RamiGO: an R/Bioconductor package providing an AmiGO visualize interface

Large-Scale Multi-Omics Studies Provide New Insights into Blood Pressure Regulation

Recent genome-wide association studies uncovered part of blood pressure’s heritability. However, there is still a vast gap between genetics and biology that needs to be bridged. Here, we followed up blood pressure genome-wide summary statistics of over 750,000 individuals, leveraging comprehensive epigenomic and transcriptomic data from blood with a follow-up in cardiovascular tissues to prioritise likely causal genes and underlying blood pressure mechanisms. We first prioritised genes based on coding consequences, multilayer molecular associations, blood pressure-associated expression levels, and coregulation evidence. Next, we followed up the prioritised genes in multilayer studies of genomics, epigenomics, and transcriptomics, functional enrichment, and their potential suitability as drug targets. Our analyses yielded 1880 likely causal genes for blood pressure, tens of which are targets of the available licensed drugs. We identified 34 novel genes for blood pressure, supported by more than one source of biological evidence. Twenty-eight (82%) of these new genes were successfully replicated by transcriptome-wide association analyses in a large independent cohort (n = ~220,000). We also found a substantial mediating role for epigenetic regulation of the prioritised genes. Our results provide new insights into genetic regulation of blood pressure in terms of likely causal genes and involved biological pathways offering opportunities for future translation into clinical practice.

Dysregulated immunological and metabolic functions discovered by a polygenic integrative analysis for PCOS

RESEARCH QUESTION

Polycystic ovary syndrome (PCOS) is a complex disease and its pathophysiology is still unclear. This polygenic study may provide some clues.

DESIGN

A polygenic, functionome-based study with the ovarian gene expression profiles downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, including 48 PCOS and 181 normal control samples. These profiles were converted to the gene set regularity (GSR) indices, which were computed by the modified differential rank conversion algorithm and were defined by the gene ontology terms.

RESULTS

Machine learning could accurately recognize the patterns of functional regularities between PCOS and normal controls. The significantly aberrant functions in PCOS included transporter activity, catalytic activity, the receptor signalling pathway via signal transducer and activator of transcription (STAT), the cellular metabolic process, and immune response.

CONCLUSION

This study provided a comprehensive view of the dysregulated functions and information for further studies on the management of PCOS.

A perturbed gene network containing PI3K-AKT, RAS-ERK and WNT-β-catenin pathways in leukocytes is linked to ASD genetics and symptom severity

Hundreds of genes are implicated in autism spectrum disorder (ASD) but the mechanisms through which they contribute to ASD pathophysiology remain elusive. Here, we analyzed leukocyte transcriptomics from 1–4 year-old male toddlers with ASD or typical development from the general population. We discovered a perturbed gene network that includes genes that are highly expressed during fetal brain development and which is dysregulated in hiPSC-derived neuron models of ASD. High-confidence ASD risk genes emerge as upstream regulators of the network, and many risk genes may impact the network by modulating RAS/ERK, PI3K/AKT, and WNT/β-catenin signaling pathways. We found that the degree of dysregulation in this network correlated with the severity of ASD symptoms in the toddlers. These results demonstrate how the heterogeneous genetics of ASD may dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms.

CirGO: an alternative circular way of visualising gene ontology terms

BACKGROUND

Prioritisation of gene ontology terms from differential gene expression analyses in a two-dimensional format remains a challenge with exponentially growing data volumes. Typically, gene ontology terms are represented as tree-maps that enclose all data into defined space. However, large datasets make this type of visualisation appear cluttered and busy, and often not informative as some labels are omitted due space limits, especially when published in two-dimensional (2D) figures.

RESULTS

Here we present an open source CirGO (Circular Gene Ontology) software that visualises non-redundant two-level hierarchically structured ontology terms from gene expression data in a 2D space. Gene ontology terms based on statistical significance were summarised with a semantic similarity algorithm and grouped by hierarchical clustering. This software visualises the most enriched gene ontology terms in an informative, comprehensive and intuitive format that is achieved by organising data from the most relevant to the least, as well as the appropriate use of colours and supporting information. Additionally, CirGO is an easy to use software that supports researchers with little computational background to present their gene ontology data in a publication ready format.

CONCLUSIONS

Our easy to use open source CirGO Python software package provides biologists with a succinct presentation of terms and functions that are most represented in a specific gene expression data set in a visually appealing 2D format (e.g. for reporting research results in scientific articles). CirGO is freely available at https://github.com/IrinaVKuznetsova/CirGO.git .

FlbA-Regulated Gene rpnR Is Involved in Stress Resistance and Impacts Protein Secretion when Aspergillus niger Is Grown on Xylose

Proteins are secreted throughout the mycelium of Aspergillus niger except for the sporulating zone. A link between sporulation and repression of protein secretion was underlined by the finding that inactivation of the sporulation gene flbA results in mycelial colonies that secrete proteins throughout the colony. However, ΔflbA strain hyphae also lyse and have thinner cell walls. This pleiotropic phenotype is associated with differential expression of 36 predicted transcription factor genes, one of which, rpnR, was inactivated in this study. Sporulation, biomass, and secretome complexity were not affected in the ΔrpnR deletion strain of the fungus. In contrast, ribosomal subunit expression and protein secretion into the medium were reduced when A. niger was grown on xylose. Moreover, the ΔrpnR strain showed decreased resistance to H₂O₂ and the proteotoxic stress-inducing agent dithiothreitol. Taking the data together, RpnR is involved in proteotoxic stress resistance and impacts protein secretion when A. niger is grown on xylose.IMPORTANCE Aspergillus niger secretes a large amount and diversity of industrially relevant enzymes into the culture medium. This makes the fungus a widely used industrial cell factory. For instance, carbohydrate-active enzymes of A. niger are used in biofuel production from lignocellulosic feedstock. These enzymes represent a major cost factor in this process. Higher production yields could substantially reduce these costs and therefore contribute to a more sustainable economy and less dependence on fossil fuels. Enzyme secretion is inhibited in A. niger by asexual reproduction. The sporulation protein FlbA is involved in this process by impacting the expression of 36 predicted transcription factor genes. Here, we show that one of these predicted transcriptional regulators, RpnR, regulates protein secretion and proteotoxic stress resistance. The gene is thus an interesting target to improve enzyme production in A. niger.

isoTar: Consensus Target Prediction with Enrichment Analysis for MicroRNAs Harboring Editing Sites and Other Variations

MicroRNAs (miRNAs) are small noncoding RNA molecules (sncRNAs) involved in gene expression regulation. Having been widely studied during last two decades, they have been associated with several diseases, including cancer. Recent improvements in high throughput sequencing technologies have revealed a more complex miRNAome, due to miRNA sequence modification phenomena, such as RNA editing and isomiRs. As a result, a new class of tools is necessary in order to appropriately investigate this emerging complexity. To address such need, we developed isoTar, a high-performance Web-based containerized application designed for miRNA consensus targeting prediction and functional enrichment analyses. In the present chapter, we provide an overview of isoTar ( https://ncrnaome.osumc.edu/isotar/ ), as well as benchmarks and a guide to its usage.

Integrating the dysregulated inflammasome-based molecular functionome in the malignant transformation of endometriosis-associated ovarian carcinoma

The coexistence of endometriosis (ES) with ovarian clear cell carcinoma (CCC) or endometrioid carcinoma (EC) suggested that malignant transformation of ES leads to endometriosis associated ovarian carcinoma (EAOC). However, there is still lack of an integrating data analysis of the accumulated experimental data to provide the evidence supporting the hypothesis of EAOC transformation. Herein we used a function-based analytic model with the publicly available microarray datasets to investigate the expression profiling between ES, CCC, and EC. We analyzed the functional regularity pattern of the three type of samples and hierarchically clustered the gene sets to identify key mechanisms regulating the malignant transformation of EAOC. We identified a list of 18 genes (NLRP3, AIM2, PYCARD, NAIP, Caspase-4, Caspase-7, Caspase-8, TLR1, TLR7, TOLLIP, NFKBIA, TNF, TNFAIP3, INFGR2, P2RX7, IL-1B, IL1RL1, IL-18) closely related to inflammasome complex, indicating an important role of inflammation/immunity in EAOC transformation. We next explore the association between these target genes and patient survival using Gene Expression Omnibus (GEO), and found significant correlation between the expression levels of the target genes and the progression-free survival. Interestingly, high expression levels of AIM2 and NLRP3, initiating proteins of inflammasomes, were significantly correlated with poor progression-free survival. Immunohistochemistry staining confirmed a correlation between high AIM2 and high Ki-67 in clinical EAOC samples, supporting its role in disease progression. Collectively, we established a bioinformatic platform of gene-set integrative molecular functionome to dissect the pathogenic pathways of EAOC, and demonstrated a key role of dysregulated inflammasome in modulating the malignant transformation of EAOC.

Gene set-based analysis of mucinous ovarian carcinoma

OBJECTIVE

Mucinous ovarian carcinoma (MOC) is an uncommon subtype of epithelial ovarian cancers, and the pathogenesis is still poorly understood because of its rarity. We conducted a gene set-based analysis to investigate the pathogenesis of MOC by integrating microarray gene expression datasets based on the regularity of functions defined by gene ontology or canonical pathway databases.

MATERIALS AND METHODS

Forty-five pairs of MOC and normal ovarian tissue sample gene expression profiles were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database. The gene expression profiles were converted to the gene set regularity indexes by measuring the change of gene expression ordering in a gene set. Then the pathogenesis of MOC was investigated with the differences of function regularity with the gene set regularity indexes between the MOC and normal control samples.

RESULTS

The informativeness of the gene set regularity indexes was sufficient for machine learning to accurately recognize and classify the functional regulation patterns with an accuracy of 99.44%. The statistical analysis revealed that the GTPase regulators and receptor tyrosine kinase erbB-2 (ERBB2) were the most important aberrations; the exploratory factor analysis revealed phosphoinositide 3-kinase-activating kinase, G-protein coupled receptor pathway, oxidoreductase activity, immune response, peptidase activity, regulation of translation, and transport and channel activity were also involved in the pathogenesis of MOC.

CONCLUSION

Investigating the pathogenesis of MOC with the functionome provided a comprehensive view of the deregulated functions of this disease. In addition to GTPase regulators and ERBB2, a plenty of deregulated functions such as phosphoinositide 3-kinase, G-protein coupled receptor pathway, and immune response also participated in the interaction network of MOC pathogenesis.

Discovering the Deregulated Molecular Functions Involved in Malignant Transformation of Endometriosis to Endometriosis-Associated Ovarian Carcinoma Using a Data-Driven, Function-Based Analysis

The clinical characteristics of clear cell carcinoma (CCC) and endometrioid carcinoma EC) are concomitant with endometriosis (ES), which leads to the postulation of malignant transformation of ES to endometriosis-associated ovarian carcinoma (EAOC). Different deregulated functional areas were proposed accounting for the pathogenesis of EAOC transformation, and there is still a lack of a data-driven analysis with the accumulated experimental data in publicly-available databases to incorporate the deregulated functions involved in the malignant transformation of EOAC. We used the microarray gene expression datasets of ES, CCC and EC downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) database. Then, we investigated the pathogenesis of EAOC by a data-driven, function-based analytic model with the quantified molecular functions defined by 1454 Gene Ontology (GO) term gene sets. This model converts the gene expression profiles to the functionome consisting of 1454 quantified GO functions, and then, the key functions involving the malignant transformation of EOAC can be extracted by a series of filters. Our results demonstrate that the deregulated oxidoreductase activity, metabolism, hormone activity, inflammatory response, innate immune response and cell-cell signaling play the key roles in the malignant transformation of EAOC. These results provide the evidence supporting the specific molecular pathways involved in the malignant transformation of EAOC.

Genetic loci associated with heart rate variability and their effects on cardiac disease risk

Reduced cardiac vagal control reflected in low heart rate variability (HRV) is associated with greater risks for cardiac morbidity and mortality. In two-stage meta-analyses of genome-wide association studies for three HRV traits in up to 53,174 individuals of European ancestry, we detect 17 genome-wide significant SNPs in eight loci. HRV SNPs tag non-synonymous SNPs (in NDUFA11 and KIAA1755), expression quantitative trait loci (eQTLs) (influencing GNG11, RGS6 and NEO1), or are located in genes preferentially expressed in the sinoatrial node (GNG11, RGS6 and HCN4). Genetic risk scores account for 0.9 to 2.6% of the HRV variance. Significant genetic correlation is found for HRV with heart rate (-0.74<r_g<-0.55) and blood pressure (-0.35<r_g<-0.20). These findings provide clinically relevant biological insight into heritable variation in vagal heart rhythm regulation, with a key role for genetic variants (GNG11, RGS6) that influence G-protein heterotrimer action in GIRK-channel induced pacemaker membrane hyperpolarization.

Effect of age on pro-inflammatory miRNAs contained in mesenchymal stem cell-derived extracellular vesicles

Stem cells possess significant age-dependent differences in their immune-response profile. These differences were analysed by Next-Generation Sequencing of six age groups from bone marrow mesenchymal stem cells. A total of 9,628 genes presenting differential expression between age groups were grouped into metabolic pathways. We focused our research on young, pre-pubertal and adult groups, which presented the highest amount of differentially expressed genes related to inflammation mediated by chemokine and cytokine signalling pathways compared with the newborn group, which was used as a control. Extracellular vesicles extracted from each group were characterized by nanoparticle tracking and flow cytometry analysis, and several micro-RNAs were verified by quantitative real-time polymerase chain reaction because of their relationship with the pathway of interest. Since miR-21-5p showed the highest statistically significant expression in extracellular vesicles from mesenchymal stem cells of the pre-pubertal group, we conducted a functional experiment inhibiting its expression and investigating the modulation of Toll-Like Receptor 4 and their link to damage-associated molecular patterns. Together, these results indicate for the first time that mesenchymal stem cell-derived extracellular vesicles have significant age-dependent differences in their immune profiles.

Working with Ontologies

Ontologies are powerful and popular tools to encode data in a structured format and manage knowledge. A large variety of existing ontologies offer users access to biomedical knowledge. This chapter contains a short theoretical background of ontologies and introduces two notable examples: The Gene Ontology and the ontology for Biological Pathways Exchange. For both ontologies a short overview and working bioinformatic applications, i.e., Gene Ontology enrichment analyses and pathway data visualization, are provided.

Knowledge-based analysis of functional impacts of mutations in microRNA seed regions

MicroRNAs are a class of important post-transcriptional regulators. Genetic and somatic mutations in miRNAs, especially those in the seed regions, have profound and broad impacts on gene expression and physiological and pathological processes. Over 500 SNPs were mapped to the miRNA seeds, which are located at position 2-8 of the mature miRNA sequences. We found that the central positions of the miRNA seeds contain fewer genetic variants and therefore are more evolutionary conserved than the peripheral positions in the seeds. We developed a knowledgebased method to analyse the functional impacts of mutations in miRNA seed regions. We computed the gene ontology-based similarity score GOSS and the GOSS percentile score for all 517 SNPs in miRNA seeds. In addition to the annotation of SNPs for their functional effects, in the present article we also present a detailed analysis pipeline for finding the key functional changes for seed SNPs. We performed a detailed gene ontology graph-based analysis of enriched functional categories for miRNA target gene sets. In the analysis of a SNP in the seed region of hsa-miR-96 we found that two key biological processes for progressive hearing loss 'Neurotrophin TRK receptor signaling pathway' and 'Epidermal growth factor receptor signaling pathway' were significantly and differentially enriched by the two sets of allele-specific target genes of miRNA hsa-miR-96.

Gene Set-Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer

Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular features. However, this hypothesis has not been experimentally demonstrated. We carried out a gene set-based analysis by integrating the microarray gene expression profiles downloaded from the publicly available databases. These quantified biological functions of EOCs were defined by 1454 Gene Ontology (GO) term and 674 Reactome pathway gene sets. The pathogenesis of the four EOC subtypes was investigated by hierarchical clustering and exploratory factor analysis. The patterns of functional regulation among the four subtypes containing 1316 cases could be accurately classified by machine learning. The results revealed that the ERBB and PI3K-related pathways played important roles in the carcinogenesis of CCC, EC and MC; while deregulation of cell cycle was more predominant in SC. The study revealed that two different functional regulation patterns exist among the four EOC subtypes, which were compatible with the type I and II classifications proposed by the dualistic model of ovarian carcinogenesis.

Gene Set-Based Functionome Analysis of Pathogenesis in Epithelial Ovarian Serous Carcinoma and the Molecular Features in Different FIGO Stages

Serous carcinoma (SC) is the most common subtype of epithelial ovarian carcinoma and is divided into four stages by the Federation of Gynecologists and Obstetrics (FIGO) staging system. Currently, the molecular functions and biological processes of SC at different FIGO stages have not been quantified. Here, we conducted a whole-genome integrative analysis to investigate the functions of SC at different stages. The function, as defined by the GO term or canonical pathway gene set, was quantified by measuring the changes in the gene expressional order between cancerous and normal control states. The quantified function, i.e., the gene set regularity (GSR) index, was utilized to investigate the pathogenesis and functional regulation of SC at different FIGO stages. We showed that the informativeness of the GSR indices was sufficient for accurate pattern recognition and classification for machine learning. The function regularity presented by the GSR indices showed stepwise deterioration during SC progression from FIGO stage I to stage IV. The pathogenesis of SC was centered on cell cycle deregulation and accompanied with multiple functional aberrations as well as their interactions.

MiRComb: An R Package to Analyse miRNA-mRNA Interactions. Examples across Five Digestive Cancers

MicroRNAs (miRNAs) are small RNAs that regulate the expression of target mRNAs by specific binding on the mRNA 3'UTR and promoting mRNA degradation in the majority of cases. It is often of interest to know the specific targets of a miRNA in order to study them in a particular disease context. In that sense, some databases have been designed to predict potential miRNA-mRNA interactions based on hybridization sequences. However, one of the main limitations is that these databases have too many false positives and do not take into account disease-specific interactions. We have developed an R package (miRComb) able to combine miRNA and mRNA expression data with hybridization information, in order to find potential miRNA-mRNA targets that are more reliable to occur in a specific physiological or disease context. This article summarizes the pipeline and the main outputs of this package by using as example TCGA data from five gastrointestinal cancers (colon cancer, rectal cancer, liver cancer, stomach cancer and esophageal cancer). The obtained results can be used to develop a huge number of testable hypotheses by other authors. Globally, we show that the miRComb package is a useful tool to deal with miRNA and mRNA expression data, that helps to filter the high amount of miRNA-mRNA interactions obtained from the pre-existing miRNA target prediction databases and it presents the results in a standardised way (pdf report). Moreover, an integrative analysis of the miRComb miRNA-mRNA interactions from the five digestive cancers is presented. Therefore, miRComb is a very useful tool to start understanding miRNA gene regulation in a specific context. The package can be downloaded in http://mircomb.sourceforge.net.

GOsummaries: an R Package for Visual Functional Annotation of Experimental Data

Functional characterisation of gene lists using Gene Ontology (GO) enrichment analysis is a common approach in computational biology, since many analysis methods end up with a list of genes as a result. Often there can be hundreds of functional terms that are significantly associated with a single list of genes and proper interpretation of such results can be a challenging endeavour. There are methods to visualise and aid the interpretation of these results, but most of them are limited to the results associated with one list of genes. However, in practice the number of gene lists can be considerably higher and common tools are not effective in such situations.

We introduce a novel R package, 'GOsummaries' that visualises the GO enrichment results as concise word clouds that can be combined together if the number of gene lists is larger. By also adding the graphs of corresponding raw experimental data, GOsummaries can create informative summary plots for various analyses such as differential expression or clustering. The case studies show that the GOsummaries plots allow rapid functional characterisation of complex sets of gene lists. The GOsummaries approach is particularly effective for Principal Component Analysis (PCA).

By adding functional annotation to the principal components, GOsummaries improves  significantly the interpretability of PCA results. The GOsummaries layout for PCA can be effective even in situations where we cannot directly apply the GO analysis. For example, in case of metabolomics or metagenomics data it is possible to show the features with significant associations to the components instead of GO terms.  

The GOsummaries package is available under GPL-2 licence at Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/GOsummaries.html).

The Cipher Code of Simple Sequence Repeats in "Vampire Pathogens"

Blood inside mammals is a forbidden area for the majority of prokaryotic microbes; however, red blood cells tropism microbes, like “vampire pathogens” (VP), succeed in matching scarce nutrients and surviving strong immunity reactions. Here, we found VP of Mycoplasma, Rhizobiales, and Rickettsiales showed significantly higher counts of (AG)_n dimeric simple sequence repeats (Di-SSRs) in the genomes, coding and non-coding regions than non Vampire Pathogens (N_VP). Regression analysis indicated a significant correlation between GC content and the span of (AG)_n-Di-SSR variation. Gene Ontology (GO) terms with abundance of (AG)₃-Di-SSRs shared by the VP strains were associated with purine nucleotide metabolism (FDR < 0.01), indicating an adaptation to the limited availability of purine and nucleotide precursors in blood. Di-amino acids coded by (AG)_n-Di-SSRs included all three six-fold code amino acids (Arg, Leu and Ser) and significantly higher counts of Di-amino acids coded by (AG)₃, (GA)₃, and (TC)₃ in VP than N_VP. Furthermore, significant differences (P < 0.001) on the numbers of triplexes formed from (AG)_n-Di-SSRs between VP and N_VP in Mycoplasma suggested the potential role of (AG)_n-Di-SSRs in gene regulation.

miR2GO: comparative functional analysis for microRNAs

miR2GO is a web-based platform for comparative analyses of human miRNA functions. It includes two programs: miRmut2GO and miRpair2GO. miRmut2GO implements a knowledge-based method to assess the functional effects of genetic and somatic mutations in microRNA seed regions. The functional effects of a mutation are analysed by semantic comparison of enriched gene ontology (GO) annotations of the target gene sets for the wild-type and mutated alleles. miRpair2GO compares the functions of two different miRNAs based on the enriched functional annotations of their target gene sets.

AVAILABILITY AND IMPLEMENTATION

The miR2GO web server is available at http://compbio.uthsc.edu/miR2GO.

In Silico Post Genome-Wide Association Studies Analysis of C-Reactive Protein Loci Suggests an Important Role for Interferons

BACKGROUND

Genome-wide association studies (GWASs) have successfully identified several single nucleotide polymorphisms (SNPs) associated with serum levels of C-reactive protein (CRP). An important limitation of GWASs is that the identified variants merely flag the nearby genomic region and do not necessarily provide a direct link to the biological mechanisms underlying their corresponding phenotype. Here we apply a bioinformatics-based approach to uncover the functional characteristics of the 18 SNPs that had previously been associated with CRP at a genome-wide significant level.

METHODS AND RESULTS

In the first phase of in silico sequencing, we explore the vicinity of GWAS SNPs to identify all linked variants. In the second phase of expression quantitative trait loci analysis, we attempt to identify all nearby genes whose expression levels are associated with the corresponding GWAS SNPs. These 2 phases generate several relevant genes that serve as input to the next phase of functional network analysis. Our in silico sequencing analysis using 1000 Genomes Project data identified 7 nonsynonymous SNPs, which are in moderate to high linkage disequilibrium (r(2)>0.5) with the GWAS SNPs. Our expression quantitative trait loci analysis, which was based on one of the largest single data sets of genome-wide expression probes (n>5000) identified 23 significantly associated expression probes belonging to 15 genes (false discovery rate <0.01). The final phase of functional network analysis revealed 93 significantly enriched biological processes (false discovery rate <0.01).

CONCLUSIONS

Our post-GWAS analysis of CRP GWAS SNPs confirmed the previously known overlap between CRP and lipids biology. Additionally, it suggested an important role for interferons in the metabolism of CRP.

FungiFun2: a comprehensive online resource for systematic analysis of gene lists from fungal species

Summary: Systematically extracting biological meaning from omics data is a major challenge in systems biology. Enrichment analysis is often used to identify characteristic patterns in candidate lists. FungiFun is a user-friendly Web tool for functional enrichment analysis of fungal genes and proteins. The novel tool FungiFun2 uses a completely revised data management system and thus allows enrichment analysis for 298 currently available fungal strains published in standard databases. FungiFun2 offers a modern Web interface and creates interactive tables, charts and figures, which users can directly manipulate to their needs.

Availability and implementation: FungiFun2, examples and tutorials are publicly available at https://elbe.hki-jena.de/fungifun/.

Contact: steffen.priebe@hki-jena.de or joerg.linde@hki-jena.de

rTANDEM, an R/Bioconductor package for MS/MS protein identification

SUMMARY

rTANDEM is an R/Bioconductor package that interfaces the X!Tandem protein identification algorithm. The package can run the multi-threaded algorithm on proteomic data files directly from R. It also provides functions to convert search parameters and results to/from R as well as functions to manipulate parameters and automate searches. An associated R package, shinyTANDEM, provides a web-based graphical interface to visualize and interpret the results. Together, those two packages form an entry point for a general MS/MS-based proteomic pipeline in R/Bioconductor.

AVAILABILITY AND IMPLEMENTATION

rTANDEM and shinyTANDEM are distributed in R/Bioconductor, http://bioconductor.org/packages/release/bioc/. The packages are under open licenses (GPL-3 and Artistice-1.0).

CONTACT

frederic.fournier@crchuq.ulaval.ca or arnaud.droit@crchuq.ulaval.ca

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.