Pangolins (order Pholidota) are the only mammals covered by scales. We have recently sequenced and analyzed the genomes of two critically endangered Asian pangolin species, namely the Malayan pangolin (Manis javanica) and the Chinese pangolin (Manis pentadactyla). These complete genome sequences will serve as reference sequences for future research to address issues of species conservation and to advance knowledge in mammalian biology and evolution. To further facilitate the global research effort in pangolin biology, we developed the Pangolin Genome Database (PGD), as a future hub for hosting pangolin genomic and transcriptomic data and annotations, and with useful analysis tools for the research community. Currently, the PGD provides the reference pangolin genome and transcriptome data, gene sequences and functional information, expressed transcripts, pseudogenes, genomic variations, organ-specific expression data and other useful annotations. We anticipate that the PGD will be an invaluable platform for researchers who are interested in pangolin and mammalian research. We will continue updating this hub by including more data, annotation and analysis tools particularly from our research consortium.Database URL: http://pangolin-genome.um.edu.my.
Bamboo and rattan are widely grown for manufacturing, horticulture, and agroforestry. Bamboo and rattan production might help reduce poverty, boost economic growth, mitigate climate change, and protect the natural environment. Despite progress in research, sufficient molecular and genomic resources to study these species are lacking. We launched the Genome Atlas of Bamboo and Rattan (GABR) project, a comprehensive, coordinated international effort to accelerate understanding of bamboo and rattan genetics through genome analysis. GABR includes 2 core subprojects: Bamboo-T1K (Transcriptomes of 1000 Bamboos) and Rattan-G5 (Genomes of 5 Rattans), and several other subprojects. Here we describe the organization, directions, and status of GABR.
Efficient extraction of knowledge from biological data requires the development of structured vocabularies to unambiguously define biological terms. This paper proposes descriptions and definitions to disambiguate the term 'single-exon gene'. Eukaryotic Single-Exon Genes (SEGs) have been defined as genes that do not have introns in their protein coding sequences. They have been studied not only to determine their origin and evolution but also because their expression has been linked to several types of human cancer and neurological/developmental disorders and many exhibit tissue-specific transcription. Unfortunately, the term 'SEGs' is rife with ambiguity, leading to biological misinterpretations. In the classic definition, no distinction is made between SEGs that harbor introns in their untranslated regions (UTRs) versus those without. This distinction is important to make because the presence of introns in UTRs affects transcriptional regulation and post-transcriptional processing of the mRNA. In addition, recent whole-transcriptome shotgun sequencing has led to the discovery of many examples of single-exon mRNAs that arise from alternative splicing of multi-exon genes, these single-exon isoforms are being confused with SEGs despite their clearly different origin. The increasing expansion of RNA-seq datasets makes it imperative to distinguish the different SEG types before annotation errors become indelibly propagated in biological databases. This paper develops a structured vocabulary for their disambiguation, allowing a major reassessment of their evolutionary trajectories, regulation, RNA processing and transport, and provides the opportunity to improve the detection of gene associations with disorders including cancers, neurological and developmental diseases.
In gene expression studies, missing values are a common problem with important consequences for the interpretation of the final data (Satija et al., Nat Biotechnol 33(5):495, 2015). Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al., Cell 173(2):371-385, 2018); thus, it is necessary to resolve the problem of missing-values imputation. This chapter presents a review of the research on missing-values imputation approaches for gene expression data. By using local and global correlation of the data, we were able to focus mostly on the differences between the algorithms. We classified the algorithms as global, hybrid, local, or knowledge-based techniques. Additionally, this chapter presents suitable assessments of the different approaches. The purpose of this review is to focus on developments in the current techniques for scientists rather than applying different or newly developed algorithms with identical functional goals. The aim was to adapt the algorithms to the characteristics of the data.
The objective of this study was to build a DNA barcode reference library for the true butterflies of Peninsula Malaysia and assess the value of attaching subspecies names to DNA barcode records. A new DNA barcode library was constructed with butterflies from the Museum of Zoology, University of Malaya collection. The library was analysed in conjunction with publicly available DNA barcodes from other Asia-Pacific localities to test the ability of the DNA barcodes to discriminate species and subspecies. Analyses confirmed the capacity of the new DNA barcode reference library to distinguish the vast majority of species (92%) and revealed that most subspecies possessed unique DNA barcodes (84%). In some cases conspecific subspecies exhibited genetic distances between their DNA barcodes that are typically seen between species, and these were often taxa that have previously been regarded as full species. Subspecies designations as shorthand for geographically and morphologically differentiated groups provide a useful heuristic for assessing how such groups correlate with clustering patterns of DNA barcodes, especially as the number of DNA barcodes per species in reference libraries increases. Our study demonstrates the value in attaching subspecies names to DNA barcode records as they can reveal a history of taxonomic concepts and expose important units of biodiversity.
Functional genomics has proven to be an efficient tool in identifying genes involved in various biological functions. However the availability of commercially important seaweed Eucheuma denticulatum functional resources is still limited. EuDBase is the first seaweed online repository that provides integrated access to ESTs of Eucheuma denticulatum generated from samples collected from Kudat and Semporna in Sabah, Malaysia. The database stored 10,031 ESTs that are clustered and assembled into 2,275 unique transcripts (UT) and 955 singletons. Raw data were automatically processed using ESTFrontier, an in-house automated EST analysis pipeline. Data was collected in MySQL database. Web interface is implemented using PHP and it allows browsing and querying EuDBase through search engine. Data is searchable via BLAST hit, domain search, Gene Ontology or KEGG Pathway. A user-friendly interface allows the identification of sequences either using a simple text query or similarity search. The development of EuDBase is initiated to store, manage and analyze the E. denticulatum ESTs and to provide accumulative digital resources for the use of global scientific community. EuDBase is freely available from http://www.inbiosis.ukm.my/eudbase/.
Laryngeal cancer is the most common head and neck cancer in the world and its incidence is on the rise. However, the
molecular mechanism underlying laryngeal cancer pathogenesis is poorly understood. The goal of this study was to
develop a protein-protein interaction (PPI) network for laryngeal cancer to predict the biological pathways that underlie
the molecular complexes in the network. Genes involved in laryngeal cancer were extracted from the OMIM database
and their interaction partners were identified via text and data mining using Agilent Literature Search, STRING and
GeneMANIA. PPI network was then integrated and visualised using Cytoscape ver3.6.0. Molecular complexes in the
network were predicted by MCODE plugin and functional enrichment analyses of the molecular complexes were performed
using BiNGO. 28 laryngeal cancer-related genes were present in the OMIM database. The PPI network associated with
laryngeal cancer contained 161 nodes, 661 edges and five molecular complexes. Some of the complexes were related to
the biological behaviour of cancer, providing the foundation for further understanding of the mechanism of laryngeal
cancer development and progression.
The world's population is increasing very rapidly, reducing the cultivable land of rice, decreasing table water, emerging new diseases and pests, and the climate changes are major issues that must be addressed to researchers to develop sustainable crop varieties with resistance to biotic and abiotic stresses. However, recent scientific discoveries and advances particularly in genetics, genomics and crop physiology have opened up new opportunities to reduce the impact of these stresses which would have been difficult if not impossible as recently as the turn of the century. Marker assisted backcrossing (MABC) is one of the most promising approaches is the use of molecular markers to identify and select genes controlling resistance to those factors. Regarding this, MABC can contribute to develop resistant or high-yielding or quality rice varieties by incorporating a gene of interest into an elite variety which is already well adapted by the farmers. MABC is newly developed efficient tool by which using large population sizes (400 or more plants) for the backcross F1 generations, it is possible to recover the recurrent parent genotype using only two or three backcrosses. So far, many high yielding, biotic and abiotic stresses tolerance, quality and fragrance rice varieties have been developed in rice growing countries through MABC within the shortest timeframe. Nowadays, MABC is being used widely in plant breeding programmes to develop new variety/lines especially in rice. This paper reviews recent literature on some examples of variety/ line development using MABC strategy.
Germplasm collections are an important source for plant breeding, especially in fruit trees which have a long duration of juvenile period. Thus, efforts have been made to study the diversity of fruit tree collections. Even though mango is an economically important crop, most of the studies on diversity in mango collections have been conducted with a small number of genetic markers.
Molecular markers have been intensively used in assisting breeding to reduce the time taken by conventional breeding as well as helping introgression of specific traits. Baseline analysis of known markers is crucial in developing a genetic database on disease and pest resistance for local rice germplasm which does not yet
exist. In this study seven local rice varieties, including the popular MR219 and MRQ 74 and MRQ 76 (newly developed aromatic rice varieties), together with a foreign variety, Intani-2, were screened for genetic markers related to pest and disease resistance. One hundred and twenty-two type-related markers (SSR, STS, InDel and Allele-specific) for genes resistant to bacterial leaf blight, blast and brown planthopper were screened using PCR amplification and validated by sequencing. It was found that each variety had its own pattern of resistance. Using allele-specific markers namely pBPH9, pTA248 and Pisbdom were found to be the most efficient way to screen for the targeted genes. Of the seven varieties, MR219 and MR232 were found to have the highest distribution of markers for resistance genes against pest and diseases studied.
Linear time series models are not able to capture the behaviour of many financial time series, as in the cases of exchange rates and stock market data. Some phenomena, such as volatility and structural breaks in time series data, cannot be modelled implicitly using linear time series models. Therefore, nonlinear time series models are typically designed to accommodate for such nonlinear features. In the present study, a nonlinearity test and a structural change test are used to detect the nonlinearity and the break date in three ASEAN currencies, namely the Indonesian Rupiah (IDR), the Malaysian Ringgit (MYR) and the Thai Baht (THB). The study finds that the null hypothesis of linearity is rejected and evidence of structural breaks exist in the exchange rates series. Therefore, the decision to use the self-exciting threshold autoregressive (SETAR) model in the present study is justified. The results showed that the SETAR model, as a regime switching model, can explain abrupt changes in a time series. To evaluate the prediction performance of SETAR model, an Autoregressive Integrated Moving Average (ARIMA) model used as a benchmark. In order to increase the accuracy of prediction, both models are combined with an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model. The prediction results showed that the construct model of SETAR-EGARCH performs better than that of the ARIMA model and the combined ARIMA and EGARCH model. The results indicated that nonlinear models give better fitting than linear models.
The present study investigated the genetic profile of the cosmopolitan cat flea, Ctenocephalides felis (Siphonaptera: Pulicidae) from Malaysia and the reference data available in the National Center for Biotechnology Information (NCBI) GenBank. A set of sequences of 100 Malaysian samples aligned as 550 characters of the cytochrome c oxidase subunit I (cox1) and 706 characters of the II (cox2) genes revealed ten haplotypes (A1-A10) and eight haplotypes (B1-B8), respectively. The concatenated sequences of cox1 and cox2 genes with a total of 1256 characters revealed 15 haplotypes (AB1-AB15). Analyses indicated that haplotype AB1 was the most frequent and the most widespread haplotype in Malaysia. Overall haplotype and nucleotide diversities of the concatenated sequences were 0.52909 and 0.00424, respectively, with moderate genetic differentiation (FST = 0.17522) and high gene flow (Nm = 1.18). The western population presented the highest genetic diversity (Hd = 0.78333, Pi = 0.01269, Nh = 9), whereas the southern population demonstrated the lowest diversity (Hd = 0.15667, Pi = 0.00019, Nh = 3). The concatenated sequences showed genetic distances ranged from 0.08 % to 4.39 %. There were three aberrant haplotypes in cox2 sequences that highly divergent, suggesting the presence of cryptic species or occurrence of introgression. In the global point of view, the aligned sequences of C. felis revealed 65 haplotypes (AA1-AA65) by the cox1 gene (n = 586), and 27 haplotypes (BB1-BB27) by the cox2 gene (n = 204). Mapping of the haplotype network showed that Malaysian C. felis possesses seven unique haplotypes in both genes with the common haplotypes demonstrated genetic affinity with C. felis from Southeast Asia for cox1 and South America for cox2. The topologies of cox1 and cox2 phylogenetic trees were concordant with relevant grouping pattern of haplotypes in the network but revealed two major lineages by which Malaysian haplotypes were closely related with haplotypes from the tropical region.
Mycobacterium abscessus is a rapidly growing non-tuberculous mycobacterial species that has been associated with a wide spectrum of human infections. As the classification and biology of this organism is still not well understood, comparative genomic analysis on members of this species may provide further insights on their taxonomy, phylogeny, pathogenicity and other information that may contribute to better management of infections. The MabsBase described in this paper is a user-friendly database providing access to whole-genome sequences of newly discovered M. abscessus strains as well as resources for whole-genome annotations and computational predictions, to support the expanding scientific community interested in M. abscessus research. The MabsBase is freely available at http://mabscessus.um.edu.my.
Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
The HUGO Pan-Asian SNP consortium conducted the largest survey to date of human genetic diversity among Asians by sampling 1,719 unrelated individuals among 71 populations from China, India, Indonesia, Japan, Malaysia, the Philippines, Singapore, South Korea, Taiwan, and Thailand. We have constructed a database (PanSNPdb), which contains these data and various new analyses of them. PanSNPdb is a research resource in the analysis of the population structure of Asian peoples, including linkage disequilibrium patterns, haplotype distributions, and copy number variations. Furthermore, PanSNPdb provides an interactive comparison with other SNP and CNV databases, including HapMap3, JSNP, dbSNP and DGV and thus provides a comprehensive resource of human genetic diversity. The information is accessible via a widely accepted graphical interface used in many genetic variation databases. Unrestricted access to PanSNPdb and any associated files is available at: http://www4a.biotec.or.th/PASNP.
For a given DNA sequence, it is well known that pair wise alignment schemes are used to determine the similarity with the DNA sequences available in the databanks. The efficiency of the alignment decides the type of amino acids and its corresponding proteins. In order to evaluate the given DNA sequence for its proteomic identity, a pattern matching approach is proposed in this paper. A block based semi-global alignment scheme is introduced to determine the similarity between the DNA sequences (known and given). The two DNA sequences are divided into blocks of equal length and alignment is performed which minimizes the computational complexity. The efficiency of the alignment scheme is evaluated using the parameter, percentage of similarity (POS). Four essential DNA version of the amino acids that emphasize the importance of proteomic functionalities are chosen as patterns and matching is performed with the known and given DNA sequences to determine the similarity between them. The ratio of amino acid counts between the two sequences is estimated and the results are compared with that of the POS value. It is found from the experimental results that higher the POS value and the pattern matching higher are the similarity between the two DNA sequences. The optimal block is also identified based on the POS value and amino acids count.
There is a need for country/population-specific databases because the existence of population-specific mutations for single gene disorders is well documented, and there is also good evidence for ethnic differences in the frequencies of genetic variations involved in complex disorders. Thus the Singapore Human Mutation/Polymorphism Database (SHMPD) was created to provide clinicians and scientists access to a central genetic database for the Singapore population. The data catalogued in the database include mutations identified in Singapore for Mendelian diseases, and frequencies of polymorphisms that have been investigated in either healthy controls or samples associated with specific phenotypes. Data from journal articles identified by searches in PubMed and other online resources, and via personal communications with researchers were compiled and assembled into a single database. Genes are categorized alphabetically and are also searchable by name and disease. The information provided for each variant of the gene includes the protein encoded, phenotype association, gender, size, and ethnic origin of the sample, as well as the reported genotype and allele frequencies, and direct links to the corresponding abstracts on PubMed. Our database will facilitate molecular diagnosis of Mendelian disorders and improve study designs for complex traits. It will be useful not only for researchers in Singapore, but also for those in countries with similar ethnic backgrounds, such as China, Taiwan, Hong Kong, Indonesia, and Malaysia.
To inform product users about the origin of timber, the implementation of a traceability system is necessary for the forestry industry. In this study, we developed a comprehensive genetic database for the important tropical timber species Merbau, Intsia palembanica, to trace its geographic origin within peninsular Malaysia. A total of 1373 individual trees representing 39 geographically distinct populations of I. palembanica were sampled throughout peninsular Malaysia. We analyzed the samples using a combination of four chloroplast DNA (cpDNA) markers and 14 short tandem repeat (STR) markers to establish both cpDNA haplotype and STR allele frequency databases. A haplotype map was generated through cpDNA sequencing for population identification, resulting in six unique haplotypes based on 10 informative intraspecifically variable sites. Subsequently, an STR allele frequency database was developed from 14 STRs allowing individual identification. Bayesian cluster analysis divided the individuals into two genetic clusters corresponding to the northern and southern regions of peninsular Malaysia. Tests of conservativeness showed that the databases were conservative after the adjustment of the θ values to 0.2000 and 0.2900 for the northern (f = 0.0163) and southern (f = 0.0285) regions, respectively. Using self-assignment tests, we observed that individuals were correctly assigned to populations at rates of 40.54-94.12% and to the identified regions at rates of 79.80-80.62%. Both the cpDNA and STR markers appear to be useful for tracking Merbau timber originating from peninsular Malaysia. The use of these forensic tools in addition to the existing paper-based timber tracking system will help to verify the legality of the origin of I. palembanica and to combat illegal logging issues associated with the species.
This chapter describes the generation of the data in the CATH-Gene3D online resource and how it can be used to study protein domains and their evolutionary relationships. Methods will be presented for: comparing protein structures, recognizing homologs, predicting domain structures within protein sequences, and subclassifying superfamilies into functionally pure families, together with a guide on using the webpages.
Head and neck cancer (HNC)-derived cell lines represent fundamental models for studying the biological mechanisms underlying cancer development and precision therapies. However, mining the genomic information of HNC cells from available databases requires knowledge on bioinformatics and computational skill sets. Here, we developed a user-friendly web resource for exploring, visualizing, and analyzing genomics information of commonly used HNC cell lines. We populated the current version of GENIPAC with 44 HNC cell lines from 3 studies: ORL Series, OPC-22, and H Series. Specifically, the mRNA expressions for all the 3 studies were derived with RNA-seq. The copy number alterations analysis of ORL Series was performed on the Genome Wide Human Cytoscan HD array, while copy number alterations for OPC-22 were derived from whole exome sequencing. Mutations from ORL Series and H Series were derived from RNA-seq information, while OPC-22 was based on whole exome sequencing. All genomic information was preprocessed with customized scripts and underwent data validation and correction through data set validator tools provided by cBioPortal. The clinical and genomic information of 44 HNC cell lines are easily assessable in GENIPAC. The functional utility of GENIPAC was demonstrated with some of the genomic alterations that are commonly reported in HNC, such as TP53, EGFR, CCND1, and PIK3CA. We showed that these genomic alterations as reported in The Cancer Genome Atlas database were recapitulated in the HNC cell lines in GENIPAC. Importantly, genomic alterations within pathways could be simultaneously visualized. We developed GENIPAC to create access to genomic information on HNC cell lines. This cancer omics initiative will help the research community to accelerate better understanding of HNC and the development of new precision therapeutic options for HNC treatment. GENIPAC is freely available at http://genipac.cancerresearch.my/ .