Motivation: Functional genomics research has expanded enormously in the last decade thanks to the cost reduction in high-throughput technologies and the development of computational tools that generate, standardize and share information on gene and protein function such as the Gene Ontology (GO). Nevertheless, many biologists, especially working with non-model organisms, still suffer from non-existing or low-coverage functional annotation, or simply struggle retrieving, summarizing and querying these data. Results: The Blast2GO Functional Annotation Repository (B2G-FAR) is a bioinformatics resource envisaged to provide functional information for otherwise uncharacterized sequence data and offers data mining tools to analyze a larger repertoire of species than currently available. This new annotation resource has been created by applying the Blast2GO functional annotation engine in a strongly high-throughput manner to the entire space of public available sequences. The resulting repository contains GO term predictions for over 13.2 million non-redundant protein sequences based on BLAST search alignments from the SIMAP database. We generated GO annotation for approximately 150 000 different taxa making available 2000 species with the highest coverage through B2G-FAR. A second section within B2G-FAR holds functional annotations for 17 non-model organism Affymetrix GeneChips. Conclusions: B2G-FAR provides easy access to exhaustive functional annotation for 2000 species offering a good balance between quality and quantity, thereby supporting functional genomics research especially in the case of non-model organisms. Availability: The annotation resource is available at http://www.b2gfar.org. Contact: aconesa@cipf.es; sgoetz@cipf.es Supplementary information: Supplementary data are available at Bioinformatics online.

Is endometrial recurrent implantation failure (RIF) only a matter of an asynchronous (displaced) window of implantation (WOI), or could it also be a pathological (disrupted) WOI? Our predictive results demonstrate that both displaced and disrupted WOIs exist and can present independently or together in the same RIF patient. Since 2002, many gene expression signatures associated with endometrial receptivity and RIF have been described. Endometrial transcriptomics prediction has been applied to the human WOI in two previous studies. One study describes endometrial RIF to be the result of a temporal displacement of the WOI. The other indicates that endometrial RIF can also result from a molecularly disrupted WOI without temporal displacement. Retrospective analysis was undertaken to compare WOI endometrial transcriptomics predictions in controls (n = 72) and RIF patients (n = 43). RIF was clinically designated by the absence of implantation after four or more transfers of high quality embryos or after the placement of 10 or more embryos in multiple transfers. Endometrial tissue samples were collected from LH + 5 to LH + 8. We compared the two molecular causes of RIF to signatures currently described in the literature. We propose a new transcriptomic RIF taxonomy to fill the gap between the two hypotheses and to guide the development of clinical detection and determination of both types of RIF. Utilizing 115 gene expression profiles, two different predictive designs were developed: one considering RIF versus controls removing menstrual cycle timing, called the disrupted or pathological model, and another stratifying the WOI in transcriptomic profiles related to timing for predicting displacements. The predictive value of each model was compared between all signatures selected. We propose a new genomic approach that distinguishes between both types of RIF in the same sample cohort. From the 16 signatures analysed, we clearly predicted two causes of RIF—both a displaced WOI and an on-time but pathologically disrupted WOI. A high predictive value related to WOI profiles associated with menstrual cycle timing was found in most of the signatures. Specifically, 69% of the signatures analysed presented an accuracy higher than expected by chance in a range from 0.87 to 0.97. Displacements and disruptions were not molecularly independent, as some signatures were moderately associated with both causes. The gene and functional comparison between signatures revealed that they were not similar, although we did find functions in common and a cluster of moderate functional concordance between some of the signatures that predicted displacements (the highest Cohen's Kappa index were between 0.55 and 0.62 depending on the functional database). We propose a new transcriptomic RIF taxonomy to fill the gap between these prior studies and to establish methodology for detecting and distinguishing both types of RIF in clinical practice. Our findings indicate these two phenotypes could present independently or together in the same RIF patient. RIF patients designated by clinical criteria have been stratified transcriptomically as 18.6% with only a displaced WOI, 53.5% with a displaced and pathological WOI, 23.3% with only a disrupted WOI, and 4.7% could be a clinical RIF with non-endometrial origin. The new RIF transcriptomic taxonomy avoids menstrual cycle timing as a confounding variable that should be controlled for, distinguishing clearly between a disrupted and a displaced WOI for precision medicine in RIF. The main objective of this study was to use transcriptomics to detect both RIF causes and to understand the role of transcriptomic signatures in these phenotypes. The predictive value in absolute terms for each signature was not indicative in these prediction designs; instead, the comparison between signatures was most important for prediction capability in the same sample cohort for both RIF causes. Clinical follow up of the RIF taxonomies proposed has not been analysed in this study, so further prospective clinical studies are necessary to determine the prevalence and penetrance of these phenotypes. The main insight from this study is a new understanding of RIF taxonomy. Understanding how to classify RIF patients to distinguish clinically between a patient who could benefit from a personalized embryo transfer day and a patient with a disrupted WOI will enable identification and stratification for the research and development of new treatments. In addition, we demonstrate that basic research designs in endometrial transcriptomics cause masking of the study variable by the menstrual cycle timing. This research has been funded by IVI-RMA; the authors do not have any competing interests.

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine. Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets. The proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.

Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC₅₀ value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).