Glioblastoma multiforme (GBM) is the most common, aggressive and malignant primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. Identification of molecular interactions that associate with disease progression may be key in finding novel treatments.Using five independent molecular and clinical datasets with a set of computational algorithms we were able to identify a gene-gene and gene-microRNA network that significantly stratifies patient prognosis. By combining gene expression microarray data with microRNA expression levels, copy number alterations, drug response and clinical data, combined with network knowledge, we were able to identify a single pathway at the core of glioblastoma.This network, the p38 network, and an associated microRNA, hsa-miR-9, facilitate prognostic stratification. The microRNA hsa-miR-9 correlated with network behavior and presents binding affinities with network members in a manner that suggests control over network behavior. A similar control over network behavior is possible through a set of drugs. These drugs are part of the treatment regimen for a subpopulation of the patients that participated in the TCGA study and for which the study provides clinical information. Interestingly, the patients that were treated with these specific sets of drugs, all of which targeted against p38 network members, demonstrate highly significant stratification of prognosis.Combined, these results call for attention to p38 network targeted treatment and present the p38 network-hsa-miR-9 control mechanism as critical in GBM progression. Glioblastoma multiforme (GBM) is the most common, aggressive and malignant primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers . This tuet al. [et al. [et al. [et al. [Merging datasets from different studies bridges biases, leads to identification of robust survival factors  and easeet al. , Murat e [et al. , Phillip [et al. , and Lee [et al. ) and one [et al. .Here, we take an approach that utilizes network graph structure and combine it with information on clinical outcome to identify curated networks that may serve as biomarkers for survival and/or to uncover molecular mechanisms that control disease course. To make use of network graph structure, we applied methods to merge expression data with network knowledge for the quantification of the network expression behavior . InteracSomatic copy number variations are extremely common in cancer, and detection and mapping of copy number abnormalities provides an approach for associating aberrations with disease phenotype and for localizing critical genes . The rolData were obtained from TCGA. This dataset comprises molecular characterizations from 373 GBM patients. For each patient, the database provides copy number , microarray  and miRNA values . In addition, the following clinical data variables were recorded for each patient: age, gender, chemotherapy status and vital status. Copy number variation levels were obtained from the Human Genome CGH 244A microarray. This Agilent 244A platform shows the highest sensitivity among microarray oligonucleotide platforms, with a single element being sufficient to detect a single-copy alteration . Comparaet al. [Validation set 1, from Freije et al. , is compet al. [Validation set 2, from Lee et al. , is compet al. [Validation set 3, from Murat et al. , is compet al. [et al. [Validation set 4, from Phillips et al.  and Cost [et al. , is compNetwork information was obtained from the National Cancer Institute's Pathway Interaction Database .Pathway consistency and pathway activity metrics were calculated according to ,21. ThesThe activity metric is a measure of the likelihood that the interaction occurs in the pathway. When taking a pathway with two genes as input and one gene as output, the algorithm calculates their probability of being in an 'up' state . The activity of this pathway is the probability that this interaction is 'active', meaning the product of the probabilities that the two genes are in the 'up' state. The consistency metric is a measure comparing the expected versus actual expression of the interaction components, obtained by calculating the probabilities (i) of an active interaction, (ii) that the output gene is in an 'up' state, and (iii) of the complementary event.P-value < 5.0 e-2 was accepted as significant. This analysis was done in order to identify pathways that could stratify prognosis in all five datasets.Kaplan-Meier survival analysis was done on all pathway measurements for all five datasets , using clinical data  to determine a pathway's survival stratification power. Log-rank tests were used to test the difference between survival groups; in all analyses a P-values (< 0.05) were then tagged as successful stratification metrics for prognosis. All the results were then compared between the five datasets to identify overlapping pathways.All values (pathway activity and consistency) were clustered using K-means clustering to stratify the patients into two distinct groups according to their pathways values. Kaplan-Meier survival analysis was performed using the groups that emerged from this K-means clustering and using the clinical outcome data . Pathways that showed significant Kaplan-Meier P-value for the generated group. Combinations that are associated with less than 20% of the patients were removed from the analysis as being insufficient. All combinations with significant P-values are shown in Additional file Kaplan-Meier survival analysis was also performed on all combinations of three drug sets; overall there were 249, 984 different combinations (constructed out of 64 drugs). In every iteration, the algorithm gathered all the patients that received one of the three drugs in question and calculated the Kaplan-Meier survival P-values (as described earlier). We performed this renormalization five times to achieve a substantial sample  to each sample in every dataset. Network information was obtained from The National Cancer Institute's Pathway Interaction Database . We thenFollowing this procedure, we were able to identify one robust pathway that stratified prognosis across all five different sources of datasets; the p38 pathway demonstrated consistent behavior across all datasets. Further, this p38 network demonstrated highly significant biomarker abilities by stratifying prognosis. Figure This pathway, when highly activated, associates with poor prognosis. This is in agreement with previous works that found that this pathway induces migration of glioblastoma cells when it is highly activated . The patThe false discovery rate calculated using the intersection of all five datasets  was 0%, which means that identifying a single robust pathway (out of 579 different pathways) that significantly stratifies prognosis in five independent datasets could not occur by chance alone.To further study the molecular characteristics of this pathway, we made use of the intensive molecular features available through TCGA, which provides genetic information for each tumor sample. We analyzed copy number profiles of the p38 pathway genes. Using a Mann-Whitney U test, a non-parametric test that assess whether two independent samples have equally large values, we examined copy number aberrations in tumors and matched normal samples to see if copy number variations in these, for each specific gene, are independent samples from identical continuous distributions with equal medians, rather than the alternative that they do not have equal medians.P-value < 0.05; Table Probe sets with an inferred log2 ratio of > 0.3 or < -0.3 were classified as gains and losses, respectively. This analysis revealed that 11 out of the 13 genes in this pathway are highly targeted for copy number changes  between the p38 network and the miRNA hsa-miR-9. Further, gene sequences revealed that 4 out of the 13 genes in the pathway have a possible binding site for hsa-miR-9  prognosis stratification . The trGBM: glioblastoma multiforme; GEO: Gene Expression Omnibus; MAPKAP: mitogen-activated protein kinase-activated protein kinase; miRNA: microRNA; TCGA: The Cancer Genome Atlas.The authors declare that they have no competing interests.RBH and SE designed the study, analyzed data and wrote the paper. All authors read and approved the final manuscript.P-valuesTable S1 - the most significant three-drug combinations with corresponding . The P-value represents significance with regard to stratification of prognosis according to Kaplan-Meier survival analysis.Click here for fileTable S2 - all drugs with their corresponding gene targets. This table also indicates whether there is a connection or not between a drug and the p38 pathway, and provides the number of patients who received each drug.Click here for fileFigure S1 - heat maps describing the false discovery rate analysis. The five heat maps illustrate the five iterations that were performed. Every row represents a different pathway , and columns represent the five datasets tested. A black line indicates a significant P-value (< 0.05) in Kaplan-Meier survival analysis. The heat map at the bottom shows the actual analysis that was performed with the p38 pathway as significant for survival in all five sets.Click here for file


NRF2 is the master transcriptional regulator of oxidative and xenobiotic stress responses. NRF2 has important roles in carcinogenesis, inflammation, and neurodegenerative diseases. We developed an online resource, NRF2-ome, to provide an integrated and systems-level database for NRF2. The database contains manually curated and predicted interactions of NRF2 as well as data from external interaction databases. We integrated NRF2 interactome with NRF2 target genes, NRF2 regulating TFs, and miRNAs. We connected NRF2-ome to signaling pathways to allow mapping upstream NRF2 regulatory components that could directly or indirectly influence NRF2 activity totaling 35,967 protein-protein and signaling interactions. The user-friendly website allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. We illustrated the applicability of the website by suggesting a posttranscriptional negative feedback of NRF2 by MAFG protein and raised the possibility of a connection between NRF2 and the JAK/STAT pathway through STAT1 and STAT3. NRF2-ome can also be used as an evaluation tool to help researchers and drug developers to understand the hidden regulatory mechanisms in the complex network of NRF2. NRF2  transcription factor is the master controller of oxidative and xenobiotic stress responses . FunctioWhile there are nearly 3000 articles for the keyword ‘‘NRF2” in PubMed (as of January 2013), major network resources  contain only a few dozen NRF2 interactors. Prompted by the lack of a systems-level NRF2-related resource, we recently collected the literature information on NRF2 interacting proteins and regulated genes, as well as predicted novel NRF2 interactors and regulators . We alsohttp://nrf2.elte.hu/) to provide an easy-to-use graphical interface allowing users to browse or download the NRF2-ome resource. Here, we report the development of a user-friendly web resource to analyze systems-level data on NRF2, which we hope will provide a help for researchers working with NRF2 or NRF2-related processes. Going beyond the creation of a database based on the datasheets on NRF2 interactome and regulome , we exteThe NRF2-ome contains a manually curated core interactome, which was extended with further protein-protein interactions (PPIs) as well as with transcriptional and posttranscriptional regulatory components and interactions. In the following, we list the compilation process of the NRF2-ome resource .in silico predictions to create the network of first neighbors of NRF2. We used the following PPI databases: InnateDB [The starting point of NRF2-ome was a set of 112 interactions between 84 NRF2 interactor proteins that we previously developed by manual curation of the literature . All theInnateDB , and BioInnateDB . InnateDInnateDB . We usedNext, we integrated regulatory information for NRF2 and for the already included first neighbors of NRF2. To identify TFs that regulate NRF2 or its first neighbors, we imported TF-target gene interactions from the following databases: ABS, ENCODE, HTRIdb, JASPAR, and ORegAnno –18. To lβ (transforming growth factor beta), WNT/Wingless, Hedgehog, JAK/STAT, Notch, and NHR (nuclear hormone receptor) [Finally, we used SignaLink 2, a signaling network resource we recently developed , 7, to ceceptor) . Integraeceptor)  and HPRDeceptor)  resourceNRF2-ome stores data in a MySQL database, which is connected to the webpage by an interface written in PHP. On the client side, the webpage uses jQuery to offer a high interactivity. It loads data asynchronously by small http requests, making possible to efficiently browse through hundreds of interactions. Data can be exported and downloaded in various formats: CSV, BioPAX, PSI-MI TAB, PSI-MI XML, SBML, and Cytoscape's CYS format. The download page offers several options to customize the network to be the downloaded; user can select desired interaction types  and filter the interactions by sources. There is an option to separate experimentally verified and predicted interactions. The customized network files are generated after the selection, by an export module running in the background, implemented in Python. This process can take few minutes. Then, for each download, the database generates a URL, where users can access the data for 14 days. Optionally, users can provide their e-mail addresses to which files smaller than 10 MB will be e-mailed. The whole dataset is also available as a standard SQL dump, so that any complex query or modification can be applied using SQL statements.interaction table. In the interaction table source and target fields are integers pointing to the primary keys of protein or mirna tables. The layer field denotes the type of the interaction, and its value determines whether the source or the target refer to a protein or miRNA. The meanings of the values in the layer field are the following: 1: PPIs in the interactome of NRF2; 4: TF-target interactions; 5: miRNA-mRNA interactions; 6: PPIs in the signaling pathways, imported from SignaLink 2; 7: TF-miRNA connections . Each interaction has three main attributes: is_directed , is_direct , and is_stimulation . In addition, interactions have one or more sources. Sources are listed in the source table, and the interaction_source table contains their assignment to the interaction table. Manually curated interactions have the literature references, contained by the interaction_reference table. In the interaction_reference table, articles are identified by their Pubmed IDs. Most of the interactions have confidence scores. These are stored as float values in the interaction_weight table, while the different types of scores are listed in weight table. Components of the NRF2-ome are listed in the protein and mirna tables. The protein table contains the uniprot_name field, which is unique, and it contains the UniProt accession number of proteins. All records imported from other databases as well as protein names from articles have been mapped to their primary UniProtKB ID. Proteins may have signaling topological properties and pathway assignments, available in protein_topology and protein_pathway tables. In the mirna table, we used miRBase AC and miRNA name to identify miRNAs. The core of NRF2-ome database is the The NRF2-ome database contains interactors and regulators of NRF2, their interactions to NRF2, and physical as well as regulatory interactions between them. Altogether, the NRF2-ome database contains 7,777 proteins and 35,967 interactions . From thThere are four types of interactions in the NRF2-ome database: (1) protein-protein interactions; (2) TF-target gene regulatory connections; (3) miRNA-mRNA regulatory interactions; (4) TF-miRNA regulatory connections. http://nrf2.elte.hu/. The aim of the website is twofold. (1) It gives an interactive opportunity to browse the NRF2-ome database and use hyperlinks to other web resources and Pubmed abstracts. (2) It provides an easy-to-use download interface, where users without computational background can select data from the content of the NRF2-ome database. The search field available on the main page autocompletes the queried terms to facilitate users' search. The search engine of NRF2-ome understands many database IDs, accession numbers, and protein names based on the mapping table of UniProt [We developed a user-friendly web interface for NRF2-ome that can be accessed at  UniProt . If the If the search is successful, a datasheet of the selected protein or miRNA will be shown Figures . The heaOn the right side of the protein datasheet page, an interactive network image is presented, using the Cytoscape Web embedded flash application . ClickinIn case of the NRF2 protein, the website provides a different view because of the high number of interactions . At the The download option is accessible under the ‘‘download” menu of the webpage. The user interface offers an easy way to select the desired parts of the database. The entire database is available as a MySQL dump file. Alternatively, for less-experienced users, we developed a BioMART-like customizable download page, where users can easily select interaction types and file format of the download. A general switch is also available to exclude all predicted interactions. The customized subnetworks can be downloaded in various formats: CSV, BioPAX, SBML, PSI-MI tab or PSI-MI XML, and in a Cytoscape CYS file.We present three examples to illustrate the applicability of the NRF2-ome website. We show how regulatory loops and pathway connections can point out important novel information about NRF2. Both approaches require the integrated transcriptional and signaling data present in NRF2-ome.To illustrate the functionality of NRF2 regulatory loops, we investigated the list of miRNA-containing regulatory loops. These loops are formed by NRF2, a target gene of NRF2 that functions as a TF, which regulates a miRNA capable to downregulate the translation of NRF2. First, we selected only those loops, where the effect of the miRNA was experimentally verified. By this, we narrowed the list of miRNAs from 63 to four miRNAs . These four miRNAs form 20 identical loops with 14 different TFs. We selected the MAFG transcription factor as an example because it was predicted to regulate the expression of two from the four miRNAs (miR-93 and miR-144) , and theAs we integrated signaling pathway data to NRF2-ome, users can examine the pathway annotations and signaling connections of known and predicted NRF2 interactors or regulators. For this analysis, we selected the JAK/STAT pathway as it has high functional overlap with NRF2. To predict and explain pathway connection between the JAK/STAT pathway and NRF2, we selected and examined two JAK/STAT pathway members: STAT1, a predicted TF of NRF2, and STAT3, a potential interactor of NRF2.STAT1 is activated in response to interferons  upon viral infection and inflammation . As NRF2Another member of the JAK/STAT pathway, STAT3 was predicted to interact with NRF2 based on domain-motif interactions. Though we found no publication that could validate STAT3-NRF2 interaction, we found several pieces of indirect, functional evidence, which support this interaction. Both STAT3 and NRF2 have anti-inflammatory and antiapoptotic functions upon ethanol exposure , 37. In In this work, we significantly extended our previously developed NRF2 interactome and regulome dataset  and devehttp://nrf2.elte.hu/) provides a user-friendly environment to interactively search, browse, or download the database. The UniProt-based, automatic mapping function of the search module allows users to search with different names or database IDs of a protein. On the website, network data on proteins directly or indirectly related to NRF2 can be easily accessed. Hyperlinks to further resources and literature references facilitate the interactive and fast exploration of protein functions. As a unique feature, we show signaling pathway connections to the proteins, including NRF2, other TFs, or first neighbors of NRF2. This option allows mapping upstream components that could directly or indirectly influence NRF2 activity. For NRF2, a specific website function is available, where users can browse regulatory loops containing possible feedback mechanisms that could up- or downregulate the expression of NRF2. After or instead of browsing the website, users can download the NRF2-ome database as a whole or a user-specified part of it. We provide a user-friendly download page allowing researchers without computational background to select and filter the NRF2-ome database. As the download is possible in CSV and Cytoscape formats, users without advanced bioinformatics knowledge can instantly examine and visualize the data of their interest.The NRF2-ome website that we developed to serve as a graphical interface to the NRF2-ome database , in most of the cases we cannot distinguish between positive and negative feedbacks.Keeping in mind the limitations listed before, the NRF2-ome is capable to provide both simple predictions , as well as complex predictions involving TFs, miRNAs, or signaling pathways. To illustrate the applicability of NRF2-ome, we presented examples about MAFG protein containing regulatory loops and raised the possibility of a connection between NRF2 and the JAK/STAT pathway through STAT1 or STAT3. All three examples are predictions; thus, further experimental validation is needed to confirm them. The NRF2-ome can also be used as an evaluation tool to help researchers explain a given expression pattern. A combination of the NRF2-ome network and user-made expression datasets with the Cytoscape application could help to uncover hidden regulatory mechanisms.NRF2-ome could also be used as a resource for network pharmacology. The medical importance of NRF2 is coming from its involvement in oxidative stress, inflammation, and many age-related diseases, including cancer, neurodegenerative diseases, and diabetes , 41. Thehttp://nrf2.elte.hu/) allows researchers without computational background to search, browse, and download the database. NRF2-ome contains integrated information on regulatory loops and pathway connections of NRF2 and its interactors. NRF2-ome is able to provide interesting predictions to be tested experimentally as well as to help researchers to evaluate experimental data or drug treatment outcomes.We have compiled a network resource, which contains a total of 7,777 manually curated, integrated, and predicted interaction data of NRF2, its first neighbor interactors, its target genes, regulating TFs, and miRNAs, as well as signaling pathways regulating NRF2. The user-friendly website (


Those in vivo demonstrated the specific expression of Meiob in early meiotic germ cells and the co-localization of MEIOB protein with RPA on chromosome axes. MEIOB localization in Dmc1−/− spermatocytes indicated that it accumulates on resected DNA. Homologous Meiob deletion in mice caused infertility in both sexes, due to a meiotic arrest at a zygotene/pachytene-like stage. DNA double strand break repair and homologous chromosome synapsis were impaired in Meiob−/− meiocytes. Interestingly MEIOB appeared to be dispensable for the initial loading of recombinases but was required to maintain a proper number of RAD51 and DMC1 foci beyond the zygotene stage. In light of these findings, we propose that RPA and this new single-strand DNA binding protein MEIOB, are essential to ensure the proper stabilization of recombinases which is required for successful homology search and meiotic recombination.Meiotic recombination is a mandatory process for sexual reproduction. We identified a protein specifically implicated in meiotic homologous recombination that we named: meiosis specific with OB domain (MEIOB). This protein is conserved among metazoan species and contains single-strand DNA binding sites similar to those of RPA1. Our studies  Meiob mutant mice were infertile and unable to complete meiotic recombination, most likely due to destabilization of DMC1 and RAD51 in the absence of MEIOB. Meiosis appears thus to be a ‘game of two pairs’ using both the canonical players in homologous recombination (RPA and RAD51) and a second set of paralogs (MEIOB and DMC1). Identifying such new players should help clarify some genetic causes of infertility and shed new light on the interplay between the molecular actors involved in maintaining genome stability.Homologous recombination allows faithful repair of damaged DNA; in mitotic cells, it necessitates the formation of single strand DNA (ssDNA), which is first protected by RPA and then coated by the RAD51 recombinase to mediate homology search. Specific modifications are made to this mechanism during meiosis, a specialized division that allows halving the ploidy of the genome and the production of haploid gametes. Among others a specialized recombinase DMC1 is added to its somatic paralog RAD51 to perform homology search. We identified a new meiotic protein that we named meiosis specific with OB domains (MEIOB). Our findings indicate that MEIOB binds ssDNA, and we propose that MEIOB is a meiotic paralog of RPA, another OB-domain containing protein.  S. cerevisiae and A. thaliana proposed that Rad51 is essential for proper Dmc1 loading and that Rad51 recombinase activity has to be inhibited to favor the homolog search bias Meiosis is a central process of sexual reproduction. This specialized cell division program allows halving the genome of diploid germ cells to produce haploid gametes. In order to ensure proper segregation of homologous chromosomes during the first meiotic division, these must become connected through chiasmata An event tightly associated with meiotic recombination is the pairing of homologous chromosomes through the formation of the synaptonemal complex (SC). SC is a tripartite structure comprising two lateral or axial elements (AE) and a central element. Early during the leptotene stage an AE is formed along each chromosome. However, it is only after successful homology search, that complete synapsis between homologs is observed connecting the AEs of homologous chromosomes with transverse filaments at the pachytene stage Meiob gene homolog hold'em (hdm) reduces meiotic crossover formation and sensitizes somatic cells to DNA-damaging agents Meiob is specifically required after DSB formation during early steps of meiosis to ensure proper DSB repair by homologous recombination, a prerequisite for efficient crossover formation and male and female fertility.In order to better understand the complex events that occur during meiosis, it is crucial to identify the specific proteins required for meiotic recombination. Hereby, we characterized a conserved and meiosis-specific protein containing ssDNA binding domains homologous to those of RPA1. We named this protein meiosis-specific with OB domains (MEIOB) in vertebrates. In fly, mutation of the likely Meiob (referred to as RIKEN cDNA 4930528F23 gene at the time) among the most differentially expressed genes in female germ cells (complete data set will be published elsewhere). The Meiob murine gene is located on chromosome 17 and is composed of 14 exons  male and female embryonic germ cells at 13.5 days post-coïtum (dpc). At this stage, female but no male germ cells enter meiosis. We identified 14 exons  coding fque band , the seqhttp://blast.ncbi.nlm.nih.gov/Blast.cgi) with either the full length sequence or short conserved motifs identified Meiob homologs in the genomes of almost all metazoans  while invertebrates displayed sequences with a much lower conservation (e.g. 23% of identity between mouse and fly). No ortholog could be retrieved in the genomes of fungi or plants, although a homolog was identified in the single-celled organism, Capsaspora owczarzaki. Interestingly, the closest Meiob paralog identified was the replication protein A large subunit (Rpa1). Consistently, InterProScan Search (http://www.ebi.ac.uk/Tools/pfa/iprscan/) identified three DNA binding domains (dbd), termed oligonucleotide/oligosaccharide binding (OB) folds, which were homologous to those of RPA1 . In the mouse ovary, Meiob expression started at 12.5 dpc, reached a maximum at 15.5 dpc and decreased to become undetectable in post natal life (Meiob expression started at 10 days post partum (dpp), reached a maximum at 20 dpp and was maintained throughout adult life  germ and somatic cells from 13.5 dpc Oct4-GFP ovaries. Meiob expression levels were 112 fold higher in germ cells compared to somatic cells . Immunostaining for MEIOB and γH2AX indicated that MEIOB was retrieved on chromosomes located in the sex body at this stage and its distribution on the X appeared similar to that observed on autosomes  oligonucleotide pull-down assays using recombinant tagged-MEIOB protein produced in a cell-free system. Pull-down assays performed with biotin 60-mer revealed a significantly greater retention of tagged-MEIOB protein on ssDNA than on dsDNA . A similIn order to evaluate the robustness of MEIOB binding to ssDNA, we performed ssDNA affinity chromatography. Protein extracts from HEK-293 cells expressing tagged-MEIOB were loaded on a column containing a ssDNA matrix, and proteins were eluted by progressively increasing salt concentration. Detection of eluted MEIOB by western blot revealed that MEIOB protein was eluted at a concentration of 0.75M NaCl  and S5C,in vitro observations and protein domain predictions we hypothesized that MEIOB could target ssDNA in vivo. We therefore compared MEIOB localization to that of RPA, RAD51 and DMC1 in spermatocyte chromosome spreads. MEIOB and RPA foci overlapped considerably  Dmc1−/− spermatocytes, MEIOB accumulated on chromosome axes suggesting that MEIOB is recruited to the DSB sites before strand invasion  confirmed the complete absence of Meiob mRNA in the adult testis of homozygous mutants and confirmed that our genetic model was indeed a null mutant . However, −/−Meiob male and female adult mice mated for four months with +/−Meiob or +/+Meiob counterparts produced no offspring, though vaginal plugs were formed normally . During post-natal life however, in 1 dpp −/−Meiob ovary, despite there being no obvious diplotene stage, the marker of late pachytene/diplotene oocytes, P63-staining, indicated the presence of numerous oocytes similar to those forming primordial follicles . AE formation is stained by SYCP3 initiated along the shared cores of sister chromatid pairs during leptotene. During zygotene a zipper like connection (stained by SYCP1) between the two AEs is initiated and completed by pachytene to fully connect homologous chromosomes. As illustrated on chromosome spreads in −/−Meiob spermatocytes, as shown with SYCP3 staining, formation of AE appear to be unaffected as cells progress from leptotene to zygotene stages  MEIOB colocalized almost perfectly with both RPA and ATR, known to sense ssDNA Our data support the idea that MEIOB binds ssDNA. First, protein domain prediction proposed that MEIOB contains three ssDNA binding domains, denoted OB domains, similar to those of RPA1 known to have a high affinity for ssDNA −/−Meiob meiocytes are arrested during prophase I of meiosis at a stage resembling late zygotene stage and characterized by persistent DSBs and defects in chromosomal pairing. This incomplete synapsis is likely the consequence of the defective DSB repair as is observed in most mutants with impaired homologous recombination −/−Meiob meiocytes the number of DSBs appears unaffected as we observed the expected number of RPA foci Rad51−/− disallows the comparison of Meiob−/− to these murine meiocytes. Of note, we would like to point out that Meiob−/− phenotype is similar to that of Dmc1−/− mice −/−Dmc1 spermatocytes, RAD51 persists in the pachytene-like stage in contrast to that observed in −/−Meiob mice  in Drosophila −/−hdm fly and Meiob−/− mice present drastic divergence during meiosis suggesting that their function may have diverged during evolution. Indeed in hdm−/− flies, some crossovers still occur though with lower frequencies compared to wild-type fly and the DSBs are repaired though with a delay. Furthermore, hdm is also involved in DNA repair in somatic cells Meiob is almost exclusively expressed in meiotic germ cells. Joyce et al. also provided evidence that hdm physically interacts with Ercc1, a member of the exchange class of gene products, and proposed that the Ercc1/Mei9/Mus81/hdm complex functions in the meiotic recombination pathway to resolve DSB-repair intermediates into crossovers −/−Meiob phenotype described here. Indeed, in mice, Ercc1 deletion does not prevent synapsis or sex body formation and males are able to produce few spermatozoa albeit with DNA damage −/−Meiob cells. Such a speculative proposition might also be fueled by the persistent colocalizsation of MEIOB with recombinases and will require additional studies. Putative MEIOB partners are under investigation and their identification should help define the precise function of MEIOB during meiotic recombination and the mechanisms allowing recombinase stabilization in this process.While meiosis recombination is a conserved process in eukaryotes, there are clear differences among organisms  homolog . One mayDmc1 and Spo11 mutant mice, w/w mice, Oct4-GFP mice .All animal studies were conducted in accordance with the guidelines for the care and use of laboratory animals of the French Ministry of Agriculture. Mice were raised and mated, and fetal gonads were isolated as previously described Meiob null allele was established at the Mouse Clinical Institute/Institut Clinique de la Souris (MCI/ICS), Illkirch, France (http://www.ics-mci.fr/). The targeting vector was constructed as follows: a 4.5 kb fragment encompassing exon 2 and part of the first intron, Red Fluorescent Protein (Rfp) cDNA, followed by a neomycin resistance cassette surrounded by two loxP sites and a 4 kb fragment encompassing exons 9 and 10  following legally induced abortions in the first trimester of pregnancy and therapeutical termination of pregnancy in the second trimester. Human fetal gonads were harvested as previously described Rabbit polyclonal anti-MEIOB antibodies were generated and affinity purified by Eurogentec  using double X protocol with ADP TEA SRN LAR QGH T and IRE NKE TNV ADE IDS polypeptides.Testicular single-cell suspensions and cell sorting were processed as previously described -actin mRNA was used as the endogenous reporter. Data are expressed as a percentage of the maximum mRNA expression unless otherwise stated in which case an external reference (F9 cells) was used to normalize the expression of numerous points.RNA extractions and RT-qPCR were performed as previously described MEIOB complete cDNA was inserted into a pCMV6-Entry plasmid (Origene cat# RC228391) containing c-Myc and Flag tags in the C-terminal domain of the protein. This plasmid was transfected into HEK-293 cells using Lipofectamine 2000 (Invitrogen) according to the manufacturer's instructions. Tagged-MEIOB was observed in both nuclear and cytoplasmic compartments of the transfected HEK-293 cells  were cultivated in DMEM High Glucose (Gibco) containing 15% FSB (Gibco). Human 93 cells .Protein extracts were produced from cell lines or tissues under native conditions. HEK-293 cells were harvested, centrifuged 5 min at 500 g and then lysed in Cell Lysis Buffer  complemented with 1 mM 2-mercaptoethanol and Complete protease inhibitor . For adult testis, the albuginea was first removed and testis was lysed in the same buffer using ceramic spheres and FastPrep-24 Instrument  with two pulses of 20 seconds. Extracts were then gently sonicated (two pulses of 30 seconds), centrifuged 10 min at 14 000 g and supernatant was immediately used for functional applications. Human recombinant tagged-MEIOB protein was produced in a cell-free system. The TnT T7 Quick Coupled Transcription/Translation System (Promega) was used with 1 µg of the tagged-MEIOB plasmid in a 50 µl reaction according to the manufacturer's instructions.Prior to gel migration, protein samples were supplemented with Laemmli buffer and resolved by 10% SDS-polyacrylamide gel electrophoresis (SDS-PAGE). Gels were electrophoretically transferred to polyvinylidene difluoride membranes (PVDF)  before hybridization with appropriate primary antibodies and fluorescent dye coupled secondary antibodies See . Images Histological sections, germ cell counting and immunohistochemical stainings using appropriate antibodies  were per−/− spermatocytes and no signal was retrieved (data not shown). Due to the lack of Rad51−/− spermatocytes , no such control was performed for the RAD51 antibodies and one cannot formally exclude that these might crossreact.Testes used for spermatocyte spread preparation were harvested from mice aged approximately 2 to 6 months. Ovaries used for the preparation of chromosome spreads were harvested in 15.5 and 17.5 dpc embryos and 1 dpp pups. Three types of meiocyte chromosome preparations were used. For γH2AX, SYCP1 and RPA2 stainings, chromosome spreads were prepared according to the drying-down technique 2 camera (Photometrics) and Leica software (Metamorph). Images were processed and specific structures were quantified with ImageJ software (Cell Counter and Foci Picker3D programs). For quantification of foci colocalization, foci overlapping for the most part were considered as colocalized without restriction to foci that share the same centroid .Spermatocyte and oocyte preparations were washed with 0.4% Photoflo 200 (Sigma) and slides were air-dried 15 minutes before permeabilizing and blocking. They were then incubated overnight with the appropriate primary antibodies in blocking solution at room temperature, followed by 1 hour incubation with secondary antibodies at 37°C . Double strand hybridizations were performed in 50 mM NaCl, 25 mM Tris-HCl, pH 7.5 buffer with complementary sequences at molecular equivalence by a denaturing step (3 minutes 94°C) and a slow progressive return to room temperature. 0.2 pMol of DNA was immobilized onto 1 µg Dynabeads M-280 Streptavidin  following the protocol supplied by the manufacturer. Protein extracts were pre-incubated on ice for 10 minutes in modified DBB  before addition of 500 µg Dynabeads with immobilized ss- or ds-DNA probes. DNA binding assays were conducted either with 3 µl of the tagged-MEIOB cell-free productions, with 600 µg of protein from HEK-293 cells transfected with hMEIOB  glycerol, Complete Protease Inhibitor, 1 mM 2-mercaptoethanol, pH 7.4), 1 ml of which was loaded in Poly-Prep columns (Biorad), previously loaded with 500 µl of ss-DNA-cellulose (Amersham Biosciences). Protein extract was incubated 90 min at 4°C under gentle agitation. Columns were washed with forty bed volumes of ss-DBB and the last 1 ml of wash was kept to ensure that complete elution occurred (0.1M LW). Columns were then washed with elution buffer containing increasing concentrations of NaCl: 0.25, 0.5, 0.75, 1, 2M. For each step columns were washed with ten bed volumes, the first 1 ml  Schematic representation of MEIOB protein, Meiob gene and construction of Meiob mutant allele. Grey exons, non coding sequences; black exons, coding sequences (see B) The full length murine Meiob transcript was amplified by RT-PCR using specific primers encompassing the ATG and STOP codons. After migration in 1% agarose gel, a single band corresponding to the predicted size (1703 bp) was observed. M, molecular weight marker. (C) Meiob and Rfp mRNA expression in adult Meiob+/+, +/− and −/− testes. Mean±SEM; n = 2. ND, not detected.(nces see  section.(TIF)Click here for additional data file.Figure S2Meiob is expressed in gonads. (A) Meiob mRNA expression was measured by RT-qPCR in various mouse fetal tissues at 13.5 dpc. O, ovary; M, mesonephros; St, stomach; Li, liver; K, kidney; H, heart; B, brain; Lu, lung; Pl, placenta; T, testis. (B) Meiob mRNA expression was measured by RT-qPCR in different adult mouse organs. T, testis; Li, liver; Sp, spleen; K, kidney; O, ovary; Pa, pancreas; Lu, lung; U, uterus. (C) Meiob and Ddx4 mRNA expression were measured using RT-qPCR in 13.5 dpc ovaries of wild type mice (wt) and mice homozygous for Kit/W allele (w/w) that are devoid of germ cells. Mean±SEM; n = 3. β-actin mRNA was used as the endogenous reporter. Data are expressed as a percentage of the maximum mRNA expression.(TIF)Click here for additional data file.Figure S3Hoechst 33342 and propidium iodide (PI) fluorescence profiles of cells from dissociated wild type adult testis acquired by FACS see  section.(TIF)Click here for additional data file.Figure S4MEIOB localization in chromosome spreads of oocytes at leptotene, zygotene and pachytene stages. Representative chromosome spreads stained for SYCP3  and MEIOB protein from 15.5 dpc wild type oocytes. SYCP3 staining was used to visualize the chromosome axes.(TIF)Click here for additional data file.Figure S5A) Subcellular localization of tagged-MEIOB expressed in HEK-293 cells using an anti-Flag antibody. MEIOB protein was observed in both the nucleus and the cytoplasm of the transfected cells. Scale bars, 25 µm. (B) Hek-293 cells expressing tagged-MEIOB protein extract was applied to beads coupled with biotine or biotinylated single strand (ss) or double strand (ds) DNA of different lengths (30 mer and 60 mer). Retained proteins were subjected to western blot hybridized with anti-β-ACTIN (green) and anti-c-MYC antibodies (red). Bands intensity quantifications are relative to pull down input protein extract. n = 4 ; Mean±SEM, ***<0.0001; **<0.001 (paired Student's t-test). (C) Controls of elution for ssDNA cellulose affinity chromatography presented in ((TIF)Click here for additional data file.Figure S6A), DMC1 (B) or RAD51 (C) in chromosome spreads of wild type leptotene, zygotene and early pachytene spermatocytes from adult testes. For each stage, foci stained for only one protein or for both were counted per cell. The percentage of foci stained for a single or both proteins was then determined. Mean±SEM ; 3 to 13 cells analyzed per stage,Quantification of co-localizations of MEIOB and RPA2 ((TIF)Click here for additional data file.Figure S7A) or ATR (B) were detected in chromosome spreads of wild type zygotene and pachytene and Dmc1−/− pachytene-like spermatocytes from adult testes. RPA2/ATR and MEIOB colocalized in Dmc1−/−. In Dmc1−/−, homologous recombination is arrested prior to strand invasion and the robust MEIOB staining indicates the presence of MEIOB on hyper-resected DNA from the DSB.MEIOB and RPA2 ((TIF)Click here for additional data file.Figure S8A) Testis to body weight ratio in Meiob+/+, +/− and −/− adult mice. Meiob−/− testis is 3.8 times smaller than Meiob+/+ or +/− testis. Gonads analyzed: n = 8, ***p<0.0001 (paired Student's t-test). (B) Section of Meiob+/+ and −/− adult epididymis. Meiob+/+ epididymis filled with spermatozoa in contrast to Meiob−/− epididymis with no spermatozoon. Scale bar, 20 µm. (C) Seminal vesicle weight of Meiob+/+, +/− and −/− adult mice. No modification of seminal vesicle weight was observed suggesting no overt alteration of the androgen levels in Meiob−/− adult male mice. Mean±SEM; n = 3.((TIF)Click here for additional data file.Figure S9A) Number of oocytes per Meiob+/+, +/− and −/− ovaries at 13.5 and 18.5 dpc, at 1, 3 and 10 dpp, and in adult mouse ovaries. The number of oocytes was similar from 13.5 dpc to 1 dpp in mice of different genotypes. At 3 dpp the oocyte number drastically decreased in Meiob−/− ovaries. From 10 dpp and onwards no oocyte was observed in Meiob−/− ovaries. Mean±SEM; Total number of mice analyzed per genotype and per age: at least n = 3. (B) Partition of follicle stages in Meiob+/+ and +/− adult mouse ovaries. No change was observed in heterozygous ovaries when compared to wild type. Total number of mice analyzed per genotype: n = 5.((TIF)Click here for additional data file.Figure S10Meiob+/+ and Meiob−/− in 1 dpp oocytes respectively at pachytene and diplotene and at pachtene-like stages of meiosis prophase I.γH2AX and SYCP3 staining in chromosome spreads from (TIF)Click here for additional data file.Figure S11−/−Meiob spermatocytes at leptotene, early-zygotene, mid-zygotene and late-zygotene/pachytene-like stages. (A) Mean of foci intensity per cell and expressed in arbitrary units (AU). In −/−Meiob the mean intensity of foci tended to decrease in the course of zygotene stage in comparison with +/+Meiob. (B) For each cell intensity were categorized in three groups: Low intensity: <500; Medium intensity: [500–1000[; High intensity: ≥1000 AU. Cells analyzed: 3 to 11 per stage and per genotype. *, p<0.05 and **, p<0.01 .RAD51 and DMC1 foci intensities. The intensities of RAD51 and DMC1 foci were measured in wild type and (TIF)Click here for additional data file.Figure S12Dmc1−/− spermatocytes. RAD51 foci persisted in pachytene-like spermatocytes, even those with a high degree of pairing.SYCP3 and RAD51 were detected in (TIF)Click here for additional data file.Table S1List of primary and secondary antibodies used in this article for western blot (WB), immunohistochemistry (IHC) and immunofluorescence (IF). P, Polyclonal antibody; M, Monoclonal antibody.(TIF)Click here for additional data file.Table S2Sequences of DNA primers used in this article for genotyping and RT-qPCR.(TIF)Click here for additional data file.


Biological pathways are important for understanding biological mechanisms. Thus, finding important pathways that underlie biological problems helps researchers to focus on the most relevant sets of genes. Pathways resemble networks with complicated structures, but most of the existing pathway enrichment tools ignore topological information embedded within pathways, which limits their applicability.A systematic and extensible pathway enrichment method in which nodes are weighted by network centrality was proposed. We demonstrate how choice of pathway structure and centrality measurement, as well as the presence of key genes, affects pathway significance. We emphasize two improvements of our method over current methods. First, allowing for the diversity of genes’ characters and the difficulty of covering gene importance from all aspects, we set centrality as an optional parameter in the model. Second, nodes rather than genes form the basic unit of pathways, such that one node can be composed of several genes and one gene may reside in different nodes. By comparing our methodology to the original enrichment method using both simulation data and real-world data, we demonstrate the efficacy of our method in finding new pathways from biological perspective.Our method can benefit the systematic analysis of biological pathways and help to extract more meaningful information from gene expression data. The algorithm has been implemented as an R package CePa, and also a web-based version of CePa is provided. As omics and high throughput technology continues to develop, researchers can increasingly understand biological phenomena at the systems level; that is, can elucidate the complicated interactions between genes and molecules responsible for biological functions . Microart-test). These statistics are then combined into a pathway level statistic by summation or averaging  . The micse (PID)  was used process . Cutoff p-values and FDRs is tabulated in Additional file p-value = 0.001, FDR = 0.025), consistent with expected biological phenomena. The differential nodes are mainly located in the downstream of the pathway; that is, transcriptional factors (e.g. FOS) or cell cycle related factors (e.g. CDK5 and CD5R1), while few of the upstream genes are included in our differential gene list. As the MAPK pathway is essentially a cascade of sequential interactions [Figure ractions , weightip-value = 0.002, FDR = 0.034 for in-degree; p-value = 0.007, FDR = 0.104 for out-degree). On the other hand, VEGFA itself is not differentially expressed, but its companion gene VEGFR2 is. Consequently, an abnormal state of the member gene results in a dysfunctional complex. This type of circumstance, which cannot be inferred by traditional ORA, emphasizes why nodes, rather than genes, should form the basic units in pathway analysis.Among 8 pathways evaluated as insignificant by traditional ORA but significant by centrality-based methods, four have been previously linked to liver cancers ,44,45. APathway analysis can assist researchers to understand biological aberrations at a systems level. The functionality of biological pathways depends upon complex gene interactions. Therefore, pathway enrichment tools should highlight genes that play important roles in the pathway from the view of topology. Here we proposed a systematic and extensible methodology, which finds significant pathways using network centrality to weight the nodes. We demonstrated that levels of pathway significance depend on choice of pathway structure and centrality measure. The method performed favorably when applied to real-world data.Centrality can reflects the central nodes in a pathway, and different centralities assign gene importance from different aspects. The use of centralities in biological networks can aid in explaining biological phenomena. In this work, we demonstrated the advantages of using multiple centrality measurements to obtain a complete view of the system. Pathway nodes, rather than genes, should form the basic units in pathway analysis, since many genes must aggregate as complexes in order to function completely. The focus on pathway nodes accommodates the fact that genes can be members of complexes or families, or may exist in many complexes. Finally, it should be noted that a high quality and non-redundant pathway structure dataset is required. Projects like BioPAX , which aOur method can reveal new findings that relate to, and can aid the understanding of, current biological problems. We consider that our method will become a valuable tool in the systematic analysis of biological pathways, and will help to extract more meaningful information from gene expression data.th 2011) are included. The pathway data are parsed from XML format file provided by the PID FTP site. The Perl code for parsing can be obtained from the author’s website (http://mcube.nju.edu.cn/jwang/lab/soft/cepa/). The general workflow of the method is illustrated in Figure To implement the method, a list of differential genes and a list of background genes, both formatted with gene identifier (e.g. gene symbol or RefSeq ID), is required. A list of pathways and their topology, and a means of mapping genes to pathway nodes, is also required. In this study, 223 NCI-Nature pathways from PID  refers to the shortest path length between nodes v and w). In a directed network, this measure is denoted as in-largest reach or out-largest reach.To measure the importance of nodes in the network from multiple aspects, we defined an additional centrality: largest reach. This centrality is based on the shortest path between two nodes and the value of the centrality is affected by all other nodes in the network. The largest reach centrality measures how far a node can send or receive information. It is defined as the largest length of the shortest path from node Users of our system can replace the provided centrality measures with their centrality measurements of interest. It is recommended that centrality choice is guided by biological plausibility and/or reality.The score is defined as the summation of the weights of differentially affected nodes in the pathways is the score of the pathway, wi is the weight of the ith node and reflects the importance of the node, n is the number of nodes in the pathway, and di identifies whether the ith node is differentially affected or not.where α is a small positive number to ensure that all weights are positive. α is chosen to exert marginal effect upon the weight. The default value of α is 1/100 of the minimum non-zero weight.In our model, we weight the nodes by network centrality. Because the network centrality can be zero, an additional term is added to the weight measure. In Equation 7, w and d are random variables; 3) w and d are independent; 4) w follows a particular discrete or continuous distribution and d is a Bernoulli random variable. Thus the pathway score, denoted as S, is also a random variable. These conditions are formally expressed asTo calculate the theoretical distribution of the pathway score, we assume that every node is a single gene and that our model satisfies the following conditions: 1) genes are independent; 2) pdiff is the probability that a gene is differentially expressed. It is calculated as the proportion of differentially expressed genes on the microarray.where s, assume that k differential genes (d = 1) and n-k non-differential genes (d = 0) exist, so that s can be written asWithin a pathway of score S is equal to or larger than s isThus the probability that pathway score k differential genes from n genes, and the second term is the probability that the sum of k differential genes’ weight is equal to or larger than s. The final probability P(S ≥ s) is the summation over all conditions of k.The binomial term of equation 12 is the probability of obtaining Pw(w) is known, the distribution of the summation of w can be calculated. For instance, given a pathway with ER random network structure in which nodes are weighted by degree, w will follow a binomial distribution and thus P(Σiwi) also follows a binomial distribution.Since genes are independent, provided that s can be generated through simulation. For every gene in a pathway, we guess whether it is differentially expressed. Similar to throwing a coin, we assume that each gene has a probability pdiff  of being differentially expressed. In each simulation, we obtain a list of simulated differentially expressed genes in the pathway. This simulated differential gene list is then mapped to the pathway nodes. The pathway structure is unchanged and the simulated pathway score is re-calculated from Equations 5 and 6. The significance is calculated as the proportion of the simulated score exceeding the real score (Equation 13).In applications, because the weight distribution is not easily determined and nodes are not independent after the mapping procedure, the theoretic distribution is difficult to calculate. A non-parametric null distribution of The ORA centrality-based enrichment method yielded plausible, biologically relevant results in the simulation study and real-world data analysis. However, an oft-mentioned drawback of ORA is that an objective cutoff is appointed in the acquisition of a differential gene list, with the following consequences: 1) The resulting pathway or network may be sensitive to the cutoff . In the s of the pathway is defined as:In the traditional univariate GSA procedure, the score f transforms the gene-level statistic to a pathway-level statistic  and g is the gene-level statistic vector which typically comprises t-values [g is a binary variant and f(g) is summation. In our model to extend GSA, gene-level statistic is first transformed to node-level statistic. We define the vector of the node-level statistics as d. When nodes in pathways comprise multiple genes, the node-level statistic can be considered as the maximum value of the corresponding member genes. Using centrality as the weight, the score is defined aswhere t-values ,10,52. Iw is the weight vector and the transformation function f acts upon the product of w and d. Equation 15 incorporates centrality weight into the original node-level statistic. To prevent w from overpowering d (or vice versa) when both vectors contain continuous variables, we propose that w and d should be normalized. The null distribution of the pathway score could then be generated by permuting the gene expression matrix.where http://www.r-project.org/). In the CePa package, four pathway catalogues, namely NCI-Nature, BioCarta, Reactome and KEGG from PID, have been integrated. Centrality calculation and network visualization are processed by igraph package [http://mcube.nju.edu.cn/cgi-bin/cepa/main.pl), in which Cytoscape Web is used for network visualization [The method proposed in the article has been implemented in an R package named CePa which is available at CRAN ( package . A web-blization .ORA, Over representation analysis; GSA: Gene set analysis; ER: Erdös-Rényi model; BA: Barabási-Albert model; PID: Pathway Interaction Database; BH: Bajamini-Hochberg process; FDR: False discovery rate.The authors declare that they have no competing interests.ZG developed the algorithm, performed the analysis, implemented the software and wrote the manuscript. JL and KC implemented the online version of the software. JZ and JW conceived the study, and helped to draft the manuscript. All authors have read and approved the final manuscript.Two random pathways generated from ER model and BA model. Number of nodes in both networks is 200.Click here for fileHistograms of in-largest reach aggregated from 1000 networks generated either by ER model or BA model.Click here for fileThe complete list of p-values of pathways generated under different centrality measurements.Click here for fileThe complete list of FDRs of pathways generated under different centrality measurements.Click here for file


Next-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects. There are approximately 141 biological human pathway resources as of Jan 2012 according to the Pathguide database. However, most currently available resources do not contain disease, drug or organ specificity information such as disease-pathway, drug-pathway, and organ-pathway associations. Systematically integrating pathway, disease, drug and organ specificity together becomes increasingly crucial for understanding the interrelationships between signaling, metabolic and regulatory pathway, drug action, disease susceptibility, and organ specificity from high-throughput omics data .Integrated Pathway Analysis Database for Systematic Enrichment Analysis , defining inter-association between pathway, disease, drug and organ specificity, based on six criteria: 1) comprehensive pathway coverage; 2) gene/protein to pathway/disease/drug/organ association; 3) inter-association between pathway, disease, drug, and organ; 4) multiple and quantitative measurement of enrichment and inter-association; 5) assessment of enrichment and inter-association analysis with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources; and 6) cross-linking of multiple available data sources.We designed the IPAD is a comprehensive database covering about 22,498 genes, 25,469 proteins, 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs integrated from databases including the BioCarta, KEGG, NCI-Nature curated, Reactome, CTD, PharmGKB, DrugBank, PharmGKB, and HOMER. The database has a web-based user interface that allows users to perform enrichment analysis from genes/proteins/molecules and inter-association analysis from a pathway, disease, drug, and organ.Moreover, the quality of the database was validated with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources. Two case studies were also presented to demonstrate: 1) self-validation of enrichment analysis and inter-association analysis on brain-specific markers, and 2) identification of previously undiscovered components by the enrichment analysis from a prostate cancer study.IPAD is a new resource for analyzing, identifying, and validating pathway, disease, drug, organ specificity and their inter-associations. The statistical method we developed for enrichment and similarity measurement and the two criteria we described for setting the threshold parameters can be extended to other enrichment applications. Enriched pathways, diseases, drugs, organs and their inter-associations can be searched, displayed, and downloaded from our online user interface. The current IPAD database can help users address a wide range of biological pathway related, disease susceptibility related, drug target related and organ specificity related questions in human disease studies. With the age of big data approaching , bioinfoAs more information is revealed through large-scale "omics" techniques, it is becoming increasingly apparent that genes do not function alone but through complex biological pathways. Unraveling these intricate pathways is essential to understanding biological mechanisms, disease states, and the function of drugs that transform them. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects.http://www.pathguide.org[http://www.biocarta.com[http://www.genome.jp/kegg/[http://pid.nci.nih.gov/PID/index.shtml[http://www.reactome.org[http://www.wikipathways.org/[Pathway databases serve as repositories of current knowledge on cell signaling, enzymatic reaction, and genetic regulation. There are more than 300 pathway repositories listed in Pathguide resource carta.com, KEGG ht.jp/kegg/, NCI-Natdex.shtml, Reactomctome.org, and Wikways.org/. Howeverways.org/. In addiways.org/. Lastly,ways.org/.Systematic collection of pathway information not only in the form of pathway databases but also including inter-association between pathway, disease, drug, and organ specificity is crucial, because 1) it provides a bridge between pathway, disease, drug and organ, and 2) this bridge can not only capture relevant biological pathways but also provide disease, drug target, and organ specificity information. For "inter-association", we refer to a biological connection between two or more biological components on basis of intermediary genes  for users to query information about genes, diseases, drugs, organ specificity, and signaling and metabolic pathways. First, we integrated data from four kinds of sources: 1) pathway databases from BioCarta [http://ctdbase.org/[http://www.pharmgkb.org[httP://www.drugbank.ca[http://discern.uits.iu.edu:8340/Homer/index.html[Therefore we created the BioCarta , KEGG [5BioCarta , and ReaBioCarta , 2) disebase.org/ and Pharrmgkb.org, 3) drugugbank.ca and Pharugbank.ca, and 4) ndex.html. Next, whttp://bioinfo.hsc.unt.edu/ipad is a comprehensive database covering about 22,498 genes, 25,469 proteins, 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs integrated from databases including the BioCarta [The Integrated Pathway Analysis Database for Systematic Enrichment Analysis (IPAD), located at BioCarta , PharmGKBioCarta , DrugBanBioCarta , and HOMBioCarta .It is the first comprehensive database that can be used to analyze, discover, and validate enrichment and inter-association between pathway, disease, drug and organ. The inter-associations allow further identification of enriched pathways, diseases, drugs and organs. The quality of the database is validated on a "gold standard" constructed from reputable and reliable sources. The ability to choose multiple quantitative parameters , Relative Enrichment Value (RE), and Mean Jaccard Index (MJI)) provides us with powerful statistics and computation to accurately calculate enrichment and inter-association. And the cross-linking of multiple data sources enables subsequent validation.Integrated Pathway Analysis Database for systematic enrichment analysis. As of the current release (May 2012), IPAD contains 25,469 proteins (IPI IDs), 22,498 genes (gene IDs), 1956 pathways covering 11663 genes, 6,704 diseases covering 17925 genes, 5,615 drugs covering 3735 genes, and 52 organs covering 5599 genes . Component similarity can also be measured by Absolute Expression Value (AE), Relative Expression Value (RE) and Mean Jaccard Index (MJI). The four measurements  can complement each other and compensate for the weaknesses inherent in each alone to create better criteria for enrichment analysis.We performed statistical testing using p-value described in the method section to describe the inter-association between pathway, disease, drug and organ in IPAD Figure . AlthougThe inter-association between the 52 organs in Figure In Figure In response to the query input, IPAD can retrieve a list of related components  in a highly flexible table, with which users can further explore details about inter-association between the components. For example, users can browse the inter-association between each component's molecule and pathway, disease, drug and organ by clicking on the link in the column of molecule, and look through the component-related inter-association between pathway, disease, drug and organ by clicking on the inter-association icon in the last column. There are totally sixteen types of inter-associations between pathway, disease, drug and organ in IPAD: Pathway-Pathway, Pathway-Disease, Pathway-Drug, Pathway-Organ, Disease-Pathway, Disease-Disease, Disease-Drug, Disease-Organ, Drug-Pathway, Drug-Disease, Drug-Drug, Drug-Organ, Organ-Pathway, Organ-Disease, Organ-Drug, and Organ-Organ. User queried inter-association pathway/disease/drug/organ data stored in IPAD can also be freely downloaded as tab-delimited text files using links below each enrichment or inter-association table.Assessing the capabilities of any pathway/disease/drug/organ enrichment analysis in real experiments is a challenge in itself because the full truth of what really occurred between the components and how they are actually inter-associated, if at all, may never be known. In the absence of a "gold standard" - a reference standard against which to establish the performance of the filter, the best alternative is to analyze the results of the enrichment analysis method in the context of the existing biological knowledge . We firsThe absence of a definitive answer regarding the involvement of a particular pathway/disease/drug/organ in a given condition makes it impossible to calculate exact values for sensitivity, specificity, ROCs, etc. Therefore, we compared the result of IPAD's enrichment analysis and inter-association analysis and tested whether the significant pathways/diseases/drugs/organs fit with the existing biological context. This type of assessment is the current best practice in this area of enrichment analysis .-3, AE ≥ 2.57, RE ≥ 13.51 and MJI ≥ 0.154; PA451581 tamoxifen, PA131301952 gefitinib, PA152241907 lapatinib, PA449509 estrogens, and PA449383 docetaxel) we identified using IPAD are all reportedly linked to breast cancer by previously published papers , we identified 18 associated pathways of which most are linked with cancer such as hsa05212 Pancreatic cancer, hsa05213 Endometrial cancer, hsa05215 Prostate cancer, hsa05223 Non-small cell lung cancer, hsa05218 Melanoma, hsa05219 Bladder cancer, hsa05200 Pathways in cancer, hsa05214 Glioma, hsa05220 Chronic myeloid leukemia, hsa05222 Small cell lung cancer, and hsa05210 Colorectal cancer , disease , and drug . Highly is measured by p-value. When the individual p-values are at least three orders of magnitude lower than current used p-value threshold, they are called "highly significant."By the inter-association, we found that the number 1 pathway  we identified from the enrichment analysis is also highly associated with the pathway . Similarly, the number 1 disease (MESH:D002528 Cerebellar Neoplasms) was found to be inter-associated with hsa05200 Pathways in cancer , MESH:D016410 Lymphoma, T-Cell, Cutaneous , and PA449780 glutathione ; and the number 1 drug (PA451581 tamoxifen) was found to be inter-associated with 211859 Biological oxidations , PA443560 Breast Neoplasms , and PA449503 estradiol .The pathway "hsa05200, pathways in cancer" and disease "MESH:D046152 Gastrointestinal Stromal Tumors" are already included in our previous enrichment analysis and were validated by the inter-association analysis. The drug PA450191 lecithin was filtered out in the enrichment analysis due to its insignificant measurement  and was discovered by the inter-association analysis as a previously undiscovered drug  from KEGG , PharmGKMain_Page. We evalCompared to sensitivity, specificity and accuracy, the prediction rates are relatively low because the size of testing set are much larger than that of the "gold standard" set. When more "gold standards" of inter-associations become available in the future, the prediction rates and F_measure can be improved because the currently unpredicted pairs will be able to be predicted correctly. Figure We show two case studies of increasing complexity and biological significance to achieve two goals: 1) to demonstrate the IPAD's ability to self-validate by using it to perform enrichment analysis and inter-association analysis on the 369 brain-specific markers, and 2) to demonstrate the ability of IPAD to identify previously undiscovered components by the enrichment analysis based on differentially expressed genes identified from a prostate cancer study.56, AE ≥ 4.86, RE ≥ 7.42 and MJI ≥ 0. 107), 92 enriched diseases , 7 enriched drugs , and 1 enriched organ   MESH:D001764, Blepharospasm, 2) MESH:D012563, Schizophrenia, Paranoid, 3) MESH:D002385, Cataplexy, 4) MESH:D020187, REM Sleep Behavior Disorder, 5) MESH:D020821, Dystonic Disorders, 6) MESH:D015877, Miosis, 7) MESH:D001925, Brain Damage, Chronic, 8) MESH:D000341, Affective Disorders, Psychotic, 9) MESH:D007415, Intestinal Obstruction, and 10) MESH:D011681, Pupil Disorders, have on average 766 inter-associations between pathway, disease, drug and organ, which shows a strong association with those 369 brain-specific markers.A blepharospasm is any abnormal contraction or twitch of the eyelid. There have been several important advances in understanding the brain mechanisms associated with blepharospasm. Baker et al. identified blinking-induced functional magnetic resonance imaging (fMRI) activation patterns in five benign essential blepharospasm (BEB) patients and five age-matched control subjects and concluded that the activations observed might represent a hyperactive cortical circuit linking visual cortex, limbic system, supplementary motor cortex, cerebellum, and supranuclear motor pathways innervating the periorbital muscles . Antal eSchizophrenia is a brain disorder that affects the way a person acts, thinks, and sees the world. People with schizophrenia have an altered perception of reality, often a significant loss of contact with reality. Chen et al. utilized a multivariate approach to identify genomic risk components associated with brain function abnormalities in schizophrenia . They fiOur 16 pathways identified by inter-association analysis using IPAD contains 1) Neurotransmitter Receptor Binding And Downstream Transmission In The Postsynaptic Cell, 2) Neuroactive ligand-receptor interaction, 3) GABAergic synapse, 4) GABA receptor activation, 5) Glutamate Binding, Activation of AMPA Receptors and Synaptic Plasticity, 6) Neurotransmitter Release Cycle, 7) GABA synthesis, release, reuptake and degradation, 8) Class C/3 (Metabotropic glutamate/pheromone receptors), and 9) GABA A receptor activation etc.The other 7 diseases  also show strong links with brain, such as Cataplexy , REM SleThe 7 identified drugs: 1) DB00349, Clobazam, 2) DB00475, Chlordiazepoxide, 3) DB00683, Midazolam, 4) DB00690, Flurazepam, 5) DB00842, Oxazepam, 6) DB01558, Bromazepam, and 7) DB01595, Nitrazepam have on average 63 inter-associations between pathway, disease, drug and organ. They show strong links with brain, such as 1) Clobazam , 2) ChloIn conclusion, this case study shows that the self-validation of IPAD is an innovation of traditional enrichment analysis and can be useful for validating any pathways, diseases, drugs or organs that users identify with their own data and methods.http://yeolab.ucsd.edu/yeolab/Papers.html and computed the digital gene expression, next identified 278 differentially expressed genes in RNA-seq data from hormone treated prostate cancer cell line samples, then performed the enrichment analysis of the 278 genes with IPAD, and lastly carried out the inter-association analysis for these enriched components with IPAD.RNA-seq is an emerging technology for surveying gene expression and transcriptome content by directly sequencing the mRNA molecules in a sample. RNA-seq can provide gene expression measurements and is regarded as an attractive approach to analyze a transcriptome in an unbiased and comprehensive manner. In this case study, we demonstrate the use of IPAD to identify previously undiscovered components by the enrichment analysis based on differentially expressed genes identified from the transcriptional profiling sequencing data . The ori-2,AE ≥ 3.45, RE ≥ 1.95 and MJI ≥ 0.040), 100 diseases, and 2 organs  for the 278 genes. And the further inter-association analysis of IPAD identified 10 pathways, 8 diseases, 2 drugs and 1 organs which are not previously discovered by the enrichment analysis of IPAD , Relative Expression Value (RE) and Mean Jaccard Index (MJI). We also described the two criteria for setting the threshold parameters: 1) p-value below the 5% quantile and 2) 1 sigma lower control limits for AE, RE and MJI. However, defining each threshold parameter and implementing them effectively can be still challenging. Because the gene list size affects the enrichment score and the sizes of four types of component are largely different  for pathway-pathway, disease-disease, drug-drug, organ-organ, pathway-disease, pathway-drug, pathway-organ, disease-drug, disease-organ, and drug-organ associations.p to be the probability of success in a Bernoulli trial where one gene in component i falls in component j, the probability of x successes isIn addition to similarity scores, we developed a statistic model based on Fisher Exact test ,67 and nL is the total number of genes in component i, M is the total number of genes in component j, N is the total number of genes in the type of component, p = M/N, x is the number of genes corresponding to component i in component j, and x genes from a set of L genes.Where i in component j is the probability of obtaining a test statistic at least as extreme as the one observed, given that the null hypothesis that there is no enrichment between component i and component j is true, and calculated according to the following formulaThe p-value for component To prevent multiple testing problem from happening, IPAD adjust the p-value by Benjamini & Hochberg method .AE) of component i in component j is defined as x, the number of genes corresponding to component i in component j. The expected enrichment value (EE) of component i in component j is defined as the expected number of genes of component i in component j under the null hypothesis that the component i and component j are independent of each other.The absolute enrichment value (RE) of component i in component j is defined as AE/EE.The relative enrichment value . We determine the parameters based on the following two criteria: 1) Associations with p-value below the 5% quantile are chosen as enriched associations based on the p-value distribution of inter-association in the Figure AE, RE and MJI. There are no upper control limits for AE, RE and MJI. AE, RE and MJI must be greater than or equal to one standard deviation from their means. Associations falling below the lower control limits are considered to be not stably enriched.We define inter-associations as enriched ones if they satisfy the thresholds in table P-value below the 5% quantile performs better than other p-value thresholds with a balanced F_measure and an appropriate total number of inter-associations Table . First, AE, RE and MJI can have the better prediction performance than other sigma thresholds.Further comparison between four sigma thresholds Table  shows thIf a user's gene list is treated as a component, then the similarity measures and the statistics for genes-pathway, genes-disease, genes-drug and genes-organ can be similarly computed with the equations in the sections: "Similarity Measure for the Inter-association Analysis" and "Statistics for the Inter-association Analysis".performance evaluation purpose only. The following measurements were involved in our evaluation. (1) Sensitivity  is the proportion of actual positive pairs which are correctly identified; (2) Specificity measures the proportion of negative pairs which are correctly identified; (3) Precision is the probability of correct positive prediction; (4) F_measure is the harmonic mean of precision and recall; (5) Accuracy is the proportion of correctly predicted pairs.A "gold standard" of 30161 inter-associations  was constructed from KEGG , PharmGKThe online version of IPAD database is a typical 3-tier web application , with anThe result tables derived from the data generation steps were imported into the SQL Server2008R2 database Figure . The patThe authors declare that they have no competing interests.RD conceived the initial work and designed the method for the database construction. FZ generated the datasets, developed the statistics method, the database backend and the web-based interface, and performed the statistical analyses of the case studies. All authors are involved in the drafting and revisions of the manuscript.


Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed. Such databases complement each other and support efficient growth in this area, although the data resources remain scattered across the World Wide Web. Here, we review available metabolome databases and summarize the present status of development of related tools, particularly focusing on the plant metabolome. Data sharing discussed here will pave way for the robust interpretation of metabolomic data and advances in plant systems biology. Metabolomics, i.e., the measurement of the full suite of metabolites in a living tissue, has expanded greatly over the last decade, especially in the context of biochemical phenotyping. Specifically, in plant science, metabolomic approaches are increasingly used for understanding regulatory networks involved in genotype comparison  and liquid chromatography–mass spectrometry (LC-MS) is not unambiguous. There are two venues to the identification of a chromatographic peak: (1) purification and NMR analysis or (2) interpretation of the spectra yielding a putative structure, followed by synthesizing or buying the compound and spiking. Detectable peaks using GC-MS and LC-MS are thought to be abundant and often authentic standards exist to identify them by spiking. Nonetheless, researchers commonly attempt to provisionally identify these peaks by comparing their mass spectra and/or the retention time or retention indices (RIs) with those present in a database build from the data of authentic standards. To validate the metabolite identification rigorously, the Metabolomics Standard Initiative (MSI), pathway enrichment analysis, and additional machine learning methods. Please note that several tools and databases presented in this review have multiple functions. Furthermore, several web-based applications for metabolomic data are available   . These databases are very useful for functional genomics and make it possible to develop additional bioinformatic tools for pre-processing of metabolomics raw data, extraction of biologically meaningful mass spectra, and reduction/correction of unwanted variation in large-scale metabolomic data.In addition to mass spectrum and compound databases, several metabolite-profiling databases have also been developed in the past few years Table . Among tPlantMetabolomics.org (Quanbeck et al., In this review, we have highlighted an extensive list of databases that incorporate both MS-based metabolomics, as well as data analysis tools. Clearly, a small, but significant, number of integrated databases, including the full annotation of metabolites, metabolite profiling, and data analysis tools are emerging, such as The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In the contexts of genomics, post-genomics and systems biology approaches, data integration presents a major concern. Databases provide crucial solutions: they store, organize and allow information to be queried, they enhance the visibility of newly produced data by comparing them with previously published results, and facilitate the exploration and development of both existing hypotheses and new ideas.++ information system was developed with the aim of using whole plant genomes as physical references in order to gather and merge available genomic data from in silico or experimental approaches. Available through a JAVA application, original interfaces and tools assist the functional study of plant genes by considering them in their specific context: chromosome, gene family, orthology group, co-expression cluster and functional network. FLAGdb++ is mainly dedicated to the exploration of large gene groups in order to decipher functional connections, to highlight shared or specific structural or functional features, and to facilitate translational tasks between plant species .The FLAGdb++ presents a powerful complementary tool for exploring plant genomes and exploiting structural and functional resources, without the need for computer programming knowledge. First launched in 2002, a 15th version of FLAGdb++ is now available and comprises four model plant genomes and over eight million genomic features.Combining original data with the output of experts and graphical displays that differ from classical plant genome browsers, FLAGdb Indeed, huge quantities of data are stored and managed by different databases, but linking this information is highly complex [++ is designed to be such a tool, efficiently navigating in and between plant model genomes in order to analyze large sets of genes. The main design criteria included (i) using a common information system for all genomes within a unified interface, (ii) providing reliable data by combining and re-analyzing raw data derived from different sources, i.e. ridding users of format heterogeneity problems, (iii) considering data in various contexts such as chromosomal location, or gene family or orthology group membership, (iv) providing access to original data through collaboration with data producers, and (v) facilitating the formulation and testing of hypotheses based on links between gene structure and function. In order to satisfy these criteria, the choice was made to develop a data warehouse connected to original interfaces and capable of helping build hypotheses based on a number of interactive graphical displays. Deciphering the functional relevance of a gene cluster and inferring hypothesis from common characteristics are both complex processes involving multiple information sources, steps and queries which may not necessarily be fully predictable at the start. In FLAGdb++, the graphical displays are centered on highly connected map-like representations, intended to act together as mnemonics to guide hypothesis establishment progression. When initially launched in 2002 FLAGdb++ focused solely on the Arabidopsis thaliana genome [++ allow the clear presentation of original data, thanks to an intuitive graphical tool box. Beyond the adding of novel data types and cross-references, the new functionalities allow the users to compare gene structures and promoters, and to navigate into gene classification, segmental duplications, feature density curves, phylogenetic profiles and orthology groups. Finally, FLAGdb++ efficiently completes other plant genome databases and browsers [Holistic approaches require the organization of data and metadata in order to allow the hypothesis-driven querying of heterogeneous objects. In many systems biology considerations, data management and integrative approaches are identified as key to the thorough exploitation of omics data and their translation into knowledge . Many bi complex . This isa genome , but hasbrowsers -7.++ is based on a client-server model. The n-tier architecture is composed of a relational database (under RDBMS PostgreSQL) and a client application, implemented in JAVA (JDK 1.6), and contains the application server and user interfaces. Communication with the database relies on the JDBC driver. The client application has to be locally installed by the users in order to query the FLAGdb++ database through the graphical interfaces. The JAVA WEB START technology is used to facilitate and automate the installation and updates of the application. The JAVA solution has been selected for its compatibility with all operating systems  and to enhance the possibilities of development around the user-side application. Concerning the database, the schema has been designed to scale well with very large quantities of diverse data, allowing the connection of features and information not only around genomic loci, but also around biological functions or gene families. Thus, this architecture proves a good compromise between performance, scalability and development issues.FLAGdb++ has been developed in a generic way in order to be applied to different genomes. Therefore, it is able to store, organize, explore and analyze numerous types of genomic resources . Data integration is based on mapping to genomic sequences using the genomic coordinates as an index system. The database schema and interfaces consider different types of data along with their origin, quality and biological relevance, and the diversity of possible queries in order to access and analyze them.FLAGdbArabidopsis thaliana genome  FLAGdb+pponbare ), Populu-1 clone ) and Vitassembly ). These ++ aims to merge different genomic resources in order to improve the structural and functional annotation of genomes. These resources derive from several origins: general or specific databases, internal and collaborative projects, experimental high-throughput approaches, manual biocuration or in silico prediction works  annotated using a semi-automatic method based on established reference sets [Gene classification is another major topic in FLAGdbtegories  and the tegories  are usedtegories . For instegories ,35. The nce sets  and clas++ also provides cross references and web links to external resources and tools , FLAGdbon group  can be r++ application allowing the user to (i) browse the segmental duplications and resulting paralogs of the Arabidopsis genome, (ii) display density curves of features or motifs along the chromosomes, (iii) extract sequences or annotations  between two chromosomal coordinates for external analyses and applications, and (iv) upload private annotations or features and overlay them with the FLAGdb++ data. User preferences are saved at the end of each session, and each graphical object (feature) can be edited in order to prepare relevant figures for use in laboratory books or manuscripts.A recently added tool dedicated to the orthology relationships makes cross-linking between the integrated genomes possible, a particularly powerful feature when inferring function and making comparative analyses. To control whether the BLAST best hits are reciprocal, all against all BLASTP comparisons are graphically represented for a selected gene Figure . Intron-++ users; they are either biologists or bioinformaticians whishing to address different queries using the database. Some are interested in gene-by-gene or high-throughput approaches, looking for either mutants in their target gene(s) or shared functional characteristics in large co-expressed gene sets. Others are focused on either gene families or large genomic segments for evolution and functional analyses. Since its first release eight years ago, we now have concrete proof of the usefulness of FLAGdb++, as it is reflected by its citation in numerous publications (see the website [We acknowledge the various skill profiles of FLAGdb website ).++ offers plant biologists access to a rich array of original genomic resources. JAVA interfaces, combined with intrinsic tools and four annotated complete plant genomes considerably help users to build hypotheses in their translational research or in comparative genomics approaches. Development and integration tasks are directed at highlighting biological correlations between data and speeding up the analyses of groups of genes in a wide range of contexts including genomic regions, gene families or gene function.Through a user friendly application, FLAGdb++. They are however extensively documented on-line [++ within the plant science community over the coming years.We have not described in this paper all the tools and types of display available in FLAGdb on-line . The dat++ home page [++ application, JAVA (JRE version 1.6 or higher) should already be installed on the computer. Database architecture, integrated data and all the pipelines developed (in Perl) to fill the database are available on request for users who want to use the FLAGdb++ environment with other eukaryotic genomes. A Perl script allowing to open the FLAGdb++ application on a specific feature is also available on request in order to create interactive links from other tools or databases. There is no restriction to the use of FLAGdb++ by non-academics.The FLAGdbome page  providesCDS: coding sequence; GFF: gene feature format; GFT: gene family tag; GO: gene ontology; GST: gene specific tag; FST: T-DNA flanking sequence tag; MPSS: massively parallel signature sequencing; PPR: pentatricopeptide repeat; TPS: terpene synthase; TSS: transcription start site.The authors declare that they have no competing interests.SD, FS, JPT, CG, SG, JCL and PL were involved in the data production, acquisition and/or integration. FS and SD carried out the JAVA software development. FS, VB, JPT and PG were involved in the database conception and management. JPT and AL helped to draft the manuscript. SA coordinated the project and drafted the manuscript. All authors read and approved the final manuscript.