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Amjad A, Ahmed S, Kabir M, Arif M, Alam T. A novel deep learning identifier for promoters and their strength using heterogeneous features. Methods 2024:S1046-2023(24)00185-3. [PMID: 39168294 DOI: 10.1016/j.ymeth.2024.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/24/2024] [Accepted: 08/17/2024] [Indexed: 08/23/2024] Open
Abstract
Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named "PROCABLES", which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron-ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.
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Affiliation(s)
- Aqsa Amjad
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan
| | - Saeed Ahmed
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan
| | - Muhammad Kabir
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan.
| | - Muhammad Arif
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.
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Kanwal S, Arif R, Ahmed S, Kabir M. A novel stacking-based predictor for accurate prediction of antimicrobial peptides. J Biomol Struct Dyn 2024:1-12. [PMID: 38500243 DOI: 10.1080/07391102.2024.2329298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/06/2024] [Indexed: 03/20/2024]
Abstract
Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel STacking-based ensemble learning framework for AntiMicrobial Peptide (STAMP) prediction. First, we constructed 84 different baseline models by using 12 different feature encoding schemes and 7 popular machine learning algorithms. Second, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, based on the feature selection strategy, we determined the optimal probabilistic feature vector, which was further utilized for the construction of our stacked model. Resultantly, the STAMP predictor achieved excellent performance during cross-validation with an accuracy and Matthew's correlation coefficient of 0.930 and 0.860, respectively. The corresponding metrics during the independent test were 0.710 and 0.464, respectively. Overall, STAMP achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, STAMP is expected to assist community-wide efforts in identifying AMPs and will contribute to the development of novel therapeutic methods and drug-design for immunity.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sameera Kanwal
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Roha Arif
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Saeed Ahmed
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Muhammad Kabir
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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3
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Zheng Y, Wang H, Ding Y, Guo F. CEPZ: A Novel Predictor for Identification of DNase I Hypersensitive Sites. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2768-2774. [PMID: 33481716 DOI: 10.1109/tcbb.2021.3053661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
DNase I hypersensitive sites (DHSs) have proven to be tightly associated with cis-regulatory elements, commonly indicating specific function on the chromatin structure. Thus, identifying DHSs plays a fundamental role in decoding gene regulatory behavior. While traditional experimental methods turn to be time-consuming and expensive, computational techniques promise to be practical to discovering and analyzing regulatory factors. In this study, we applied an efficient model that considered composition information and physicochemical properties and effectively selected features with a boosting algorithm. CEPZ, our predictor, greatly improved a Matthews correlation coefficient and accuracy of 0.7740 and 0.9113 respectively, more competitive than any predictor before. This result suggests that it may become a useful tool for DHSs research in the human and other complex genomes. Our research was anchored on the properties of dinucleotides and we identified several dinucleotides with significant differences in the distribution of DHS and non-DHS samples, which are likely to have a special meaning in the chromatin structure. The datasets, feature sets and the relevant algorithm are available at https://github.com/YanZheng-16/CEPZ_DHS/.
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Goel N, Singh S, Aseri TC. Global sequence features based translation initiation site prediction in human genomic sequences. Heliyon 2020; 6:e04825. [PMID: 32964155 PMCID: PMC7490824 DOI: 10.1016/j.heliyon.2020.e04825] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Revised: 05/25/2020] [Accepted: 08/26/2020] [Indexed: 11/26/2022] Open
Abstract
Gene prediction has been increasingly important in genome annotation due to advancements in sequencing technology. Genome annotation further helps in determining the structure and function of these genes. Translation initiation site prediction (TIS) in human genomic sequences is one of the fundamental and essential steps in gene prediction. Thus, accurate prediction of TIS in these sequences is highly desirable. Although many computational methods were developed for this problem, none of them focused on finding these sites in human genomic sequences. In this paper, a new TIS prediction method is proposed by incorporating global sequence based features. Support vector machine is used to assess the prediction power of these features. The proposed method achieved accuracy of above 90% when tested for genomic as well as cDNA sequences. The experimental results indicate that the method works well for both genomic and cDNA sequences. The method can be integrated into gene prediction system in future.
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Affiliation(s)
- Neelam Goel
- Department of Information Technology, University Institute of Engineering and Technology, Sector-25, Panjab University, Chandigarh 160014, India
| | - Shailendra Singh
- Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Sector-12, Chandigarh 160012, India
| | - Trilok Chand Aseri
- Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Sector-12, Chandigarh 160012, India
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SDBP-Pred: Prediction of single-stranded and double-stranded DNA-binding proteins by extending consensus sequence and K-segmentation strategies into PSSM. Anal Biochem 2019; 589:113494. [PMID: 31693872 DOI: 10.1016/j.ab.2019.113494] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 10/24/2019] [Accepted: 10/31/2019] [Indexed: 11/24/2022]
Abstract
Identification of DNA-binding proteins (DNA-BPs) is a hot issue in protein science due to its key role in various biological processes. These processes are highly concerned with DNA-binding protein types. DNA-BPs are classified into single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). SSBs mainly involved in DNA recombination, replication, and repair, while DSBs regulate transcription process, DNA cleavage, and chromosome packaging. In spite of the aforementioned significance, few methods have been proposed for discrimination of SSBs and DSBs. Therefore, more predictors with favorable performance are indispensable. In this work, we present an innovative predictor, called SDBP-Pred with a novel feature descriptor, named consensus sequence-based K-segmentation position-specific scoring matrix (CSKS-PSSM). We encoded the local discriminative features concealed in PSSM via K-segmentation strategy and the global potential features by applying the notion of the consensus sequence. The obtained feature vector then input to support vector machine (SVM) with linear, polynomial and radial base function (RBF) kernels. Our model with SVM-RBF achieved the highest accuracies on three tests namely jackknife, 10-fold, and independent tests, respectively than the recent method. The obtained prediction results illustrate the superlative prediction performance of SDBP-Pred over existing studies in the literature so far.
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Ali F, Ahmed S, Swati ZNK, Akbar S. DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information. J Comput Aided Mol Des 2019; 33:645-658. [PMID: 31123959 DOI: 10.1007/s10822-019-00207-x] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 05/18/2019] [Indexed: 12/28/2022]
Abstract
DNA-binding proteins (DBPs) participate in various biological processes including DNA replication, recombination, and repair. In the human genome, about 6-7% of these proteins are utilized for genes encoding. DBPs shape the DNA into a compact structure known chromatin while some of these proteins regulate the chromosome packaging and transcription process. In the pharmaceutical industry, DBPs are used as a key component of antibiotics, steroids, and cancer drugs. These proteins also involve in biophysical, biological, and biochemical studies of DNA. Due to the crucial role in various biological activities, identification of DBPs is a hot issue in protein science. A series of experimental and computational methods have been proposed, however, some methods didn't achieve the desired results while some are inadequate in its accuracy and authenticity. Still, it is highly desired to present more intelligent computational predictors. In this work, we introduce an innovative computational method namely DP-BINDER based on physicochemical and evolutionary information. We captured local highly decisive features from physicochemical properties of primary protein sequences via normalized Moreau-Broto autocorrelation (NMBAC) and evolutionary information by position specific scoring matrix-transition probability composition (PSSM-TPC) and pseudo-position specific scoring matrix (PsePSSM) using training and independent datasets. The optimal features were selected by the support vector machine-recursive feature elimination and correlation bias reduction (SVM-RFE + CBR) from fused features and were fed into random forest (RF) and support vector machine (SVM). Our method attained 92.46% and 89.58% accuracy with jackknife and ten-fold cross-validation, respectively on the training dataset, while 81.17% accuracy on the independent dataset for prediction of DBPs. These results demonstrate that our method attained the highest success rate in the literature. The superiority of DP-BINDER over existing approaches due to several reasons including abstraction of local dominant features via effective feature descriptors, utilization of appropriate feature selection algorithms and effective classifier.
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Affiliation(s)
- Farman Ali
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Saeed Ahmed
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Zar Nawab Khan Swati
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
- Department of Computer Science, Karakoram International University, Gilgit, Gilgit-Baltistan, 15100, Pakistan
| | - Shahid Akbar
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan
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Akbar S, Hayat M, Kabir M, Iqbal M. iAFP-gap-SMOTE: An Efficient Feature Extraction Scheme Gapped Dipeptide Composition is Coupled with an Oversampling Technique for Identification of Antifreeze Proteins. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666180816101653] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Antifreeze proteins (AFPs) perform distinguishable roles in maintaining homeostatic conditions of living organisms and protect their cell and body from freezing in extremely cold conditions. Owing to high diversity in protein sequences and structures, the discrimination of AFPs from non- AFPs through experimental approaches is expensive and lengthy. It is, therefore, vastly desirable to propose a computational intelligent and high throughput model that truly reflects AFPs quickly and accurately. In a sequel, a new predictor called “iAFP-gap-SMOTE” is proposed for the identification of AFPs. Protein sequences are expressed by adopting three numerical feature extraction schemes namely; Split Amino Acid Composition, G-gap di-peptide Composition and Reduce Amino Acid alphabet composition. Usually, classification hypothesis biased towards majority class in case of the imbalanced dataset. Oversampling technique Synthetic Minority Over-sampling Technique is employed in order to increase the instances of the lower class and control the biasness. 10-fold cross-validation test is applied to appraise the success rates of “iAFP-gap-SMOTE” model. After the empirical investigation, “iAFP-gap-SMOTE” model obtained 95.02% accuracy. The comparison suggested that the accuracy of” iAFP-gap-SMOTE” model is higher than that of the present techniques in the literature so far. It is greatly recommended that our proposed model “iAFP-gap-SMOTE” might be helpful for the research community and academia.
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Affiliation(s)
- Shahid Akbar
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
| | - Muhammad Kabir
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
| | - Muhammad Iqbal
- Department of Computer Science, Abdul Wali Khan University, Mardan, KP 23200, Pakistan
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Kabir M, Ahmad S, Iqbal M, Hayat M. iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families. Genomics 2019; 112:276-285. [PMID: 30779939 DOI: 10.1016/j.ygeno.2019.02.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 01/09/2019] [Accepted: 02/07/2019] [Indexed: 12/25/2022]
Abstract
Nuclear receptor proteins (NRPs) perform a vital role in regulating gene expression. With the rapidity growth of NRPs in post-genomic era, it is highly recommendable to identify NRPs and their sub-families accurately from their primary sequences. Several conventional methods have been used for discrimination of NRPs and their sub-families, but did not achieve considerable results. In a sequel, a two-level new computational model "iNR-2 L" is developed. Two discrete methods namely: Dipeptide Composition and Tripeptide Composition were used to formulate NRPs sequences. Further, both the descriptor spaces were merged to construct hybrid space. Furthermore, feature selection technique minimum redundancy and maximum relevance was employed in order to select salient features as well as reduce the noise and redundancy. The experiential outcomes exhibited that the proposed model iNR-2 L achieved outstanding results. It is anticipated that the proposed computational model might be a practical and effective tool for academia and research community.
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Affiliation(s)
- Muhammad Kabir
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
| | - Saeed Ahmad
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Muhammad Iqbal
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
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9
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Prediction of membrane protein types by exploring local discriminative information from evolutionary profiles. Anal Biochem 2019; 564-565:123-132. [DOI: 10.1016/j.ab.2018.10.027] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Revised: 10/23/2018] [Accepted: 10/25/2018] [Indexed: 11/17/2022]
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10
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Zhang M, Xu Y, Li L, Liu Z, Yang X, Yu DJ. Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble. Anal Biochem 2018; 550:41-48. [DOI: 10.1016/j.ab.2018.03.027] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Revised: 03/27/2018] [Accepted: 03/28/2018] [Indexed: 11/25/2022]
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11
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Sabooh MF, Iqbal N, Khan M, Khan M, Maqbool HF. Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC. J Theor Biol 2018; 452:1-9. [PMID: 29727634 DOI: 10.1016/j.jtbi.2018.04.037] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 04/24/2018] [Accepted: 04/27/2018] [Indexed: 02/02/2023]
Abstract
This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (m5C) plays a vital role in a number of biological processes. For better comprehension of the biological functions and mechanism it is necessary to recognize m5C sites in RNA precisely. The laboratory techniques and procedures are available to identify m5C sites in RNA, but these procedures require a lot of time and resources. This study develops a new computational method for extracting the features of RNA sequence. In this method, first the RNA sequence is encoded via composite feature vector, then, for the selection of discriminate features, the minimum-redundancy-maximum-relevance algorithm was used. Secondly, the classification method used has been based on a support vector machine by using jackknife cross validation test. The suggested method efficiently identifies m5C sites from non- m5C sites and the outcome of the suggested algorithm is 93.33% with sensitivity of 90.0 and specificity of 96.66 on bench mark datasets. The result exhibits that proposed algorithm shown significant identification performance compared to the existing computational techniques. This study extends the knowledge about the occurrence sites of RNA modification which paves the way for better comprehension of the biological uses and mechanism.
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Affiliation(s)
- M Fazli Sabooh
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
| | - Nadeem Iqbal
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
| | - Mukhtaj Khan
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
| | - Muslim Khan
- Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
| | - H F Maqbool
- University of Engineering & Technology Lahore, Pakistan
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iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition. J Theor Biol 2018; 442:11-21. [DOI: 10.1016/j.jtbi.2018.01.008] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 12/23/2017] [Accepted: 01/10/2018] [Indexed: 02/08/2023]
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13
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Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins. J Theor Biol 2017; 435:116-124. [DOI: 10.1016/j.jtbi.2017.09.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Revised: 09/12/2017] [Accepted: 09/15/2017] [Indexed: 02/08/2023]
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Du PF, Zhao W, Miao YY, Wei LY, Wang L. UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences. Int J Mol Sci 2017; 18:ijms18112400. [PMID: 29135934 PMCID: PMC5713368 DOI: 10.3390/ijms18112400] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 11/01/2017] [Accepted: 11/03/2017] [Indexed: 01/12/2023] Open
Abstract
With the avalanche of biological sequences in public databases, one of the most challenging problems in computational biology is to predict their biological functions and cellular attributes. Most of the existing prediction algorithms can only handle fixed-length numerical vectors. Therefore, it is important to be able to represent biological sequences with various lengths using fixed-length numerical vectors. Although several algorithms, as well as software implementations, have been developed to address this problem, these existing programs can only provide a fixed number of representation modes. Every time a new sequence representation mode is developed, a new program will be needed. In this paper, we propose the UltraPse as a universal software platform for this problem. The function of the UltraPse is not only to generate various existing sequence representation modes, but also to simplify all future programming works in developing novel representation modes. The extensibility of UltraPse is particularly enhanced. It allows the users to define their own representation mode, their own physicochemical properties, or even their own types of biological sequences. Moreover, UltraPse is also the fastest software of its kind. The source code package, as well as the executables for both Linux and Windows platforms, can be downloaded from the GitHub repository.
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Affiliation(s)
- Pu-Feng Du
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
| | - Wei Zhao
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
| | - Yang-Yang Miao
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
- School of Chemical Engineering, Tianjin University, Tianjin 300350, China.
| | - Le-Yi Wei
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
| | - Likun Wang
- Institute of Systems Biomedicine, Beijing Key Laboratory of Tumor Systems Biology, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China.
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A Two-Layer Computational Model for Discrimination of Enhancer and Their Types Using Hybrid Features Pace of Pseudo K-Tuple Nucleotide Composition. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2017. [DOI: 10.1007/s13369-017-2818-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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16
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Khan M, Hayat M, Khan SA, Iqbal N. Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC. J Theor Biol 2017; 415:13-19. [DOI: 10.1016/j.jtbi.2016.12.004] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Revised: 10/24/2016] [Accepted: 12/07/2016] [Indexed: 01/22/2023]
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17
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Improving N(6)-methyladenosine site prediction with heuristic selection of nucleotide physical-chemical properties. Anal Biochem 2016; 508:104-13. [PMID: 27293216 DOI: 10.1016/j.ab.2016.06.001] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2016] [Revised: 05/31/2016] [Accepted: 06/01/2016] [Indexed: 12/28/2022]
Abstract
N(6)-methyladenosine (m(6)A) is one of the most common and abundant post-transcriptional RNA modifications found in viruses and most eukaryotes. m(6)A plays an essential role in many vital biological processes to regulate gene expression. Because of its widespread distribution across the genomes, the identification of m(6)A sites from RNA sequences is of significant importance for better understanding the regulatory mechanism of m(6)A. Although progress has been achieved in m(6)A site prediction, challenges remain. This article aims to further improve the performance of m(6)A site prediction by introducing a new heuristic nucleotide physical-chemical property selection (HPCS) algorithm. The proposed HPCS algorithm can effectively extract an optimized subset of nucleotide physical-chemical properties under the prescribed feature representation for encoding an RNA sequence into a feature vector. We demonstrate the efficacy of the proposed HPCS algorithm under different feature representations, including pseudo dinucleotide composition (PseDNC), auto-covariance (AC), and cross-covariance (CC). Based on the proposed HPCS algorithm, we implemented an m(6)A site predictor, called M6A-HPCS, which is freely available at http://csbio.njust.edu.cn/bioinf/M6A-HPCS. Experimental results over rigorous jackknife tests on benchmark datasets demonstrated that the proposed M6A-HPCS achieves higher success rates and outperforms existing state-of-the-art sequence-based m(6)A site predictors.
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18
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Iqbal M, Hayat M. "iSS-Hyb-mRMR": Identification of splicing sites using hybrid space of pseudo trinucleotide and pseudo tetranucleotide composition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 128:1-11. [PMID: 27040827 DOI: 10.1016/j.cmpb.2016.02.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Accepted: 02/16/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Gene splicing is a vital source of protein diversity. Perfectly eradication of introns and joining exons is the prominent task in eukaryotic gene expression, as exons are usually interrupted by introns. Identification of splicing sites through experimental techniques is complicated and time-consuming task. With the avalanche of genome sequences generated in the post genomic age, it remains a complicated and challenging task to develop an automatic, robust and reliable computational method for fast and effective identification of splicing sites. METHODS In this study, a hybrid model "iSS-Hyb-mRMR" is proposed for quickly and accurately identification of splicing sites. Two sample representation methods namely; pseudo trinucleotide composition (PseTNC) and pseudo tetranucleotide composition (PseTetraNC) were used to extract numerical descriptors from DNA sequences. Hybrid model was developed by concatenating PseTNC and PseTetraNC. In order to select high discriminative features, minimum redundancy maximum relevance algorithm was applied on the hybrid feature space. The performance of these feature representation methods was tested using various classification algorithms including K-nearest neighbor, probabilistic neural network, general regression neural network, and fitting network. Jackknife test was used for evaluation of its performance on two benchmark datasets S1 and S2, respectively. RESULTS The predictor, proposed in the current study achieved an accuracy of 93.26%, sensitivity of 88.77%, and specificity of 97.78% for S1, and the accuracy of 94.12%, sensitivity of 87.14%, and specificity of 98.64% for S2, respectively. CONCLUSION It is observed, that the performance of proposed model is higher than the existing methods in the literature so for; and will be fruitful in the mechanism of RNA splicing, and other research academia.
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Affiliation(s)
- Muhammad Iqbal
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan.
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19
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Ahmad K, Waris M, Hayat M. Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition. J Membr Biol 2016; 249:293-304. [PMID: 26746980 DOI: 10.1007/s00232-015-9868-8] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 12/30/2015] [Indexed: 12/15/2022]
Abstract
Mitochondrion is the key organelle of eukaryotic cell, which provides energy for cellular activities. Submitochondrial locations of proteins play crucial role in understanding different biological processes such as energy metabolism, program cell death, and ionic homeostasis. Prediction of submitochondrial locations through conventional methods are expensive and time consuming because of the large number of protein sequences generated in the last few decades. Therefore, it is intensively desired to establish an automated model for identification of submitochondrial locations of proteins. In this regard, the current study is initiated to develop a fast, reliable, and accurate computational model. Various feature extraction methods such as dipeptide composition (DPC), Split Amino Acid Composition, and Composition and Translation were utilized. In order to overcome the issue of biasness, oversampling technique SMOTE was applied to balance the datasets. Several classification learners including K-Nearest Neighbor, Probabilistic Neural Network, and support vector machine (SVM) are used. Jackknife test is applied to assess the performance of classification algorithms using two benchmark datasets. Among various classification algorithms, SVM achieved the highest success rates in conjunction with the condensed feature space of DPC, which are 95.20 % accuracy on dataset SML3-317 and 95.11 % on dataset SML3-983. The empirical results revealed that our proposed model obtained the highest results so far in the literatures. It is anticipated that our proposed model might be useful for future studies.
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Affiliation(s)
- Khurshid Ahmad
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Muhammad Waris
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan.
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