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Lung cancer, one of the most common causes of cancer-related death worldwide, has been associated with high treatment cost and imposed great burdens. The 5-year postoperative survival rate of lung cancer (13%) is lower than many other leading cancers indicating the urgent needs to dissect its pathogenic mechanisms and discover specific biomarkers. Although several proteins have been proposed to be potential candidates for the diagnosis of lung cancer, they present low accuracy in clinical settings. Metabolomics has thus emerged as a very promising tool for biomarker discovery. To date, many lung cancer-related metabolites have been highlighted in the literature but no database is available for scientists to retrieve this information. Herein, we construct and introduce the first Lung Cancer Metabolome Database (LCMD), a freely available online database depositing 2013 lung cancer-related metabolites identified from 65 mass spectrometry-based lung cancer metabolomics studies. Researchers are able to explore LCMD via two ways. Firstly, by applying various filters in the “Browse Metabolites” mode, users can access a list of lung cancer-related metabolites that satisfy the filter specifications. For each metabolite, users can acquire the value of the fold change (cancer/normal), statistical significance (p-value) of the fold change, and the comparative research designs of all the mass spectrometry-based lung cancer metabolomics studies that identify this metabolite. Secondly, by applying various filters in the “Browse Studies” mode, users can obtain a list of mass spectrometry-based lung cancer metabolomics studies that satisfy the filter specifications. For each study, users can view the type of studied specimen, mass spectrometry (MS) method, MS data processing software, and differential analysis method, as well as all the identified lung cancer-related metabolites. Furthermore, the overview of each study is clearly illustrated by a graphical summary. The LCMD (http://cosbi7.ee.ncku.edu.tw/LCMD/) is the first database that brings together the meaningful information of lung cancer-related metabolites. The development of the LCMD is envisioned to promote the biomarker discovery of lung cancer.

Containing the largest number of species, the orchid family provides not only materials for studying plant evolution and environmental adaptation, but economically and culturally important ornamental plants for human society. Previously, we collected genome and transcriptome information of Dendrobium catenatum, Phalaenopsis equestris, and Apostasia shenzhenica which belong to two different subfamilies of Orchidaceae, and developed user-friendly tools to explore the orchid genetic sequences in the OrchidBase 4.0. The OrchidBase 4.0 offers the opportunity for plant science community to compare orchid genomes and transcriptomes and retrieve orchid sequences for further study.In the year 2022, two whole-genome sequences of Orchidoideae species, Platanthera zijinensis and Platanthera guangdongensis, were de novo sequenced, assembled and analyzed. In addition, systemic transcriptomes from these two species were also established. Therefore, we included these datasets to develop the new version of OrchidBase 5.0. In addition, three new functions including synteny, gene order, and miRNA information were also developed for orchid genome comparisons and miRNA characterization.OrchidBase 5.0 extended the genetic information to three orchid subfamilies (including five orchid species) and provided new tools for orchid researchers to analyze orchid genomes and transcriptomes. The online resources can be accessed at https://cosbi.ee.ncku.edu.tw/orchidbase5/

The formation of biomolecular condensates has emerged as a critical mechanism for compartmentation in living cells. Despite interactions between distinct condensates having been reported, the biological relevance of these interactions remains elusive. In germ cells, small RNA silencing factors are enriched in germ granule condensates, where distinct factors are organized into sub-compartments with specific functions linked to genome surveillance or transgenerational gene silencing. Here we showed that perinuclear germ granules are coated by P body condensates, which are known for housing translationally-inactive mRNAs and mRNA degradation factors. Disruption of P body factors, including CGH-1/DDX6 and CAR-1/LSM14, lead to dispersal of small RNA factors from perinuclear germ granules and disorganization of sub-compartments within germ granules. We further found that CAR-1 promotes the interaction between CGH-1 and germ granule factors, and these interactions are critical for the ability of CGH-1 to promote piRNA-mediated gene silencing. Importantly, we observed thatcgh-1mutants are competent in triggering gene silencing but exhibit defects in maintaining gene silencing in subsequent generations. Small RNA sequencing further showed thatcgh-1mutants exhibit defects in amplifying secondary small RNAs, known carriers of gene silencing memories. Together, our results uncover the function of P body factors in small RNA-mediated transgenerational gene silencing and highlight how the formation and function of one condensate can be regulated by an adjacent, interacting condensate in cells.

Kawasaki disease (KD), a multisystem inflammatory syndrome that occurs in children, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19) may share some overlapping mechanisms. The purpose of this study was to analyze the differences in single-cell RNA sequencing between KD and COVID-19. We performed single-cell RNA sequencing in KD patients (within 24 hours before IVIG treatment) and age-matched fever controls. The single-cell RNA sequencing data of COVID-19, influenza, and health controls were downloaded from the Sequence Read Archive (GSE149689/PRJNA629752). In total, 22 single-cell RNA sequencing data with 102,355 nuclei were enrolled in this study. After performing hierarchical and functional clustering analyses, two enriched gene clusters demonstrated similar patterns in severe COVID-19 and KD, heightened neutrophil activation, and decreased MHC class II expression. Furthermore, comparable dysregulation of neutrophilic granulopoiesis representing two pronounced hyperinflammatory states was demonstrated, which play a critical role in the overactivated and defective aging program of granulocytes, in patients with KD as well as those with severe COVID-19. In conclusion, both neutrophil activation and MHC class II reduction play a crucial role and thus may provide potential treatment targets for KD and severe COVID-19.

BackgroundPIWI-interacting RNAs (piRNAs) protect genome integrity by silencing transposon mRNAs and some endogenous mRNAs in various animals. However, C. elegans piRNAs fail to trigger gene silencing at many sequence-based predicted targeting sites.ResultsTo gain insights into the mechanisms that control piRNA silencing capability, we compared the transcriptome-wide predicted piRNA targeting sites to the in vivo piRNA binding sites. Surprisingly, while predicted piRNA targeting sites are enriched in 3’ UTRs, we found that C. elegans piRNAs preferentially bind to coding regions (CDS) of target mRNAs, leading to preferential production of secondary silencing small RNAs in the CDS. Furthermore, our analyses suggest that Argonaute protein CSR-1 protects mRNAs from piRNA silencing through two distinct mechanisms – by inhibiting piRNA binding across the entire CSR-1 targeted transcript, and by inhibiting secondary silencing small RNA production locally at CSR-1 bound sites. However, CSR-1 is not responsible for the piRNA binding preference for the CDS.ConclusionsOur work identifies the CDS as the critical region that is uniquely competent for piRNA silencing in C. elegans. We speculate that the preference for CDS recognition by piRNAs may represent a mechanism to counteract the evolution of foreign protein-coding RNAs that evade piRNA surveillance.

Background: The malignant transformation of oral potentially malignant disorders (OPMDs) is a potential cause of oral cancers. However, epidemiological studies on malignant transformation of OPMDs are lacking. The present study is aimed to investigate the percentage of OPMD in the oral mucosal screening database and the malignant transformation (%) across different regions of Taiwan. Methods: We determined the cases of malignant transformation of OPMD to oral cancers during the 2-year follow-up by the oral mucosal screening database and the cancer registry database contained in the National Health Insurance Research Database. Patients with oral cancer history before OPMD diagnosis were excluded from the study. We collected data including clinical diagnosis, biopsy result, follow-up, and place of residence of the patient. The most widely used definition from the Council for Economic Planning and Development in Taiwan was used in this study for the classification into Southern, Northern, Eastern, and Western Taiwan regions. Differences in the malignant transformation (%) and ranking between regions were evaluated using analysis of variance test. Results: Chiayi city located in Southern Taiwan has the highest percentage of OPMD in oral screening citizen. The percentage of OPMD in the oral mucosal screening database was highest in Southern Taiwan and lowest in Eastern Taiwan; however, the malignant transformation (%) was highest in the Eastern Taiwan and lowest in Northern Taiwan. Conclusion: Dentists and otolaryngologists in Southern Taiwan, especially Chiayi city, should be cautious when screening the patient because of the high percentage of OPMD in oral mucosal screening database. The percentage of OPMD in the oral mucosal screening database and the malignant transformation (%) are different among the different regions in Taiwan. Further research is needed to clarify the reasons for the different malignant transformation (%) between regions.

Along with the prosperity and rapid development of the national economy, the transportation industry has rapidly developed in China. However, overloaded vehicles have been causing frequent traffic accidents. Thus, to alleviate or resolve the corresponding problems associated with highway engineering safety and the market economy, an improved technique for overload management is urgently required. In this study, to analyze the overload data on expressways and highways in China, we developed a machine learning model by comparing the performances of cluster analysis, backpropagation neural network (BPNN), generalized regression neural network (GRNN), and wavelet neural network (WNN) in analyzing global and local time series overload data. In a case study, our results revealed the trends of overloading on highways in Jiangsu Province. Given sufficient data, BPNN performed better than GRNN and WNN. As the amount of training data increased, GRNN performed better, but the runtime increased. WNN had the shortest runtime among the three methods and could reflect the future trends of the overload rate in the monthly data prediction of overload. Our model provides information with potential value for expressway network management departments through data mining. This information could help management departments allocate resources reasonably and optimize the information utilization rate.

Most oral squamous cell carcinoma (OSCC) tumors arise from oral premalignant lesions. Oral submucous fibrosis (OSF), usually occurring in male chewers of betel quid, is a premalignant stromal disease characterized by a high malignant transformation rate and high prevalence. Although a relationship between the inhabited microbiome and carcinogenesis has been proposed, no detailed information regarding the oral microbiome of patients with OSF exists; the changes of the salivary microbiome during cancer formation remain unclear. This study compared the salivary microbiomes of male patients with OSCC and a predisposing OSF background (OSCC-OSF group) and those with OSF only (OSF group). The results of high-throughput sequencing of the bacterial 16S rRNA gene indicated that OSF-related carcinogenesis and smoking status significantly contributed to phylogenetic composition variations in the salivary microbiome, leading to considerable reductions in species richness and phylogenetic diversity. The microbiome profile of OSF-related malignancy was associated with increased microbial stochastic fluctuation, which dominated the salivary microbiome assembly and caused species co-occurrence network collapse. Artificial intelligence selection algorithms consistently identified 5 key species in the OSCC-OSF group: Porphyromonas catoniae, Prevotella multisaccharivorax, Prevotella sp. HMT-300, Mitsuokella sp. HMT-131, and Treponema sp. HMT-927. Robust accuracy in predicting oral carcinogenesis was obtained with our exploratory and validation data sets. In functional analysis, the microbiome of the OSCC-OSF group had greater potential for S-adenosyl-l-methionine and norspermidine synthesis but lower potential for l-ornithine and pyrimidine deoxyribonucleotide synthesis and formaldehyde metabolism. These findings indicated that the salivary microbiome plays important roles in modulating microbial metabolites during oral carcinogenesis. In conclusion, our results provided new insights into salivary microbiome alterations during the malignant transformation of OSF.

The ability to distinguish non-self from self is the key characteristic for any defense system. piRNAs function as guardians of the genome by silencing non- self nucleic acids and transposable elements in animals. Many piRNA factors are enriched in perinuclear germ granules, but whether their localization is required for piRNA biogenesis or function is not known. Here we show that GLH/VASA helicase mutants exhibit defects in forming perinuclear condensates containing PIWI and other small RNA cofactors. These mutant animals produce largely normal levels of piRNA but are defective in triggering piRNA silencing. Strikingly, while many piRNA targets are activated in GLH mutants, we observed that hundreds of endogenous genes are aberrantly silenced by piRNAs. This defect in self versus non-self recognition was also observed in other mutants where perinuclear P granules are disrupted. Together, our results argue that perinuclear germ granules function critically to promote the fidelity of piRNA- based transcriptome surveillance in C. elegans and preserve self versus non-self distinction.

Non-coding RNAs, such as miRNAs and piRNAs, play critical roles in gene regulation through base-pairing interactions with their target molecules. The recent development of the crosslinking, ligation, and sequencing of hybrids (CLASH) method has allowed scientists to map transcriptome-wide RNA–RNA interactions by identifying chimeric reads consisting of fragments from regulatory RNAs and their targets. However, analyzing CLASH data requires scientists to use advanced bioinformatics, and currently available tools are limited for users with little bioinformatic experience. In addition, many published CLASH studies do not show the full scope of RNA–RNA interactions that were captured, highlighting the importance of reanalyzing published data. Here, we present CLASH Analyst, a web server that can analyze raw CLASH data within a fully customizable and easy-to-use interface. CLASH Analyst accepts raw CLASH data as input and identifies the RNA chimeras containing the regulatory and target RNAs according to the user’s interest. Detailed annotation of the captured RNA–RNA interactions is then presented for the user to visualize within the server or download for further analysis. We demonstrate that CLASH Analyst can identify miRNA- and piRNA-targeting sites reported from published CLASH data and should be applicable to analyze other RNA–RNA interactions. CLASH Analyst is freely available for academic use.

Background Piwi-interacting RNAs (piRNAs) are the small non-coding RNAs (ncRNAs) that silence genomic transposable elements. And researchers found out that piRNA also regulates various endogenous transcripts. However, there is no systematic understanding of the piRNA binding patterns and how piRNA targets genes. While various prediction methods have been developed for other similar ncRNAs (e.g., miRNAs), piRNA holds distinctive characteristics and requires its own computational model for binding target prediction. Results Recently, transcriptome-wide piRNA binding events in C. elegans were probed by PRG-1 CLASH experiments. Based on the probed piRNA-messenger RNAs (mRNAs) binding pairs, in this research, we devised the first deep learning architecture based on multi-head attention to computationally identify piRNA targeting mRNA sites. In the devised deep network, the given piRNA and mRNA segment sequences are first one-hot encoded and undergo a combined operation of convolution and squeezing-extraction to unravel motif patterns. And we incorporate a novel multi-head attention sub-network to extract the hidden piRNA binding rules that can simulate the biological piRNA target recognition process. Finally, the true piRNA–mRNA binding pairs are identified by a deep fully connected sub-network. Our model obtains a supreme discriminatory power of AUC $$=$$ = 93.3% on an independent test set and successfully extracts the verified binding pattern of a synthetic piRNA. These results demonstrated that the devised model achieves high prediction performance and suggests testable potential biological piRNA binding rules. Conclusions In this research, we developed the first deep learning method to identify piRNA targeting sites on C. elegans mRNAs. And the developed deep learning method is demonstrated to be of high accuracy and can provide biological insights into piRNA–mRNA binding patterns. The piRNA binding target identification network can be downloaded from http://cosbi2.ee.ncku.edu.tw/data_download/piRNA_mRNA_binding.

Comparative analysis among multiple gene lists on their functional features is now a routine task due to the advancement of high-throughput experiments. Several enrichment analysis tools were developed in the past. However, these tools mainly focus on one gene list and contain only gene ontology or interaction features. What makes it worse, comparative investigation and customized feature set reanalysis are still unavailable. Therefore, we constructed the YMLA (Yeast Multiple List Analyzer) platform in this research. YMLA includes 39 yeast features and facilitates comparative analysis among multiple gene lists via tabular views, heatmaps, and network plots. Moreover, the customized feature set reanalysis function was implemented in YMLA to help form mechanism hypotheses based on a selected enriched feature subset. We demonstrated the biological applicability of YMLA via example lists consisting of genes with top/bottom translation efficiency values. The analysis results provided by YMLA reveal novel facts consistent with previous experiments. YMLA is available at https://cosbi7.ee.ncku.edu.tw/YMLA/.

Kawasaki disease (KD) is a form of acute systemic vasculitis that primarily affects children and has become the most common cause of acquired heart disease. While the etiopathogenesis of KD remains unknown, the diagnostic criteria of KD have been well established. Nevertheless, the diagnosis of KD is currently based on subjective clinical symptoms, and no molecular biomarker is yet available. We have previously performed and combined methylation array (Illumina HumanMethylation450 BeadChip) and transcriptome array (Affymetrix GeneChip Human Transcriptome Array 2.0) to identify genes that are differentially methylated/expressed in KD patients compared with control subjects. We have found that decreased methylation levels combined with elevated gene expression can indicate genes (e.g., toll-like receptors and CD177) involved in the disease mechanisms of KD. In this study, we constructed a database called KDmarkers to allow researchers to access these valuable potential KD biomarkers identified via methylation array and transcriptome array. KDmarkers provides three search modes. First, users can search genes differentially methylated and/or differentially expressed in KD patients compared with control subjects. Second, users can check the KD patient groups in which a given gene is differentially methylated and/or differentially expressed. Third, users can explore the DNA methylation levels and gene expression levels in all samples (KD patients and controls) for a particular gene of interest. We further demonstrated that the results in KDmarkers are strongly associated with KD immune responses. All analysis results can be downloaded for downstream experimental designs. KDmarkers is available online at https://cosbi.ee.ncku.edu.tw/KDmarkers/.

Exploring microbial community compositions in humans with healthy versus diseased states is crucial to understand the microbe-host interplay associated with the disease progression. Although the relationship between oral cancer and microbiome was previously established, it remained controversial, and yet the ecological characteristics and their responses to oral carcinogenesis have not been well studied. Here, using the bacterial 16S rRNA gene amplicon sequencing along with the in silico function analysis by PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2), we systematically characterized the compositions and the ecological drivers of saliva microbiome in the cohorts of orally healthy, non-recurrent oral verrucous hyperplasia (a pre-cancer lesion), and oral verrucous hyperplasia–associated oral cancer at taxonomic and function levels, and compared them with the re-analysis of publicly available datasets. Diversity analyses showed that microbiome dysbiosis in saliva was significantly linked to oral health status. As oral health deteriorated, the number of core species declined, and metabolic pathways predicted by PICRUSt2 were dysregulated. Partitioned beta-diversity revealed an extremely high species turnover but low function turnover. Functional beta-diversity in saliva microbiome shifted from turnover to nestedness during oral carcinogenesis, which was not observed at taxonomic levels. Correspondingly, the quantitative analysis of stochasticity ratios showed that drivers of microbial composition and functional gene content of saliva microbiomes were primarily governed by the stochastic processes, yet the driver of functional gene content shifted toward deterministic processes as oral cancer developed. Re-analysis of publicly accessible datasets supported not only the distinctive family taxa of Veillonellaceae and Actinomycetaceae present in normal cohorts but also that Flavobacteriaceae and Peptostreptococcaceae as well as the dysregulated metabolic pathways of nucleotides, amino acids, fatty acids, and cell structure were related to oral cancer. Using predicted functional profiles to elucidate the correlations to the oral health status shows superior performance than using taxonomic data among different studies. These findings advance our understanding of the oral ecosystem in relation to oral carcinogenesis and provide a new direction to the development of microbiome-based tools to study the interplay of the oral microbiome, metabolites, and host health.

Transcript isoforms regulated by alternative splicing can substantially impact carcinogenesis, leading to a need to obtain clues for both gene differential expression and malfunctions of isoform distributions in cancer studies. The Cancer Genome Atlas (TCGA) project was launched in 2008 to collect cancer-related genome mutation raw data from the population. While many repositories tried to add insights into the raw data in TCGA, no existing database provides both comprehensive gene-level and isoform-level cancer stage marker investigation and survival analysis. We constructed Cancer DEIso to facilitate in-depth analyses for both gene-level and isoform-level human cancer studies. Patient RNA-seq data, sample sheets, patient clinical data, and human genome datasets were collected and processed in Cancer DEIso. And four functions to search differentially expressed genes/isoforms between cancer stages were implemented: (i) Search potential gene/isoform markers for a specified cancer type and its two stages; (ii) Search potentially induced cancer types and stages for a gene/isoform; (iii) Expression survival analysis on a given gene/isoform for some cancer; (iv) Gene/isoform stage expression comparison visualization. As an example, we demonstrate that Cancer DEIso can indicate potential colorectal cancer isoform diagnostic markers that are not easily detected when only gene-level expressions are considered. Cancer DEIso is available at http://cosbi4.ee.ncku.edu.tw/DEIso/.

Background Translational regulation is one important aspect of gene expression regulation. Dysregulation of translation results in abnormal cell physiology and leads to diseases. Ribosome profiling (RP), also called ribo-seq, is a powerful experimental technique to study translational regulation. It can capture a snapshot of translation by deep sequencing of ribosome-protected mRNA fragments. Many ribosome profiling data processing tools have been developed. However, almost all tools analyze ribosome profiling data at the gene level. Since different isoforms of a gene may produce different proteins with distinct biological functions, it is advantageous to analyze ribosome profiling data at the isoform level. To meet this need, previously we developed a pipeline to analyze 610 public human ribosome profiling data at the isoform level and constructed HRPDviewer database. Results To allow other researchers to use our pipeline as well, here we implement our pipeline as an easy-to-use software tool called RPiso. Compared to Ribomap (a widely used tool which provides isoform-level ribosome profiling analyses), our RPiso (1) estimates isoform abundance more accurately, (2) supports analyses on more species, and (3) provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. Conclusions In this study, we developed RPiso software tool (http://cosbi7.ee.ncku.edu.tw/RPiso/) to provide isoform-level ribosome profiling analyses. RPiso is very easy to install and execute. RPiso also provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. We believe that RPiso is a useful tool for researchers to analyze and visualize their own ribosome profiling data at the isoform level.

Graphical abstract, Highlights • YPIBP contains 679 PI-binding yeast proteins and their PI-binding evidence. • YPIBP contains 181 lipid-binding domains and 103 lipid-related domains. • YPIBP provides PI-binding enrichment analysis and domain enrichment analysis. • YPIBP provides visualization of PI-domain-protein interactome., Phosphoinositides (PIs) are a family of eight lipids consisting of phosphatidylinositol (PtdIns) and its seven phosphorylated forms. PIs have important regulatory functions in the cell including lipid signaling, protein transport, and membrane trafficking. Yeast has been recognized as a eukaryotic model system to study lipid-protein interactions. Hundreds of yeast PI-binding proteins have been identified, but this research knowledge remains scattered. Besides, the complete PI-binding spectrum and potential PI-binding domains have not been interlinked. No comprehensive databases are available to support the lipid-protein interaction research on phosphoinositides. Here we constructed the first knowledgebase of Yeast Phosphoinositide-Binding Proteins (YPIBP), a repository consisting of 679 PI-binding proteins collected from high-throughput proteome-array and lipid-array studies, QuickGO, and a rigorous literature mining. The YPIBP also contains protein domain information in categories of lipid-binding domains, lipid-related domains and other domains. The YPIBP provides search and browse modes along with two enrichment analyses (PI-binding enrichment analysis and domain enrichment analysis). An interactive visualization is given to summarize the PI-domain-protein interactome. Finally, three case studies were given to demonstrate the utility of YPIBP. The YPIBP knowledgebase consolidates the present knowledge and provides new insights of the PI-binding proteins by bringing comprehensive and in-depth interaction network of the PI-binding proteins. YPIBP is available at http://cosbi7.ee.ncku.edu.tw/YPIBP/.

Background Recent advances in ChIP-seq technologies have led to the identification of thousands of TP53 binding loci in various cell types, providing unmatched opportunities for analysis and comparison of the TP53 genomewide binding patterns under different experimental conditions. These ChIP-seq datasets provide valuable resources for studying the function of TP53. However, there are currently no databases available for easily comparing and analyzing TP53 genome-wide binding patterns derived from different cell lines. Moreover, the TP53 ChIP-seq datasets are scattered among different papers, so extensive work is required to collect and process them for further analysis. Description To solve these problems, we comprehensively collected 13 publicly available TP53 ChIP-seq datasets derived from various cell lines. We re-mapped these 13 ChIP-seq datasets to the most updated reference human genome hg38 and identified the binding peaks (regions with significant enrichment of TP53 binding) and the target genes of TP53 in the human genome using the same data processing pipeline. Note that processing these 13 ChIP-seq datasets using the same pipeline is very crucial because it makes comparing the identified peaks and target genes of TP53 from different datasets possible. Finally, we developed a webbased platform (called the p53BLD), which provides a browse mode to visualize the binding loci of TP53 in the genome and a search mode to retrieve genes whose promoters are bound by TP53. The search mode is very powerful. Users can apply union, intersect, and/or difference operations on the 13 ChIP-seq datasets to generate a list of TP53 binding target genes that satisfies the users’ specifications. The generated gene list can then be downloaded for further analysis. Therefore, the p53BLD can also be regarded as a discovery tool that helps users to generate interesting gene lists for studying TP53. Conclusions Here we presented the first p53 Binding Loci Database (p53BLD). In the case study, we showed that using p53BLD can identify novel TP53 binding targets (KAT6A and KMT2A) in specific cancer cell lines. We believe that p53BLD is a useful resource for studying the function of TP53 in different cancer cell lines. P53BLD is available online at link1/, link2/, or link3/.

We used yeast proteome microarrays (∼5800 purified proteins) to conduct a high-throughput and systematic screening of PI5P-interacting proteins with PI5P-tagged fluorescent liposomal nanovesicles. Lissamine rhodamine B-dipalmitoyl phosphatidylethanol was incorporated into the liposome bilayer to provide the nanovesicles with fluorescence without any encapsulants, which not only made the liposome fabrication much easier without the need for purification but also improved the chip-probing quality. A special chip assay was washed very gently without the traditional spin-dry step. Forty-five PI5P-interacting proteins were identified in triplicate with this special chip assay. Subsequently, we used flow cytometry to validate these interactions, and a total of 41 PI5P-interacting proteins were confirmed. Enrichment analysis revealed that these proteins have significant functions associated with ribosome biogenesis, rRNA processing, ribosome binding, GTP binding, and hydrolase activity. Their component enrichment is located in the nucleolus. The InterPro domain analysis indicated that PI5P-interacting proteins are enriched in the P-loop containing nucleoside triphosphate hydrolases domain (P-loop). Additionally, using the MEME program, we identified a consensus motif (IVGPAGTGKSTLF) that contains the Walker A sequence, a well-known nucleotide-binding motif. Furthermore, using a quartz crystal microbalance, both the consensus motif and Walker A motif showed strong affinities to PI5P-containing liposomes but not to PI5P-deprived liposomes or PI-containing liposomes. Additionally, the glycine (G6) and lysine (K7) residues of the Walker A motif (-GPAGTG6K7S-) were found to be critical to the PI5P-binding ability. This study not only identified an additional set of PI5P-interacting proteins but also revealed the strong PI5P-binding affinity (Kd = 1.81 × 10-7 M) of the Walker A motif beyond the motif's nucleotide-binding characteristic.

Transcriptional regulators are prevalent among identified prions in Saccharomyces cerevisiae, however, it is unclear how prions affect genome-wide transcription. We show here that the prion ([SWI+]) and mutant (swi1∆) forms of Swi1, a subunit of the SWI/SNF chromatin-remodeling complex, confer dramatically distinct transcriptomic profiles. In [SWI+] cells, genes encoding for 34 transcription factors (TFs) and 24 Swi1-interacting proteins can undergo transcriptional modifications. Several TFs show enhanced aggregation in [SWI+] cells. Further analyses suggest that such alterations are key factors in specifying the transcriptomic signatures of [SWI+] cells. Interestingly, swi1∆ and [SWI+] impose distinct and oftentimes opposite effects on cellular functions. Translation-associated activities, in particular, are significantly reduced in swi1∆ cells. Although both swi1∆ and [SWI+] cells are similarly sensitive to thermal, osmotic and drought stresses, harmful, neutral or beneficial effects were observed for a panel of tested chemical stressors. Further analyses suggest that the environmental stress response (ESR) is mechanistically different between swi1∆ and [SWI+] cells—stress-inducible ESR (iESR) are repressed by [SWI+] but unchanged by swi1∆ while stress-repressible ESR (rESR) are induced by [SWI+] but repressed by swi1∆. Our work thus demonstrates primarily gain-of-function outcomes through transcriptomic modifications by [SWI+] and highlights a prion-mediated regulation of transcription and phenotypes in yeast.

Oral cancer is one of the leading causes of cancer death, which are mostly preceded by oral potentially malignant disorders (OPMDs). Taiwanese government launched a free oral cancer screening program. The aim of this study was to analyze the malignant transformation rate of OPMDs. This study was based on national-wide oral screening databases. 3,362,232 people were enrolled. Patients clinically diagnosed with leukoplakia, erythroplakia, oral submucosal fibrosis (OSF), oral verrucous hyperplasia (OVH), and oral lichen planus (OLP), from 2010 to 2013, were identified. We followed up OPMD patients in cancer registry databases to analyze the malignant transformation rate. The malignant transformation rates from the highest to the lowest were: OVH > OSF > erythroplakia > OLP > leukoplakia. The malignant transformation rate was 24.55, 12.76, 9.75, 4.23, and 0.60 per 1000 person-years in the OVH, OSF, erythroplakia, leukoplakia, and comparison cohort. The hazard ratio was 8.19 times higher in the OPMD group compared with comparison cohort group, after age and habit adjustment. Female patients with OPMDs had a high risk of malignant transformation. Nationwide screening is very important for early diagnosis. OVH had the highest malignant transformation possibility. Female OPMD patients are a rare but have a relatively high malignant transformation rate.

Background: The association between microbiome and host disease has been documented in oral cancer, one of the leading cancers worldwide. Huge efforts are made to use the profile of oral microbiome and distinct signature species as markers to distinguish oral cancer patients from healthy individuals. The previous results, however, remain inconclusive. The assembly mechanisms of oral microbiome and their response to changes in oral carcinogenesis also have not been characterized. Here, using 16S rRNA gene amplicon sequencing and in-silico function prediction approaches, we analyzed the saliva microbiome in cohorts of orally healthy, oral verrucous hyperplasia, and oral cancer at taxon and function levels, and compared their corresponding predictive performance of oral cancer using machine learning algorithms.Results: Analyses of diversity and phylogenetic profiles of bacterial communities in saliva showed that microbiome dysbiosis was significantly linked to oral health status. As oral health deteriorated, the number of core species (>75% prevalence) as a percentage of overall species richness declined. In line with the null model-based analysis, taxonomic and functional assemblies of saliva microbiomes were primarily governed by the stochastic processes. Correspondingly, the quantitative assessment of partitioned beta-diversity suggested extremely high species turnover but low function turnover, revealing a functional redundancy of the oral ecosystem. Functional beta-diversity in salvia microbiome shifted from turnover to nestedness during carcinogenesis of oral verrucous hyperplasia, but this pattern was not observed at the taxon level. Moreover, using both taxon and function data as training features for machine learning-aided prediction on host health status supports a superior predictive performance when using functional profiling. Similar results were also obtained and validated using publicly accessible data.Conclusions: Our results suggest that altered oral bacterial communities are highly associated with carcinogenesis of oral verrucous hyperplasia. Partly owing to high taxonomic turnover and stochastic assembly processes of the oral ecosystem, discovering oral microbial consortia as universal biomarkers for oral cancer may prove difficult and arduous. Functional profiles are relatively stable and evolve a nestedness pattern during oral carcinogenesis, serving as a new benchmark to study the interplay of the oral microbiome and host health in the future.

Nowadays high-throughput omics technologies are routinely used in biological research. From the omics data, researchers can easily get two gene lists (e.g. stress-induced genes vs. stress-repressed genes) related to their biological question. The next step would be to apply enrichment analysis tools to identify distinct functional/regulatory features between these two gene lists for further investigation. Although various enrichment analysis tools are already available, two challenges remain to be addressed. First, most existing tools are designed to analyze only one gene list, so they cannot directly compare two gene lists. Second, almost all existing tools focus on identifying the enriched qualitative features (e.g. gene ontology [GO] terms, pathways, domains, etc.). Many quantitative features (e.g. number of mRNA isoforms of a gene, mRNA half-life, protein half-life, transcriptional plasticity, translational efficiency, etc.) are available in the yeast, but no existing tools provide analyses on these quantitative features. To address these two challenges, here we present Yeast Quantitative Features Comparator (YQFC) that can directly compare various quantitative features between two yeast gene lists. In YQFC, we comprehensively collected and processed 85 quantitative features from the yeast literature and yeast databases. For each quantitative feature, YQFC provides three statistical tests (t-test, U test and KS test) to test whether this quantitative feature is statistically different between the two input yeast gene lists. The distinct quantitative features identified by YQFC may help researchers to study the underlying molecular mechanisms that differentiate the two input yeast gene lists. We believe that YQFC is a useful tool to expedite the biological research that uses high-throughput omics technologies.Database URLhttp://cosbi2.ee.ncku.edu.tw/YQFC/

Self-defense by avoiding self-targeting By silencing transposons, Piwi-interacting RNAs (piRNAs) protect the stability of animal genomes in germ lines. However, many piRNAs do not map to transposons, and their functions have remained undefined. Zhang et al. described the piRNA targeting logic in Caenorhabditis elegans and identified an intrinsic sequence signal in endogenous germline genes that confer resistance to piRNA silencing. Thus, diverse piRNAs silence foreign nucleic acids but spare self genes to defend the C. elegans genome. In addition, multiple foreign transgenes can be engineered to escape piRNA targeting, allowing successful expression in the germline. Science , this issue p. 587

Antimicrobial peptides (AMPs) have potential antifungal activities, however, their intracellular protein targets are poorly reported. Proteome microarray is an effective tool with high-throughput and rapid platform that systematically identifies the protein targets. In this study, we have used yeast proteome microarrays for systematical identification of the yeast protein targets of Lactoferricin B (Lfcin B) and Histatin-5. A total of 140 and 137 protein targets were identified from the triplicate yeast proteome microarray assays for Lfcin B and Histatin-5, respectively. The Gene Ontology (GO) enrichment analysis showed that Lfcin B targeted more enrichment categories than Histatin-5 did in all GO biological processes, molecular functions, and cellular components. This might be one of the reasons that Lfcin B has a lower minimum inhibitory concentration (MIC) than Histatin-5. Moreover, pairwise essential proteins that have lethal effects on yeast were analyzed through synthetic lethality. A total of 11 synthetic lethal pairs were identified within the protein targets of Lfcin B. However, only three synthetic lethal pairs were identified within the protein targets of Histatin-5. The higher number of synthetic lethal pairs identified within the protein targets of Lfcin B might also be the reason for Lfcin B to have lower MIC than Histatin-5. Furthermore, two synthetic lethal pairs were identified between the unique protein targets of Lfcin B and Histatin-5. Both the identified synthetic lethal pairs proteins are part of the Spt-Ada-Gcn5 acetyltransferase (SAGA) protein complex that regulates gene expression via histone modification. Identification of synthetic lethal pairs between Lfcin B and Histatin-5 and their involvement in the same protein complex indicated synergistic combination between Lfcin B and Histatin-5. This hypothesis was experimentally confirmed by growth inhibition assay.

Post-translational modifications of histones (e.g. acetylation, methylation, phosphorylation and ubiquitination) play crucial roles in regulating gene expression by altering chromatin structures and creating docking sites for histone/chromatin regulators. However, the combination patterns of histone modifications, regulatory proteins and their corresponding target genes remain incompletely understood. Therefore, it is advantageous to have a tool for the enrichment/depletion analysis of histone modifications and histone/chromatin regulators from a gene list. Many ChIP-chip/ChIP-seq datasets of histone modifications and histone/chromatin regulators in yeast can be found in the literature. Knowing the needs and having the data motivate us to develop a web tool, called Yeast Histone Modifications Identifier (YHMI), which can identify the enriched/depleted histone modifications and the enriched histone/chromatin regulators from a list of yeast genes. Both tables and figures are provided to visualize the identification results. Finally, the high-quality and biological insight of the identification results are demonstrated by two case studies. We believe that YHMI is a valuable tool for yeast biologists to do epigenetics research.

Due to the low operation temperature and quick start-up, proton exchange membrane fuel cells (PEMFCs) have been investigated nowadays. To improve the power density and short lifetime, much attention has been focused on the catalyst fabrication technique to attain high catalytic activity. Electrodeposition is widely applied using different electrochemical techniques. With a three-electrode electrochemical cell, Pt catalysts were directly electrodeposited onto the gas diffusion layer. The electrodeposition electrolyte was the hexachloroplatinic acid (H2PtCl6), supporting electrolyte (e.g. Boric acid, perchloric acid or sulfuric acid, etc.) and dispersant (e.g. Ethylene Glycol). The fabrication process of electrodeposited Pt catalyst was conducted under at a specific reduction potential and oxidation potential. By changing the supporting electrolyte, dispersant and applied potentials in fabrication process, the catalytic efficiency of the Pt catalyst electrode was improved. The Pt dendritic nanostructure was manufactured in an electrolyte containing sulfuric acid or perchloric acid, which was used as a supporting electrolyte. If boric acid was served as the supporting electrolyte, the morphology of the Pt particles were cubic structures instead of dendritic structures. Electrochemical characteristics of catalytic efficiency on the anode and cathode were investigated via cyclic voltammetry analysis in a sulfuric acid solution. Finally, a single cell test was also demonstrated.

Arsenic is a toxic metalloid. Moderate levels of arsenic exposure from drinking water can cause various human health problems such as skin lesions, circulatory disorders and cancers. Thus, arsenic toxicity is a key focus area for environmental and toxicological investigations. Many arsenic-related genes in yeast have been identified by experimental strategies such as phenotypic screening and transcriptional profiling. These identified arsenic-related genes are valuable information for studying arsenic toxicity. However, the literature about these identified arsenic-related genes is widely dispersed and cannot be easily acquired by researchers. This prompts us to develop YARG (Yeast Arsenic-Related Genes) database, which comprehensively collects 3396 arsenic-related genes in the literature. For each arsenic-related gene, the number and types of experimental evidence (phenotypic screening and/or transcriptional profiling) are provided. Users can use both search and browse modes to query arsenic-related genes in YARG. We used two case studies to show that YARG can return biologically meaningful arsenic-related information for the query gene(s). We believe that YARG is a useful resource for arsenic toxicity research. YARG is available at http://cosbi4.ee.ncku.edu.tw/YARG/.

Translational regulation plays an important role in protein synthesis. Dysregulation of translation causes abnormal cell physiology and leads to diseases such as inflammatory disorders and cancers. An emerging technique, called ribosome profiling (ribo-seq), was developed to capture a snapshot of translation. It is based on deep sequencing of ribosome-protected mRNA fragments. A lot of ribo-seq data have been generated in various studies, so databases are needed for depositing and visualizing the published ribo-seq data. Nowadays, GWIPS-viz, RPFdb and TranslatomeDB are the three largest databases developed for this purpose. However, two challenges remain to be addressed. First, GWIPS-viz and RPFdb databases align the published ribo-seq data to the genome. Since ribo-seq data aim to reveal the actively translated mRNA transcripts, there are advantages of aligning ribo-req data to the transcriptome over the genome. Second, TranslatomeDB does not provide any visualization and the other two databases only provide visualization of the ribo-seq data around a specific genomic location, while simultaneous visualization of the ribo-seq data on multiple mRNA transcripts produced from the same gene or different genes is desired. To address these two challenges, we developed the Human Ribosome Profiling Data viewer (HRPDviewer). HRPDviewer (i) contains 610 published human ribo-seq datasets from Gene Expression Omnibus, (ii) aligns the ribo-seq data to the transcriptome and (iii) provides visualization of the ribo-seq data on the selected mRNA transcripts. Using HRPDviewer, researchers can compare the ribosome binding patterns of multiple mRNA transcripts from the same gene or different genes to gain an accurate understanding of protein synthesis in human cells. We believe that HRPDviewer is a useful resource for researchers to study translational regulation in human. Database URL: http://cosbi4.ee.ncku.edu.tw/HRPDviewer/ or http://cosbi5.ee.ncku.edu.tw/HRPDviewer/

pirScan is a web-based tool for identifying C. elegans piRNA-targeting sites within a given mRNA or spliced DNA sequence. The purpose of our tool is to allow C. elegans researchers to predict piRNA targeting sites and to avoid the persistent germline silencing of transgenes that has rendered many constructs unusable. pirScan fulfills this purpose by first enumerating the predicted piRNA-targeting sites present in an input sequence. This prediction can be exported in a tabular or graphical format. Subsequently, pirScan suggests silent mutations that can be introduced to the input sequence that would allow the modified transgene to avoid piRNA targeting. The user can customize the piRNA targeting stringency and the silent mutations that he/she wants to introduce into the sequence. The modified sequences can be re-submitted to be certain that any previously present piRNA-targeting sites are now absent and no new piRNA-targeting sites are accidentally generated. This revised sequence can finally be downloaded as a text file and/or visualized in a graphical format. pirScan is freely available for academic use at http://cosbi4.ee.ncku.edu.tw/pirScan/.

The Fanconi anemia pathway in coordination with homologous recombination is essential to repair interstrand crosslinks (ICLs) caused by cisplatin. TIP60 belongs to the MYST family of acetyltransferases and is involved in DNA repair and regulation of gene transcription. Although the physical interaction between the TIP60 and FANCD2 proteins has been identified that is critical for ICL repair, it is still elusive whether TIP60 regulates the expression of FA and HR genes. In this study, we found that the chemoresistant nasopharyngeal carcinoma cells, derived from chronic treatment of cisplatin, show elevated expression of TIP60. Furthermore, TIP60 binds to the promoters of FANCD2 and BRCA1 by using the chromatin immunoprecipitation experiments and promote the expression of FANCD2 and BRCA1. Importantly, the depletion of TIP60 significantly reduces sister chromatid exchange, a measurement of HR efficiency. The similar results were also shown in the FNACD2-, and BRCA1-deficient cells. Additionally, these TIP60-deficient cells encounter more frequent stalled forks, as well as more DNA double-strand breaks resulting from the collapse of stalled forks. Taken together, our results suggest that TIP60 promotes the expression of FA and HR genes that are important for ICL repair and the chemoresistant phenotype under chronic treatment with cisplatin.

By organizing the genome into gene modules (GMs), a living cell coordinates the activities of a set of genes to properly respond to environmental changes. The transcriptional regulation of the expression of a GM is usually carried out by a cooperative transcription factor set (CoopTFS) consisting of several cooperative transcription factors (TFs). Therefore, a database which provides CoopTFSs and their target GMs is useful for studying the cellular responses to internal or external stimuli. To address this need, here we constructed YGMD (Yeast Gene Module Database) to provide 34120 CoopTFSs, each of which consists of two to five cooperative TFs, and their target GMs. The cooperativity between TFs in a CoopTFS is suggested by physical/genetic interaction evidence or/and predicted by existing algorithms. The target GM regulated by a CoopTFS is defined as the common target genes of all the TFs in that CoopTFS. The regulatory association between any TF in a CoopTFS and any gene in the target GM is supported by experimental evidence in the literature. In YGMD, users can (i) search the GM regulated by a specific CoopTFS of interest or (ii) search all possible CoopTFSs whose target GMs contain a specific gene of interest. The biological relevance of YGMD is shown by a case study which demonstrates that YGMD can provide a GM enriched with genes known to be regulated by the query CoopTFS (Cbf1-Met4-Met32). We believe that YGMD provides a valuable resource for yeast biologists to study the transcriptional regulation of GMs. Database URL: http://cosbi4.ee.ncku.edu.tw/YGMD/, http://cosbi5.ee.ncku.edu.tw/YGMD/ or http://cosbi.ee.ncku.edu.tw/YGMD/

During recent years, the Taiwan government launches issues of green building , sustainable engineering , carbon reduction and etc. , to promote balance construction and environment ecology. One of the most important materials in those engineering issues is cement which is used widely in construction industry. Cement emits 0.85 tons of carbon dioxide (CO2) for each ton of cement used in average. This not only causes the waste of energy but also againsts the conception of environmental protection. This research uses the rice husk ash (RHA) which people always regards as the waste material as a pozzolanic material to replace part of cement to make high performance concrete (HPC). Then makes the value analysis and properties investigation for HPC. Based on this research, rice husk (RH) will contain lots of silicon dioxide (SiO2) after burning into RHA. If use RHA to replace part of cement, it can get appropriate properties of compressive strength and durability. More important is that by replacing part of cement, it not only will reduce the emission of CO2 and save money but also reach the goal of global village protection on earth.

In eukaryotes, the precise transcriptional control of gene expression is typically achieved through combinatorial regulation using cooperative transcription factors (TFs). Therefore, a database which provides regulatory associations between cooperative TFs and their target genes is helpful for biologists to study the molecular mechanisms of transcriptional regulation of gene expression. Because there is no such kind of databases in the public domain, this prompts us to construct a database, called Yeast Combinatorial Regulation Database (YCRD), which deposits 434,197 regulatory associations between 2535 cooperative TF pairs and 6243 genes. The comprehensive collection of more than 2500 cooperative TF pairs was retrieved from 17 existing algorithms in the literature. The target genes of a cooperative TF pair (e.g. TF1-TF2) are defined as the common target genes of TF1 and TF2, where a TF's experimentally validated target genes were downloaded from YEASTRACT database. In YCRD, users can (i) search the target genes of a cooperative TF pair of interest, (ii) search the cooperative TF pairs which regulate a gene of interest and (iii) identify important cooperative TF pairs which regulate a given set of genes. We believe that YCRD will be a valuable resource for yeast biologists to study combinatorial regulation of gene expression. YCRD is available at http://cosbi.ee.ncku.edu.tw/YCRD/ or http://cosbi2.ee.ncku.edu.tw/YCRD/.

Background Missing value imputation is important for microarray data analyses because microarray data with missing values would significantly degrade the performance of the downstream analyses. Although many microarray missing value imputation algorithms have been developed, an objective and comprehensive performance comparison framework is still lacking. To solve this problem, we previously proposed a framework which can perform a comprehensive performance comparison of different existing algorithms. Also the performance of a new algorithm can be evaluated by our performance comparison framework. However, constructing our framework is not an easy task for the interested researchers. To save researchers’ time and efforts, here we present an easy-to-use web tool named MVIAeval (Missing Value Imputation Algorithm evaluator) which implements our performance comparison framework. Results MVIAeval provides a user-friendly interface allowing users to upload the R code of their new algorithm and select (i) the test datasets among 20 benchmark microarray (time series and non-time series) datasets, (ii) the compared algorithms among 12 existing algorithms, (iii) the performance indices from three existing ones, (iv) the comprehensive performance scores from two possible choices, and (v) the number of simulation runs. The comprehensive performance comparison results are then generated and shown as both figures and tables. Conclusions MVIAeval is a useful tool for researchers to easily conduct a comprehensive and objective performance evaluation of their newly developed missing value imputation algorithm for microarray data or any data which can be represented as a matrix form (e.g. NGS data or proteomics data). Thus, MVIAeval will greatly expedite the progress in the research of missing value imputation algorithms.

The eukaryotic cell cycle is a complex process and is precisely regulated at many levels. Many genes specific to the cell cycle are regulated transcriptionally and are expressed just before they are needed. To understand the cell cycle process, it is important to identify the cell cycle transcription factors (TFs) that regulate the expression of cell cycle-regulated genes. Here, we describe a computational method to identify cell cycle TFs in yeast by integrating current ChIP-chip, mutant, transcription factor-binding site (TFBS), and cell cycle gene expression data. For each identified cell cycle TF, our method also assigned specific cell cycle phases in which the TF functions and identified the time lag for the TF to exert regulatory effects on its target genes. Moreover, our method can identify novel cell cycle-regulated genes as a by-product.

The strength and durability properties of concrete with or without three types of rice husk ash (RHA), namely, amorphous, partial crystalline, and crystalline RHA, were investigates. The three types of RHA were added into concrete at a 20% replacement level. Consequently, the pozzolanic reactivity of amorphous RHA was higher than that of partial crystalline and crystalline RHA. Concrete added with amorphous RHA showed excellent characteristics in its mechanical and durability properties. The results showed that the higher the amount of crystalline silica in RHA, the lower the concrete resistivity value became. When compared with each other, concretes with 20% of the cement replaced with these types of RHA achieved similar ultrasonic pulse velocity values, but all were lower than that of the control concrete. The incorporation of these kinds of RHA significantly reduced chloride penetration. The results not only encourage the use of amorphous materials, they also support the application of crystalline or partial crystalline RHA as mineral and pozzolanic admixtures for cement.

Background Computational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface. Results The developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses. Conclusions Allowing users to select eight existing performance indices and 15 existing algorithms for comparison, our web tool benefits researchers who are eager to comprehensively and objectively evaluate the performance of their newly developed algorithm. Thus, our tool greatly expedites the progress in the research of computational identification of cooperative TF pairs.

Identifying cell cycle transcription factors (TFs) is important for understanding the transcriptional regulation of the cell cycle process which controls the growth and development of all organisms. Existing computational approaches for identifying cell cycle TFs are mainly based on methods with a fixed selection criterion. That is, the same criterion was applied to each TF to determine whether it is a cell cycle TF or not. Since the characteristic of each TF may be quite different, it is not suitable to use a fixed selection criterion in identifying cell cycle TFs. Instead of using a fixed selection criterion, we propose a method with variable selection criteria to identify cell cycle TFs in yeast by integrating the ChIP-chip and cell cycle gene expression data. Our method is shown to outperform five existing methods which used the same ChIP-chip dataset as we did. Fifteen cell cycle TFs were identified by our approach, 12 of which are known cell cycle TFs, while the remaining three (Hap4, Reb1 and Tye7) are novel cell cycle TFs. The biological significance of our predictions is shown by four lines of indirect evidence derived from the protein-protein interaction data, TF mutant data, ChIP-chip data and the results of the previous computational studies.

Transcription factors control gene expression by binding to short specific DNA sequences, called transcription factor binding sites (TFBSs), in the promoter of a gene. Thus, studying the spatial distribution of TFBSs in the promoters may provide insights into the molecular mechanisms of gene regulation. I developed a method to construct the spatial distribution of TFBSs for any set of genes of interest. I found that different functional gene clusters have different spatial distributions of TFBSs, indicating that gene regulation mechanisms may be very different among different functional gene clusters. I also found that the binding sites for different transcription factors (TFs) may have different spatial distributions: a sharp peak, a plateau or no dominant single peak. The spatial distributions of binding sites for many TFs derived from my analyses are valuable prior information for TFBS prediction algorithm because different regions of a promoter can assign different possibilities for TFBS occurrence.

ChIP-Chip data, which shows binding of transcription factors (TFs) to promoter regions in vivo, are widely used by biologists to identify the regulatory targets of TFs. However, the binding of a TF to a gene does not necessarily imply regulation. Thus, it is important to develop computational methods which can extract a TF's regulatory targets from its binding targets. We developed a method, called REgulatory Targets Extraction Algorithm (RETEA), which uses partial correlation analysis on gene expression data to extract a TF's regulatory targets from its binding targets inferred from ChIP-chip data. We applied RETEA to yeast cell cycle microarray data and identified the plausible regulatory targets of eleven known cell cycle TFs. We validated our predictions by checking the enrichments for cell cycle-regulated genes, common cellular processes and common molecular functions. Finally, we showed that RETEA performs better than three published methods (MA-Network, TRIA and Garten et al's method).

This study presents the Yeast Promoter Atlas (YPA, http://ypa.ee.ncku.edu.tw/ or http://ypa.csbb.ntu.edu.tw/) database, which aims to collect comprehensive promoter features in Saccharomyces cerevisiae. YPA integrates nine kinds of promoter features including promoter sequences, genes' transcription boundaries-transcription start sites (TSSs), five prime untranslated regions (5'-UTRs) and three prime untranslated regions (3'UTRs), TATA boxes, transcription factor binding sites (TFBSs), nucleosome occupancy, DNA bendability, transcription factor (TF) binding, TF knockout expression and TF-TF physical interaction. YPA is designed to present data in a unified manner as many important observations are revealed only when these promoter features are considered altogether. For example, DNA rigidity can prevent nucleosome packaging, thereby making TFBSs in the rigid DNA regions more accessible to TFs. Integrating nucleosome occupancy, DNA bendability, TF binding, TF knockout expression and TFBS data helps to identify which TFBS is actually functional. In YPA, various promoter features can be accessed in a centralized and organized platform. Researchers can easily view if the TFBSs in an interested promoter are occupied by nucleosomes or located in a rigid DNA segment and know if the expression of the downstream gene responds to the knockout of the corresponding TFs. Compared to other established yeast promoter databases, YPA collects not only TFBSs but also many other promoter features to help biologists study transcriptional regulation.

Unicellular organisms such as yeasts have evolved mechanisms to respond to environmental stresses by rapidly reorganizing the gene expression program. Although many stress-response genes in yeast have been discovered by DNA microarrays, the stress-response transcription factors (TFs) that regulate these stress-response genes remain to be investigated. In this study, we use a dynamic system model of gene regulation to describe the mechanism of how TFs may control a gene's expression. Then, based on the dynamic system model, we develop the Stress Regulator Identification Algorithm (SRIA) to identify stress-response TFs for six kinds of stresses. We identified some general stress-response TFs that respond to various stresses and some specific stress-response TFs that respond to one specific stress. The biological significance of our findings is validated by the literature. We found that a small number of TFs is probably sufficient to control a wide variety of expression patterns in yeast under different stresses. Two implications can be inferred from this observation. First, the response mechanisms to different stresses may have a bow-tie structure. Second, there may be regulatory cross-talks among different stress responses. In conclusion, this study proposes a network of stress-response regulators and the details of their actions.

The eukaryotic cell cycle is a complex process and is precisely regulated at many levels. Many genes specific to the cell cycle are regulated transcriptionally and are expressed just before they are needed. To understand the cell cycle process, it is important to identify the cell cycle transcription factors (TFs) that regulate the expression of cell cycle-regulated genes. Here, we describe a computational method to identify cell cycle TFs in yeast by integrating current ChIP-chip, mutant, transcription factor-binding site (TFBS), and cell cycle gene expression data. For each identified cell cycle TF, our method also assigned specific cell cycle phases in which the TF functions and identified the time lag for the TF to exert regulatory effects on its target genes. Moreover, our method can identify novel cell cycle-regulated genes as a by-product.

DNA double-strand breaks (DSBs) represent one of the most threatening lesions to the integrity of genomes. In yeast Saccharomyces cerevisiae, NuA4, a histone acetylation complex, is recruited to DSBs, wherein it acetylates histones H2A and H4, presumably relaxing the chromatin and allowing access to repair proteins. Two subunits of NuA4, Yng2 and Eaf3, can interact in vitro with methylated H3K4 and H3K36 via their plant homeodomain (PHD) and chromodomain. However, the roles of the two domains and how they interact in a combinatorial fashion are still poorly characterized. In this study, we generated mutations in the PHD and chromodomain that disrupt their interaction with methylated H3K4 and H3K36. We demonstrate that the combined mutations in both the PHD and chromodomain impair the NuA4 recruitment, reduce H4K12 acetylation at the DSB site, and confer sensitivity to bleomycin that induces DSBs. In addition, the double mutant cells are defective in DSB repair as judged by Southern blot and exhibit prolonged activation of phospho-S129 of H2A. Cells harboring the H3K4R, H3K4R, K36R, or set1Δ set2Δ mutant that disrupts H3K4 and H3K36 methylation also show very similar phenotypes to the PHD and chromodomain double mutant. Our results suggest that multivalent interactions between the PHD, chromodomain, and methylated H3K4 and H3K36 act in a combinatorial manner to recruit NuA4 and regulate the NuA4 activity at the DSB site.

Background Eukaryotic transcriptional regulation is known to be highly connected through the networks of cooperative transcription factors (TFs). Measuring the cooperativity of TFs is helpful for understanding the biological relevance of these TFs in regulating genes. The recent advances in computational techniques led to various predictions of cooperative TF pairs in yeast. As each algorithm integrated different data resources and was developed based on different rationales, it possessed its own merit and claimed outperforming others. However, the claim was prone to subjectivity because each algorithm compared with only a few other algorithms and only used a small set of performance indices for comparison. This motivated us to propose a series of indices to objectively evaluate the prediction performance of existing algorithms. And based on the proposed performance indices, we conducted a comprehensive performance evaluation. Results We collected 14 sets of predicted cooperative TF pairs (PCTFPs) in yeast from 14 existing algorithms in the literature. Using the eight performance indices we adopted/proposed, the cooperativity of each PCTFP was measured and a ranking score according to the mean cooperativity of the set was given to each set of PCTFPs under evaluation for each performance index. It was seen that the ranking scores of a set of PCTFPs vary with different performance indices, implying that an algorithm used in predicting cooperative TF pairs is of strength somewhere but may be of weakness elsewhere. We finally made a comprehensive ranking for these 14 sets. The results showed that Wang J's study obtained the best performance evaluation on the prediction of cooperative TF pairs in yeast. Conclusions In this study, we adopted/proposed eight performance indices to make a comprehensive performance evaluation on the prediction results of 14 existing cooperative TFs identification algorithms. Most importantly, these proposed indices can be easily applied to measure the performance of new algorithms developed in the future, thus expedite progress in this research field.

Background Defining a measure for regulatory similarity (RS) of two genes is an important step toward identifying co-regulated genes. To date, transcription factor binding sites (TFBSs) have been widely used to measure the RS of two genes because transcription factors (TFs) binding to TFBSs in promoters is the most crucial and well understood step in gene regulation. However, existing TFBS-based RS measures consider the relation of a TFBS to a gene as a Boolean (either 'presence' or 'absence') without utilizing the information of TFBS locations in promoters. Results Functional TFBSs of many TFs in yeast are known to have a strong positional preference to occur in a small region in the promoters. This biological knowledge prompts us to develop a novel RS measure that exploits the TFBS location information. The performances of different RS measures are evaluated by the fraction of gene pairs that are co-regulated (validated by literature evidence) by at least one common TF under different RS scores. The experimental results show that the proposed RS measure is the best co-regulation indicator among the six compared RS measures. In addition, the co-regulated genes identified by the proposed RS measure are also shown to be able to benefit three co-regulation-based applications: detecting gene co-function, gene co-expression and protein-protein interactions. Conclusions The proposed RS measure provides a good indicator for gene co-regulation. Besides, its good performance reveals the importance of the location information in TFBS-based RS measures.

Background Cis-regulatory modules (CRMs), or the DNA sequences required for regulating gene expression, play the central role in biological researches on transcriptional regulation in metazoan species. Nowadays, the systematic understanding of CRMs still mainly resorts to computational methods due to the time-consuming and small-scale nature of experimental methods. But the accuracy and reliability of different CRM prediction tools are still unclear. Without comparative cross-analysis of the results and combinatorial consideration with extra experimental information, there is no easy way to assess the confidence of the predicted CRMs. This limits the genome-wide understanding of CRMs. Description It is known that transcription factor binding and epigenetic profiles tend to determine functions of CRMs in gene transcriptional regulation. Thus integration of the genome-wide epigenetic profiles with systematically predicted CRMs can greatly help researchers evaluate and decipher the prediction confidence and possible transcriptional regulatory functions of these potential CRMs. However, these data are still fragmentary in the literatures. Here we performed the computational genome-wide screening for potential CRMs using different prediction tools and constructed the pioneer database, cisMEP (cis-regulatory module epigenetic profile database), to integrate these computationally identified CRMs with genomic epigenetic profile data. cisMEP collects the literature-curated TFBS location data and nine genres of epigenetic data for assessing the confidence of these potential CRMs and deciphering the possible CRM functionality. Conclusions cisMEP aims to provide a user-friendly interface for researchers to assess the confidence of different potential CRMs and to understand the functions of CRMs through experimentally-identified epigenetic profiles. The deposited potential CRMs and experimental epigenetic profiles for confidence assessment provide experimentally testable hypotheses for the molecular mechanisms of metazoan gene regulation. We believe that the information deposited in cisMEP will greatly facilitate the comparative usage of different CRM prediction tools and will help biologists to study the modular regulatory mechanisms between different TFs and their target genes.

MicroRNAs (miRNAs) are functional RNA molecules which play important roles in the post-transcriptional regulation. miRNAs regulate their target genes by repressing translation or inducing degradation of the target genes' mRNAs. Many databases have been constructed to provide computationally predicted miRNA targets. However, they cannot provide the miRNA targets expressed in a specific tissue and related to a specific disease at the same time. Moreover, they cannot provide the common targets of multiple miRNAs and the common miRNAs of multiple genes at the same time. To solve these two problems, we construct a database called CSmiRTar (Condition-Specific miRNA Targets). CSmiRTar collects computationally predicted targets of 2588 human miRNAs and 1945 mouse miRNAs from four most widely used miRNA target prediction databases (miRDB, TargetScan, microRNA.org and DIANA-microT) and implements functional filters which allows users to search (i) a miRNA's targets expressed in a specific tissue or/and related to a specific disease, (ii) multiple miRNAs' common targets expressed in a specific tissue or/and related to a specific disease, (iii) a gene's miRNAs related to a specific disease, and (iv) multiple genes' common miRNAs related to a specific disease. We believe that CSmiRTar will be a useful database for biologists to study the molecular mechanisms of post-transcriptional regulation in human or mouse. CSmiRTar is available at http://cosbi.ee.ncku.edu.tw/CSmiRTar/ or http://cosbi4.ee.ncku.edu.tw/CSmiRTar/.

Missing value imputation is crucial for the microarray data analysis since the missing values would degrade the performance of the downstream analysis, e.g. differentially expressed genes identification, gene clustering or classification. Although many missing value imputation algorithms has been proposed, convenient software tools are still lacking. The existing tools are not easy to use and cannot tell users how to choose the optimal imputation algorithm for their dataset. In this paper, we present an easy-to-use web server named IMDE (Impute Missing Data Easily). IMDE has two unique features. First, it provides much more missing value imputation algorithms than any existing tool. Second, it can suggest the optimal imputation algorithm for users' dataset after doing the performance evaluation.We used four different datasets to show that different optimal algorithms may be chosen for different datasets and for different selection schemes. We expect that IMDE will be a very useful server for solving the missing value problem in the microarray data.