TRUNCATULIX – a data warehouse for the legume community

Background  

Databases for either sequence, annotation, or microarray experiments data are extremely beneficial to the research community, as they centrally gather information from experiments performed by different scientists. However, data from different sources develop their full capacities only when combined. The idea of a data warehouse directly adresses this problem and solves it by integrating all required data into one single database – hence there are already many data warehouses available to genetics. For the model legume Medicago truncatula, there is currently no such single data warehouse that integrates all freely available gene sequences, the corresponding gene expression data, and annotation information. Thus, we created the data warehouse TRUNCATULIX, an integrative database of Medicago truncatula sequence and expression data.     

2DDB – a bioinformatics solution for analysis of quantitative proteomics data

Background  

We present 2DDB, a bioinformatics solution for storage, integration and analysis of quantitative proteomics data. As the data complexity and the rate with which it is produced increases in the proteomics field, the need for flexible analysis software increases.     

Squid – a simple bioinformatics grid

Background  

BLAST is a widely used genetic research tool for analysis of similarity between nucleotide and protein sequences. This paper presents a software application entitled "Squid" that makes use of grid technology. The current version, as an example, is configured for BLAST applications, but adaptation for other computing intensive repetitive tasks can be easily accomplished in the open source version. This enables the allocation of remote resources to perform distributed computing, making large BLAST queries viable without the need of high-end computers.     

LC-MSsim – a simulation software for liquid chromatography mass spectrometry data

Background  

Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms.     

Applying bioinformatics for antibody epitope prediction using affinity-selected mimotopes – relevance for vaccine design

To properly characterize protective polyclonal antibody responses, it is necessary to examine epitope specificity. Most antibody epitopes are conformational in nature and, thus, cannot be identified using synthetic linear peptides. Cyclic peptides can function as mimetics of conformational epitopes (termed mimotopes), thereby providing targets, which can be selected by immunoaffinity purification. However, the management of large collections of random cyclic peptides is cumbersome. Filamentous bacteriophage provides a useful scaffold for the expression of random peptides (termed phage display) facilitating both the production and manipulation of complex peptide libraries. Immunoaffinity selection of phage displaying random cyclic peptides is an effective strategy for isolating mimotopes with specificity for a given antiserum. Further epitope prediction based on mimotope sequence is not trivial since mimotopes generally display only small homologies with the target protein. Large numbers of unique mimotopes are required to provide sufficient sequence coverage to elucidate the target epitope. We have developed a method based on pattern recognition theory to deal with the complexity of large collections of conformational mimotopes. The analysis consists of two phases: 1) The learning phase where a large collection of epitope-specific mimotopes is analyzed to identify epitope specific "signs" and 2) The identification phase where immunoaffinity-selected mimotopes are interrogated for the presence of the epitope specific "signs" and assigned to specific epitopes. We are currently using computational methods to define epitope "signs" without the need for prior knowledge of specific mimotopes. This technology provides an important tool for characterizing the breadth of antibody specificities within polyclonal antisera.     

NeuroTerrain – a client-server system for browsing 3D biomedical image data sets

Background  

Three dimensional biomedical image sets are becoming ubiquitous, along with the canonical atlases providing the necessary spatial context for analysis. To make full use of these 3D image sets, one must be able to present views for 2D display, either surface renderings or 2D cross-sections through the data. Typical display software is limited to presentations along one of the three orthogonal anatomical axes (coronal, horizontal, or sagittal). However, data sets precisely oriented along the major axes are rare. To make fullest use of these datasets, one must reasonably match the atlas' orientation; this involves resampling the atlas in planes matched to the data set. Traditionally, this requires the atlas and browser reside on the user's desktop; unfortunately, in addition to being monolithic programs, these tools often require substantial local resources. In this article, we describe a network-capable, client-server framework to slice and visualize 3D atlases at off-axis angles, along with an open client architecture and development kit to support integration into complex data analysis environments.     

stam – a Bioconductor compliant R package for structured analysis of microarray data

Background  

Genome wide microarray studies have the potential to unveil novel disease entities. Clinically homogeneous groups of patients can have diverse gene expression profiles. The definition of novel subclasses based on gene expression is a difficult problem not addressed systematically by currently available software tools.     

CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data

Background  

For the last eight years, microarray-based classification has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the so-called "p ≫ n" setting where the number of predictors p by far exceeds the number of observations n, hence the term "ill-posed-problem". Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for statisticians without experience in this area or for scientists with limited statistical background. The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers.     

Is DIA proteomics data FAIR? Current data sharing practices,available bioinformatics infrastructure and recommendations for the future

Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements.     

Can we integrate bioinformatics data on the Internet?

The NETTAB (Network Tools and Applications in Biology) 2001 Workshop entitled 'CORBA and XML: towards a bioinformatics-integrated network environment' was held at the Advanced Biotechnology Centre, Genoa, Italy, 17-18 May 2001.     

Data – ova – gene – data

In this essay, I observe that data is valuable not only for what it is, but also for what it will become: that is, that data is a form of potential. I explore two aspects of this by drawing two comparisons with other forms of potential: ova and genes. First, building on ethnographic fieldwork with environmental scientists and technicians in the Brazilian Amazon, I compare data processing with ova donation in the United Kingdom in order to explore how data processing might be considered a form of reproductive labour. I then turn to emergent big data infrastructures in the environmental sciences, and compare the environmental sciences with genomics, in order to gesture towards some critical questions that need to be asked of such open data initiatives. I end with a reflection on comparison as a privileged means of drawing out the forms understood to be latent within data.     

Habitat suitability for wisents in the Carpathians – a model based on presence only data

The study tested an alternative method for the evaluation of habitat preferences of wisents (Bison bonasus) based upon possibly non-biased dataset – presence only data. Data on spatial distribution of free-ranging wisents (3055 individual presence records) were collected over a period of 10 years in the Bieszczady Mountains, south-eastern Poland. Using a nonparametric method of kernel estimators, the density of animal occurrence was calculated. The obtained dot map distribution allowed for spatial analyses of seasonal (winter and vegetative) presence that is related to environmental parameters such as the type of forest stand, dominating tree species, terrain and aspect. According to the final log-linear model, based upon presence-only data, significant differences exist in wisent seasonal preferences for habitat type. Wisents were more commonly found, in both winter and vegetative seasons, on sites with broken canopy closure (85.71% and 86.44%, respectively), as well as on slopes with steepness of 13-17^o, with rate of presence of 89.83% and 91.84%, respectively. The most frequented tree association was alnus/pine irrespective of season. During both seasons, the percentage of records of wisent presence was the highest within sites of north aspect (93.22% in winter and 93.88% in vegetative), and on sloping terrain (94.92% and 93.88%, respectively), rather than within valleys or areas of relative flatness. The obtained results allow identification of conditions optimal for those animals unbiased by subjective evaluation of habitat conditions. This approach can be crucial in selecting sites potentially suitable for future reintroductions of wisents in the whole Carpathian range.     

TableButler – a Windows based tool for processing large data tables generated with high-throughput methods

Background  

High-throughput "omics" based data analysis play emerging roles in life sciences and molecular diagnostics. This emphasizes the urgent need for user-friendly windows-based software interfaces that could process the diversity of large tab-delimited raw data files generated by these methods. Depending on the study, dozens to hundreds of these data tables are generated. Before the actual statistical or cluster analysis, these data tables have to be combined and merged to expression matrices (e.g., in case of gene expression analysis). Gene annotations as well as information concerning the samples analyzed may be appended, renewed or extended. Often additional data values shall be computed or certain features must be filtered out.     

tacg – a grep for DNA

Background  

Pattern matching is the core of bioinformatics; it is used in database searching, restriction enzyme mapping, and finding open reading frames. It is done repeatedly over increasingly long sequences, thus codes must be efficient and insensitive to sequence length. Such patterns of interest include simple motifs with IUPAC degeneracies, regular expressions, patterns allowing mismatches, and probability matrices.     

CGHPRO – A comprehensive data analysis tool for array CGH

Background  

Array CGH (Comparative Genomic Hybridisation) is a molecular cytogenetic technique for the genome wide detection of chromosomal imbalances. It is based on the co-hybridisation of differentially labelled test and reference DNA onto arrays of genomic BAC clones, cDNAs or oligonucleotides, and after correction for various intervening variables, loss or gain in the test DNA can be indicated from spots showing aberrant signal intensity ratios.     

ACE for all – a molecular perspective

Angiotensin-I converting enzyme (ACE, EC 3.4.15.1) is a zinc dependent dipeptidyl carboxypeptidase with an essential role in mammalian blood pressure regulation as part of the renin-angiotensin aldosterone system (RAAS). As such, it has long been targeted in the treatment of hypertension through the use of ACE inhibitors. Although ACE has been studied since the 1950s, only recently have the full range of functions of this enzyme begun to truly be appreciated. ACE homologues have been found in a host of other organisms, and are now known to be conserved in insects. Insect ACE homologues typically share over 30 % amino acid sequence identity with human ACE. Given that insects lack a mammalian type circulatory system, they must have crucial roles in other physiological processes. The first ACE crystal structures were reported during the last decade and have enabled these enzymes to be studied from an entirely different perspective. Here we review many of these key developments and the implications that they have had on our understanding of the diverse functions of these enzymes. Specifically, we consider how structural information is being used in the design of a new generation of ACE inhibitors with increased specificity, and how the structures of ACE homologues are related to their functions. The Anopheles gambiae genome is predicted to code for ten ACE homologues, more than any genome studied so far. We have modelled the active sites of some of these as yet uncharacterised enzymes to try and infer more about their potential roles at the molecular level.