HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks.

The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are available, they often fail to detect peaks with low quality and do not report cases with low confidence. Furthermore, visual examination of all chromatograms is still necessary to identify uncertain or erroneous cases. This study introduces HeapMS, a web service that uses artificial intelligence to assist with peak picking and the quality assessment of MRM chromatograms. HeapMS applies a rule-based filter to remove chromatograms with low interference and high-confidence peak boundaries detected by Skyline. Additionally, it transforms two histograms (representing light and heavy peptides) into a single encoded heatmap and performs a two-step evaluation (quality detection and peak picking) using image convolutional neural networks. HeapMS offers three categories of peak picking: uncertain peak picking that requires manual inspection, deletion peak picking that requires removal or manual re-examination, and automatic peak picking. HeapMS acquires the chromatogram and peak-picking boundaries directly from Skyline output. The output results are imported back into Skyline for further manual inspection, facilitating integration with Skyline. HeapMS offers the benefit of detecting chromatograms that should be deleted or require human inspection. Based on defined categories, it can significantly reduce human workload and provide consistent results. Furthermore, by using heatmaps instead of histograms, HeapMS can adapt to future updates in image recognition models. The HeapMS is available at: https://github.com/ccllabe/HeapMS.

mSignatureDB: a database for deciphering mutational signatures in human cancers.

Cancer is a genetic disease caused by somatic mutations; however, the understanding of the causative biological processes generating these mutations is limited. A cancer genome bears the cumulative effects of mutational processes during tumor development. Deciphering mutational signatures in cancer is a new topic in cancer research. The Wellcome Trust Sanger Institute (WTSI) has categorized 30 reference signatures in the COSMIC database based on the analyses of ∼10 000 sequencing datasets from TCGA and ICGC. Large cohorts and bioinformatics skills are required to perform the same analysis as WTSI. The quantification of known signatures in custom cohorts is not possible under the current framework of the COSMIC database, which motivates us to construct a database for mutational signatures in cancers and make such analyses more accessible to general researchers. mSignatureDB (http://tardis.cgu.edu.tw/msignaturedb) integrates R packages and in-house scripts to determine the contributions of the published signatures in 15 780 individual tumors from 73 TCGA/ICGC cancer projects, making comparison of signature patterns within and between projects become possible. mSignatureDB also allows users to perform signature analysis on their own datasets, quantifying contributions of signatures at sample resolution, which is a unique feature of mSignatureDB not available in other related databases.

Pathogenic Protist Transmembranome database (PPTdb): a web-based platform for searching and analysis of protist transmembrane proteins.

BACKGROUND: Pathogenic protist membrane transporter proteins play important roles not only in exchanging molecules into and out of cells but also in acquiring nutrients and biosynthetic compounds from their hosts. Currently, there is no centralized protist membrane transporter database published, which makes system-wide comparisons and studies of host-pathogen membranomes difficult to achieve. RESULTS: We analyzed over one million protein sequences from 139 protists with full or partial genome sequences. Putative transmembrane proteins were annotated by primary sequence alignments, conserved secondary structural elements, and functional domains. We have constructed the PPTdb (Pathogenic Protist Transmembranome database), a comprehensive membrane transporter protein portal for pathogenic protists and their human hosts. The PPTdb is a web-based database with a user-friendly searching and data querying interface, including hierarchical transporter classification (TC) numbers, protein sequences, functional annotations, conserved functional domains, batch sequence retrieving and downloads. The PPTdb also serves as an analytical platform to provide useful comparison/mining tools, including transmembrane ability evaluation, annotation of unknown proteins, informative visualization charts, and iterative functional mining of host-pathogen transporter proteins. CONCLUSIONS: The PPTdb collected putative protist transporter proteins and offers a user-friendly data retrieving interface. Moreover, a pairwise functional comparison ability can provide useful information for identifying functional uniqueness of each protist. Finally, the host and non-host protein similarity search can fulfill the needs of comprehensive studies of protists and their hosts. The PPTdb is freely accessible at http://pptdb.cgu.edu.tw .

Large-Scale Proteomic Identification of Targets of Cellular miR-197 Downregulated by Enterovirus A71.

MicroRNAs are noncoding RNA species comprising 18-23 nucleotides that regulate host-virus interaction networks. Here, we show that enterovirus A71 infection in human rhabdomyosarcoma (RD) is regulated by miR-197 expression. Transfection of miR-197 mimic into RD cells inhibited virus replication by interfering with the viral RNA synthesis. We employed a combination of mass-spectrometry-based quantitative proteomics with the stable isotope labeling with amino acids in cell culture (SILAC) approach for the identification of the miR-197 target genes in RD cells and to investigate the differential expression of the prospective target proteins. A total of 1822 proteins were repeatedly identified in miR-197-transfected RD cells, 106 of which were predicted to have seed sites by TargetScan. Notably, seven of eight selected genes potentially related to viral replication and immune response were validated as direct miR-197 targets, using a luciferase 3'-untranslated region (UTR) reporter assay. The expression levels of three selected endogenous molecules (ITGAV, ETF1, and MAP2K1/MEK1) were significantly reduced when RD cells were transfected with a miR-197 mimic. Our results provide a comprehensive database of miR-197 targets, which might provide better insights into the understanding of host-virus interaction.

VAReporter: variant reporter for cancer research of massive parallel sequencing.

BACKGROUND: High throughput sequencing technologies have been an increasingly critical aspect of precision medicine owing to a better identification of disease targets, which contributes to improved health care cost and clinical outcomes. In particular, disease-oriented targeted enrichment sequencing is becoming a widely-accepted application for diagnostic purposes, which can interrogate known diagnostic variants as well as identify novel biomarkers from panels of entire human coding exome or disease-associated genes. RESULTS: We introduce a workflow named VAReporter to facilitate the management of variant assessment in disease-targeted sequencing, the identification of pathogenic variants, the interpretation of biological effects and the prioritization of clinically actionable targets. State-of-art algorithms that account for mutation phenotypes are used to rank the importance of mutated genes through visual analytic strategies. We established an extensive annotation source by integrating a wide variety of biomedical databases and followed the American College of Medical Genetics and Genomics (ACMG) guidelines for interpretation and reporting of sequence variations. CONCLUSIONS: In summary, VAReporter is the first web server designed to provide a "one-stop" resource for individual's diagnosis and large-scale cohort studies, and is freely available at http://rnd.cgu.edu.tw/vareporter .

Traffic Intensity of Patients and Physicians in the Emergency Department: A Queueing Approach for Physician Utilization.

BACKGROUND: The unpredictable nature of patient visits poses considerable challenges to the staffing of emergency department (ED) medical personnel. There is a lack of common physician usage parameters at present. OBJECTIVE: The aim of this study was to quantify the ED traffic intensity of patients and physicians using a queueing model approach. METHODS: A retrospective administrative electronic data analysis was conducted in a tertiary medical center. All patients who registered at the ED in 2013 were included. Precisely recorded patient waiting time, service time, and disposition time were obtained. An M/M/s (Markovian patient arrival, Markovian patient service, s servers) queueing model was used, while taking account of the actual physician number and number of patients managed simultaneously. Physician utilization and performance indicators were measured. RESULTS: A total of 148,581 patients were analyzed after exclusion. The overall mean waiting time, service time, and disposition time were 0.23 (standard deviation [SD] = 0.24), 2.31 (SD = 3.89), and 2.54 (SD = 3.90) hours, respectively. Hourly physician utilization (ρ), stratified by different patient entities, was ρ = 0.75 ± 0.17 for adult non-trauma, ρ = 0.75 ± 0.28 for pediatric, and ρ = 0.53 ± 0.18 for trauma (p = 0.0004). There was a surge of utility for pediatric non-trauma patients in the late evening (ρ = 1.4 at 11 pm). The distribution of number of patients in the system was derived and compared by different patient entities and time points. CONCLUSIONS: A queueing model was built to model traffic intensity of physicians and patients, the physician utility trend disclosed the fluctuation of manpower utility. The estimated parameters serve as important factors for developing tailored staffing policies for minimizing ED waiting and improving ED crowding.

CMPD: cancer mutant proteome database.

Whole-exome sequencing, which centres on the protein coding regions of disease/cancer associated genes, represents the most cost-effective method to-date for deciphering the association between genetic alterations and diseases. Large-scale whole exome/genome sequencing projects have been launched by various institutions, such as NCI, Broad Institute and TCGA, to provide a comprehensive catalogue of coding variants in diverse tissue samples and cell lines. Further functional and clinical interrogation of these sequence variations must rely on extensive cross-platforms integration of sequencing information and a proteome database that explicitly and comprehensively archives the corresponding mutated peptide sequences. While such data resource is a critical for the mass spectrometry-based proteomic analysis of exomic variants, no database is currently available for the collection of mutant protein sequences that correspond to recent large-scale genomic data. To address this issue and serve as bridge to integrate genomic and proteomics datasets, CMPD (http://cgbc.cgu.edu.tw/cmpd) collected over 2 millions genetic alterations, which not only facilitates the confirmation and examination of potential cancer biomarkers but also provides an invaluable resource for translational medicine research and opportunities to identify mutated proteins encoded by mutated genes.

PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms.

BACKGROUND: Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared. RESULTS: Here, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours. CONCLUSIONS: In this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw .

Distinct expression of interferon-induced protein with tetratricopeptide repeats (IFIT) 1/2/3 and other antiviral genes between subsets of dendritic cells induced by dengue virus 2 infection.

Dengue virus (DENV) infection is an emerging public health hazard threatening inhabitants of the tropics and sub-tropics. Dendritic cells (DCs) are one of the major targets of DENV and the initiators of the innate immune response against the virus. However, current in vitro research on the DENV-DC interaction is hampered by the low availability of ex vivo DCs and donor variation. In the current study, we attempted to develop a novel in vitro DC model using immature DCs derived from the myeloid leukaemia cell line MUTZ-3 (IMDCs) to investigate the DENV-DC interaction. The IMDCs morphologically and phenotypically resembled human immature monocyte-derived dendritic cells (IMMoDCs). However, the permissiveness of IMDCs to DENV2 was lower than that of IMMoDCs. RT-PCR arrays showed that a group of type I interferon (IFN) -inducible genes, especially IFIT1, IFITM1, and IFI27, were significantly up-regulated in IMMoDCs but not in IMDCs after DENV2 infection. Further investigation revealed that IFIT genes were spontaneously expressed at both transcriptional and protein levels in the naive IMDCs but not in the naive IMMoDCs. It is possible that the poor permissiveness of IMDCs to DENV2 was a result of the high basal levels of IFIT proteins. We conclude that the IMDC model, although less permissive to DENV2, is a useful platform for studying the suppression mechanism of DENV2 and we expand the knowledge of cellular factors that modulate DENV2 infection in the human body.

A cytoplasmic RNA virus generates functional viral small RNAs and regulates viral IRES activity in mammalian cells.

The roles of virus-derived small RNAs (vsRNAs) have been studied in plants and insects. However, the generation and function of small RNAs from cytoplasmic RNA viruses in mammalian cells remain unexplored. This study describes four vsRNAs that were detected in enterovirus 71-infected cells using next-generation sequencing and northern blots. Viral infection produced substantial levels (>10(5) copy numbers per cell) of vsRNA1, one of the four vsRNAs. We also demonstrated that Dicer is involved in vsRNA1 generation in infected cells. vsRNA1 overexpression inhibited viral translation and internal ribosomal entry site (IRES) activity in infected cells. Conversely, blocking vsRNA1 enhanced viral yield and viral protein synthesis. We also present evidence that vsRNA1 targets stem-loop II of the viral 5' untranslated region and inhibits the activity of the IRES through this sequence-specific targeting. Our study demonstrates the ability of a cytoplasmic RNA virus to generate functional vsRNA in mammalian cells. In addition, we also demonstrate a potential novel mechanism for a positive-stranded RNA virus to regulate viral translation: generating a vsRNA that targets the IRES.

Vanno: a visualization-aided variant annotation tool.

Next-generation sequencing (NGS) technologies have revolutionized the field of genetics and are trending toward clinical diagnostics. Exome and targeted sequencing in a disease context represent a major NGS clinical application, considering its utility and cost-effectiveness. With the ongoing discovery of disease-associated genes, various gene panels have been launched for both basic research and diagnostic tests. However, the fundamental inconsistencies among the diverse annotation sources, software packages, and data formats have complicated the subsequent analysis. To manage disease-associated NGS data, we developed Vanno, a Web-based application for in-depth analysis and rapid evaluation of disease-causative genome sequence alterations. Vanno integrates information from biomedical databases, functional predictions from available evaluation models, and mutation landscapes from TCGA cancer types. A highly integrated framework that incorporates filtering, sorting, clustering, and visual analytic modules is provided to facilitate exploration of oncogenomics datasets at different levels, such as gene, variant, protein domain, or three-dimensional structure. Such design is crucial for the extraction of knowledge from sequence alterations and translating biological insights into clinical applications. Taken together, Vanno supports almost all disease-associated gene tests and exome sequencing panels designed for NGS, providing a complete solution for targeted and exome sequencing analysis. Vanno is freely available at http://cgts.cgu.edu.tw/vanno.

Adaptive responses to glucose restriction enhance cell survival, antioxidant capability, and autophagy of the protozoan parasite Trichomonas vaginalis.

BACKGROUND: To establish an infection in the vagina, Trichomonas vaginalis must adapt to various environmental cues for survival and further replication. Nutrient competition by lactobacilli, the major normal vaginal flora, is one of the mechanisms to limit the growth of other microorganisms. Additionally, lactobacilli produce H2O2 that can reduce the genital infections caused by other pathogens. Thus, the ability to overcome the metabolic stresses, such as glucose restriction (GR), as well as the oxidative stresses, is critical for T. vaginalis to establish an infection. METHODS: To gain insights into the molecular mechanisms of adaptation to GR, we utilized next-generation RNA sequencing (RNA-seq) to quantify the gene expression changes upon GR. Autophagy, a cytoprotective response to starvation, was monitored by using autophagy-specific staining, autophagy inhibition assay, and co-localization of autophagosomes with lysosomes. RESULTS: We demonstrated that GR promotes the survival of T. vaginalis. Besides, GR-cultivated cells exhibit higher H2O2 resistance. Our RNA-seq data revealed that genes involved in general energy metabolism were downregulated, whereas genes encoding glutamate metabolism-related aminotransferases were strikingly upregulated under GR. Furthermore, autophagy was first identified and characterized in T. vaginalis under GR. CONCLUSIONS: These data suggest that GR induces a metabolic reprogramming, enhancing antioxidant ability and autophagy for cellular homeostasis to maintain survival. GENERAL SIGNIFICANCE: Our work not only led to significant advances in understanding the transcriptional changes in response to GR but also provided possible strategies elicited by GR for T. vaginalis to adapt to the vaginal microenvironment.

Is the whole greater than the sum of its parts? De novo assembly strategies for bacterial genomes based on paired-end sequencing.

BACKGROUND: Whole genome sequence construction is becoming increasingly feasible because of advances in next generation sequencing (NGS), including increasing throughput and read length. By simply overlapping paired-end reads, we can obtain longer reads with higher accuracy, which can facilitate the assembly process. However, the influences of different library sizes and assembly methods on paired-end sequencing-based de novo assembly remain poorly understood. RESULTS: We used 250 bp Illumina Miseq paired-end reads of different library sizes generated from genomic DNA from Escherichia coli DH1 and Streptococcus parasanguinis FW213 to compare the assembly results of different library sizes and assembly approaches. Our data indicate that overlapping paired-end reads can increase read accuracy but sometimes cause insertion or deletions. Regarding genome assembly, merged reads only outcompete original paired-end reads when coverage depth is low, and larger libraries tend to yield better assembly results. These results imply that distance information is the most critical factor during assembly. Our results also indicate that when depth is sufficiently high, assembly from subsets can sometimes produce better results. CONCLUSIONS: In summary, this study provides systematic evaluations of de novo assembly from paired end sequencing data. Among the assembly strategies, we find that overlapping paired-end reads is not always beneficial for bacteria genome assembly and should be avoided or used with caution especially for genomes containing high fraction of repetitive sequences. Because increasing numbers of projects aim at bacteria genome sequencing, our study provides valuable suggestions for the field of genomic sequence construction.

Novel insights into the molecular events linking to cell death induced by tetracycline in the amitochondriate protozoan Trichomonas vaginalis.

Trichomonas vaginalis colonizes the human urogenital tract and causes trichomoniasis, the most common nonviral sexually transmitted disease. Currently, 5-nitroimidazoles are the only recommended drugs for treating trichomoniasis. However, increased resistance of the parasite to 5-nitroimidazoles has emerged as a highly problematic public health issue. Hence, it is essential to identify alternative chemotherapeutic agents against refractory trichomoniasis. Tetracycline (TET) is a broad-spectrum antibiotic with activity against several protozoan parasites, but the mode of action of TET in parasites remains poorly understood. The in vitro effect of TET on the growth of T. vaginalis was examined, and the mode of cell death was verified by various apoptosis-related assays. Next-generation sequencing-based RNA sequencing (RNA-seq) was employed to elucidate the transcriptome of T. vaginalis in response to TET. We show that TET has a cytotoxic effect on both metronidazole (MTZ)-sensitive and -resistant T. vaginalis isolates, inducing some features resembling apoptosis. RNA-seq data reveal that TET significantly alters the transcriptome via activation of specific pathways, such as aminoacyl-tRNA synthetases and carbohydrate metabolism. Functional analyses demonstrate that TET disrupts the hydrogenosomal membrane potential and antioxidant system, which concomitantly elicits a metabolic shift toward glycolysis, suggesting that the hydrogenosomal function is impaired and triggers cell death. Collectively, we provide in vitro evidence that TET is a potential alternative therapeutic choice for treating MTZ-resistant T. vaginalis. The in-depth transcriptomic signatures in T. vaginalis upon TET treatment presented here will shed light on the signaling pathways linking to cell death in amitochondriate organisms.

Exploring microRNA-mediated alteration of EGFR signaling pathway in non-small cell lung cancer using an mRNA:miRNA regression model supported by target prediction databases.

EGFR signaling pathway and microRNAs (miRNAs) are two important factors for development and treatment in non-small cell lung cancer (NSCLC). Microarray analysis enables the genome-wide expression profiling. However, the information from microarray data may not be fully deciphered through the existing approaches. In this study we present an mRNA:miRNA stepwise regression model supported by miRNA target prediction databases. This model is applied to explore the roles of miRNAs in the EGFR signaling pathway. The results show that miR-145 is positively associated with epidermal growth factor (EGF) in the pre-surgery NSCLC group and miR-199a-5p is positively associated with EGF in the post-surgery NSCLC group. Surprisingly, miR-495 is positively associated with protein tyrosine kinase 2 (PTK2) in both groups. The coefficient of determination (R(2)) and leave-one-out cross-validation (LOOCV) demonstrate good performance of our regression model, indicating that it can identify the miRNA roles as oncomirs and tumor suppressor mirs in NSCLC.

ChIPseek, a web-based analysis tool for ChIP data.

BACKGROUND: Chromatin is a dynamic but highly regulated structure. DNA-binding proteins such as transcription factors, epigenetic and chromatin modifiers are responsible for regulating specific gene expression pattern and may result in different phenotypes. To reveal the identity of the proteins associated with the specific region on DNA, chromatin immunoprecipitation (ChIP) is the most widely used technique. ChIP assay followed by next generation sequencing (ChIP-seq) or microarray (ChIP-chip) is often used to study patterns of protein-binding profiles in different cell types and in cancer samples on a genome-wide scale. However, only a limited number of bioinformatics tools are available for ChIP datasets analysis. RESULTS: We present ChIPseek, a web-based tool for ChIP data analysis providing summary statistics in graphs and offering several commonly demanded analyses. ChIPseek can provide statistical summary of the dataset including histogram of peak length distribution, histogram of distances to the nearest transcription start site (TSS), and pie chart (or bar chart) of genomic locations for users to have a comprehensive view on the dataset for further analysis. For examining the potential functions of peaks, ChIPseek provides peak annotation, visualization of peak genomic location, motif identification, sequence extraction, and comparison between datasets. Beyond that, ChIPseek also offers users the flexibility to filter peaks and re-analyze the filtered subset of peaks. ChIPseek supports 20 different genome assemblies for 12 model organisms including human, mouse, rat, worm, fly, frog, zebrafish, chicken, yeast, fission yeast, Arabidopsis, and rice. We use demo datasets to demonstrate the usage and intuitive user interface of ChIPseek. CONCLUSIONS: ChIPseek provides a user-friendly interface for biologists to analyze large-scale ChIP data without requiring any programing skills. All the results and figures produced by ChIPseek can be downloaded for further analysis. The analysis tools built into ChIPseek, especially the ones for selecting and examine a subset of peaks from ChIP data, provides invaluable helps for exploring the high through-put data from either ChIP-seq or ChIP-chip. ChIPseek is freely available at http://chipseek.cgu.edu.tw.

Comparative studies on the proteomic expression patterns in the third- and fifth-stage larvae of Angiostrongylus cantonensis.

Angiostrongylus cantonensis is an important zoonotic parasite causing eosinophilic meningitis and eosinophilic meningoencephalitis in humans. In this study, the protein expression profiles of the infective third- and pathogenic fifth-stage larvae (L3 and L5) of this parasite were compared by proteomic techniques. Isolated protein samples were separated by two-dimensional gel electrophoresis (2-DE), stained with silver nitrate, and analyzed by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS). Proteins from L5 were mainly at pH 5-7 and with molecular weight (MW) 40-100 kDa, whereas those from L3 were at pH 5-6 and with 5-35 kDa. Of 100 protein spots identified, 33 were from L3 whereas 67 from L5 and 63 had known identities, whereas 37 were hypothetical proteins. There were 15 spots of stress proteins, and HSP60 was the most frequently found heat stress proteins in L5. More binding and protein transport-related proteins were found in L5 including peptidylprolyl isomerase (cyclophilin)-like 2, serum albumin, preproalbumin precursor, and dilute class unconventional myosin. L3 had a higher expression of cytoskeleton and membrane proteins than L5. In addition, four protein spots were identified in the sera of the rat host by Western blot analysis. The present proteomic study revealed different protein expression profiles in L3 and L5 of A. cantonensis. These changes may reflect the development of L3 from the poikilothermic snails to L5 in the homoeothemic rats. This information may be useful for the finding of stage-specific proteins and biomarker for diagnosis of angiostrongyliasis.

ProFun: A web server for functional enrichment analysis of parasitic protozoan genes.

BACKGROUND: The initial step to interpreting putative biological functions from comparative multi-omics studies usually starts from a differential expressed gene list followed by functional enrichment analysis (FEA). However, most FEA packages are designed exclusively for humans and model organisms. Although parasitic protozoan is the most important pathogen in the tropics, no FEA package is available for protozoan functional (ProFun) enrichment analysis. To speed up comparative multi-omics research on parasitic protozoans, we constructed ProFun, a web-based, user-friendly platform for the research community. METHODS: ProFun utilizes the Docker container, ShinyProxy, and R Shiny to construct a scalable web service with load-balancing infrastructure. We have integrated a series of visual analytic functions, in-house scripts, and custom-made annotation packages to create three analytical modules for 40 protozoan species: (1) Gene Overlaps; (2) Over-representation Analysis (ORA); (3) Gene Set Enrichment Analysis (GSEA). RESULTS: We have established ProFun, a web server for functional enrichment analysis of differentially expressed genes. FEA becomes as simple as pasting a list of gene IDs into the textbox of our website. Users can customize enrichment parameters and results with just one click. The intuitive web interface and publication-ready charts enable users to reveal meaningful biological events and pinpoint potential targets for further studies. CONCLUSION: ProFun is the first web application that enables gene functional enrichment analysis of parasitic protozoans. In addition to supporting FEA analysis, ProFun also allows the comparison of FEA results across complicated experimental designs. ProFun is freely available at http://dalek.cgu.edu.tw:8080/app/profun.

The core exosome proteome of Trichomonas vaginalis.

BACKGROUND: Trichomonas vaginalis is parasitic protozoan that causes human urogenital infections. Accumulated reports indicated that exosomes released by this parasite play a crucial role in transmitting information and substances between cells during host-parasite interactions. Current knowledge on the protein contents in T. vaginalis exosome is mainly generated from three previous studies that used different T. vaginalis isolates as an experimental model. Whether T. vaginalis exosomes comprise a common set of proteins (core exosome proteome) is still unclear. METHODS: To explore the core exosome proteome in T. vaginalis, we used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify the contents of sucrose ultracentrifugation-enriched exosome and supernatant fractions isolated from six isolates. RESULTS: Transmission electron microscopy (TEM) confirmed the presence of exosomes in the enriched fraction. Proteomic analysis identified a total of 1870 proteins from exosomal extracts. There were 1207 exosomal-specific proteins after excluding 436 'non-core exosomal proteins'. Among these, 72 common exosomal-specific proteins were expressed in all six isolates. Compared with three published T. vaginalis exosome proteome datasets, we identified 16 core exosomal-specific proteins. These core exosomal-specific proteins included tetraspanin (TvTSP1), the classical exosome marker, and proteins mainly involved in catalytic activity and binding such as ribosomal proteins, ras-associated binding (Rab) proteins, and heterotrimeric G proteins. CONCLUSIONS: Our study highlighted the importance of using supernatant fraction from exosomal extract as a control to eliminate 'non-core exosomal proteins'. We compiled a reference core exosome proteome of T. vaginalis, which is essential for developing a fundamental understanding of exosome-mediated cell communication and host-parasite interaction.

CPAP: Cancer Panel Analysis Pipeline.

Targeted sequencing using next-generation sequencing technologies is currently being rapidly adopted for clinical sequencing and cancer marker tests. However, no existing bioinformatics tool is available for the analysis and visualization of multiple targeted sequencing datasets. In the present study, we use cancer panel targeted sequencing datasets generated by the Life Technologies Ion Personal Genome Machine Sequencer as an example to illustrate how to develop an automated pipeline for the comparative analyses of multiple datasets. Cancer Panel Analysis Pipeline (CPAP) uses standard output files from variant calling software to generate a distribution map of SNPs among all of the samples in a circular diagram generated by Circos. The diagram is hyperlinked to a dynamic HTML table that allows the users to identify target SNPs by using different filters. CPAP also integrates additional information about the identified SNPs by linking to an integrated SQL database compiled from SNP-related databases, including dbSNP, 1000 Genomes Project, COSMIC, and dbNSFP. CPAP only takes 17 min to complete a comparative analysis of 500 datasets. CPAP not only provides an automated platform for the analysis of multiple cancer panel datasets but can also serve as a model for any customized targeted sequencing project.