Chien-Ming Chen

An online conserved SSR discovery through cross-species comparison.

Simple sequence repeats (SSRs) play important roles in gene regulation and genome evolution. Although there exist several online resources for SSR mining, most of them only extract general SSR patterns without providing functional information. Here, an online search tool, CG-SSR (Comparative Genomics SSR discovery), has been developed for discovering potential functional SSRs from vertebrate genomes through cross-species comparison. In addition to revealing SSR candidates in conserved regions among various species, it also combines accurate coordinate and functional genomics information. CG-SSR is the first comprehensive and efficient online tool for conserved SSR discovery.

EFFICIENT ALGORITHMS FOR IDENTIFYING ORTHOLOGOUS SIMPLE SEQUENCE REPEATS OF DISEASE GENES

Dynamic mutations of simple sequence repeats (SSRs) have been demonstrated to affect normal gene function and cause different genetic disorders. Several conserved and even partial functional SSR patterns are discovered in inherited orthologous disease genes. To explore a wide range of SSRs in genetic diseases, a comprehensive system focusing on identifying orthologous SSRs of disease genes through a comparative genomics mechanism is constructed and accomplished by adopting online Mendelian inheritance in man (OMIM) and NCBI HomoloGene databases as the fundamental resources of human genetic diseases and homologous gene information. In addition, an efficient and effective algorithm for searching SSR patterns is also developed for providing annotated SSR information among various model species. By integrating these data resources and mining technologies, biologists and doctors can systematically retrieve novel and important conserved SSR information among orthologous disease genes. The proposed system, Orthologous SSR for Disease Genes (OSDG), is the first comprehensive framework for identifying orthologous SSRs as potential causative factors of genetic disorders and is freely available at http://osdg.cs.ntou.edu.tw/.

Transcriptome sequencing of an Antarctic microalga, Chlorella sp. (Trebouxiophyceae, Chlorophyta) subjected to short-term ultraviolet radiation stress

Stratospheric ozone depletion has led to increasing levels of ultraviolet radiation (UVR) reaching the Earth’s surface. Elevated UVR, particularly in the high latitudes, potentially causes shifts in species composition and diversity in various ecosystems, consequently altering the biogeochemical cycles. Microalgae are not only ecologically important as primary producers, generating atmospheric oxygen and sequestering carbon dioxide; they are also economically important as sources of health supplement, pigments, biofuel and others. Changes to the size and composition of algal communities can lead to profound impacts to the fisheries productivity. There have been studies on the effects of UVR on the growth, photosynthesis and biochemical composition of microalgae, but limited information on the underlying molecular mechanisms involved in the response and adaptation of microalgae to UVR is available. We employed RNA-seq to quantitatively evaluate and compare the transcriptomes of an Antarctic freshwater Chlorella sp. grown at ambient versus elevated UVR conditions. Differentially expressed genes, relating to the fatty acid degradation, amino acid metabolism, starch and sucrose metabolism and peroxisome pathways, suggest conservation and remobilisation of energy resources, maintenance of newly synthesised protein and inhibition of protein degradation, ensuring membrane lipid homeostasis and regulating antioxidative mechanisms, as the acclimation strategies in response to UVR. These findings expand current knowledge of gene expression in polar Chlorella sp. in response to short-term UVR. Studies on stress tolerance mechanisms are important to understand and predict future impacts of climate change. Genes, proteins and pathways identified from these adaptable polar algae have potentially far-reaching biotechnological applications.

Mining Polymorphic SSRs from Individual Genome Sequences

Simple Sequence Repeats (SSRs) are abundant in genome sequences and become popular biomarkers for genetic studies. Several SSRs were proved essential for gene regulation, abnormal repeat patterns of these critical SSRs might cause lethal diseases. The Next Generation Sequencing technologies provided efficient approaches for SSR polymorphism detection. However, inefficient and manually curated processes were unavoidable for identifying SSR markers in previous approaches. An automatic and efficient system for detecting polymorphic SSRs at genomic scales was proposed without manual curated and examining works. The workflow accepted multiple NGS sequencing datasets and started with assembly by de novo or reference mapping approaches. The consensus sequences were then obtained from previously assembled contigs, and calibrated coordinates in each individual contig were aligned according to the selected reference sequences. Next, the mining SSR mechanism was designed to retrieve all potential polymorphic SSRs whenever the circumstances were occurred due to insertion or deletion mechanisms. The 1000 genomes Trio projects were employed as the testing sequence datasets, and the CODIS SSR markers and 9 well known disease-related SSR motifs were verified as the testing targets. The results have shown the proposed method could identify the known polymorphic SSRs as well as novel SSR markers when there was no sequencing or mapping errors within the consensus sequences. The proposed method employed NGS technologies to identify SSR polymorphism and accelerate related researches, which facilitates novel SSR biomarker selection and regulatory elements discovery.

Biological Pathway Analysis for De Novo Transcriptomes through Multiple Reference Species Selections

For de novo transcriptome analysis, choosing a closest reference model specie in terms of evolutionary distance is a general approach for gene mapping and genome annotations. However, not every selected reference model species possesses comprehensive genome annotations and curated information, and the total number of mapped genes from the selected reference species could not be fully expected either. Due to inefficient mapped genes from the selected reference model species, the following functional pathway analysis on transcriptome datasets would be seriously affected. To solve this problem, we proposed an improved approach based on multiple reference model species selection, especially for KEGG pathway analysis on differentially expressed genes. Applying union operations on individually mapped genes from different selected species, we could significantly promote the integrity of gene mapping results in KEGG pathways and provide realistic P-values for each identified pathway. Furthermore, based on mapped genes and KGML datasets, we applied various gray-levels, colors and shapes to present gene expression conditions on each biological pathway. Taking NGS transcriptomic datasets from an unknown Antarctic green alga species as an experimental example and selecting three published known species including Chlamydomonas reinhardtii, Chlorella variabilis, and Coccomyxa subellipsoidea as candidate reference species, we compared the results of pathway enrichment analysis by adopting different selections of reference species. We found that integrating all mapped genes from various model species provided a better result compared to using any single reference species. Some missed important biological pathways could be retrieved under an identical threshold setting of P-value, such as Ribosome, Pyrimidine metabolism and ABC transporters pathways. Therefore, we believe appropriate selection of multiple reference species is necessary and significant for transcriptome analysis on de novo species.

Gene Ontology based housekeeping gene selection for RNA-seq normalization.

RNA-seq analysis provides a powerful tool for revealing relationships between gene expression level and biological function of proteins. In order to identify differentially expressed genes among various RNA-seq datasets obtained from different experimental designs, an appropriate normalization method for calibrating multiple experimental datasets is the first challenging problem. We propose a novel method to facilitate biologists in selecting a set of suitable housekeeping genes for inter-sample normalization. The approach is achieved by adopting user defined experimentally related keywords, GO annotations, GO term distance matrices, orthologous housekeeping gene candidates, and stability ranking of housekeeping genes. By identifying the most distanced GO terms from query keywords and selecting housekeeping gene candidates with low coefficients of variation among different spatio-temporal datasets, the proposed method can automatically enumerate a set of functionally irrelevant housekeeping genes for pratical normalization. Novel and benchmark testing RNA-seq datasets were applied to demostrate that different selections of housekeeping gene lead to strong impact on differential gene expression analysis, and compared results have shown that our proposed method outperformed other traditional approaches in terms of both sensitivity and specificity. The proposed mechanism of selecting appropriate houskeeping genes for inter-dataset normalization is robust and accurate for differential expression analyses.

Length Encoded Secondary Structure Profile for Remote Homologous Protein Detection

Protein data has an explosive increasing rate both in volume and diversity, yet many of its structures remain unresolved, as well their functions remain to be identified. The conventional sequence alignment tools are insufficient in remote homology detection, while the current structural alignment tools would encounter the difficulties for proteins of unresolved structure. Here, we aimed to overcome the combination of two major obstacles for detecting remote homologous proteins: proteins with unresolved structure, and proteins of low sequence identity but high structural similarity. We proposed a novel method for improving the performance of protein matching problem, especially for mining remote homologous proteins. In this study, existing secondary structure prediction techniques were applied to provide the locations of secondary structure elements of proteins. The proposed LESS (Length Encoded Secondary Structure) profile was then constructed for segment-based similarity comparison in parallel computing. As compared to a conventional residue-based sequence alignment tool, detection of remote protein homologies through LESS profile is favourable in terms of speed and high sequence diversity, and its accuracy and performance can improve the deficiencies of the traditional primary sequence alignment methodology. This method may further support biologists in protein folding, evolution, and function prediction.

Gene expression rate comparison for multiple high-throughput datasets

Microarray provides genome-wide transcript profiles, whereas RNA-seq is an alternative approach applied for transcript discovery and genome annotation. Both high-throughput techniques show quantitative measurement of gene expression. To explore differential gene expression rates and understand biological functions, the authors designed a system which utilises annotations from Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways and Gene Ontology (GO) associations for integrating multiple RNA-seq or microarray datasets. The developed system is initiated by either estimating gene expression levels from mapping next generation sequencing short reads onto reference genomes or performing intensity analysis from microarray raw images. Normalisation procedures on expression levels are evaluated and compared through different approaches including Reads Per Kilobase per Million mapped reads (RPKM) and housekeeping gene selection. Such gene expression levels are shown in different colour shades and graphically displayed in designed temporal pathways. To enhance importance of functional relationships of clustered genes, representative GO terms associated with differentially expressed gene cluster are visually illustrated in a tag cloud representation.

Gene Ontology Based Clustering Analysis for Functionally Linked Genes and Cross-Species Comparison for SSR Biomarkers

Molecular biomarkers are important and commonly used as classification features for detecting function performances of target genes and distinguishing characteristics of different species or strains. As expeditious development in Next Generation Sequencing technologies, whole genome sequencing delivers a comprehensive view and new insights for novel species. However, utilization of whole genome sequences to discover genetic biomarkers for specific functional gene groups still possess many challenges. To solve this problem, we utilized Gene Ontology definitions and Ensembl orthologous information to decide a set of associated functional genes according to users query keywords. Then, previously assembled NGS contigs were matched against the selected gene set from model species, and an SSR searching algorithm and a cross-species comparison mechanism were performed to reveal potential polymorphic SSR biomarkers. Each identified SSR would be annotated and classified by important attributes, such as lengths, genetic locations, fundamental repeat patterns, tolerant rates of retrieved SSRs. The developed system could identify novel SSR biomarkers for a specified functional gene group regarding conserved and unique features between the query and the target model species, and which could facilitate biologists in finding potential markers for elucidating biological functions and distinguishable features of a novel species.

Exclusive Genomic Pathway Analysis for Groupers Infected by Different Iridovirus

Grouper is one of the most important aquaculture fish in the world. Due to high density of grouper farming, the aquaculture industry is threatened by iridovirus infections in recent years. In this study, we used next-generation sequencing technologies to analyze gene differential expression of groupers infected by two different types of iridoviruses, including Grouper Iridovirus of Taiwan (TGIV) of Megalocytivirus and Grouper Iridovirus (GIV) of Ranavirus. Zebra fish (Danio rerio) was selected as the reference model for gene functional annotations and pathway analyses. According to different fold change levels and identified associated KEGG pathways from defined gene sets, we found exclusive biological characteristics of groupers infected by two different iridoviruses. The results have shown that two specific gene sets, including the first set of PSMB2, CASP3, and U2AF2 for TGIV and the second set of PTGES, PDE3, and THBS1 for GIV were significantly and exclusively expressed. Furthermore, we also found two important pathways, Proteasome and ECM-receptor interaction pathways, were identified by employing gene set/pathway enrichment analysis. These results could facilitate biologists realizing comprehensive gene regulation of groupers infected by different iridoviruses and developing specific vaccines for grouper aquaculture.