UNLABELLED: We announce the release of kSNP3.0, a program for SNP identification and phylogenetic analysis without genome alignment or the requirement for reference genomes. kSNP3.0 is a significantly improved version of kSNP v2. AVAILABILITY AND IMPLEMENTATION: kSNP3.0 is implemented as a package of stand-alone executables for Linux and Mac OS X under the open-source BSD license. The executable packages, source code and a full User Guide are freely available at https://sourceforge.net/projects/ksnp/files/ CONTACT: [email protected].

BACKGROUND: Pairing up primers to amplify desired targets and avoid undesired cross reactions can be a combinatorial challenge. Effective prediction of specificity and inclusivity from multiplexed primers and TaqMan®/Luminex® probes is a critical step in PCR design. RESULTS: Code is described to identify all primer and probe combinations from a list of unpaired, unordered candidates that should produce a product. It predicts and extracts all amplicon sequences in a large sequence database from a list of primers and probes, allowing degenerate bases and user-specified levels of primer-target mismatch tolerance. Amplicons hit by TaqMan®/Luminex® probes are indicated, and products may be annotated with gene information from NCBI. Fragment length distributions are calculated to predict electrophoretic gel banding patterns. CONCLUSIONS: Simulate_PCR is the only freely available software that can be run from the command line for high throughput applications which can calculate all products from large lists of primers and probes compared to a large sequence database such as nt. It requires no prior knowledge of how primers should be paired. Degenerate bases are allowed and entire amplicon sequences are extracted and annotated with gene information. Examples are provided for sets of TaqMan®/Luminex® PCR signatures predicted to amplify all HIV-1 genomes, all Coronaviridae genomes, and a group of antibiotic resistance genes. The software is a command line perl script freely available as open source.

MOTIVATION: Genomic-based methods have significant potential for fast and accurate identification of organisms or even genes of interest in complex environmental samples (air, water, soil, food, etc.), especially when isolation of the target organism cannot be performed by a variety of reasons. Despite this potential, the presence of the unknown, variable and usually large quantities of background DNA can cause interference resulting in false positive outcomes. RESULTS: In order to estimate how the genomic diversity of the background (total length of all of the different genomes present in the background), target length and target mutation rate affect the probability of misidentifications, we introduce a mathematical definition for the quality of an individual signature in the presence of a background based on its length and number of mismatches needed to transform the signature into the closest subsequence present in the background. This definition, in conjunction with a probabilistic framework, allows one to predict the minimal signature length required to identify the target in the presence of different sizes of backgrounds and the effect of the target's mutation rate on the quality of its identification. The model assumptions and predictions were validated using both Monte Carlo simulations and real genomic data examples. The proposed model can be used to determine appropriate signature lengths for various combinations of target and background genome sizes. It also predicted that any genomic signatures will be unable to identify target if its mutation rate is >5%. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: MannDB was created to meet a need for rapid, comprehensive automated protein sequence analyses to support selection of proteins suitable as targets for driving the development of reagents for pathogen or protein toxin detection. Because a large number of open-source tools were needed, it was necessary to produce a software system to scale the computations for whole-proteome analysis. Thus, we built a fully automated system for executing software tools and for storage, integration, and display of automated protein sequence analysis and annotation data. DESCRIPTION: MannDB is a relational database that organizes data resulting from fully automated, high-throughput protein-sequence analyses using open-source tools. Types of analyses provided include predictions of cleavage, chemical properties, classification, features, functional assignment, post-translational modifications, motifs, antigenicity, and secondary structure. Proteomes (lists of hypothetical and known proteins) are downloaded and parsed from Genbank and then inserted into MannDB, and annotations from SwissProt are downloaded when identifiers are found in the Genbank entry or when identical sequences are identified. Currently 36 open-source tools are run against MannDB protein sequences either on local systems or by means of batch submission to external servers. In addition, BLAST against protein entries in MvirDB, our database of microbial virulence factors, is performed. A web client browser enables viewing of computational results and downloaded annotations, and a query tool enables structured and free-text search capabilities. When available, links to external databases, including MvirDB, are provided. MannDB contains whole-proteome analyses for at least one representative organism from each category of biological threat organism listed by APHIS, CDC, HHS, NIAID, USDA, USFDA, and WHO. CONCLUSION: MannDB comprises a large number of genomes and comprehensive protein sequence analyses representing organisms listed as high-priority agents on the websites of several governmental organizations concerned with bio-terrorism. MannDB provides the user with a BLAST interface for comparison of native and non-native sequences and a query tool for conveniently selecting proteins of interest. In addition, the user has access to a web-based browser that compiles comprehensive and extensive reports. Access to MannDB is freely available at http://manndb.llnl.gov/.

A team at the Lawrence Livermore National Laboratory (LLNL) was given the task of using computational tools to speed up the development of DNA diagnostics for pathogen detection. This work will be described in another paper in this issue (see pages 133-149). To achieve this goal it was necessary to understand the merits and limitations of the various available comparative genomics tools. A review of some recent tools for multisequence/genome alignment and substring comparison is presented, within the general framework of applicability to a large-scale application. We note that genome alignments are important for many things, only one of which is pathogen detection. Understanding gene function, gene regulation, gene networks, phylogenetic studies and other aspects of evolution all depend on accurate nucleic acid and protein sequence alignment. Selecting appropriate tools can make a large difference in the quality of results obtained and the effort required.

Rapid advances in the genomic sequencing of bacteria and viruses over the past few years have made it possible to consider sequencing the genomes of all pathogens that affect humans and the crops and livestock upon which our lives depend. Recent events make it imperative that full genome sequencing be accomplished as soon as possible for pathogens that could be used as weapons of mass destruction or disruption. This sequence information must be exploited to provide rapid and accurate diagnostics to identify pathogens and distinguish them from harmless near-neighbours and hoaxes. The Chem-Bio Non-Proliferation (CBNP) programme of the US Department of Energy (DOE) began a large-scale effort of pathogen detection in early 2000 when it was announced that the DOE would be providing bio-security at the 2002 Winter Olympic Games in Salt Lake City, Utah. Our team at the Lawrence Livermore National Lab (LLNL) was given the task of developing reliable and validated assays for a number of the most likely bioterrorist agents. The short timeline led us to devise a novel system that utilised whole-genome comparison methods to rapidly focus on parts of the pathogen genomes that had a high probability of being unique. Assays developed with this approach have been validated by the Centers for Disease Control (CDC). They were used at the 2002 Winter Olympics, have entered the public health system, and have been in continual use for non-publicised aspects of homeland defence since autumn 2001. Assays have been developed for all major threat list agents for which adequate genomic sequence is available, as well as for other pathogens requested by various government agencies. Collaborations with comparative genomics algorithm developers have enabled our LLNL team to make major advances in pathogen detection, since many of the existing tools simply did not scale well enough to be of practical use for this application. It is hoped that a discussion of a real-life practical application of comparative genomics algorithms may help spur algorithm developers to tackle some of the many remaining problems that need to be addressed. Solutions to these problems will advance a wide range of biological disciplines, only one of which is pathogen detection. For example, exploration in evolution and phylogenetics, annotating gene coding regions, predicting and understanding gene function and regulation, and untangling gene networks all rely on tools for aligning multiple sequences, detecting gene rearrangements and duplications, and visualising genomic data. Two key problems currently needing improved solutions are: (1) aligning incomplete, fragmentary sequence (eg draft genome contigs or arbitrary genome regions) with both complete genomes and other fragmentary sequences; and (2) ordering, aligning and visualising non-colinear gene rearrangements and inversions in addition to the colinear alignments handled by current tools.

There has been a significant increase, fueled by technologies front the human genome project, in the availability of nucleic acid sequence information for viruses and bacteria. This paper presents a computer-assisted process that begins with nucleic acid sequence information and produces highly specific pathogen signatures. When combined with instrumentation using the polymerase chain reaction, the resulting diagnostics are both specific and sensitive. The computational and engineering aspects of converting raw sequence data into pathogen-specific and instrument-ready assays are presented. Examples and data are presented for specific pathogens, including foot-and-mouth disease virus and the human immunodeficiency virus.

Data warehouses and data marts have been successfully applied to a multitude of commercial business applications. They have proven to be invaluable tools by integrating information from distributed, heterogeneous sources and summarizing this data for use throughout the enterprise. Although the need for information dissemination is as vital in science as in business, working warehouses in this community are scarce because traditional warehousing techniques do not transfer to scientific environments. There are two primary reasons for this difficulty. First, schema integration is more difficult for scientific databases than for business sources, because of the complexity of the concepts and the associated relationships. While this difference has not yet been fully explored, it is an important consideration when determining how to integrate autonomous sources. Second, scientific data sources have highly dynamic data representations (schemata). When a data source participating in a warehouse changes its schema, both the mediator transferring data to the warehouse and the warehouse itself need to be updated to reflect these modifications. The cost of repeatedly performing these updates in a traditional warehouse, as is required in a dynamic environment, is prohibitive. This paper discusses these issues within the context of the DataFoundry project, an ongoing research effort at Lawrence Livermore National Laboratory. DataFoundry utilizes a unique integration strategy to identify corresponding instances while maintaining differences between data from different sources, and a novel architecture and an extensive meta-data infrastructure, which reduce the cost of maintaining a warehouse.

The determination of the sequence of all nucleotide base-pairs in a DNA molecule, from restriction-fragment data, is a complex task and can be posed as the problem of finding the optima of a multimodal function. A genetic algorithm that uses multiniche crowding permits us to do this. Performance of this algorithm is first tested using a standard suite of test functions. The algorithm is next tested using two data sets obtained from the Human Genome Project at the Lawrence Livermore National Laboratory. The new method holds promise in automating the sequencing computations.