Displaying publications 41 - 60 of 119 in total

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  1. Fulltext The genome of newly classified Ochroconis mirabilis: Insights into fungal adaptation to different living conditions
    Yew SM, Chan CL, Kuan CS, Toh YF, Ngeow YF, Na SL, et al.
    BMC Genomics, 2016 Feb 03;17:91.
    PMID: 26842951 DOI: 10.1186/s12864-016-2409-8
    Ochroconis mirabilis, a recently introduced water-borne dematiaceous fungus, is occasionally isolated from human skin lesions and nails. We identified an isolate of O. mirabilis from a skin scraping with morphological and molecular studies. Its genome was then sequenced and analysed for genetic features related to classification and biological characteristics.
    Matched MeSH terms: Computational Biology/methods
  2. Shotgun Proteomics and Mass Spectrometry as a Tool for Protein Identification and Profiling of Bio-Carrier-Based Therapeutics on Human Cancer Cells
    Abidin SAZ, Othman I, Naidu R
    Methods Mol Biol, 2021;2211:233-240.
    PMID: 33336281 DOI: 10.1007/978-1-0716-0943-9_16
    Shotgun proteomics has been widely applied to study proteins in complex biological samples. Combination of high-performance liquid chromatography with mass spectrometry has allowed for comprehensive protein analysis with high resolution, sensitivity, and mass accuracy. Prior to mass spectrometry analysis, proteins are extracted from biological samples and subjected to in-solution trypsin digestion. The digested proteins are subjected for clean-up and injected into the liquid chromatography-mass spectrometry system for peptide mass identification. Protein identification is performed by analyzing the mass spectrometry data on a protein search engine software such as PEAKS studio loaded with protein database for the species of interest. Results such as protein score, protein coverage, number of peptides, and unique peptides identified will be obtained and can be used to determine proteins identified with high confidence. This method can be applied to understand the proteomic changes or profile brought by bio-carrier-based therapeutics in vitro. In this chapter, we describe methods in which proteins can be extracted for proteomic analysis using a shotgun approach. The chapter outlines important in vitro techniques and data analysis that can be applied to investigate the proteome dynamics.
    Matched MeSH terms: Computational Biology/methods
  3. Fulltext Evolutionary and genomic analysis of the caleosin/peroxygenase (CLO/PXG) gene/protein families in the Viridiplantae
    Rahman F, Hassan M, Rosli R, Almousally I, Hanano A, Murphy DJ
    PLoS One, 2018;13(5):e0196669.
    PMID: 29771926 DOI: 10.1371/journal.pone.0196669
    Bioinformatics analyses of caleosin/peroxygenases (CLO/PXG) demonstrated that these genes are present in the vast majority of Viridiplantae taxa for which sequence data are available. Functionally active CLO/PXG proteins with roles in abiotic stress tolerance and lipid droplet storage are present in some Trebouxiophycean and Chlorophycean green algae but are absent from the small number of sequenced Prasinophyceaen genomes. CLO/PXG-like genes are expressed during dehydration stress in Charophyte algae, a sister clade of the land plants (Embryophyta). CLO/PXG-like sequences are also present in all of the >300 sequenced Embryophyte genomes, where some species contain as many as 10-12 genes that have arisen via selective gene duplication. Angiosperm genomes harbour at least one copy each of two distinct CLO/PX isoforms, termed H (high) and L (low), where H-forms contain an additional C-terminal motif of about 30-50 residues that is absent from L-forms. In contrast, species in other Viridiplantae taxa, including green algae, non-vascular plants, ferns and gymnosperms, contain only one (or occasionally both) of these isoforms per genome. Transcriptome and biochemical data show that CLO/PXG-like genes have complex patterns of developmental and tissue-specific expression. CLO/PXG proteins can associate with cytosolic lipid droplets and/or bilayer membranes. Many of the analysed isoforms also have peroxygenase activity and are involved in oxylipin metabolism. The distribution of CLO/PXG-like genes is consistent with an origin >1 billion years ago in at least two of the earliest diverging groups of the Viridiplantae, namely the Chlorophyta and the Streptophyta, after the Viridiplantae had already diverged from other Archaeplastidal groups such as the Rhodophyta and Glaucophyta. While algal CLO/PXGs have roles in lipid packaging and stress responses, the Embryophyte proteins have a much wider spectrum of roles and may have been instrumental in the colonisation of terrestrial habitats and the subsequent diversification as the major land flora.
    Matched MeSH terms: Computational Biology/methods
  4. Fulltext Comparative genomic and transcriptomic analysis of selected fatty acid biosynthesis genes and CNL disease resistance genes in oil palm
    Rosli R, Amiruddin N, Ab Halim MA, Chan PL, Chan KL, Azizi N, et al.
    PLoS One, 2018;13(4):e0194792.
    PMID: 29672525 DOI: 10.1371/journal.pone.0194792
    Comparative genomics and transcriptomic analyses were performed on two agronomically important groups of genes from oil palm versus other major crop species and the model organism, Arabidopsis thaliana. The first analysis was of two gene families with key roles in regulation of oil quality and in particular the accumulation of oleic acid, namely stearoyl ACP desaturases (SAD) and acyl-acyl carrier protein (ACP) thioesterases (FAT). In both cases, these were found to be large gene families with complex expression profiles across a wide range of tissue types and developmental stages. The detailed classification of the oil palm SAD and FAT genes has enabled the updating of the latest version of the oil palm gene model. The second analysis focused on disease resistance (R) genes in order to elucidate possible candidates for breeding of pathogen tolerance/resistance. Ortholog analysis showed that 141 out of the 210 putative oil palm R genes had homologs in banana and rice. These genes formed 37 clusters with 634 orthologous genes. Classification of the 141 oil palm R genes showed that the genes belong to the Kinase (7), CNL (95), MLO-like (8), RLK (3) and Others (28) categories. The CNL R genes formed eight clusters. Expression data for selected R genes also identified potential candidates for breeding of disease resistance traits. Furthermore, these findings can provide information about the species evolution as well as the identification of agronomically important genes in oil palm and other major crops.
    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 Comparative genome-wide analysis of WRKY, MADS-box and MYB transcription factor families in Arabidopsis and rice
    Abdullah-Zawawi MR, Ahmad-Nizammuddin NF, Govender N, Harun S, Mohd-Assaad N, Mohamed-Hussein ZA
    Sci Rep, 2021 10 04;11(1):19678.
    PMID: 34608238 DOI: 10.1038/s41598-021-99206-y
    Transcription factors (TFs) form the major class of regulatory genes and play key roles in multiple plant stress responses. In most eukaryotic plants, transcription factor (TF) families (WRKY, MADS-box and MYB) activate unique cellular-level abiotic and biotic stress-responsive strategies, which are considered as key determinants for defense and developmental processes. Arabidopsis and rice are two important representative model systems for dicot and monocot plants, respectively. A comprehensive comparative study on 101 OsWRKY, 34 OsMADS box and 122 OsMYB genes (rice genome) and, 71 AtWRKY, 66 AtMADS box and 144 AtMYB genes (Arabidopsis genome) showed various relationships among TFs across species. The phylogenetic analysis clustered WRKY, MADS-box and MYB TF family members into 10, 7 and 14 clades, respectively. All clades in WRKY and MYB TF families and almost half of the total number of clades in the MADS-box TF family are shared between both species. Chromosomal and gene structure analysis showed that the Arabidopsis-rice orthologous TF gene pairs were unevenly localized within their chromosomes whilst the distribution of exon-intron gene structure and motif conservation indicated plausible functional similarity in both species. The abiotic and biotic stress-responsive cis-regulatory element type and distribution patterns in the promoter regions of Arabidopsis and rice WRKY, MADS-box and MYB orthologous gene pairs provide better knowledge on their role as conserved regulators in both species. Co-expression network analysis showed the correlation between WRKY, MADs-box and MYB genes in each independent rice and Arabidopsis network indicating their role in stress responsiveness and developmental processes.
    Matched MeSH terms: Computational Biology/methods
  7. Fulltext Protein-Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome
    Ramly B, Afiqah-Aleng N, Mohamed-Hussein ZA
    Int J Mol Sci, 2019 Jun 18;20(12).
    PMID: 31216618 DOI: 10.3390/ijms20122959
    Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein-protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher's exact test and comorbidity data were used to identify PCOS-disease subnetworks. Pathway enrichment analysis was performed on the PCOS-disease subnetworks to identify significant pathways that are highly involved in the PCOS-disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.
    Matched MeSH terms: Computational Biology/methods
  8. 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*
  9. Fulltext Molecular study of wound healing after using biosynthesized BNC/Fe3O4 nanocomposites assisted with a bioinformatics approach
    Moniri M, Boroumand Moghaddam A, Azizi S, Abdul Rahim R, Zuhainis Saad W, Navaderi M, et al.
    Int J Nanomedicine, 2018;13:2955-2971.
    PMID: 29861630 DOI: 10.2147/IJN.S159637
    Background: Molecular investigation of wound healing has allowed better understanding about interaction of genes and pathways involved in healing progression.

    Objectives: The aim of this study was to prepare magnetic/bacterial nanocellulose (Fe3O4/BNC) nanocomposite films as ecofriendly wound dressing in order to evaluate their physical, cytotoxicity and antimicrobial properties. The molecular study was carried out to evaluate expression of genes involved in healing of wounds after treatment with BNC/Fe3O4 films.

    Study design materials and methods: Magnetic nanoparticles were biosynthesized by using Aloe vera extract in new isolated bacterial nanocellulose (BNC) RM1. The nanocomposites were characterized using X-ray diffraction, Fourier transform infrared, and field emission scanning electron microscopy. Moreover, swelling property and metal ions release profile of the nanocomposites were investigated. The ability of nanocomposites to promote wound healing of human dermal fibroblast cells in vitro was examined. Bioinformatics databases were used to identify genes with important healing effect. Key genes which interfered with healing were studied by quantitative real time PCR.

    Results: Spherical magnetic nanoparticles (15-30 nm) were formed and immobilized within the structure of BNC. The BNC/Fe3O4 was nontoxic (IC50>500 μg/mL) with excellent wound healing efficiency after 48 hours. The nanocomposites showed good antibacterial activity ranging from 6±0.2 to 13.40±0.10 mm against Staphylococcus aureus, Staphylococcus epidermidis and Pseudomonas aeruginosa. The effective genes for the wound healing process were TGF-B1, MMP2, MMP9, Wnt4, CTNNB1, hsa-miR-29b, and hsa-miR-29c with time dependent manner. BNC/Fe3O4 has an effect on microRNA by reducing its expression and therefore causing an increase in the gene expression of other genes, which consequently resulted in wound healing.

    Conclusion: This eco-friendly nanocomposite with excellent healing properties can be used as an effective wound dressing for treatment of cutaneous wounds.

    Matched MeSH terms: Computational Biology/methods
  10. Detection of copy number variations in epilepsy using exome data
    Tsuchida N, Nakashima M, Kato M, Heyman E, Inui T, Haginoya K, et al.
    Clin Genet, 2018 03;93(3):577-587.
    PMID: 28940419 DOI: 10.1111/cge.13144
    Epilepsies are common neurological disorders and genetic factors contribute to their pathogenesis. Copy number variations (CNVs) are increasingly recognized as an important etiology of many human diseases including epilepsy. Whole-exome sequencing (WES) is becoming a standard tool for detecting pathogenic mutations and has recently been applied to detecting CNVs. Here, we analyzed 294 families with epilepsy using WES, and focused on 168 families with no causative single nucleotide variants in known epilepsy-associated genes to further validate CNVs using 2 different CNV detection tools using WES data. We confirmed 18 pathogenic CNVs, and 2 deletions and 2 duplications at chr15q11.2 of clinically unknown significance. Of note, we were able to identify small CNVs less than 10 kb in size, which might be difficult to detect by conventional microarray. We revealed 2 cases with pathogenic CNVs that one of the 2 CNV detection tools failed to find, suggesting that using different CNV tools is recommended to increase diagnostic yield. Considering a relatively high discovery rate of CNVs (18 out of 168 families, 10.7%) and successful detection of CNV with <10 kb in size, CNV detection by WES may be able to surrogate, or at least complement, conventional microarray analysis.
    Matched MeSH terms: Computational Biology/methods
  11. Fulltext NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning
    Høie MH, Kiehl EN, Petersen B, Nielsen M, Winther O, Nielsen H, et al.
    Nucleic Acids Res, 2022 Jul 05;50(W1):W510-W515.
    PMID: 35648435 DOI: 10.1093/nar/gkac439
    Recent advances in machine learning and natural language processing have made it possible to profoundly advance our ability to accurately predict protein structures and their functions. While such improvements are significantly impacting the fields of biology and biotechnology at large, such methods have the downside of high demands in terms of computing power and runtime, hampering their applicability to large datasets. Here, we present NetSurfP-3.0, a tool for predicting solvent accessibility, secondary structure, structural disorder and backbone dihedral angles for each residue of an amino acid sequence. This NetSurfP update exploits recent advances in pre-trained protein language models to drastically improve the runtime of its predecessor by two orders of magnitude, while displaying similar prediction performance. We assessed the accuracy of NetSurfP-3.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features, with a runtime that is up to to 600 times faster than the most commonly available methods performing the same tasks. The tool is freely available as a web server with a user-friendly interface to navigate the results, as well as a standalone downloadable package.
    Matched MeSH terms: Computational Biology/methods
  12. Fulltext Identification of NRAS Diagnostic Biomarkers and Drug Targets for Endometrial Cancer-An Integrated in Silico Approach
    Alessandro L, Low KE, Abushelaibi A, Lim SE, Cheng WH, Chang SK, et al.
    Int J Mol Sci, 2022 Nov 18;23(22).
    PMID: 36430761 DOI: 10.3390/ijms232214285
    The diagnosis of endometrial cancer involves sequential, invasive tests to assess the thickness of the endometrium by a transvaginal ultrasound scan. In 6−33% of cases, endometrial biopsy results in inadequate tissue for a conclusive pathological diagnosis and 6% of postmenopausal women with non-diagnostic specimens are later discovered to have severe endometrial lesions. Thus, identifying diagnostic biomarkers could offer a non-invasive diagnosis for community or home-based triage of symptomatic or asymptomatic women. Herein, this study identified high-risk pathogenic nsSNPs in the NRAS gene. The nsSNPs of NRAS were retrieved from the NCBI database. PROVEAN, SIFT, PolyPhen-2, SNPs&GO, PhD-SNP and PANTHER were used to predict the pathogenicity of the nsSNPs. Eleven nsSNPs were identified as “damaging”, and further stability analysis using I-Mutant 2.0 and MutPred 2 indicated eight nsSNPs to cause decreased stability (DDG scores < −0.5). Post-translational modification and protein−protein interactions (PPI) analysis showed putative phosphorylation sites. The PPI network indicated a GFR-MAPK signalling pathway with higher node degrees that were further evaluated for drug targets. The P34L, G12C and Y64D showed significantly lower binding affinity towards GTP than wild-type. Furthermore, the Kaplan−Meier bioinformatics analyses indicated that the NRAS gene deregulation affected the overall survival rate of patients with endometrial cancer, leading to prognostic significance. Findings from this could be considered novel diagnostic and therapeutic markers.
    Matched MeSH terms: Computational Biology/methods
  13. 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*
  14. Fulltext Evidence-based gene models for structural and functional annotations of the oil palm genome
    Chan KL, Tatarinova TV, Rosli R, Amiruddin N, Azizi N, Halim MAA, et al.
    Biol. Direct, 2017 Sep 08;12(1):21.
    PMID: 28886750 DOI: 10.1186/s13062-017-0191-4
    BACKGROUND: Oil palm is an important source of edible oil. The importance of the crop, as well as its long breeding cycle (10-12 years) has led to the sequencing of its genome in 2013 to pave the way for genomics-guided breeding. Nevertheless, the first set of gene predictions, although useful, had many fragmented genes. Classification and characterization of genes associated with traits of interest, such as those for fatty acid biosynthesis and disease resistance, were also limited. Lipid-, especially fatty acid (FA)-related genes are of particular interest for the oil palm as they specify oil yields and quality. This paper presents the characterization of the oil palm genome using different gene prediction methods and comparative genomics analysis, identification of FA biosynthesis and disease resistance genes, and the development of an annotation database and bioinformatics tools.

    RESULTS: Using two independent gene-prediction pipelines, Fgenesh++ and Seqping, 26,059 oil palm genes with transcriptome and RefSeq support were identified from the oil palm genome. These coding regions of the genome have a characteristic broad distribution of GC3 (fraction of cytosine and guanine in the third position of a codon) with over half the GC3-rich genes (GC3 ≥ 0.75286) being intronless. In comparison, only one-seventh of the oil palm genes identified are intronless. Using comparative genomics analysis, characterization of conserved domains and active sites, and expression analysis, 42 key genes involved in FA biosynthesis in oil palm were identified. For three of them, namely EgFABF, EgFABH and EgFAD3, segmental duplication events were detected. Our analysis also identified 210 candidate resistance genes in six classes, grouped by their protein domain structures.

    CONCLUSIONS: We present an accurate and comprehensive annotation of the oil palm genome, focusing on analysis of important categories of genes (GC3-rich and intronless), as well as those associated with important functions, such as FA biosynthesis and disease resistance. The study demonstrated the advantages of having an integrated approach to gene prediction and developed a computational framework for combining multiple genome annotations. These results, available in the oil palm annotation database ( http://palmxplore.mpob.gov.my ), will provide important resources for studies on the genomes of oil palm and related crops.

    REVIEWERS: This article was reviewed by Alexander Kel, Igor Rogozin, and Vladimir A. Kuznetsov.

    Matched MeSH terms: Computational Biology/methods
  15. In silico approaches for probiotic-derived bioactives
    Tan PL, Liong MT
    Trends Biotechnol, 2014 Dec;32(12):599-601.
    PMID: 25457386 DOI: 10.1016/j.tibtech.2014.09.011
    Matched MeSH terms: Computational Biology/methods*
  16. Fulltext Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context
    Nazri A, Lio P
    PLoS One, 2012;7(1):e28713.
    PMID: 22253694 DOI: 10.1371/journal.pone.0028713
    The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.
    Matched MeSH terms: Computational Biology/methods*
  17. In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
    Ealam Selvan M, Lim KS, Teo CH, Lim YY
    J Vis Exp, 2022 Oct 21.
    PMID: 36342167 DOI: 10.3791/64565
    Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as regulators of various biological processes. Notably, emerging evidence demonstrates that host circRNAs can be differentially expressed (DE) upon infection with pathogens (e.g., influenza and coronaviruses), suggesting a role for circRNAs in regulating host innate immune responses. However, investigations on the role of circRNAs during pathogenic infections are limited by the knowledge and skills required to carry out the necessary bioinformatic analysis to identify DE circRNAs from RNA sequencing (RNA-seq) data. Bioinformatics prediction and identification of circRNAs is crucial before any verification, and functional studies using costly and time-consuming wet-lab techniques. To solve this issue, a step-by-step protocol of in silico prediction and characterization of circRNAs using RNA-seq data is provided in this manuscript. The protocol can be divided into four steps: 1) Prediction and quantification of DE circRNAs via the CIRIquant pipeline; 2) Annotation via circBase and characterization of DE circRNAs; 3) CircRNA-miRNA interaction prediction through Circr pipeline; 4) functional enrichment analysis of circRNA parental genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). This pipeline will be useful in driving future in vitro and in vivo research to further unravel the role of circRNAs in host-pathogen interactions.
    Matched MeSH terms: Computational Biology/methods
  18. From discovery to spread: The evolution and phylogeny of Getah virus
    Li YY, Liu H, Fu SH, Li XL, Guo XF, Li MH, et al.
    Infect Genet Evol, 2017 11;55:48-55.
    PMID: 28827175 DOI: 10.1016/j.meegid.2017.08.016
    Getah virus (GETV) was first isolated in Malaysia in 1955. Since then, epidemics in horses and pigs caused by GETV have resulted in huge economic losses. At present, GETV has spread across Eurasia and Southeast Asia, including mainland China, Korea, Japan, Mongolia, and Russia. Data show that the Most Recent Common Ancestor (MRCA) of GETV existed about 145years ago (95% HPD: 75-244) and gradually evolved into four distinct evolutionary populations: Groups I-IV. The MRCA of GETVs in Group III, which includes all GETVs isolated from mosquitoes, pigs, horses, and other animals since the 1960s (from latitude 19°N to 60°N), existed about 51years ago (95% HPD: 51-72). Group III is responsible for most viral epidemics among domestic animals. An analysis of the GETV E2 protein sequence and structure revealed seven common amino acid mutation sites. These sites are responsible for the structural and electrostatic differences detected between widespread Group III isolates and the prototype strain MM2021. These differences may account for the recent geographical radiation of the virus. Considering the economic significance of GETV infection in pigs and horses, we recommend the implementation of strict viral screening and monitoring programs.
    Matched MeSH terms: Computational Biology/methods
  19. Fulltext Large-scale 3D chromatin reconstruction from chromosomal contacts
    Zhang Y, Liu W, Lin Y, Ng YK, Li S
    BMC Genomics, 2019 Apr 04;20(Suppl 2):186.
    PMID: 30967119 DOI: 10.1186/s12864-019-5470-2
    BACKGROUND: Recent advances in genome analysis have established that chromatin has preferred 3D conformations, which bring distant loci into contact. Identifying these contacts is important for us to understand possible interactions between these loci. This has motivated the creation of the Hi-C technology, which detects long-range chromosomal interactions. Distance geometry-based algorithms, such as ChromSDE and ShRec3D, have been able to utilize Hi-C data to infer 3D chromosomal structures. However, these algorithms, being matrix-based, are space- and time-consuming on very large datasets. A human genome of 100 kilobase resolution would involve ∼30,000 loci, requiring gigabytes just in storing the matrices.

    RESULTS: We propose a succinct representation of the distance matrices which tremendously reduces the space requirement. We give a complete solution, called SuperRec, for the inference of chromosomal structures from Hi-C data, through iterative solving the large-scale weighted multidimensional scaling problem.

    CONCLUSIONS: SuperRec runs faster than earlier systems without compromising on result accuracy. The SuperRec package can be obtained from http://www.cs.cityu.edu.hk/~shuaicli/SuperRec .

    Matched MeSH terms: Computational Biology/methods*
  20. Fulltext Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design
    Kazi A, Chuah C, Majeed ABA, Leow CH, Lim BH, Leow CY
    Pathog Glob Health, 2018 05;112(3):123-131.
    PMID: 29528265 DOI: 10.1080/20477724.2018.1446773
    Immunoinformatics plays a pivotal role in vaccine design, immunodiagnostic development, and antibody production. In the past, antibody design and vaccine development depended exclusively on immunological experiments which are relatively expensive and time-consuming. However, recent advances in the field of immunological bioinformatics have provided feasible tools which can be used to lessen the time and cost required for vaccine and antibody development. This approach allows the selection of immunogenic regions from the pathogen genomes. The ideal regions could be developed as potential vaccine candidates to trigger protective immune responses in the hosts. At present, epitope-based vaccines are attractive concepts which have been successfully trailed to develop vaccines which target rapidly mutating pathogens. In this article, we provide an overview of the current progress of immunoinformatics and their applications in the vaccine design, immune system modeling and therapeutics.
    Matched MeSH terms: Computational Biology/methods*
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