MOTIVATION: PCR, hybridization, DNA sequencing and other important methods in molecular diagnostics rely on both sequence-specific and sequence group-specific oligonucleotide primers and probes. Their design depends on the identification of oligonucleotide signatures in whole genome or marker gene sequences. Although genome and gene databases are generally available and regularly updated, collections of valuable signatures are rare. Even for single requests, the search for signatures becomes computationally expensive when working with large collections of target (and non-target) sequences. Moreover, with growing dataset sizes, the chance of finding exact group-matching signatures decreases, necessitating the application of relaxed search methods. The resultant substantial increase in complexity is exacerbated by the dearth of algorithms able to solve these problems efficiently. RESULTS: We have developed CaSSiS, a fast and scalable method for computing comprehensive collections of sequence- and sequence group-specific oligonucleotide signatures from large sets of hierarchically clustered nucleic acid sequence data. Based on the ARB Positional Tree (PT-)Server and a newly developed BGRT data structure, CaSSiS not only determines sequence-specific signatures and perfect group-covering signatures for every node within the cluster (i.e. target groups), but also signatures with maximal group coverage (sensitivity) within a user-defined range of non-target hits (specificity) for groups lacking a perfect common signature. An upper limit of tolerated mismatches within the target group, as well as the minimum number of mismatches with non-target sequences, can be predefined. Test runs with one of the largest phylogenetic gene sequence datasets available indicate good runtime and memory performance, and in silico spot tests have shown the usefulness of the resulting signature sequences as blueprints for group-specific oligonucleotide probes. AVAILABILITY: Software and Supplementary Material are available at http://cassis.in.tum.de/.

MOTIVATION: Nucleic acid diagnostics has high demands for non-heuristic exact and approximate oligonucleotide string matching concerning in silico primer/probe design in huge nucleic acid sequence collections. Unfortunately, public sequence repositories grow much faster than computer hardware performance and main memory capacity do. This growth imposes severe problems on existing oligonucleotide primer/probe design applications necessitating new approaches based on space-efficient indexing structures. RESULTS: We developed PTPan (spoken Peter Pan, 'PT' is for Position Tree, the earlier name of suffix trees), a space-efficient indexing structure for approximate oligonucleotide string matching in nucleic acid sequence data. Based on suffix trees, it combines partitioning, truncation and a new suffix tree stream compression to deal with large amounts of aligned and unaligned data. PTPan operates efficiently in main memory and on secondary storage, balancing between memory consumption and runtime during construction and application. Based on PTPan, applications supporting similarity search and primer/probe design have been implemented, namely FindFamily, ProbeMatch and ProbeDesign. All three use a weighted Levenshtein distance metric for approximative queries to find and rate matches with indels as well as substitutions. We integrated PTPan in the worldwide used software package ARB to demonstrate usability and performance. Comparing PTPan and the original ARB index for the very large ssu-rRNA database SILVA, we recognized a shorter construction time, extended functionality and dramatically reduced memory requirements at the price of expanded, but very reasonable query times. PTPan enables indexing of huge nucleic acid sequence collections at reasonable application response times. Not being limited by main memory, PTPan constitutes a major advancement regarding rapid oligonucleotide string matching in primer/probe design now and in the future facing the enormous growth of molecular sequence data. AVAILABILITY: Supplementary Material, PTPan stand-alone library and ARB-PTPan binary on http://ptpan.lrr.in.tum.de/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Microbial diversity is still largely unknown in most environments, such as soils. In order to get access to this microbial 'black-box', the development of powerful tools such as microarrays are necessary. However, the reliability of this approach relies on probe efficiency, in particular sensitivity, specificity and explorative power, in order to obtain an image of the microbial communities that is close to reality. RESULTS: We propose a new probe design algorithm that is able to select microarray probes targeting SSU rRNA at any phylogenetic level. This original approach, implemented in a program called 'PhylArray', designs a combination of degenerate and non-degenerate probes for each target taxon. Comparative experimental evaluations indicate that probes designed with PhylArray yield a higher sensitivity and specificity than those designed by conventional approaches. Applying the combined PhyArray/GoArrays strategy helps to optimize the hybridization performance of short probes. Finally, hybridizations with environmental targets have shown that the use of the PhylArray strategy can draw attention to even previously unknown bacteria.

BACKGROUND: Availability of high-resolution RNA crystal structures for the 30S and 50S ribosomal subunits and the subsequent validation of comparative secondary structure models have prompted the biologists to use three-dimensional structure of ribosomal RNA (rRNA) for evaluating sequence alignments of rRNA genes. Furthermore, the secondary and tertiary structural features of rRNA are highly useful and successfully employed in designing rRNA targeted oligonucleotide probes intended for in situ hybridization experiments. RNA3D, a program to combine sequence alignment information with three-dimensional structure of rRNA was developed. Integration into ARB software package, which is used extensively by the scientific community for phylogenetic analysis and molecular probe designing, has substantially extended the functionality of ARB software suite with 3D environment. RESULTS: Three-dimensional structure of rRNA is visualized in OpenGL 3D environment with the abilities to change the display and overlay information onto the molecule, dynamically. Phylogenetic information derived from the multiple sequence alignments can be overlaid onto the molecule structure in a real time. Superimposition of both statistical and non-statistical sequence associated information onto the rRNA 3D structure can be done using customizable color scheme, which is also applied to a textual sequence alignment for reference. Oligonucleotide probes designed by ARB probe design tools can be mapped onto the 3D structure along with the probe accessibility models for evaluation with respect to secondary and tertiary structural conformations of rRNA. CONCLUSION: Visualization of three-dimensional structure of rRNA in an intuitive display provides the biologists with the greater possibilities to carry out structure based phylogenetic analysis. Coupled with secondary structure models of rRNA, RNA3D program aids in validating the sequence alignments of rRNA genes and evaluating probe target sites. Superimposition of the information derived from the multiple sequence alignment onto the molecule dynamically allows the researchers to observe any sequence inherited characteristics (phylogenetic information) in real-time environment. The extended ARB software package is made freely available for the scientific community via http://www.arb-home.de.

MOTIVATION: The computation of large phylogenetic trees with statistical models such as maximum likelihood or bayesian inference is computationally extremely intensive. It has repeatedly been demonstrated that these models are able to recover the true tree or a tree which is topologically closer to the true tree more frequently than less elaborate methods such as parsimony or neighbor joining. Due to the combinatorial and computational complexity the size of trees which can be computed on a Biologist's PC workstation within reasonable time is limited to trees containing approximately 100 taxa. RESULTS: In this paper we present the latest release of our program RAxML-III for rapid maximum likelihood-based inference of large evolutionary trees which allows for computation of 1.000-taxon trees in less than 24 hours on a single PC processor. We compare RAxML-III to the currently fastest implementations for maximum likelihood and bayesian inference: PHYML and MrBayes. Whereas RAxML-III performs worse than PHYML and MrBayes on synthetic data it clearly outperforms both programs on all real data alignments used in terms of speed and final likelihood values. Availability SUPPLEMENTARY INFORMATION: RAxML-III including all alignments and final trees mentioned in this paper is freely available as open source code at http://wwwbode.cs.tum/~stamatak CONTACT: [email protected].

BACKGROUND: Taxon specific hybridization probes in combination with a variety of commonly used hybridization formats nowadays are standard tools in microbial identification. A frequently applied technology, fluorescence in situ hybridization (FISH), besides single cell identification, allows the localization and functional studies of the microbial community composition. Careful in silico design and evaluation of potential oligonucleotide probe targets is therefore crucial for performing successful hybridization experiments. RESULTS: The PROBE Design tools of the ARB software package take into consideration several criteria such as number, position and quality of diagnostic sequence differences while designing oligonucleotide probes. Additionally, new visualization tools were developed to enable the user to easily examine further sequence associated criteria such as higher order structure, conservation, G+C content, transition-transversion profiles and in situ target accessibility patterns. The different types of sequence associated information (SAI) can be visualized by user defined background colors within the ARB primary and secondary structure editors as well as in the PROBE Match tool. CONCLUSION: Using this tool, in silico probe design and evaluation can be performed with respect to in situ probe accessibility data. The evaluation of proposed probe targets with respect to higher-order rRNA structure is of importance for successful design and performance of in situ hybridization experiments. The entire ARB software package along with the probe accessibility data is available from the ARB home page http://www.arb-home.de

Abstract Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the maximum likelihood method is computationally intensive. We present simple heuristics which yield accurate trees for synthetic as well as real data and significantly reduce execution time. Those heuristics have been implemented in a sequential, parallel, and distributed program called RAxML‐II, which is freely available as open source code. We compare the performance of the sequential program with PHYML and MrBayes which—to the best of our knowledge—are currently the fastest and most accurate programs for phylogenetic tree inference based on statistical methods. Experiments are conducted using 50 synthetic 100 taxon alignments as well as nine real‐world alignments comprising 101 up to 1000 sequences. RAxML‐II outperforms MrBayes for real‐world data both in terms of speed and final likelihood values. Furthermore, for real data RAxML‐II requires less time (a factor of 2–8) than PHYML to reach PHYML's final likelihood values and yields better final trees due to its more exhaustive search strategy. For synthetic data MrBayes is slightly more accurate than RAxML‐II and PHYML but significantly slower. The non‐deterministic parallel program shows good speedup values and has been used to infer a 10 000‐taxon tree comprising organisms from the domains Eukarya, Bacteria, and Archaea. Copyright © 2005 John Wiley & Sons, Ltd.

Summary form only given. Inference of phylogenetic trees comprising thousands of taxa using maximum likelihood is computationally extremely expensive. We present simple heuristics which yield accurate trees for simulated as well as real data and reduce execution time. The new heuristics have been implemented in a program called RAxML which is freely available. Furthermore, we present a distributed version of our algorithm which is implemented in an MPI-based prototype. This prototype is being used to implement an http-based seti@home-like version of RaxML. We compare our program with PHYML and MrBayes which are currently the fastest and most accurate programs for phylogenetic tree inference. Experiments are conducted using 50 simulated 100 taxon alignments as well as real-world alignments with up to 1000 sequences. RAxML outperforms MrBayes for real-world data both in terms of speed and final likelihood values. Furthermore, for real data RAxML outperforms PHYML by factor 2-8 and yields better final trees due to its more exhaustive search strategy. For synthetic data MrBayes is slightly more accurate than RAxML and PHYML but significantly slower.

Abstract We describe an accurate method for the automatic parallel generation of oligonucleotide probe sets for DNA microarrays. This approach includes a component for high‐performance specificity evaluation of designed probes in large data sets. The three main algorithmic components of the method, namely probe preselection, hybridization prediction and probe selection are explained in detail. We introduce new combinatorial techniques for the efficient selection of probe sets of high differentiation capability even from sequence databases of conserved homologous genes. These techniques include the automatic generation of group specific probes as well as the design of excluding probes. A basic prototype has been implemented including a shared memory parallelization. Test runs have been performed on a multiprocessor personal computer with subsets of a small subunit ribosomal ribonucleic acid database, containing very conserved sequence data. The applicability of our program is pointed out by designing a set of oligonucleotide probes that shall allow a comprehensive parallel identification and differentiation of several groups of extremophilic prokaryotes by DNA microarray. The probe set is accessible via the Internet. On applying the parallel version on a dual processor system an efficiency of 80% was achieved. Copyright © 2004 John Wiley & Sons, Ltd.

Abstract Inference of phylogenetic (evolutionary) trees comprising hundreds or thousands of organisms based on the maximum likelihood criterion is a computationally extremely intensive task. This paper describes the evolution of the AxML program family which provides novel algorithmic as well as technical solutions for the maximum likelihood‐based inference of huge phylogenetic trees. Algorithmic optimizations and a new tree building algorithm yield runtime improvements of a factor greater than 4 compared with fastDNAml and parallel fastDNAml, returning equally good trees at the same time. Various parallel, distributed, and Grid‐based implementations of AxML give the program the capability to acquire the large amount of required computational resources for the inference of huge high‐quality trees. Copyright © 2004 John Wiley & Sons, Ltd.

Heuristics for calculating phylogenetic trees for a large sets of aligned rRNA sequences based on the maximum likelihood method are computationally expensive. The core of most parallel algorithms, which accounts for the greatest part of computation time, is the tree evaluation function, that calculates the likelihood value for each tree topology. This paper describes and uses Subtree Equality Vectors (SEVs) to reduce the number of required floating point operations during topology evaluation. We integrated our optimizations into various sequential programs and into parallel fastDNAml, one of the most common and efficient parallel programs for calculating large phylogenetic trees. Experimental results for our parallel program, which renders exactly the same output as parallel fastDNAml show global runtime improvements of 26% to 65%. The optimization scales best on clusters of PCs, which also implies a substantial cost saving factor for the determination of large trees.