The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort.[Supplemental material is available online at www.genome.org. Bioperl is available as open-source software free of charge and is licensed under the Perl Artistic License (http://www.perl.com/pub/a/language/misc/Artistic.html). It is available for download at http://www.bioperl.org. Support inquiries should be addressed to bioperl-l@bioperl.org.]

The oocyte is the only cell of the body that can reprogram transplanted somatic nuclei and sets the gold standard for all reprogramming methods. Therefore, an in-depth characterization of its proteome holds promise to advance our understanding of reprogramming and germ cell biology. To date, limitations on oocyte numbers and proteomic technology have impeded this task, and the search for reprogramming factors has been conducted in embryonic stem (ES) cells instead. Here, we present the proteome of metaphase II mouse oocytes to a depth of 3699 proteins, which substantially extends the number of proteins identified until now in mouse oocytes and is comparable by size to the proteome of undifferentiated mouse ES cells. Twenty-eight oocyte proteins, also detected in ES cells, match the criteria of our multilevel approach to screen for reprogramming factors, namely nuclear localization, chromatin modification, and catalytic activity. Our oocyte proteome catalog thus advances the definition of the "reprogrammome", the set of molecules--proteins, RNAs, lipids, and small molecules--that enable reprogramming.

The long-standing view of 'immortal germline vs mortal soma' poses a fundamental question in biology concerning how oocytes age in molecular terms. A mainstream hypothesis is that maternal ageing of oocytes has its roots in gene transcription. Investigating the proteins resulting from mRNA translation would reveal how far the levels of functionally available proteins correlate with mRNAs and would offer novel insights into the changes oocytes undergo during maternal ageing. Gene ontology (GO) semantic analysis revealed a high similarity of the detected proteome (2324 proteins) to the transcriptome (22 334 mRNAs), although not all proteins had a cognate mRNA. Concerning their dynamics, fourfold changes of abundance were more frequent in the proteome (3%) than the transcriptome (0.05%), with no correlation. Whereas proteins associated with the nucleus (e.g. structural maintenance of chromosomes and spindle-assembly checkpoints) were largely represented among those that change in oocytes during maternal ageing; proteins associated with oxidative stress/damage (e.g. superoxide dismutase) were infrequent. These quantitative alterations are either impoverishing or enriching. Using GO analysis, these alterations do not relate in any simple way to the classic signature of ageing known from somatic tissues. Given the lack of correlation, we conclude that proteome analysis of mouse oocytes may not be surrogated with transcriptome analysis. Furthermore, we conclude that the classic features of ageing may not be transposed from somatic tissues to oocytes in a one-to-one fashion. Overall, there is more to the maternal ageing of oocytes than mere cellular deterioration exemplified by the notorious increase of meiotic aneuploidy.

BackgroundExperimentalists are overwhelmed by high-throughput data and there is an urgent need to condense information into simple hypotheses. For example, large amounts of microarray and deep sequencing data are becoming available, describing a variety of experimental conditions such as gene knockout and knockdown, the effect of interventions, and the differences between tissues and cell lines.ResultsTo address this challenge, we developed a method, implemented as a Cytoscape plugin called ExprEssence. As input we take a network of interaction, stimulation and/or inhibition links between genes/proteins, and differential data, such as gene expression data, tracking an intervention or development in time. We condense the network, highlighting those links across which the largest changes can be observed. Highlighting is based on a simple formula inspired by the law of mass action. We can interactively modify the threshold for highlighting and instantaneously visualize results. We applied ExprEssence to three scenarios describing kidney podocyte biology, pluripotency and ageing: 1) We identify putative processes involved in podocyte (de-)differentiation and validate one prediction experimentally. 2) We predict and validate the expression level of a transcription factor involved in pluripotency. 3) Finally, we generate plausible hypotheses on the role of apoptosis, cell cycle deregulation and DNA repair in ageing data obtained from the hippocampus.ConclusionReducing the size of gene/protein networks to the few links affected by large changes allows to screen for putative mechanistic relationships among the genes/proteins that are involved in adaptation to different experimental conditions, yielding important hypotheses, insights and suggestions for new experiments. We note that we do not focus on the identification of 'active subnetworks'. Instead we focus on the identification of single links (which may or may not form subnetworks), and these single links are much easier to validate experimentally than submodules. ExprEssence is available at http://sourceforge.net/projects/expressence/.

BackgroundAnalysis of the mechanisms underlying pluripotency and reprogramming would benefit substantially from easy access to an electronic network of genes, proteins and mechanisms. Moreover, interpreting gene expression data needs to move beyond just the identification of the up-/downregulation of key genes and of overrepresented processes and pathways, towards clarifying the essential effects of the experiment in molecular terms.Methodology/Principal FindingsWe have assembled a network of 574 molecular interactions, stimulations and inhibitions, based on a collection of research data from 177 publications until June 2010, involving 274 mouse genes/proteins, all in a standard electronic format, enabling analyses by readily available software such as Cytoscape and its plugins. The network includes the core circuit of Oct4 (Pou5f1), Sox2 and Nanog, its periphery (such as Stat3, Klf4, Esrrb, and c-Myc), connections to upstream signaling pathways (such as Activin, WNT, FGF, BMP, Insulin, Notch and LIF), and epigenetic regulators as well as some other relevant genes/proteins, such as proteins involved in nuclear import/export. We describe the general properties of the network, as well as a Gene Ontology analysis of the genes included. We use several expression data sets to condense the network to a set of network links that are affected in the course of an experiment, yielding hypotheses about the underlying mechanisms.Conclusions/SignificanceWe have initiated an electronic data repository that will be useful to understand pluripotency and to facilitate the interpretation of high-throughput data. To keep up with the growth of knowledge on the fundamental processes of pluripotency and reprogramming, we suggest to combine Wiki and social networking software towards a community curation system that is easy to use and flexible, and tailored to provide a benefit for the scientist, and to improve communication and exchange of research results. A PluriNetWork tutorial is available at http://www.ibima.med.uni-rostock.de/IBIMA/PluriNetWork/.

Despite increasing research efforts, there is a lack of consensus on defining aging or health. To understand the underlying processes, and to foster the development of targeted interventions towards increasing one’s health, there is an urgent need to find a broadly acceptable and useful definition of health, based on a list of (molecular) features; to operationalize features of health so that it can be measured; to identify predictive biomarkers and (molecular) pathways of health; and to suggest interventions, such as nutrition and exercise, targeted at putative causal pathways and processes. Based on a survey of the literature, we propose to define health as a state of an individual characterized by the core features of physiological, cognitive, physical and reproductive function, and a lack of disease. We further define aging as the aggregate of all processes in an individual that reduce its wellbeing , that is, its health or survival or both. We define biomarkers of health by their attribute of predicting future health better than chronological age. We define healthspan pathways as molecular features of health that relate to each other by belonging to the same molecular pathway. Our conceptual framework may integrate diverse operationalizations of health and guide precision prevention efforts.

Early mouse embryos have an atypical translational machinery that consists of cytoplasmic lattices and is poorly competent for translation. Hence, the impact of transcriptomic changes on the operational level of proteins is predicted to be relatively modest. To investigate this, we performed liquid chromatography–tandem mass spectrometry and mRNA sequencing at seven developmental stages, from the mature oocyte to the blastocyst, and independently validated our data by immunofluorescence and qPCR. We detected and quantified 6,550 proteins and 20,535 protein-coding transcripts. In contrast to the transcriptome – where changes occur early, mostly at the 2-cell stage – our data indicate that the most substantial changes in the proteome take place towards later stages, between the morula and blastocyst. We also found little to no concordance between the changes in protein and transcript levels, especially for early stages, but observed that the concordance increased towards the morula and blastocyst, as did the number of free ribosomes. These results are consistent with the cytoplasmic lattice-to-free ribosome transition being a key mediator of developmental regulation. Finally, we show how these data can be used to appraise the strengths and limitations of mRNA-based studies of pre-implantation development and expand on the list of known developmental markers.

The reprogramming process that leads to induced pluripotent stem cells (iPSCs) may benefit from adding oocyte factors to Yamanaka's reprogramming cocktail (OCT4, SOX2, KLF4, with or without MYC; OSK(M)). We previously searched for such facilitators of reprogramming (the reprogrammome) by applying label-free LC-MS/MS analysis to mouse oocytes, producing a catalog of 28 candidates that are (i) able to robustly access the cell nucleus and (ii) shared between mature mouse oocytes and pluripotent embryonic stem cells. In the present study, we hypothesized that our 28 reprogrammome candidates would also be (iii) abundant in mature oocytes, (iv) depleted after the oocyte-to-embryo transition, and (v) able to potentiate or replace the OSKM factors. Using LC-MS/MS and isotopic labeling methods, we found that the abundance profiles of the 28 proteins were below those of known oocyte-specific and housekeeping proteins. Of the 28 proteins, only arginine methyltransferase 7 (PRMT7) changed substantially during mouse embryogenesis and promoted the conversion of mouse fibroblasts into iPSCs. Specifically, PRMT7 replaced SOX2 in a factor-substitution assay, yielding iPSCs. These findings exemplify how proteomics can be used to prioritize the functional analysis of reprogrammome candidates. The LC-MS/MS data are available via ProteomeXchange with identifier PXD003093.