet: Deep Learning Network Models for Predicting Function of Non-coding DNA

The human genome consists of 98.5% non-coding DNA sequences, and most of them have no known function. However, a majority of disease-associated variants lie in these regions. Therefore, it is critical to predict the function of non-coding DNA. Hence, we propose the NCNet, which integrates deep residual learning and sequence-to-sequence learning networks, to predict the transcription factor (TF) binding sites, which can then be used to predict non-coding functions. In NCNet, deep residual learning networks are used to enhance the identification rate of regulatory patterns of motifs, so that the sequence-to-sequence learning network may make the most out of the sequential dependency between the patterns. With the identity shortcut technique and deep architectures of the networks, NCNet achieves significant improvement compared to the original hybrid model in identifying regulatory markers.

Identification of Phage Viral Proteins With Hybrid Sequence Features

The uniqueness of bacteriophages plays an important role in bioinformatics research. In real applications, the function of the bacteriophage virion proteins is the main area of interest. Therefore, it is very important to classify bacteriophage virion proteins and non-phage virion proteins accurately. Extracting comprehensive and effective sequence features from proteins plays a vital role in protein classification. In order to more fully represent protein information, this paper is more comprehensive and effective by combining the features extracted by the feature information representation algorithm based on sequence information (CCPA) and the feature representation algorithm based on sequence and structure information. After extracting features, the Max-Relevance-Max-Distance (MRMD) algorithm is used to select the optimal feature set with the strongest correlation between class labels and low redundancy between features. Given the randomness of the samples selected by the random forest classification algorithm and the randomness features for producing each node variable, a random forest method is employed to perform 10-fold cross-validation on the bacteriophage protein classification. The accuracy of this model is as high as 93.5% in the classification of phage proteins in this study. This study also found that, among the eight physicochemical properties considered, the charge property has the greatest impact on the classification of bacteriophage proteins These results indicate that the model discussed in this paper is an important tool in bacteriophage protein research.

Scalable Extraction of Big Macromolecular Data in Azure Data Lake Environment

Calculation of structural features of proteins, nucleic acids, and nucleic acid-protein complexes on the basis of their geometries and studying various interactions within these macromolecules, for which high-resolution structures are stored in Protein Data Bank (PDB), require parsing and extraction of suitable data stored in text files. To perform these operations on large scale in the face of the growing amount of macromolecular data in public repositories, we propose to perform them in the distributed environment of Azure Data Lake and scale the calculations on the Cloud. In this paper, we present dedicated data extractors for PDB files that can be used in various types of calculations performed over protein and nucleic acids structures in the Azure Data Lake. Results of our tests show that the Cloud storage space occupied by the macromolecular data can be successfully reduced by using compression of PDB files without significant loss of data processing efficiency. Moreover, our experiments show that the performed calculations can be significantly accelerated when using large sequential files for storing macromolecular data and by parallelizing the calculations and data extractions that precede them. Finally, the paper shows how all the calculations can be performed in a declarative way in U-SQL scripts for Data Lake Analytics.

M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

N6-methyladenosine (m⁶A) modification is the most abundant RNA methylation modification and involves various biological processes, such as RNA splicing and degradation. Recent studies have demonstrated the feasibility of identifying m⁶A peaks using high-throughput sequencing techniques. However, such techniques cannot accurately identify specific methylated sites, which is important for a better understanding of m⁶A functions. In this study, we develop a novel machine learning-based predictor called M6APred-EL for the identification of m⁶A sites. To predict m⁶A sites accurately within genomic sequences, we trained an ensemble of three support vector machine classifiers that explore the position-specific information and physical chemical information from position-specific k-mer nucleotide propensity, physical-chemical properties, and ring-function-hydrogen-chemical properties. We examined and compared the performance of our predictor with other state-of-the-art methods of benchmarking datasets. Comparative results showed that the proposed M6APred-EL performed more accurately for m⁶A site identification. Moreover, a user-friendly web server that implements the proposed M6APred-EL is well established and is currently available at http://server.malab.cn/M6APred-EL/. It is expected to be a practical and effective tool for the investigation of m⁶A functional mechanisms.

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

BACKGROUND

Promoter is an important sequence regulation element, which is in charge of gene transcription initiation. In prokaryotes, σ70 promoters regulate the transcription of most genes. The promoter recognition has been a crucial part of gene structure recognition. It's also the core issue of constructing gene transcriptional regulation network. With the successfully completion of genome sequencing from an increasing number of microbe species, the accurate identification of σ70 promoter regions in DNA sequence is not easy.

RESULTS

In order to improve the prediction accuracy of sigma70 promoters in prokaryote, a promoter recognition model 70ProPred was established. In this work, two sequence-based features, including position-specific trinucleotide propensity based on single-stranded characteristic (PSTNPss) and electron-ion potential values for trinucleotides (PseEIIP), were assessed to build the best prediction model. It was found that 79 features of PSTNPSS combined with 64 features of PseEIIP obtained the best performance for sigma70 promoter identification, with a promising accuracy and the Matthews correlation coefficient (MCC) at 95.56% and 0.90, respectively.

CONCLUSION

The jackknife tests showed that 70ProPred outperforms the existing sigma70 promoter prediction approaches in terms of accuracy and stability. Additionally, this approach can also be extended to predict promoters of other species. In order to facilitate experimental biologists, an online web server for the proposed method was established, which is freely available at http://server.malab.cn/70ProPred/ .