Wolfberry genome database: integrated genomic datasets for studying molecular biology.

Wolfberry, also known as goji berry or Lycium barbarum, is a highly valued fruit with significant health benefits and nutritional value. For more efficient and comprehensive usage of published L. barbarum genomic data, we established the Wolfberry database. The utility of the Wolfberry Genome Database (WGDB) is highlighted through the Genome browser, which enables the user to explore the L. barbarum genome, browse specific chromosomes, and access gene sequences. Gene annotation features provide comprehensive information about gene functions, locations, expression profiles, pathway involvement, protein domains, and regulatory transcription factors. The transcriptome feature allows the user to explore gene expression patterns using transcripts per kilobase million (TPM) and fragments per kilobase per million mapped reads (FPKM) metrics. The Metabolism pathway page provides insights into metabolic pathways and the involvement of the selected genes. In addition to the database content, we also introduce six analysis tools developed for the WGDB. These tools offer functionalities for gene function prediction, nucleotide and amino acid BLAST analysis, protein domain analysis, GO annotation, and gene expression pattern analysis. The WGDB is freely accessible at https://cosbi7.ee.ncku.edu.tw/Wolfberry/. Overall, WGDB serves as a valuable resource for researchers interested in the genomics and transcriptomics of L. barbarum. Its user-friendly web interface and comprehensive data facilitate the exploration of gene functions, regulatory mechanisms, and metabolic pathways, ultimately contributing to a deeper understanding of wolfberry and its potential applications in agronomy and nutrition.

FL-circAS: an integrative resource and analysis for full-length sequences and alternative splicing of circular RNAs with nanopore sequencing.

Circular RNAs (circRNAs) are RNA molecules with a continuous loop structure characterized by back-splice junctions (BSJs). While analyses of short-read RNA sequencing have identified millions of BSJ events, it is inherently challenging to determine exact full-length sequences and alternatively spliced (AS) isoforms of circRNAs. Recent advances in nanopore long-read sequencing with circRNA enrichment bring an unprecedented opportunity for investigating the issues. Here, we developed FL-circAS (https://cosbi.ee.ncku.edu.tw/FL-circAS/), which collected such long-read sequencing data of 20 cell lines/tissues and thereby identified 884 636 BSJs with 1 853 692 full-length circRNA isoforms in human and 115 173 BSJs with 135 617 full-length circRNA isoforms in mouse. FL-circAS also provides multiple circRNA features. For circRNA expression, FL-circAS calculates expression levels for each circRNA isoform, cell line/tissue specificity at both the BSJ and isoform levels, and AS entropy for each BSJ across samples. For circRNA biogenesis, FL-circAS identifies reverse complementary sequences and RNA binding protein (RBP) binding sites residing in flanking sequences of BSJs. For functional patterns, FL-circAS identifies potential microRNA/RBP binding sites and several types of evidence for circRNA translation on each full-length circRNA isoform. FL-circAS provides user-friendly interfaces for browsing, searching, analyzing, and downloading data, serving as the first resource for discovering full-length circRNAs at the isoform level.

Interventional chemoembolization for the treatment of severe ulcerative bleeding caused by advanced breast cancer: A report of two cases.

Local ulcerative cutaneous hemorrhage resulting from breast cancer profoundly effects the quality of life of patients, at times even posing a threat to life. While early diagnosis rates of breast cancer have shown improvement, some patients may present at an advanced stage upon consultation. Presently, there is no standardized treatment approach for these patients. In this context, the present study presented two case studies detailing the use of interventional embolization chemotherapy for addressing severe local ulcerative hemorrhage associated with breast cancer. Post-treatment, there was a notable amelioration in the mammary ulceration among the patients, an elevated hemoglobin level compared with baseline and a consequent enhancement in their overall quality of life. These cases may serve as valuable references for the management of such clinical situations.

Identifying Human miRNA Target Sites via Learning the Interaction Patterns between miRNA and mRNA Segments.

miRNAs (microRNAs) target specific mRNA (messenger RNA) sites to regulate their translation expression. Although miRNA targeting can rely on seed region base pairing, animal miRNAs, including human miRNAs, typically cooperate with several cofactors, leading to various noncanonical pairing rules. Therefore, identifying the binding sites of animal miRNAs remains challenging. Because experiments for mapping miRNA targets are costly, computational methods are preferred for extracting potential miRNA-mRNA fragment binding pairs first. However, existing prediction tools can have significant false positives due to the prevalent noncanonical miRNA binding behaviors and the information-biased training negative sets that were used while constructing these tools. To overcome these obstacles, we first prepared an information-balanced miRNA binding pair ground-truth data set. A miRNA-mRNA interaction-aware model was then designed to help identify miRNA binding events. On the test set, our model (auROC = 94.4%) outperformed existing models by at least 2.8% in auROC. Furthermore, we showed that this model can suggest potential binding patterns for miRNA-mRNA sequence interacting pairs. Finally, we made the prepared data sets and the designed model available at http://cosbi2.ee.ncku.edu.tw/mirna_binding/download.

Identification of Xenobiotic Biotransformation Products Using Mass Spectrometry-Based Metabolomics Integrated with a Structural Elucidation Strategy by Assembling Fragment Signatures.

The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4'-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.

Condensate cooperativity underlies transgenerational gene silencing.

Biomolecular condensates have been shown to interact in vivo, yet it is unclear whether these interactions are functionally meaningful. Here, we demonstrate that cooperativity between two distinct condensates-germ granules and P bodies-is required for transgenerational gene silencing in C. elegans. We find that P bodies form a coating around perinuclear germ granules and that P body components CGH-1/DDX6 and CAR-1/LSM14 are required for germ granules to organize into sub-compartments and concentrate small RNA silencing factors. Functionally, while the P body mutant cgh-1 is competent to initially trigger gene silencing, it is unable to propagate the silencing to subsequent generations. Mechanistically, we trace this loss of transgenerational silencing to defects in amplifying secondary small RNAs and the stability of WAGO-4 Argonaute, both known carriers of gene silencing memories. Together, these data reveal that cooperation between condensates results in an emergent capability of germ cells to establish heritable memory.

Transcriptome-wide analyses of piRNA binding sites suggest distinct mechanisms regulate piRNA binding and silencing in C. elegans.

PIWI-interacting RNAs (piRNAs) protect genome integrity by silencing transposon mRNAs and some endogenous mRNAs in various animals. However, C. elegans piRNAs only trigger gene silencing at select predicted targeting sites, suggesting additional cellular mechanisms regulate piRNA silencing. To gain insight into possible mechanisms, we compared the transcriptome-wide predicted piRNA targeting sites to the in vivo piRNA binding sites. Surprisingly, while sequence-based 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. However, our analyses suggest that this CDS binding preference cannot be explained by the action of antisilencing Argonaute CSR-1. Instead, our analyses imply that 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. Together, our work identifies the CDS as the critical region that is uniquely competent for piRNA binding in C. elegans. We speculate the CDS binding preference may have evolved to allow the piRNA pathway to maintain robust recognition of RNA targets in spite of genetic drift. Together, our analyses revealed that distinct mechanisms are responsible for restricting piRNA binding and silencing to achieve proper transcriptome surveillance.

YMLA: A comparative platform to carry out functional enrichment analysis for multiple gene lists in yeast.

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/.

OrchidBase 5.0: updates of the orchid genome knowledgebase.

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/.

Comparable bidirectional neutrophil immune dysregulation between Kawasaki disease and severe COVID-19.

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.

GLH/VASA helicases promote germ granule formation to ensure the fidelity of piRNA-mediated transcriptome surveillance.

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 observe that hundreds of endogenous genes are aberrantly silenced by piRNAs. This defect in self versus non-self recognition is also observed in other mutants where perinuclear germ 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.

KDmarkers: A biomarker database for investigating epigenetic methylation and gene expression levels in Kawasaki disease.

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/.

LCMD: Lung Cancer Metabolome Database.

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.

CLASH Analyst: A Web Server to Identify In Vivo RNA-RNA Interactions from CLASH Data.

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.

Taxonomic and Functional Dysregulation in Salivary Microbiomes During Oral Carcinogenesis.

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.

Identifying piRNA targets on mRNAs in C. elegans using a deep multi-head attention network.

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 [Formula: see text] 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 .

Cancer DEIso: An integrative analysis platform for investigating differentially expressed gene-level and isoform-level human cancer markers.

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/.

OrchidBase 4.0: a database for orchid genomics and molecular biology.

BACKGROUND: The Orchid family is the largest families of the monocotyledons and an economically important ornamental plant worldwide. Given the pivotal role of this plant to humans, botanical researchers and breeding communities should have access to valuable genomic and transcriptomic information of this plant. Previously, we established OrchidBase, which contains expressed sequence tags (ESTs) from different tissues and developmental stages of Phalaenopsis as well as biotic and abiotic stress-treated Phalaenopsis. The database includes floral transcriptomic sequences from 10 orchid species across all the five subfamilies of Orchidaceae. DESCRIPTION: Recently, the whole-genome sequences of Apostasia shenzhenica, Dendrobium catenatum, and Phalaenopsis equestris were de novo assembled and analyzed. These datasets were used to develop OrchidBase 4.0, including genomic and transcriptomic data for these three orchid species. OrchidBase 4.0 offers information for gene annotation, gene expression with fragments per kilobase of transcript per millions mapped reads (FPKM), KEGG pathways and BLAST search. In addition, assembled genome sequences and location of genes and miRNAs could be visualized by the genome browser. The online resources in OrchidBase 4.0 can be accessed by browsing or using BLAST. Users can also download the assembled scaffold sequences and the predicted gene and protein sequences of these three orchid species. CONCLUSIONS: OrchidBase 4.0 is the first database that contain the whole-genome sequences and annotations of multiple orchid species. OrchidBase 4.0 is available at http://orchidbase.itps.ncku.edu.tw/.

YPIBP: A repository for phosphoinositide-binding proteins in yeast.

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/.

A tool for analyzing and visualizing ribo-seq data at the isoform level.

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.