Displaying publications 1 - 20 of 116 in total

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  1. Fulltext Efficient feature selection and classification of protein sequence data in bioinformatics
    Iqbal MJ, Faye I, Samir BB, Said AM
    ScientificWorldJournal, 2014;2014:173869.
    PMID: 25045727 DOI: 10.1155/2014/173869
    Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth.
    Matched MeSH terms: Computational Biology/methods*
  2. Fulltext NMF-Based Approach for Missing Values Imputation of Mass Spectrometry Metabolomics Data
    Xu J, Wang Y, Xu X, Cheng KK, Raftery D, Dong J
    Molecules, 2021 Sep 24;26(19).
    PMID: 34641330 DOI: 10.3390/molecules26195787
    In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data.
    Matched MeSH terms: Computational Biology/methods*
  3. iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions
    Naseer S, Ali RF, Khan YD, Dominic PDD
    J Biomol Struct Dyn, 2022;40(22):11691-11704.
    PMID: 34396935 DOI: 10.1080/07391102.2021.1962738
    Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou's Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D DominicCommunicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Computational Biology/methods
  4. A review on the computational approaches for gene regulatory network construction
    Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z
    Comput Biol Med, 2014 May;48:55-65.
    PMID: 24637147 DOI: 10.1016/j.compbiomed.2014.02.011
    Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
    Matched MeSH terms: Computational Biology/methods*
  5. Fulltext Simulation of a Petri net-based model of the terpenoid biosynthesis pathway
    Hawari AH, Mohamed-Hussein ZA
    BMC Bioinformatics, 2010;11:83.
    PMID: 20144236 DOI: 10.1186/1471-2105-11-83
    The development and simulation of dynamic models of terpenoid biosynthesis has yielded a systems perspective that provides new insights into how the structure of this biochemical pathway affects compound synthesis. These insights may eventually help identify reactions that could be experimentally manipulated to amplify terpenoid production. In this study, a dynamic model of the terpenoid biosynthesis pathway was constructed based on the Hybrid Functional Petri Net (HFPN) technique. This technique is a fusion of three other extended Petri net techniques, namely Hybrid Petri Net (HPN), Dynamic Petri Net (HDN) and Functional Petri Net (FPN).
    Matched MeSH terms: Computational Biology/methods*
  6. Fulltext birgHPC: creating instant computing clusters for bioinformatics and molecular dynamics
    Chew TH, Joyce-Tan KH, Akma F, Shamsir MS
    Bioinformatics, 2011 May 1;27(9):1320-1.
    PMID: 21398666 DOI: 10.1093/bioinformatics/btr109
    birgHPC, a bootable Linux Live CD has been developed to create high-performance clusters for bioinformatics and molecular dynamics studies using any Local Area Network (LAN)-networked computers. birgHPC features automated hardware and slots detection as well as provides a simple job submission interface. The latest versions of GROMACS, NAMD, mpiBLAST and ClustalW-MPI can be run in parallel by simply booting the birgHPC CD or flash drive from the head node, which immediately positions the rest of the PCs on the network as computing nodes. Thus, a temporary, affordable, scalable and high-performance computing environment can be built by non-computing-based researchers using low-cost commodity hardware.
    Matched MeSH terms: Computational Biology/methods*
  7. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing
    Ahmad M, Jung LT, Bhuiyan AA
    Comput Methods Programs Biomed, 2017 Oct;149:11-17.
    PMID: 28802326 DOI: 10.1016/j.cmpb.2017.06.021
    BACKGROUND AND OBJECTIVE: Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals.

    METHODS: This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise.

    RESULTS: Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.

    CONCLUSION: This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.

    Matched MeSH terms: Computational Biology/methods*
  8. Fulltext Pattern Matching for DNA Sequencing Data Using Multiple Bloom Filters
    Najam M, Rasool RU, Ahmad HF, Ashraf U, Malik AW
    Biomed Res Int, 2019;2019:7074387.
    PMID: 31111064 DOI: 10.1155/2019/7074387
    Storing and processing of large DNA sequences has always been a major problem due to increasing volume of DNA sequence data. However, a number of solutions have been proposed but they require significant computation and memory. Therefore, an efficient storage and pattern matching solution is required for DNA sequencing data. Bloom filters (BFs) represent an efficient data structure, which is mostly used in the domain of bioinformatics for classification of DNA sequences. In this paper, we explore more dimensions where BFs can be used other than classification. A proposed solution is based on Multiple Bloom Filters (MBFs) that finds all the locations and number of repetitions of the specified pattern inside a DNA sequence. Both of these factors are extremely important in determining the type and intensity of any disease. This paper serves as a first effort towards optimizing the search for location and frequency of substrings in DNA sequences using MBFs. We expect that further optimizations in the proposed solution can bring remarkable results as this paper presents a proof of concept implementation for a given set of data using proposed MBFs technique. Performance evaluation shows improved accuracy and time efficiency of the proposed approach.
    Matched MeSH terms: Computational Biology/methods*
  9. CircPrime: a web-based platform for design of specific circular RNA primers
    Sharko F, Rbbani G, Siriyappagouder P, Raeymaekers JAM, Galindo-Villegas J, Nedoluzhko A, et al.
    BMC Bioinformatics, 2023 May 19;24(1):205.
    PMID: 37208611 DOI: 10.1186/s12859-023-05331-y
    BACKGROUND: Circular RNAs (circRNAs) are covalently closed-loop RNAs with critical regulatory roles in cells. Tens of thousands of circRNAs have been unveiled due to the recent advances in high throughput RNA sequencing technologies and bioinformatic tools development. At the same time, polymerase chain reaction (PCR) cross-validation for circRNAs predicted by bioinformatic tools remains an essential part of any circRNA study before publication.

    RESULTS: Here, we present the CircPrime web-based platform, providing a user-friendly solution for DNA primer design and thermocycling conditions for circRNA identification with routine PCR methods.

    CONCLUSIONS: User-friendly CircPrime web platform ( http://circprime.elgene.net/ ) works with outputs of the most popular bioinformatic predictors of circRNAs to design specific circular RNA primers. CircPrime works with circRNA coordinates and any reference genome from the National Center for Biotechnology Information database).

    Matched MeSH terms: Computational Biology/methods
  10. Fulltext Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features
    Mahmud SMH, Goh KOM, Hosen MF, Nandi D, Shoombuatong W
    Sci Rep, 2024 Feb 05;14(1):2961.
    PMID: 38316843 DOI: 10.1038/s41598-024-52653-9
    DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.
    Matched MeSH terms: Computational Biology/methods
  11. Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure
    Al-Khatib RM, Rashid NA, Abdullah R
    J Biomol Struct Dyn, 2011 Aug;29(1):1-26.
    PMID: 21696223
    The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.
    Matched MeSH terms: Computational Biology/methods*
  12. Fulltext Challenges of the next decade for the Asia Pacific region: 2010 International Conference in Bioinformatics (InCoB 2010)
    Ranganathan S, Schönbach C, Nakai K, Tan TW
    BMC Genomics, 2010;11 Suppl 4:S1.
    PMID: 21143792 DOI: 10.1186/1471-2164-11-S4-S1
    The 2010 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation formed in 1998, was organized as the 9th International Conference on Bioinformatics (InCoB), Sept. 26-28, 2010 in Tokyo, Japan. Initially, APBioNet created InCoB as forum to foster bioinformatics in the Asia Pacific region. Given the growing importance of interdisciplinary research, InCoB2010 included topics targeting scientists in the fields of genomic medicine, immunology and chemoinformatics, supporting translational research. Peer-reviewed manuscripts that were accepted for publication in this supplement, represent key areas of research interests that have emerged in our region. We also highlight some of the current challenges bioinformatics is facing in the Asia Pacific region and conclude our report with the announcement of APBioNet's 100 BioDatabases (BioDB100) initiative. BioDB100 will comply with the database criteria set out earlier in our proposal for Minimum Information about a Bioinformatics and Investigation (MIABi), setting the standards for biocuration and bioinformatics research, on which we will report at the next InCoB, Nov. 27 - Dec. 2, 2011 at Kuala Lumpur, Malaysia.
    Matched MeSH terms: Computational Biology/methods
  13. A Review of Gene Knockout Strategies for Microbial Cells
    Tang PW, Chua PS, Chong SK, Mohamad MS, Choon YW, Deris S, et al.
    Recent Pat Biotechnol, 2015;9(3):176-97.
    PMID: 27185502
    BACKGROUND: Predicting the effects of genetic modification is difficult due to the complexity of metabolic net- works. Various gene knockout strategies have been utilised to deactivate specific genes in order to determine the effects of these genes on the function of microbes. Deactivation of genes can lead to deletion of certain proteins and functions. Through these strategies, the associated function of a deleted gene can be identified from the metabolic networks.

    METHODS: The main aim of this paper is to review the available techniques in gene knockout strategies for microbial cells. The review is done in terms of their methodology, recent applications in microbial cells. In addition, the advantages and disadvantages of the techniques are compared and discuss and the related patents are also listed as well.

    RESULTS: Traditionally, gene knockout is done through wet lab (in vivo) techniques, which were conducted through laboratory experiments. However, these techniques are costly and time consuming. Hence, various dry lab (in silico) techniques, where are conducted using computational approaches, have been developed to surmount these problem.

    CONCLUSION: The development of numerous techniques for gene knockout in microbial cells has brought many advancements in the study of gene functions. Based on the literatures, we found that the gene knockout strategies currently used are sensibly implemented with regard to their benefits.

    Matched MeSH terms: Computational Biology/methods
  14. An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
    Ismail AM, Mohamad MS, Abdul Majid H, Abas KH, Deris S, Zaki N, et al.
    Biosystems, 2017 Dec;162:81-89.
    PMID: 28951204 DOI: 10.1016/j.biosystems.2017.09.013
    Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.
    Matched MeSH terms: Computational Biology/methods*
  15. Random forests for predicting species identity of forensically important blow flies (Diptera: Calliphoridae) and flesh flies (Diptera: Sarcophagidae) using geometric morphometric data: Proof of concept
    Khang TF, Mohd Puaad NAD, Teh SH, Mohamed Z
    J Forensic Sci, 2021 May;66(3):960-970.
    PMID: 33438785 DOI: 10.1111/1556-4029.14655
    Wing shape variation has been shown to be useful for delineating forensically important fly species in two Diptera families: Calliphoridae and Sarcophagidae. Compared to DNA-based identification, the cost of geometric morphometric data acquisition and analysis is relatively much lower because the tools required are basic, and stable softwares are available. However, to date, an explicit demonstration of using wing geometric morphometric data for species identity prediction in these two families remains lacking. Here, geometric morphometric data from 19 homologous landmarks on the left wing of males from seven species of Calliphoridae (n = 55), and eight species of Sarcophagidae (n = 40) were obtained and processed using Generalized Procrustes Analysis. Allometric effect was removed by regressing centroid size (in log10 ) against the Procrustes coordinates. Subsequently, principal component analysis of the allometry-adjusted Procrustes variables was done, with the first 15 principal components used to train a random forests model for species prediction. Using a real test sample consisting of 33 male fly specimens collected around a human corpse at a crime scene, the estimated percentage of concordance between species identities predicted using the random forests model and those inferred using DNA-based identification was about 80.6% (approximate 95% confidence interval = [68.9%, 92.2%]). In contrast, baseline concordance using naive majority class prediction was 36.4%. The results provide proof of concept that geometric morphometric data has good potential to complement morphological and DNA-based identification of blow flies and flesh flies in forensic work.
    Matched MeSH terms: Computational Biology/methods*
  16. Identification of metabolic pathways using pathfinding approaches: a systematic review
    Abd Algfoor Z, Shahrizal Sunar M, Abdullah A, Kolivand H
    Brief Funct Genomics, 2017 03 01;16(2):87-98.
    PMID: 26969656 DOI: 10.1093/bfgp/elw002
    Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.
    Matched MeSH terms: Computational Biology/methods*
  17. Fulltext The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies
    Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan KG, et al.
    F1000Res, 2018;7.
    PMID: 30026930 DOI: 10.12688/f1000research.14509.2
    Next-Generation Sequencing (NGS) technologies are expected to play a crucial role in the surveillance of infectious diseases, with their unprecedented capabilities for the characterisation of genetic information underlying the virulence and antimicrobial resistance (AMR) properties of microorganisms.  In the implementation of any novel technology for regulatory purposes, important considerations such as harmonisation, validation and quality assurance need to be addressed.  NGS technologies pose unique challenges in these regards, in part due to their reliance on bioinformatics for the processing and proper interpretation of the data produced.  Well-designed benchmark resources are thus needed to evaluate, validate and ensure continued quality control over the bioinformatics component of the process.  This concept was explored as part of a workshop on "Next-generation sequencing technologies and antimicrobial resistance" held October 4-5 2017.   Challenges involved in the development of such a benchmark resource, with a specific focus on identifying the molecular determinants of AMR, were identified. For each of the challenges, sets of unsolved questions that will need to be tackled for them to be properly addressed were compiled. These take into consideration the requirement for monitoring of AMR bacteria in humans, animals, food and the environment, which is aligned with the principles of a "One Health" approach.
    Matched MeSH terms: Computational Biology/methods*
  18. An analysis of simple computational strategies to facilitate the design of functional molecular information processors
    Lee Y, Roslan R, Azizan S, Firdaus-Raih M, Ramlan EI
    BMC Bioinformatics, 2016 Oct 28;17(1):438.
    PMID: 27793081
    BACKGROUND: Biological macromolecules (DNA, RNA and proteins) are capable of processing physical or chemical inputs to generate outputs that parallel conventional Boolean logical operators. However, the design of functional modules that will enable these macromolecules to operate as synthetic molecular computing devices is challenging.

    RESULTS: Using three simple heuristics, we designed RNA sensors that can mimic the function of a seven-segment display (SSD). Ten independent and orthogonal sensors representing the numerals 0 to 9 are designed and constructed. Each sensor has its own unique oligonucleotide binding site region that is activated uniquely by a specific input. Each operator was subjected to a stringent in silico filtering. Random sensors were selected and functionally validated via ribozyme self cleavage assays that were visualized via electrophoresis.

    CONCLUSIONS: By utilising simple permutation and randomisation in the sequence design phase, we have developed functional RNA sensors thus demonstrating that even the simplest of computational methods can greatly aid the design phase for constructing functional molecular devices.

    Matched MeSH terms: Computational Biology/methods*
  19. Samira-VP: A simple protein alignment method with rechecking the alphabet vector positions
    Fotoohifiroozabadi S, Mohamad MS, Deris S
    J Bioinform Comput Biol, 2017 Apr;15(2):1750004.
    PMID: 28274174 DOI: 10.1142/S0219720017500044
    Protein structure alignment and comparisons that are based on an alphabetical demonstration of protein structure are more simple to run with faster evaluation processes; thus, their accuracy is not as reliable as three-dimension (3D)-based tools. As a 1D method candidate, TS-AMIR used the alphabetic demonstration of secondary-structure elements (SSE) of proteins and compared the assigned letters to each SSE using the [Formula: see text]-gram method. Although the results were comparable to those obtained via geometrical methods, the SSE length and accuracy of adjacency between SSEs were not considered in the comparison process. Therefore, to obtain further information on accuracy of adjacency between SSE vectors, the new approach of assigning text to vectors was adopted according to the spherical coordinate system in the present study. Moreover, dynamic programming was applied in order to account for the length of SSE vectors. Five common datasets were selected for method evaluation. The first three datasets were small, but difficult to align, and the remaining two datasets were used to compare the capability of the proposed method with that of other methods on a large protein dataset. The results showed that the proposed method, as a text-based alignment approach, obtained results comparable to both 1D and 3D methods. It outperformed 1D methods in terms of accuracy and 3D methods in terms of runtime.
    Matched MeSH terms: Computational Biology/methods*
  20. The Extended Polydimensional Immunome Characterization (EPIC) web-based reference and discovery tool for cytometry data
    Yeo JG, Wasser M, Kumar P, Pan L, Poh SL, Ally F, et al.
    Nat Biotechnol, 2020 06;38(6):679-684.
    PMID: 32440006 DOI: 10.1038/s41587-020-0532-1
    Matched MeSH terms: Computational Biology/methods*
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