Description: Motivation: The research area metabolomics achieved tremendous popularity and development in the last couple of years. Owing to its unique interdisciplinarity, it requires to combine knowledge from various scientific disciplines. Advances in the high-throughput technology and the consequently growing quality and quantity of data put new demands on applied analytical and computational methods. Exploration of finally generated and analyzed datasets furthermore relies on powerful tools for data mining and visualization. Results: To cover and keep up with these requirements, we have created MeltDB 2.0, a next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions. Availability: The system is publicly available at https://meltdb.cebitec.uni-bielefeld.de . A login is required but freely available. Contact: nkessler@cebitec.uni-bielefeld.de

Description: Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N -dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application. Results: We applied WHIDE to a set of MBI recorded using the multitag fluorescence imaging Toponome Imaging System. The MBI show field of view in tissue sections from a colon cancer study and we compare tissue from normal/healthy colon with tissue classified as tumor. Our results show, that WHIDE efficiently reduces the complexity of the data by mapping each of the pixels to a cluster, referred to as Molecular Co-Expression Phenotypes and provides a structural basis for a sophisticated multimodal visualization, which combines topology preserving pseudocoloring with information visualization. The wide range of WHIDE's applicability is demonstrated with examples from toponome imaging, high content screens and MALDI imaging (shown in the Supplementary Material ). Availability and implementation: The WHIDE tool can be accessed via the BioIMAX website http://ani.cebitec.uni-bielefeld.de/BioIMAX/ ; Login: whidetestuser; Password: whidetest . Supplementary information: Supplementary data are available at Bioinformatics online. Contact: tim.nattkemper@uni-bielefeld.de

Description: Use of automated image analysis to detect changes in megafaunal densities at HAUSGARTEN (79°N west off Svalbard) between 2002 and 2004High latitudes are amongst the most sensitive ecosystems with respect to climate change, which prompted the launch of the first and only deep-sea long-term observatory beyond the polar circle, HAUSGARTEN (eastern Fram Strait), in 1999. An understanding of the abundance and spatial distribution of organisms is vital to assess the effects of global change. To map the distribution of megafaunal organisms deep-sea research relies strongly on the use of high-resolution cameras that are fitted to towed sledges, drop-down frames and remotely operated or autonomous underwater vehicles. Inevitably, such techniques generate large quantities of footage. The visual analysis of images is labour-intensive, time-consuming and subjective. Over recent years, the increase in computer power has facilitated advances to automate image analysis. Here, we present a novel approach for the automatic detection and classification of particular biological classes within image data. For this purpose, we applied machine-learning algorithms to two photographic transects from the HAUSGARTEN central station (2500m) taken by an ocean floor observation system in 2002 and 2004. Each transect contains some 700 photographs. The main goal is the automatic identification of important biological classes (e.g. sea cucumbers, sea lilies) to assess the densities of the most frequent organisms over time. So far, our system shows a promising performance in detecting sea cucumbers and sea lilies with sensitivities and positive predictive values between 75 - 80%.Results from manual analysis of 66 images taken at the central part of the transect indicate a significant decline in the mean density of sea cucumbers (Elpidia glacialis), sea lilies (Bathycrinus cf. carpenteri), burrow entrances and total megafaunal densities from 2002 to 2004 which concurs with a decrease in sea ice coverage, particulate flux to the sea floor, sediment-bound nutrients and pigments, microbial biomass and changes in meiofaunal community structure. Results from automated image analysis will increase the spatial resolution and statistical power of our analyses as it enables us to process larger quantities of images.

Description: Deep seafloor communities, especially those from the ice-covered Arctic, are subject to severe food limitation as the amount of particulate organic matter (POM) from the surface is attenuated with increasing depth. Here, we use naturally occurring stable isotope tracers (δ15N) to broaden our rudimentary knowledge of food web structure and the response of benthic organisms to decreasing food supplies along the bathymetric transect of the deep-sea observatory HAUSGARTEN. Encompassing five trophic levels, the HAUSGARTEN food web is among the longest indicating continuous recycling of organic material typical of food-limited deep-sea ecosystems. The δ15N signatures ranged from 3.0 for Foraminifera to 21.4 (±0.4) for starfish (Poraniomorpha tumida). The majority of organisms occupied the second and third trophic level. Demersal fish fed at the third trophic level, consistent with results from stomach contents analysis. There were significant differences in the δ15N signatures of different functional groups with highest δ15N values in predators/scavengers (13.2 ± 0.2) followed by suspension feeders (11.2 ± 0.2) and deposit feeders (10.2 ± 0.3). Depth (=increasing food limitation) affected functional groups in different ways. While the isotopic signatures of predators/ scavengers did not change, those of suspension feeders increased with depth, and the reverse was found for deposit feeders. In contrast to the results of other studies, the δ15N signatures in POM samples obtained below 800 m did not vary significantly with depths indicating that changes in δ15N values could not be held responsible for the depth-related δ15N signature changes observed for benthic consumers. However, the δ15N signatures of sediments decreased with increasing depth, which also explains the decrease found for deposit feeders. Suspension feeders may rely increasingly on particles trickling down the HAUSGARTEN slope and carrying higher δ15N signatures than the decreasing POM supplies, which elevates the δ15N value of their tissues. Our results imply that a depth-stratified approach should be taken to avoid a misinterpretation of data obtained at different depths.

Description: Far-sighted marine research institutions around the globe are capturing images from the seafloor at a scale of hundreds of thousands. Only a small part of these data have been accessed to date, as manual analyses are time-consuming and automated evaluation approaches are still under development. Machine learning and neural networks have been identified as a promising algorithmic approach to automate analysis of images from the seafloor. These algorithms need ground-truth data about the objects to be detected. As the information provided by one human expert lacks reproducibility, the expertise of a group of individuals has to be employed to collect training data as well as to evaluate the performance of an automated detection. In this paper we show that the inter-and intra-observer agreements of these human experts is a critical factor for the training of a learning architecture and has shown to be conditional to image quality for some object classes. A supervised automated detection approach is evaluated where five experts marked the positions of eight distinct object classes within seventy images taken at the HAUSGARTEN observatory (eastern Fram Strait, Arctic). Support Vector Machines were trained to detect and classify objects in the images with an overall sensitivity of 0.87 and precision of 0.67. A detailed comparison of the human expert agreements showed interesting correlations with the system's performance and pointed us towards new strategies for (semi-) automated underwater image analysis.