Search results

Whereas alignment has been fundamental to sequence-based assessments of protein homology, it is ineffective for intrinsically disordered regions (IDRs) due to their lowered sequence conservation and unique sequence properties. Here, we present a web server implementation of SHARK (bio-shark.org), an alignment-free algorithm for homology classification that compares the overall amino acid composition and short regions (k-mers) shared between sequences (SHARK-scores). The output of such k-mer-based comparisons is used by SHARK-dive, a machine learning classifier to detect homology between unalignable, disordered sequences. SHARK-web provides sequence-versus-database assessment of protein sequence homology akin to conventional tools such as BLAST and HMMER. Additionally, we provide precomputed sets of IDR sequences from 16 model organism proteomes facilitating searches against species-specific IDR-omes. SHARK-dive offers superior overall homology detection performance to BLAST and HMMER, driven by a large increase in sensitivity to low sequence identity homologs, and can be used to facilitate the study of sequence-function relationships in disordered, difficult-to-align regions.

We present an efficient raycasting algorithm for rendering Volumetric Depth Images (VDIs), and we show how it can be used in a remote visualization setting with VDIs generated and streamed from a remote server. VDIs are compact view-dependent volume representations that enable interactive visualization of large volumes at high frame rates by decoupling viewpoint changes from expensive rendering calculations. However, current rendering approaches for VDIs struggle with achieving interactive frame rates at high image resolutions. Here, we exploit the properties of perspective projection to simplify intersections of rays with the view-dependent frustums in a VDI and leverage spatial smoothness in the volume data to minimize memory accesses. Benchmarks show that responsive frame rates can be achieved close to the viewpoint of generation for HD display resolutions, providing high-fidelity approximate renderings of Gigabyte-sized volumes. We also propose a method to subsample the VDI for preview rendering, maintaining high frame rates even for large viewpoint deviations. We provide our implementation as an extension of an established open-source visualization library.

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at: www.print-db.org.

Site-specific recombinases have become a resourceful tool for genome engineering, allowing sophisticated in vivo DNA modifications and rearrangements, including the precise removal of integrated retroviruses from host genomes. In a recent study, a mutant form of Cre recombinase has been used to excise the provirus of a specific HIV-1 strain from the human genome. To achieve provirus excision, the Cre recombinase had to be evolved to recombine an asymmetric locus of recombination (lox)-like sequence present in the long terminal repeat (LTR) regions of a HIV-1 strain. One pre-requisite for this type of work is the identification of degenerate lox-like sites in genomic sequences. Given their nature-two inverted repeats flanking a spacer of variable length-existing search tools like BLAST or RepeatMasker perform poorly. To address this lack of available algorithms, we have developed the web-server SeLOX, which can identify degenerate lox-like sites within genomic sequences. SeLOX calculates a position weight matrix based on lox-like sequences, which is used to search genomic sequences. For computational efficiency, we transform sequences into binary space, which allows us to use a bit-wise AND Boolean operator for comparisons. Next to finding lox-like sites for Cre type recombinases in HIV LTR sequences, we have used SeLOX to identify lox-like sites in HIV LTRs for six yeast recombinases. We finally demonstrate the general usefulness of SeLOX in identifying lox-like sequences in large genomes by searching Cre type recombination sites in the entire human genome. SeLOX is freely available at http://selox.mpi-cbg.de/cgi-bin/selox/index.

Homology-driven proteomics is a major tool to characterize proteomes of organisms with unsequenced genomes. This paper addresses practical aspects of automated homology-driven protein identifications by LC-MS/MS on a hybrid LTQ Orbitrap mass spectrometer. All essential software elements supporting the presented pipeline are either hosted at the publicly accessible web server, or are available for free download.

BioBayesNet is a new web application that allows the easy modeling and classification of biological data using Bayesian networks. To learn Bayesian networks the user can either upload a set of annotated FASTA sequences or a set of pre-computed feature vectors. In case of FASTA sequences, the server is able to generate a wide range of sequence and structural features from the sequences. These features are used to learn Bayesian networks. An automatic feature selection procedure assists in selecting discriminative features, providing an (locally) optimal set of features. The output includes several quality measures of the overall network and individual features as well as a graphical representation of the network structure, which allows to explore dependencies between features. Finally, the learned Bayesian network or another uploaded network can be used to classify new data. BioBayesNet facilitates the use of Bayesian networks in biological sequences analysis and is flexible to support modeling and classification applications in various scientific fields. The BioBayesNet server is available at http://biwww3.informatik.uni-freiburg.de:8080/BioBayesNet/.

BACKGROUND: The functional annotation of proteins relies on published information concerning their close and remote homologues in sequence databases. Evidence for remote sequence similarity can be further strengthened by a similar biological background of the query sequence and identified database sequences. However, few tools exist so far, that provide a means to include functional information in sequence database searches. RESULTS: We present ProFAT, a web-based tool for the functional annotation of protein sequences based on remote sequence similarity. ProFAT combines sensitive sequence database search methods and a fold recognition algorithm with a simple text-mining approach. ProFAT extracts identified hits based on their biological background by keyword-mining of annotations, features and most importantly, literature associated with a sequence entry. A user-provided keyword list enables the user to specifically search for weak, but biologically relevant homologues of an input query. The ProFAT server has been evaluated using the complete set of proteins from three different domain families, including their weak relatives and could correctly identify between 90% and 100% of all domain family members studied in this context. ProFAT has furthermore been applied to a variety of proteins from different cellular contexts and we provide evidence on how ProFAT can help in functional prediction of proteins based on remotely conserved proteins. CONCLUSION: By employing sensitive database search programs as well as exploiting the functional information associated with database sequences, ProFAT can detect remote, but biologically relevant relationships between proteins and will assist researchers in the prediction of protein function based on remote homologies.

A new software system designed for use in high-throughput DNA sequencing laboratories is described. The Genome Reconstruction Manager (GRM) was developed from requirements derived from ongoing large-scale DNA sequencing projects. Object-oriented principles were followed in designing the system, and tools supporting object-oriented system development were employed for its implementation. GRM provides several advances in software support for high-throughput DNA sequencing: support for random, directed, and mixed sequencing strategies; a novel system for fragment assembly; a commercial object data-base management system for data storage; a client/server architecture for using network computational servers; and an underlying data model that can evolve to support fully automatic sequence reconstruction. GRM is currently being deployed for production use in high-throughput DNA sequencing projects.