The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user- and context-dependent visual exploration systems.

/pdf/feedback-driven-interactive-exploration-of-large-1yxxrlgcz0.pdf

Feedback-driven interactive exploration of large multidimensional data supported by visual classifier

BLOSUM matrices belong to the most commonly used substitution matrix series for protein homology search and sequence alignments since their publication in 1992. In 2008, Styczynski et al. discovered miscalculations in the clustering step of the matrix computation. Still, the RBLOSUM64 matrix based on the corrected BLOSUM code was reported to perform worse at a statistically significant level than the BLOSUM62. Here, we present a further correction of the (R)BLOSUM code and provide a thorough performance analysis of BLOSUM-, RBLOSUM- and the newly derived CorBLOSUM-type matrices. Thereby, we assess homology search performance of these matrix-types derived from three different BLOCKS databases on all versions of the ASTRAL20, ASTRAL40 and ASTRAL70 subsets resulting in 51 different benchmarks in total. Our analysis is focused on two of the most popular BLOSUM matrices — BLOSUM50 and BLOSUM62. Our study shows that fixing small errors in the BLOSUM code results in substantially different substitution matrices with a beneficial influence on homology search performance when compared to the original matrices. The CorBLOSUM matrices introduced here performed at least as good as their BLOSUM counterparts in ∼75 % of all test cases. On up-to-date ASTRAL databases BLOSUM matrices were even outperformed by CorBLOSUM matrices in more than 86 % of the times. In contrast to the study by Styczynski et al., the tested RBLOSUM matrices also outperformed the corresponding BLOSUM matrices in most of the cases. Comparing the CorBLOSUM with the RBLOSUM matrices revealed no general performance advantages for either on older ASTRAL releases. On up-to-date ASTRAL databases however CorBLOSUM matrices performed better than their RBLOSUM counterparts in ∼74 % of the test cases. Our results imply that CorBLOSUM type matrices outperform the BLOSUM matrices on a statistically significant level in most of the cases, especially on up-to-date databases such as ASTRAL ≥2.01. Additionally, CorBLOSUM matrices are closer to those originally intended by Henikoff and Henikoff on a conceptual level. Hence, we encourage the usage of CorBLOSUM over (R)BLOSUM matrices for the task of homology search.

/pdf/addressing-inaccuracies-in-blosum-computation-improves-1xci0jfhv3.pdf

Addressing inaccuracies in BLOSUM computation improves homology search performance

The rising quantity and complexity of data creates a need to design and optimize data processing pipelines—the set of data processing steps, parameters and algorithms that perform operations on the data. Visualization can support this process but, although there are many examples of systems for visual parameter analysis, there remains a need to systematically assess users’ requirements and match those requirements to exemplar visualization methods. This article presents a new characterization of the requirements for pipeline design and optimization. This characterization is based on both a review of the literature and first-hand assessment of eight application case studies. We also match these requirements with exemplar functionality provided by existing visualization tools. Thus, we provide end-users and visualization developers with a way of identifying functionality that addresses data processing problems in an application. We also identify seven future challenges for visualization research that are not met by the capabilities of today's systems.

/pdf/visualization-system-requirements-for-data-processing-3tb7nl9o4l.pdf

Visualization System Requirements for Data Processing Pipeline Design and Optimization

The analysis of complex systems frequently poses the challenge to distinguish correlation from causation. Statistical physics has inspired very promising approaches to search for correlations in time series; the transfer entropy in particular (Hlavackova-Schindler et al., 2007). Now, methods from computational statistics can quantitatively assign significance to such correlation measures. In this study, we propose and apply a procedure to statistically assess transfer entropies by one-sided tests. We introduce to null models of vanishing correlations for time series with memory. We implemented them in an OpenMP-based, parallelized C++ package for multi-core CPUs. Using template meta-programming, we enable a compromise between memory and run time efficiency.

/pdf/efficient-computation-and-statistical-assessment-of-transfer-44e7080pym.pdf

Efficient computation and statistical assessment of transfer entropy

Estimating phylogenetic trees is an important problem in evolutionary biology, environmental policy, and medicine. Although trees are estimated, their uncertainties are generally discarded in stati...

Uncertainty in Phylogenetic Tree Estimates

Evolutionary relationships between organisms are frequently derived as phylogenetic trees inferred from multiple sequence alignments (MSAs). The MSA parameter space is exponentially large, so tens of thousands of potential trees can emerge for each dataset. A proposed visual-analytics approach can reveal the parameters' impact on the trees. Given input trees created with different parameter settings, it hierarchically clusters the trees according to their structural similarity. The most important clusters of similar trees are shown together with their parameters. This view offers interactive parameter exploration and automatic identification of relevant parameters. Biologists applied this approach to real data of 16S ribosomal RNA and protein sequences of ion channels. It revealed which parameters affected the tree structures. This led to a more reliable selection of the best trees.

Visual Exploration of Parameter Influence on Phylogenetic Trees

We present a massively-parallel implementation of the computation of (co)evolutionary signals from biomolecular sequence alignments based on mutual information (MI) and a normalization procedure to neutral evolution. The MI is computed for two- and three-point correlations within any multiple-sequence alignment. The high computational demand in the normalization procedure is efficiently met by an implementation on Graphics Processing Units (GPUs) using NVIDIA's CUDA framework. GPU computation serves as an enabling technology here insofar as MI normalization is also possible using traditional computational methods but only GPU computation makes MI normalization for sequence analysis feasible in a statistically sufficient sample and in acceptable time. In particular, the normalization of the MI for three-point 'cliques' of amino acids or nucleotides requires large sampling numbers in the normalization, that can only be achieved using GPUs.We illustrate a) the computational efficiency and b) the biological usefulness of two- and three-point MI by an application to the well-known protein calmodulin. Here, we find striking coevolutionary patterns and distinct information on the molecular evolution of this molecule.

https://www.gcc.tu-darmstadt.de/media/gcc/papers/Waechter-2012-ITA.pdf

Information-theoretic analysis of molecular (co)evolution using graphics processing units

We present a massively parallel implementation of the computation of coevolutionary signals from biomolecular sequence alignments based on mutual information MI and a normalization procedure to neutral evolution. The MI is computed for two-point and three-point correlations within any multiple sequence alignment. We meet the high computational demand in the normalization procedure efficiently with an implementation on Graphics Processing Units GPUs using NVIDIA's CUDA framework. In particular, the normalization of the MI for three-point 'cliques' of amino acids or nucleotides requires large sampling numbers in the normalization, which we achieve by using GPUs. GPU computation serves as an enabling technology here insofar as MI normalization is also possible using traditional computational methods [1] or cluster computation, but only GPU computation makes MI normalization for sequence analysis feasible in a statistically sufficient sample and in acceptable time given affordable commodity hardware. We illustrate i the computational efficiency and ii the biological usefulness of two-point and three-point MI by applications to the well-known protein calmodulin and the variable surface glycoprotein VSG of Trypanosoma brucei, which are subject to involved evolutionary pressure. Here, we find striking coevolutionary patterns and distinct information on the molecular evolution of these molecules that question previous work that relied on non-efficient MI computations. Copyright © 2013 John Wiley & Sons, Ltd.

Stephanie Weissgraeber

Papers

Visual Exploration of Parameter Influence on Phylogenetic Trees

Information-theoretic analysis of molecular (co)evolution using graphics processing units

Using graphics processing units to investigate molecular coevolution