Genome-wide signatures of flowering adaptation to climate temperature: Regional analyses in a highly diverse native range of Arabidopsis thaliana

Current global change is fueling an interest to understand the genetic and molecular mechanisms of plant adaptation to climate. In particular, altered flowering time is a common strategy for escape from unfavourable climate temperature. In order to determine the genomic bases underlying flowering time adaptation to this climatic factor, we have systematically analysed a collection of 174 highly diverse Arabidopsis thaliana accessions from the Iberian Peninsula. Analyses of 1.88 million single nucleotide polymorphisms provide evidence for a spatially heterogeneous contribution of demographic and adaptive processes to geographic patterns of genetic variation. Mountains appear to be allele dispersal barriers, whereas the relationship between flowering time and temperature depended on the precise temperature range. Environmental genome-wide associations supported an overall genome adaptation to temperature, with 9.4% of the genes showing significant associations. Furthermore, phenotypic genome-wide associations provided a catalogue of candidate genes underlying flowering time variation. Finally, comparison of environmental and phenotypic genome-wide associations identified known (Twin Sister of FT, FRIGIDA-like 1, and Casein Kinase II Beta chain 1) and new (Epithiospecifer Modifier 1 and Voltage-Dependent Anion Channel 5) genes as candidates for adaptation to climate temperature by altered flowering time. Thus, this regional collection provides an excellent resource to address the spatial complexity of climate adaptation in annual plants.

miRNA profiling during antigen-dependent T cell activation: A role for miR-132-3p

microRNAs (miRNAs) are tightly regulated during T lymphocyte activation to enable the establishment of precise immune responses. Here, we analyzed the changes of the miRNA profiles of T cells in response to activation by cognate interaction with dendritic cells. We also studied mRNA targets common to miRNAs regulated in T cell activation. pik3r1 gene, which encodes the regulatory subunits of PI3K p50, p55 and p85, was identified as target of miRNAs upregulated after T cell activation. Using 3′UTR luciferase reporter-based and biochemical assays, we showed the inhibitory relationship between miR-132-3p upregulation and expression of the pik3r1 gene. Our results indicate that specific miRNAs whose expression is modulated during T cell activation might regulate PI3K signaling in T cells.

Gene signature associated with benign neurofibroma transformation to malignant peripheral nerve sheath tumors

Benign neurofibromas, the main phenotypic manifestations of the rare neurological disorder neurofibromatosis type 1, degenerate to malignant tumors associated to poor prognosis in about 10% of patients. Despite efforts in the field of (epi)genomics, the lack of prognostic biomarkers with which to predict disease evolution frustrates the adoption of appropriate early therapeutic measures. To identify potential biomarkers of malignant neurofibroma transformation, we integrated four human experimental studies and one for mouse, using a gene score-based meta-analysis method, from which we obtained a score-ranked signature of 579 genes. Genes with the highest absolute scores were classified as promising disease biomarkers. By grouping genes with similar neurofibromatosis-related profiles, we derived panels of potential biomarkers. The addition of promoter methylation data to gene profiles indicated a panel of genes probably silenced by hypermethylation. To identify possible therapeutic treatments, we used the gene signature to query drug expression databases. Trichostatin A and other histone deacetylase inhibitors, as well as cantharidin and tamoxifen, were retrieved as putative therapeutic means to reverse the aberrant regulation that drives to malignant cell proliferation and metastasis. This in silico prediction corroborated reported experimental results that suggested the inclusion of these compounds in clinical trials. This experimental validation supported the suitability of the meta-analysis method used to integrate several sources of public genomic information, and the reliability of the gene signature associated to the malignant evolution of neurofibromas to generate working hypotheses for prognostic and drug-responsive biomarkers or therapeutic measures, thus showing the potential of this in silico approach for biomarker discovery.

Distinct Molecular Signature of Murine Fetal Liver and Adult Hematopoietic Stem Cells Identify Novel Regulators of Hematopoietic Stem Cell Function

During ontogeny, fetal liver (FL) acts as a major site for hematopoietic stem cell (HSC) maturation and expansion, whereas HSCs in the adult bone marrow (ABM) are largely quiescent. HSCs in the FL possess faster repopulation capacity as compared with ABM HSCs. However, the molecular mechanism regulating the greater self-renewal potential of FL HSCs has not yet extensively been assessed. Recently, we published RNA sequencing-based gene expression analysis on FL HSCs from 14.5-day mouse embryo (E14.5) in comparison to the ABM HSCs. We reanalyzed these data to identify key transcriptional regulators that play important roles in the expansion of HSCs during development. The comparison of FL E14.5 with ABM HSCs identified more than 1,400 differentially expressed genes. More than 200 genes were shortlisted based on the gene ontology (GO) annotation term "transcription." By morpholino-based knockdown studies in zebrafish, we assessed the function of 18 of these regulators, previously not associated with HSC proliferation. Our studies identified a previously unknown role for tdg, uhrf1, uchl5, and ncoa1 in the emergence of definitive hematopoiesis in zebrafish. In conclusion, we demonstrate that identification of genes involved in transcriptional regulation differentially expressed between expanding FL HSCs and quiescent ABM HSCs, uncovers novel regulators of HSC function.

Notch-regulated miR-223 targets the aryl hydrocarbon receptor pathway and increases cytokine production in macrophages from rheumatoid arthritis patients

Evidence links aryl hydrocarbon receptor (AHR) activation to rheumatoid arthritis (RA) pathogenesis, although results are inconsistent. AHR agonists inhibit pro-inflammatory cytokine expression in macrophages, pivotal cells in RA aetiopathogenesis, which hints at specific circuits that regulate the AHR pathway in RA macrophages. We compared microRNA (miR) expression in CD14⁺ cells from patients with active RA or with osteoarthritis (OA). Seven miR were downregulated and one (miR-223) upregulated in RA compared to OA cells. miR-223 upregulation correlated with reduced Notch3 and Notch effector expression in RA patients. Overexpression of the Notch-induced repressor HEY-1 and co-culture of healthy donor monocytes with Notch ligand-expressing cells showed direct Notch-mediated downregulation of miR-223. Bioinformatics predicted the AHR regulator ARNT (AHR nuclear translocator) as a miR-223 target. Pre-miR-223 overexpression silenced ARNT 3’UTR-driven reporter expression, reduced ARNT (but not AHR) protein levels and prevented AHR/ARNT-mediated inhibition of pro-inflammatory cytokine expression. miR-223 counteracted AHR/ARNT-induced Notch3 upregulation in monocytes. Levels of ARNT and of CYP1B1, an AHR/ARNT signalling effector, were reduced in RA compared to OA synovial tissue, which correlated with miR-223 levels. Our results associate Notch signalling to miR-223 downregulation in RA macrophages, and identify miR-223 as a negative regulator of the AHR/ARNT pathway through ARNT targeting.

Highly proliferative primitive fetal liver hematopoietic stem cells are fueled by oxidative metabolic pathways

Hematopoietic stem cells (HSCs) in the fetal liver (FL) unlike adult bone marrow (BM) proliferate extensively, posing different metabolic demands. However, metabolic pathways responsible for the production of energy and cellular building blocks in FL HSCs have not been described. Here, we report that FL HSCs use oxygen dependent energy generating pathways significantly more than their BM counterparts. RNA-Seq analysis of E14.5 FL versus BM derived HSCs identified increased expression levels of genes involved in oxidative phosphorylation (OxPhos) and the citric acid cycle (TCA). We demonstrated that FL HSCs contain more mitochondria than BM HSCs, which resulted in increased levels of oxygen consumption and reactive oxygen species (ROS) production. Higher levels of DNA repair and antioxidant pathway gene expression may prevent ROS-mediated (geno)toxicity in FL HSCs. Thus, we here for the first time highlight the underestimated importance of oxygen dependent pathways for generating energy and building blocks in FL HSCs.

NFFinder: an online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning

Drug repositioning, using known drugs for treating conditions different from those the drug was originally designed to treat, is an important drug discovery tool that allows for a faster and cheaper development process by using drugs that are already approved or in an advanced trial stage for another purpose. This is especially relevant for orphan diseases because they affect too few people to make drug research de novo economically viable. In this paper we present NFFinder, a bioinformatics tool for identifying potential useful drugs in the context of orphan diseases. NFFinder uses transcriptomic data to find relationships between drugs, diseases and a phenotype of interest, as well as identifying experts having published on that domain. The application shows in a dashboard a series of graphics and tables designed to help researchers formulate repositioning hypotheses and identify potential biological relationships between drugs and diseases. NFFinder is freely available at http://nffinder.cnb.csic.es.

A subpopulation of CD103(pos) ICOS(pos) Treg cells occurs at high frequency in lymphopenic mice and represents a lymph node specific differentiation stage

Regulatory T (Treg) cells are pivotal for the maintenance of peripheral tolerance by controlling self-reactive, chronic, and homeostatic T-cell responses. Here, we report that the increase in Treg-cell suppressive function observed in lymphopenic mice correlates with the degree of lymphopenia and is caused by a higher frequency of a novel subpopulation of CD103(pos) ICOS(pos) Treg cells. Though present in the thymus, CD103(pos) ICOS(pos) Treg cells are not generated there but recirculate from the periphery to that site. The acquisition and maintenance of this distinctive phenotype requires the LN microenvironment and the in situ availability of antigen. Contrary to conventional effector and other Treg cells, the cellularity of CD103(pos) ICOS(pos) Treg cells is not affected by the absence of IL-7 and thymic stroma lymphopoetin. Given their increased frequency in lymphopenia, the absolute number of CD103(pos) ICOS(pos) Treg cells remains unchanged in the periphery irrespective of a paucity of total Treg cells. We furthermore demonstrate, with cell transfers in mice, that the CD103(pos) ICOS(pos) phenotype represents a LN-specific differentiation stage arrived at by several other Treg-cell subsets. Thus, tissue-specific cues determine the overall potency of the peripheral Treg-cell pool by shaping its subset composition.

NMF-mGPU: non-negative matrix factorization on multi-GPU systems

BACKGROUND

In the last few years, the Non-negative Matrix Factorization ( NMF ) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster. In this paper, we present NMF-mGPU, an efficient and easy-to-use implementation of the NMF algorithm that takes advantage of the high computing performance delivered by Graphics-Processing Units ( GPUs ). Driven by the ever-growing demands from the video-games industry, graphics cards usually provided in PCs and laptops have evolved from simple graphics-drawing platforms into high-performance programmable systems that can be used as coprocessors for linear-algebra operations. However, these devices may have a limited amount of on-board memory, which is not considered by other NMF implementations on GPU.

RESULTS

NMF-mGPU is based on CUDA ( Compute Unified Device Architecture ), the NVIDIA's framework for GPU computing. On devices with low memory available, large input matrices are blockwise transferred from the system's main memory to the GPU's memory, and processed accordingly. In addition, NMF-mGPU has been explicitly optimized for the different CUDA architectures. Finally, platforms with multiple GPUs can be synchronized through MPI ( Message Passing Interface ). In a four-GPU system, this implementation is about 120 times faster than a single conventional processor, and more than four times faster than a single GPU device (i.e., a super-linear speedup).

CONCLUSIONS

Applications of GPUs in Bioinformatics are getting more and more attention due to their outstanding performance when compared to traditional processors. In addition, their relatively low price represents a highly cost-effective alternative to conventional clusters. In life sciences, this results in an excellent opportunity to facilitate the daily work of bioinformaticians that are trying to extract biological meaning out of hundreds of gigabytes of experimental information. NMF-mGPU can be used "out of the box" by researchers with little or no expertise in GPU programming in a variety of platforms, such as PCs, laptops, or high-end GPU clusters. NMF-mGPU is freely available at https://github.com/bioinfo-cnb/bionmf-gpu .

Improving miRNA-mRNA interaction predictions

Background

MicroRNAs are short RNA molecules that post-transcriptionally regulate gene expression. Today, microRNA target prediction remains challenging since very few have been experimentally validated and sequence-based predictions have large numbers of false positives. Furthermore, due to the different measuring rules used in each database of predicted interactions, the selection of the most reliable ones requires extensive knowledge about each algorithm.

Results

Here we propose two methods to measure the confidence of predicted interactions based on experimentally validated information. The output of the methods is a combined database where new scores and statistical confidences are re-assigned to each predicted interaction. The new scores allow the robust combination of several databases without the effect of low-performing algorithms dragging down good-performing ones. The combined databases obtained using both algorithms described in this paper outperform each of the existing predictive algorithms that were considered for the combination.

Conclusions

Our approaches are a useful way to integrate predicted interactions from different databases. They reduce the selection of interactions to a unique database based on an intuitive score and allow comparing databases between them.

Sumoylated hnRNPA2B1 controls the sorting of miRNAs into exosomes through binding to specific motifs

Exosomes are released by most cells to the extracellular environment and are involved in cell-to-cell communication. Exosomes contain specific repertoires of mRNAs, microRNAs (miRNAs) and other non-coding RNAs that can be functionally transferred to recipient cells. However, the mechanisms that control the specific loading of RNA species into exosomes remain unknown. Here we describe sequence motifs present in miRNAs that control their localization into exosomes. The protein heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) specifically binds exosomal miRNAs through the recognition of these motifs and controls their loading into exosomes. Moreover, hnRNPA2B1 in exosomes is sumoylated, and sumoylation controls the binding of hnRNPA2B1 to miRNAs. The loading of miRNAs into exosomes can be modulated by mutagenesis of the identified motifs or changes in hnRNPA2B1 expression levels. These findings identify hnRNPA2B1 as a key player in miRNA sorting into exosomes and provide potential tools for the packaging of selected regulatory RNAs into exosomes and their use in biomedical applications.

Cells secrete micro-RNAs by packaging them into exosomes; however, the mechanisms by which this packaging occurs are unclear. Here, the authors identify a sequence motif that confers exosomal targeting to micro-RNAs and identify a ribonucleoprotein complex that plays a role in this process.

Surfing transcriptomic landscapes. A step beyond the annotation of chromosome 16 proteome

The Spanish team of the Human Proteome Project (SpHPP) marked the annotation of Chr16 and data analysis as one of its priorities. Precise annotation of Chromosome 16 proteins according to C-HPP criteria is presented. Moreover, Human Body Map 2.0 RNA-Seq and Encyclopedia of DNA Elements (ENCODE) data sets were used to obtain further information relative to cell/tissue specific chromosome 16 coding gene expression patterns and to infer the presence of missing proteins. Twenty-four shotgun 2D-LC-MS/MS and gel/LC-MS/MS MIAPE compliant experiments, representing 41% coverage of chromosome 16 proteins, were performed. Furthermore, mapping of large-scale multicenter mass spectrometry data sets from CCD18, MCF7, Jurkat, and Ramos cell lines into RNA-Seq data allowed further insights relative to correlation of chromosome 16 transcripts and proteins. Detection and quantification of chromosome 16 proteins in biological matrices by SRM procedures are also primary goals of the SpHPP. Two strategies were undertaken: one focused on known proteins, taking advantage of MS data already available, and the second, aimed at the detection of the missing proteins, is based on the expression of recombinant proteins to gather MS information and optimize SRM methods that will be used in real biological samples. SRM methods for 49 known proteins and for recombinant forms of 24 missing proteins are reported in this study.

Chromothripsis: breakage-fusion-bridge over and over again

The acquisition of massive but localized chromosome translocations, a phenomenon termed chromothripsis, has received widespread attention since its discovery over a year ago. Until recently, chromothripsis was believed to originate from a single catastrophic event, but the molecular mechanisms leading to this event are yet to be uncovered. Because a thorough interpretation of the data are missing, the phenomenon itself has wrongly acquired the status of a mechanism used to justify many kinds of complex rearrangements. Although the assumption that all translocations in chromothripsis originate from a single event has met with criticism, satisfactory explanations for the intense but localized nature of this phenomenon are still missing. Here, we show why the data used to describe massive catastrophic rearrangements are incompatible with a model comprising a single event only and propose a molecular mechanism in which a combination of known cellular pathways accounts for chromothripsis. Instead of a single traumatic event, the protection of undamaged chromosomes by telomeres can limit repetitive breakage-fusion-bridge events to a single chromosome arm. Ultimately, common properties of chromosomal instability, such as aneuploidy and centromere fission, might establish the complex genetic pattern observed in this genomic state.

3DEM Loupe: Analysis of macromolecular dynamics using structures from electron microscopy

Electron microscopy (EM) provides access to structural information of macromolecular complexes in the 3-20 Å resolution range. Normal mode analysis has been extensively used with atomic resolution structures and successfully applied to EM structures. The major application of normal modes is the identification of possible conformational changes in proteins. The analysis can throw light on the mechanism following ligand binding, protein-protein interactions, channel opening and other functional macromolecular movements. In this article, we present a new web server, 3DEM Loupe, which allows normal mode analysis of any uploaded EM volume using a user-friendly interface and an intuitive workflow. Results can be fully explored in 3D through animations and movies generated by the server. The application is freely available at http://3demloupe.cnb.csic.es.

MicroRNAs control the maintenance of thymic epithelia and their competence for T lineage commitment and thymocyte selection

Thymic epithelial cells provide unique cues for the lifelong selection and differentiation of a repertoire of functionally diverse T cells. Rendered microRNA (miRNA) deficient, these stromal cells in the mouse lose their capacity to instruct the commitment of hematopoietic precursors to a T cell fate, to effect thymocyte positive selection, and to achieve promiscuous gene expression required for central tolerance induction. Over time, the microenvironment created by miRNA-deficient thymic epithelia assumes the cellular composition and structure of peripheral lymphoid tissue, where thympoiesis fails to be supported. These findings emphasize a global role for miRNA in the maintenance and function of the thymic epithelial cell scaffold and establish a novel mechanism how these cells control peripheral tissue Ag expression to prompt central immunological tolerance.

GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics

Since its first release in 2007, GeneCodis has become a valuable tool to functionally interpret results from experimental techniques in genomics. This web-based application integrates different sources of information to finding groups of genes with similar biological meaning. This process, known as enrichment analysis, is essential in the interpretation of high-throughput experiments. The frequent feedbacks and the natural evolution of genomics and bioinformatics have allowed the growth of the tool and the development of this third release. In this version, a special effort has been made to remove noisy and redundant output from the enrichment results with the inclusion of a recently reported algorithm that summarizes significantly enriched terms and generates functionally coherent modules of genes and terms. A new comparative analysis has been added to allow the differential analysis of gene sets. To expand the scope of the application, new sources of biological information have been included, such as genetic diseases, drugs-genes interactions and Pubmed information among others. Finally, the graphic section has been renewed with the inclusion of new interactive graphics and filtering options. The application is freely available at http://genecodis.cnb.csic.es.

Quantification of miRNA-mRNA interactions

miRNAs are small RNA molecules (′ 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely. Recently, it has been proposed to combine expression measurement from both miRNA and mRNA and sequence based predictions to achieve more accurate relationships. In our work, we use LASSO regression with non-positive constraints to integrate both sources of information. LASSO enforces the sparseness of the solution and the non-positive constraints restrict the search of miRNA targets to those with down-regulation effects on the mRNA expression. We named this method TaLasso (miRNA-Target LASSO).

We used TaLasso on two public datasets that have paired expression levels of human miRNAs and mRNAs. The top ranked interactions recovered by TaLasso are especially enriched (more than using any other algorithm) in experimentally validated targets. The functions of the genes with mRNA transcripts in the top-ranked interactions are meaningful. This is not the case using other algorithms.

TaLasso is available as Matlab or R code. There is also a web-based tool for human miRNAs at http://talasso.cnb.csic.es/.

Assessment of NER solutions against the first and second CALBC Silver Standard Corpus

Background

Competitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions.

Results

All four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided pre-annotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I.

The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants’ solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and PRGE.

Conclusions

The SSC-I delivers a large set of annotations (1,121,705) for a large number of documents (100,000 Medline abstracts). The annotations cover four different semantic groups and are sufficiently homogeneous to be reproduced with a trained classifier leading to an average F-measure of 85%. Benchmarking the annotation solutions against the SSC-II leads to better performance for the CPs’ annotation solutions in comparison to the SSC-I.

Functional analysis beyond enrichment: non-redundant reciprocal linkage of genes and biological terms

Functional analysis of large sets of genes and proteins is becoming more and more necessary with the increase of experimental biomolecular data at omic-scale. Enrichment analysis is by far the most popular available methodology to derive functional implications of sets of cooperating genes. The problem with these techniques relies in the redundancy of resulting information, that in most cases generate lots of trivial results with high risk to mask the reality of key biological events. We present and describe a computational method, called GeneTerm Linker, that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. The algorithm is tested with a small set of well known interacting proteins from yeast and with a large collection of reference sets from three heterogeneous resources: multiprotein complexes (CORUM), cellular pathways (SGD) and human diseases (OMIM). Statistical Precision, Recall and balanced F-score are calculated showing robust results, even when different levels of random noise are included in the test sets. Although we could not find an equivalent method, we present a comparative analysis with a widely used method that combines enrichment and functional annotation clustering. A web application to use the method here proposed is provided at http://gtlinker.cnb.csic.es.

MARQ: an online tool to mine GEO for experiments with similar or opposite gene expression signatures

The enormous amount of data available in public gene expression repositories such as Gene Expression Omnibus (GEO) offers an inestimable resource to explore gene expression programs across several organisms and conditions. This information can be used to discover experiments that induce similar or opposite gene expression patterns to a given query, which in turn may lead to the discovery of new relationships among diseases, drugs or pathways, as well as the generation of new hypotheses. In this work, we present MARQ, a web-based application that allows researchers to compare a query set of genes, e.g. a set of over- and under-expressed genes, against a signature database built from GEO datasets for different organisms and platforms. MARQ offers an easy-to-use and integrated environment to mine GEO, in order to identify conditions that induce similar or opposite gene expression patterns to a given experimental condition. MARQ also includes additional functionalities for the exploration of the results, including a meta-analysis pipeline to find genes that are differentially expressed across different experiments. The application is freely available at http://marq.dacya.ucm.es.

Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling

Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein–protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.

Moara: a Java library for extracting and normalizing gene and protein mentions

Background

Gene/protein recognition and normalization are important preliminary steps for many biological text mining tasks, such as information retrieval, protein-protein interactions, and extraction of semantic information, among others. Despite dedication to these problems and effective solutions being reported, easily integrated tools to perform these tasks are not readily available.

Results

This study proposes a versatile and trainable Java library that implements gene/protein tagger and normalization steps based on machine learning approaches. The system has been trained for several model organisms and corpora but can be expanded to support new organisms and documents.

Conclusions

Moara is a flexible, trainable and open-source system that is not specifically orientated to any organism and therefore does not requires specific tuning in the algorithms or dictionaries utilized. Moara can be used as a stand-alone application or can be incorporated in the workflow of a more general text mining system.

GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biological information

GeneCodis is a web server application for functional analysis of gene lists that integrates different sources of information and finds modular patterns of interrelated annotations. This integrative approach has proved to be useful for the interpretation of high-throughput experiments and therefore a new version of the system has been developed to expand its functionality and scope. GeneCodis now expands the functional information with regulatory patterns and user-defined annotations, offering the possibility of integrating all sources of information in the same analysis. Traditional singular enrichment is now permitted and more organisms and gene identifiers have been added to the database. The application has been re-engineered to improve performance, accessibility and scalability. In addition, GeneCodis can now be accessed through a public SOAP web services interface, enabling users to perform analysis from their own scripts and workflows. The application is freely available at http://genecodis.dacya.ucm.es

SENT: semantic features in text

We present SENT (semantic features in text), a functional interpretation tool based on literature analysis. SENT uses Non-negative Matrix Factorization to identify topics in the scientific articles related to a collection of genes or their products, and use them to group and summarize these genes. In addition, the application allows users to rank and explore the articles that best relate to the topics found, helping put the analysis results into context. This approach is useful as an exploratory step in the workflow of interpreting and understanding experimental data, shedding some light into the complex underlying biological mechanisms. This tool provides a user-friendly interface via a web site, and a programmatic access via a SOAP web server. SENT is freely accessible at http://sent.dacya.ucm.es.

Comparison of molecular dynamics and superfamily spaces of protein domain deformation

BACKGROUND

It is well known the strong relationship between protein structure and flexibility, on one hand, and biological protein function, on the other hand. Technically, protein flexibility exploration is an essential task in many applications, such as protein structure prediction and modeling. In this contribution we have compared two different approaches to explore the flexibility space of protein domains: i) molecular dynamics (MD-space), and ii) the study of the structural changes within superfamily (SF-space).

RESULTS

Our analysis indicates that the MD-space and the SF-space display a significant overlap, but are still different enough to be considered as complementary. The SF-space space is wider but less complex than the MD-space, irrespective of the number of members in the superfamily. Also, the SF-space does not sample all possibilities offered by the MD-space, but often introduces very large changes along just a few deformation modes, whose number tend to a plateau as the number of related folds in the superfamily increases.

CONCLUSION

Theoretically, we obtained two conclusions. First, that function restricts the access to some flexibility patterns to evolution, as we observe that when a superfamily member changes to become another, the path does not completely overlap with the physical deformability. Second, that conformational changes from variation in a superfamily are larger and much simpler than those allowed by physical deformability. Methodologically, the conclusion is that both spaces studied are complementary, and have different size and complexity. We expect this fact to have application in fields as 3D-EM/X-ray hybrid models or ab initio protein folding.

CentrosomeDB: a human centrosomal proteins database

Active research on the biology of the centrosome during the past decades has allowed the identification and characterization of many centrosomal proteins. Unfortunately, the accumulated data is still dispersed among heterogeneous sources of information. Here we present centrosome:db, which intends to compile and integrate relevant information related to the human centrosome. We have compiled a set of 383 likely human centrosomal genes and recorded the associated supporting evidences. Centrosome:db offers several perspectives to study the human centrosome including evolution, function and structure. The database contains information on the orthology relationships with other species, including fungi, nematodes, arthropods, urochordates and vertebrates. Predictions of the domain organization of centrosome:db proteins are graphically represented at different sections of the database, including sets of alternative protein isoforms, interacting proteins, groups of orthologs and the homologs identified with blast. Centrosome:db also contains information related to function, gene–disease associations, SNPs and the 3D structure of proteins. Apart from important differences in the coverage of the set of centrosomal genes, our database differentiates from other similar initiatives in the way information is treated and analyzed. Centrosome:db is publicly available at http://centrosome.dacya.ucm.es.

Assessment of protein set coherence using functional annotations

Background

Analysis of large-scale experimental datasets frequently produces one or more sets of proteins that are subsequently mined for functional interpretation and validation. To this end, a number of computational methods have been devised that rely on the analysis of functional annotations. Although current methods provide valuable information (e.g. significantly enriched annotations, pairwise functional similarities), they do not specifically measure the degree of homogeneity of a protein set.

Results

In this work we present a method that scores the degree of functional homogeneity, or coherence, of a set of proteins on the basis of the global similarity of their functional annotations. The method uses statistical hypothesis testing to assess the significance of the set in the context of the functional space of a reference set. As such, it can be used as a first step in the validation of sets expected to be homogeneous prior to further functional interpretation.

Conclusion

We evaluate our method by analysing known biologically relevant sets as well as random ones. The known relevant sets comprise macromolecular complexes, cellular components and pathways described for Saccharomyces cerevisiae, which are mostly significantly coherent. Finally, we illustrate the usefulness of our approach for validating 'functional modules' obtained from computational analysis of protein-protein interaction networks. Matlab code and supplementary data are available at

bioNMF: a web-based tool for nonnegative matrix factorization in biology

In the last few years, advances in high-throughput technologies are generating large amounts of biological data that require analysis and interpretation. Nonnegative matrix factorization (NMF) has been established as a very effective method to reveal information about the complex latent relationships in experimental data sets. Using this method as part of the exploratory data analysis, workflow would certainly help in the process of interpreting and understanding the complex biology mechanisms that are underlying experimental data. We have developed bioNMF, a web-based tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications in biology. This online tool provides a user-friendly interface, combined with a computational efficient parallel implementation of the NMF methods to explore the data in different analysis scenarios. In addition to the online access, bioNMF also provides the same functionality included in the website as a public web services interface, enabling users with more computer expertise to launch jobs into bioNMF server from their own scripts and workflows. bioNMF application is freely available at http://bionmf.dacya.ucm.es.

ChIPCodis: mining complex regulatory systems in yeast by concurrent enrichment analysis of chip-on-chip data

MOTIVATION

Eukaryotic genes are often regulated by multiple transcription factors (TFs). Depending on the interactions among different TFs the expression of a gene can be tuned to respond to diverse environmental conditions. Chip-on-chip experiments provide a snapshot of which TF are in vivo bound to which genes in a particular condition, and have been applied to characterize the regulatory code of yeast under several experimental settings. ChIPCodis mines this data to provide new insights about how the expression of a particular group of genes is regulated. For a given list of yeast genes ChIPCodis determines which combinations of TFs are significantly over-represented in a series of environmental conditions.

AVAILABILITY

http://chipcodis.dacya.ucm.es

The cyclic AMP receptor protein modulates quorum sensing, motility and multiple genes that affect intestinal colonization in Vibrio cholerae

Vibrio cholerae is the causative agent of cholera, which continues to be a major public health concern in Asia, Africa and Latin America. The bacterium can persist outside the human host and alternates between planktonic and biofilm community lifestyles. Transition between the different lifestyles is mediated by multiple signal transduction pathways including quorum sensing. Expression of the Zn-metalloprotease haemagglutinin (HA)/protease is subject to a dual regulation which involves the quorum-sensing regulator HapR and the cAMP receptor protein. In a previous study, we observed that a mutant defective in the cAMP-receptor protein (CRP) expressed lower levels of HapR. To further investigate the role of CRP in modulating HapR and other signal transduction pathways, we performed global gene expression profiling of a Deltacrp mutant of El Tor biotype V. cholerae. Here we show that CRP is required for the biosynthesis of cholera autoinducer 1 (CAI-1) and affects the expression of multiple HapR-regulated genes. As expected, the Deltacrp mutant produced more cholera toxin and enhanced biofilm. Expression of flagellar genes, reported to be affected in DeltahapR mutants, was diminished in the Deltacrp mutant. However, an epistasis analysis indicated that cAMP-CRP affects motility by a mechanism independent of HapR. Inactivation of crp inhibited the expression of multiple genes reported to be strongly induced in vivo and to affect the ability of V. cholerae to colonize the small intestine and cause disease. These genes included ompU, ompT and ompW encoding outer-membrane proteins, the alternative sigma factor sigma(E) required for intestinal colonization, and genes involved in anaerobic energy metabolism. Our results indicate that CRP plays a crucial role in the V. cholerae life cycle by affecting quorum sensing and multiple genes required for survival of V. cholerae in the human host and the environment.

GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists

GENECODIS, a web-based tool for finding annotations that frequently co-occur in a set of genes and ranking them by their statistical significance, is presented.

We present GENECODIS, a web-based tool that integrates different sources of information to search for annotations that frequently co-occur in a set of genes and rank them by statistical significance. The analysis of concurrent annotations provides significant information for the biologic interpretation of high-throughput experiments and may outperform the results of standard methods for the functional analysis of gene lists. GENECODIS is publicly available at .

bioNMF: a versatile tool for non-negative matrix factorization in biology

Background

In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about the complex latent relationships in experimental data sets. This method, and some of its variants, has been successfully applied to gene expression, sequence analysis, functional characterization of genes and text mining. Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types of data analysis in an integrated environment.

Results

In this work we propose a versatile and user-friendly tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications of this new methodology. This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression. The tool, which is named bioNMF, also contains a user-friendly graphical interface to explore results in an interactive manner and facilitate in this way the exploratory data analysis process.

Conclusion

bioNMF is a standalone versatile application which does not require any special installation or libraries. It can be used for most of the multiple applications proposed in the bioinformatics field or to support new research using this method. This tool is publicly available at .

A literature-based similarity metric for biological processes

Background

Recent analyses in systems biology pursue the discovery of functional modules within the cell. Recognition of such modules requires the integrative analysis of genome-wide experimental data together with available functional schemes. In this line, methods to bridge the gap between the abstract definitions of cellular processes in current schemes and the interlinked nature of biological networks are required.

Results

This work explores the use of the scientific literature to establish potential relationships among cellular processes. To this end we haveused a document based similarity method to compute pair-wise similarities of the biological processes described in the Gene Ontology (GO). The method has been applied to the biological processes annotated for the Saccharomyces cerevisiae genome. We compared our results with similarities obtained with two ontology-based metrics, as well as with gene product annotation relationships. We show that the literature-based metric conserves most direct ontological relationships, while reveals biologically sounded similarities that are not obtained using ontology-based metrics and/or genome annotation.

Conclusion

The scientific literature is a valuable source of information from which to compute similarities among biological processes. The associations discovered by literature analysis are a valuable complement to those encoded in existing functional schemes, and those that arise by genome annotation. These similarities can be used to conveniently map the interlinked structure of cellular processes in a particular organism.

Nonsmooth nonnegative matrix factorization (nsNMF)

We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization" (nsNMF), corresponds to the optimization of an unambiguous cost function designed to explicitly represent sparseness, in the form of nonsmoothness, which is controlled by a single parameter. In general, this method produces a set of basis and encoding vectors that are not only capable of representing the original data, but they also extract highly localized patterns, which generally lend themselves to improved interpretability. The properties of this new method are illustrated with several data sets. Comparisons to previously published methods show that the new nsNMF method has some advantages in keeping faithfulness to the data in the achieving a high degree of sparseness for both the estimated basis and the encoding vectors and in better interpretability of the factors.

Biclustering of gene expression data by Non-smooth Non-negative Matrix Factorization

Background

The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states.

Results

In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions.

Conclusion

The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms.

Integrated analysis of gene expression by Association Rules Discovery

Background

Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process.

Results

In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work.

Conclusion

The integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Engene software package.

Discovering semantic features in the literature: a foundation for building functional associations

Background

Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.

Results

We present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes.

Conclusion

The presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data.

Host response to the attenuated poxvirus vector NYVAC: upregulation of apoptotic genes and NF-kappaB-responsive genes in infected HeLa cells

NYVAC has been engineered as a safe, attenuated vaccinia virus (VV) vector for use in vaccination against a broad spectrum of pathogens and tumors. Due to the interest in NYVAC-based vectors as vaccines and current phase I/II clinical trials with this vector, there is a need to analyze the human host response to NYVAC infection. Using high-density cDNA microarrays, we found 368 differentially regulated genes after NYVAC infection of HeLa cells. Clustering of the regulated genes identified six discrete gene clusters with altered expression patterns. Clusters 1 to 3 represented 47.5% of the regulated genes, with three patterns of gene activation kinetics, whereas clusters 4 to 6 showed distinct repression kinetics. Quantitative real-time reverse transcription-PCR analysis of selected genes validated the array data. Upregulated transcripts correlated with genes implicated in immune responses, including those encoding interleukin-1 receptor 2 (IL-1R2), IL-6, ISG-15, CD-80, and TNFSF7. NYVAC upregulated several intermediates of apoptotic cascades, including caspase-9, correlating with its ability to induce apoptosis. NYVAC infection also stimulated the expression of NF-kappaB1 and NF-kappaB2 as well as that of NF-kappaB target genes. Expression of the VV host range K1L gene during NYVAC infection prevented NF-kappaB activation, but not the induction of apoptosis. This study is the first overall analysis of the transcriptional response of human cells to NYVAC infection and provides a framework for future functional studies to evaluate this vector and its derivatives as human vaccines.

XMIPP: a new generation of an open-source image processing package for electron microscopy

X-windows based microscopy image processing package (Xmipp) is a specialized suit of image processing programs, primarily aimed at obtaining the 3D reconstruction of biological specimens from large sets of projection images acquired by transmission electron microscopy. This public-domain software package was introduced to the electron microscopy field eight years ago, and since then it has changed drastically. New methodologies for the analysis of single-particle projection images have been added to classification, contrast transfer function correction, angular assignment, 3D reconstruction, reconstruction of crystals, etc. In addition, the package has been extended with functionalities for 2D crystal and electron tomography data. Furthermore, its current implementation in C++, with a highly modular design of well-documented data structures and functions, offers a convenient environment for the development of novel algorithms. In this paper, we present a general overview of a new generation of Xmipp that has been re-engineered to maximize flexibility and modularity, potentially facilitating its integration in future standardization efforts in the field. Moreover, by focusing on those developments that distinguish Xmipp from other packages available, we illustrate its added value to the electron microscopy community.

Microarray analysis reveals characteristic changes of host cell gene expression in response to attenuated modified vaccinia virus Ankara infection of human HeLa cells

The potential use of the modified vaccinia virus Ankara (MVA) strain as a live recombinant vector to deliver antigens and elicit protective immune responses against infectious diseases demands a comprehensive understanding of the effect of MVA infection on human host gene expression. We used microarrays containing more than 15,000 human cDNAs to identify gene expression changes in human HeLa cell cultures at 2, 6, and 16 h postinfection. Clustering of the 410 differentially regulated genes identified 11 discrete gene clusters with altered expression patterns after MVA infection. Clusters 1 and 2 (accounting for 16.59% [68 of 410] of the genes) contained 68 transcripts showing a robust induction pattern that was maintained during the course of infection. Changes in cellular gene transcription detected by microarrays after MVA infection were confirmed for selected genes by Northern blot analysis and by real-time reverse transcription-PCR. Upregulated transcripts in clusters 1 and 2 included 20 genes implicated in immune responses, including interleukin 1A (IL-1A), IL-6, IL-7, IL-8, and IL-15 genes. MVA infection also stimulated the expression of NF-kappaB and components of the NF-kappaB signal transduction pathway, including p50 and TRAF-interacting protein. A marked increase in the expression of histone family members was also induced during MVA infection. Expression of the Wiskott-Aldrich syndrome family members WAS, WASF1, and the small GTP-binding protein RAC-1, which are involved in actin cytoskeleton reorganization, was enhanced after MVA infection. This study demonstrates that MVA infection triggered the induction of groups of genes, some of which may be involved in host resistance and immune modulation during virus infection.

Cellular gene expression survey of vaccinia virus infection of human HeLa cells

Vaccinia virus (VV) is a cytocidal virus that causes major changes in host cell machinery shortly after infecting cells. To define the consequences of virus infection on host gene expression, we used microarrays of approximately 15,000 human cDNAs to examine expression levels of mRNAs isolated at 2, 6, and 16 h postinfection from cultures of infected HeLa cells. The majority of profiling changes during VV infection corresponded to downregulation of genes at 16 h postinfection. Differentially expressed genes were clustered into seven groups to identify common regulatory pathways, with most of them (90%) belonging to clusters 6 and 7, which represent genes whose expression was repressed after infection. Cluster 1, however, contained 37 transcripts (2.81%) showing a robust pattern of induction that was maintained during the course of infection. Genes in cluster 1 included those for Wiskott-Aldrich syndrome protein (WASP) family member WASF1, thymosine, adenosine A2a receptor, glutamate decarboxylase 2, CD-80 antigen, KIAA0888 protein, selenophosphate synthetase, pericentrin, and attractin as well as several expressed sequence tags. We analyzed in more detail the fate of WASP protein in VV-infected cells, because a related family member, N-WASP, is involved in viral motility. WASP protein accumulated in the course of infection; its increase required viral DNA replication and de novo protein synthesis, and it localized in cytoplasmic structures distinct from uninfected cells. This study is the first quantitative analysis of host gene expression following VV infection of cultured human cells, demonstrating global changes in the expression profile, and identifies upregulated genes with potential roles in the virus replication cycle.

Engene: the processing and exploratory analysis of gene expression data

Engene is a versatile, and platform-independent web tool for exploratory analysis of gene expression data that aims at storing, visualizing and processing large sets of gene expression patterns.

Modeling shape and topology of low-resolution density maps of biological macromolecules

In the present work we develop an efficient way of representing the geometry and topology of volumetric datasets of biological structures from medium to low resolution, aiming at storing and querying them in a database framework. We make use of a new vector quantization algorithm to select the points within the macromolecule that best approximate the probability density function of the original volume data. Connectivity among points is obtained with the use of the alpha shapes theory. This novel data representation has a number of interesting characteristics, such as 1) it allows us to automatically segment and quantify a number of important structural features from low-resolution maps, such as cavities and channels, opening the possibility of querying large collections of maps on the basis of these quantitative structural features; 2) it provides a compact representation in terms of size; 3) it contains a subset of three-dimensional points that optimally quantify the densities of medium resolution data; and 4) a general model of the geometry and topology of the macromolecule (as opposite to a spatially unrelated bunch of voxels) is easily obtained by the use of the alpha shapes theory.

Quantitative self-organizing maps for clustering electron tomograms

Tomography emerges as a powerful methodology for determining the complex architectures of biological specimens that are better regarded from the structural point of view as singular entities. However, once the structure of a sufficiently large number of singular specimens is solved, quite possibly structural patterns start to emerge. This latter situation is addressed here, where the clustering of a set of 3D reconstructions using a novel quantitative approach is presented. In general terms, we propose a new variant of a self-organizing neural network for the unsupervised classification of 3D reconstructions. The novelty of the algorithm lies in its rigorous mathematical formulation that, starting from a large set of noisy input data, finds a set of "representative" items, organized onto an ordered output map, such that the probability density of this set of representative items resembles at its possible best the probability density of the input data. In this study, we evaluate the feasibility of application of the proposed neural approach to the problem of identifying similar 3D motifs within tomograms of insect flight muscle. Our experimental results prove that this technique is suitable for this type of problem, providing the electron microscopy community with a new tool for exploring large sets of tomogram data to find complex patterns.

A novel neural network technique for analysis and classification of EM single-particle images

We propose a novel self-organizing neural network for the unsupervised classification of electron microscopy (EM) images of biological macromolecules. The radical novelty of the algorithm lies in its rigorous mathematical formulation that, starting from a large set of possibly very noisy input data, finds a set of "representative" data items, organized onto an ordered output map, such that the probability density of this set of representative items resembles at its possible best the probability density of the input data. In a way, it summarizes large amounts of information into a concise description that rigorously keeps the basic pattern of the input data distribution. In this application to the field of three-dimensional EM of single particles, two different data sets have been used; one comprised 2458 rotational power spectra of individual negative stain images of the G40P helicase of Bacillus subtilis bacteriophage SPP1, and the other contained 2822 cryoelectron images of SV40 large T-antigen. Our experimental results prove that this technique is indeed very successful, providing the user with the capability of exploring complex patterns in a succinct, informative, and objective manner. The above facts, together with the consideration that the integration of this new algorithm with commonly used software packages is immediate, prompt us to propose it as a valuable new tool in the analysis of large collections of noisy data.