BBMS + + – basic bioinformatics meta-searcher

In this paper we present a Basic Bioinformatics Meta-searcher (BBMS), a web-based service aiming to simplify and integrate biological data searching through selected biological databases. BBMS facilitates biological data searching enabling multiple sources transparently, increasing research productivity as it avoids time consuming learning and parameterization of different search engines. As a complementary service, BBMS provides insight and links to common online bioinformatics tools. Users’ feedback when evaluating BBMS in terms of usability, usefulness and efficiency was very positive

‘Sciencenet’—towards a global search and share engine for all scientific knowledge

Summary: Modern biological experiments create vast amounts of data which are geographically distributed. These datasets consist of petabytes of raw data and billions of documents. Yet to the best of our knowledge, a search engine technology that searches and cross-links all different data types in life sciences does not exist

Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutaminemediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington’s Disease (HD) model.National Institutes of Health (U.S.) (Grant

An incremental approach to automated protein localisation

Tscherepanow M, Jensen N, Kummert F. An incremental approach to automated protein localisation. BMC Bioinformatics. 2008;9(1): 445.Background: The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches – i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards – has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected. Results: We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided. Conclusion: We have shown that several aspects required for developing an automatic protein localisation technique – namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations – can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments

Genetic variability of populations of Nyssomyia neivai in the Northern State of Paraná, Brazil

ABSTRACT The genetic study of sandfly populations needs to be further explored given the importance of these insects for public health. Were sequenced the NDH4 mitochondrial gene from populations of Nyssomyia neivai from Doutor Camargo, Lobato, Japira, and Porto Rico, municipalities in the State of Paraná, Brazil, to understand the genetic structure and gene flow. Eighty specimens of Ny. Neivai were sequenced, 20 from each municipality, and 269 base pairs were obtained. A total of 27 haplotypes and 28 polymorphic sites were found, along with a haplotypic diversity of 0.80696 and a nucleotide diversity of 0.00567. Haplotype H5, with 33 specimens, was the most common among the four populations. Only haplotypes H5 and H7 were present in all four populations. The population from Doutor Camargo showed the highest genetic diversity, and only this population shared haplotypes with those from the other municipalities. The highest number of haplotypes was sheared with Lobato which also had the highest number of unique haplotypes. This probably occurred because of constant anthropic changes that happened in the environment during the first half of the twentieth century, mainly after 1998. There was no significant correlation between genetic and geographical distances regarding these populations. However, the highest genetic and geographical distances, and the lowest gene flow were observed between Japira and Porto Rico. Geographical distance is a possible barrier between these municipalities through the blocking of haplotype sharing

Imaging the boundaries—innovative tools for microscopy of living cells and real-time imaging

Recently, light microscopy moved back into the spotlight, which is mainly due to the development of revolutionary technologies for imaging real-time events in living cells. It is truly fascinating to see enzymes “at work” and optically acquired images certainly help us to understand biological processes better than any abstract measurements. This review aims to point out elegant examples of recent cell-biological imaging applications that have been developed with a chemical approach. The discussed technologies include nanoscale fluorescence microscopy, imaging of model membranes, automated high-throughput microscopy control and analysis, and fluorescent probes with a special focus on visualizing enzyme activity, free radicals, and protein–protein interaction designed for use in living cells