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The dramatic decrease in time and cost for generating genetic sequence data has opened up vast opportunities in molecular systematics, one of which is the ability to decipher the evolutionary history of strains of a species. Under this fine systematic resolution, the standard markers are too crude to provide a phylogenetic signal. Nevertheless, among prokaryotes, genome dynamics in the form of horizontal gene transfer (HGT) between organisms and gene loss seem to provide far richer information by affecting both gene order and gene content. The “synteny index” (SI) between a pair of genomes combines these latter two factors, allowing comparison of genomes with unequal gene content, together with order considerations of their common genes. Although this approach is useful for classifying close relatives, no rigorous statistical modeling for it has been suggested. Such modeling is valuable, as it allows observed measures to be transformed into estimates of time periods during evolution, yielding the “additivity” of the measure. To the best of our knowledge, there is no other additivity proof for other gene order/content measures under HGT. Here, we provide a first statistical model and analysis for the SI measure. We model the “gene neighborhood” as a “birth–death–immigration” process affected by the HGT activity over the genome, and analytically relate the HGT rate and time to the expected SI. This model is asymptotic and thus provides accurate results, assuming infinite size genomes. Therefore, we also developed a heuristic model following an “exponential decay” function, accounting for biologically realistic values, which performed well in simulations. Applying this model to 1,133 prokaryotes partitioned to 39 clusters by the rank of genus yields that the average number of genome dynamics events per gene in the phylogenetic depth of genus is around half with significant variability between genera. This result extends and confirms similar results obtained for individual genera in different manners.

Summary Coronavirus Disease 2019 (COVID-19) is a mild to moderate respiratory tract infection, however, a subset of patients progresses to severe disease and respiratory failure. The mechanism of protective immunity in mild forms and the pathogenesis of severe COVID-19, associated with increased neutrophil counts and dysregulated immune responses, remains unclear. In a dual-center, two-cohort study, we combined single-cell RNA-sequencing and single-cell proteomics of whole blood and peripheral blood mononuclear cells to determine changes in immune cell composition and activation in mild vs. severe COVID-19 (242 samples from 109 individuals) over time. HLA-DRhiCD11chi inflammatory monocytes with an interferon-stimulated gene signature were elevated in mild COVID-19. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils, and HLA-DRlo monocytes. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and it reveals profound alterations in the myeloid cell compartment associated with severe COVID-19., Highlights ● SARS-CoV-2 infection induces profound alterations of the myeloid compartment ● Mild COVID-19 is marked by inflammatory HLA-DRhiCD11chi CD14+ monocytes ● Dysfunctional HLA-DRloCD163hi and HLA-DRloS100Ahi CD14+ monocytes in severe COVID-19 ● Emergency myelopoiesis with immature and dysfunctional neutrophils in severe COVID-19

16 páginas, 1 tabla, 3 figuras., Mesenteric infection by the parasitic blood fluke Schistosoma bovis is a common veterinary problem in Africa and the Middle East and occasionally in the Mediterranean Region. The species also has the ability to form interspecific hybrids with the human parasite S. haematobium with natural hybridisation observed in West Africa, presenting possible zoonotic transmission. Additionally, this exchange of alleles between species may dramatically influence disease dynamics and parasite evolution. We have generated a 374 Mb assembly of the S. bovis genome using Illumina and PacBio-based technologies. Despite infecting different hosts and organs, the genome sequences of S. bovis and S. haematobium appeared strikingly similar with 97% sequence identity. The two species share 98% of protein-coding genes, with an average sequence identity of 97.3% at the amino acid level. Genome comparison identified large continuous parts of the genome (up to several 100 kb) showing almost 100% sequence identity between S. bovis and S. haematobium. It is unlikely that this is a result of genome conservation and provides further evidence of natural interspecific hybridization between S. bovis and S. haematobium. Our results suggest that foreign DNA obtained by interspecific hybridization was maintained in the population through multiple meiosis cycles and that hybrids were sexually reproductive, producing viable offspring. The S. bovis genome assembly forms a highly valuable resource for studying schistosome evolution and exploring genetic regions that are associated with species-specific phenotypic traits., This work was supported by grants from the QIMR Berghofer Medical Research Institute (Chenhall Estate) and the Australian Infectious Diseases Research Centre. NDY was supported by a National Health and Medical Research Council (NHMRC) CDF1, PKK by an NHMRC ECRF and DPM by an NHMRC SPRF (APP1102926). The work has been supported by NHMRC project grant APP1102322.

Temporal resolution of cellular features associated with a severe COVID-19 disease trajectory is required for understanding skewed immune responses and finding outcome predictors. Here, we performed a longitudinal multi-omics study using a two-centre cohort of 14 patients. We analysed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. Validation was performed in two independent cohorts of COVID-19 patients. Severe COVID-19 was characterized by an increase of proliferating, metabolically hyperactive plasmablasts. Coinciding with critical illness, we also identified an expansion of IFN-activated circulating megakaryocytes and increased erythropoiesis with features of hypoxic signalling. Megakaryocyte- and erythroid cell-derived co-expression modules were predictive of fatal disease outcome. The study demonstrates broad cellular effects of SARS-CoV-2 infection beyond classical immune cells and may serve as an entry point to develop biomarkers and targeted treatments of patients with COVID-19., Graphical Abstract, Highlights • SARS-CoV2 infection elicits dynamic changes of circulating cells in the blood • Severe COVID-19 is characterized by increased metabolically active plasmablasts • Elevation of IFN-activated megakaryocytes and erythroid cells in severe COVID-19 • Cell-type specific expression signatures are associated with a fatal COVID-19 outcome, Bernardes et al. explore COVID-19 disease trajectories by performing longitudinal multi-omics analyses in peripheral blood samples from hospitalized patients. The analyses identify increased numbers of plasmablasts, interferon-activated megakaryocytes and erythroid cells as hallmarks of severe disease, and define molecular signatures linked to a fatal COVID-19 disease outcome.

Background The characterization of microbial communities based on sequencing and analysis of their genetic information has become a popular approach also referred to as metagenomics; in particular, the recent advances in sequencing technologies have enabled researchers to study even the most complex communities. Metagenome analysis, the assignment of sequences to taxonomic and functional entities, however, remains a tedious task: large amounts of data need to be processed. There are a number of approaches addressing particular aspects, but scientific questions are often too specific to be answered by a general-purpose method. Results We present MGX, a flexible and extensible client/server-framework for the management and analysis of metagenomic datasets; MGX features a comprehensive set of adaptable workflows required for taxonomic and functional metagenome analysis, combined with an intuitive and easy-to-use graphical user interface offering customizable result visualizations. At the same time, MGX allows to include own data sources and devise custom analysis pipelines, thus enabling researchers to perform basic as well as highly specific analyses within a single application. Conclusions With MGX, we provide a novel metagenome analysis platform giving researchers access to the most recent analysis tools. MGX covers taxonomic and functional metagenome analysis, statistical evaluation, and a wide range of visualizations easing data interpretation. Its default taxonomic classification pipeline provides equivalent or superior results in comparison to existing tools. Electronic supplementary material The online version of this article (10.1186/s40168-018-0460-1) contains supplementary material, which is available to authorized users.

Metagenomics has proven to be one of the most important research fields for microbial ecology during the last decade. Starting from 16S rRNA marker gene analysis for the characterization of community compositions to whole metagenome shotgun sequencing which additionally allows for functional analysis, metagenomics has been applied in a wide spectrum of research areas. The cost reduction paired with the increase in the amount of data due to the advent of next-generation sequencing led to a rapidly growing demand for bioinformatic software in metagenomics. By now, a large number of tools that can be used to analyze metagenomic datasets has been developed. The Bielefeld-Giesen center for microbial bioinformatics as part of the German Network for Bioinformatics Infrastructure bundles and imparts expert knowledge in the analysis of metagenomic datasets, especially in research on microbial communities involved in anaerobic digestion residing in biogas reactors. In this review, we give an overview of the field of metagenomics, introduce into important bioinformatic tools and possible workflows, accompanied by application examples of biogas surveys successfully conducted at the Center for Biotechnology of Bielefeld University.

Many important questions in molecular biology, evolution, and biomedicine can be addressed by comparative genomic approaches. One of the basic tasks when comparing genomes is the definition of measures of similarity (or dissimilarity) between two genomes, for example, to elucidate the phylogenetic relationships between species. The power of different genome comparison methods varies with the underlying formal model of a genome. The simplest models impose the strong restriction that each genome under study must contain the same genes, each in exactly one copy. More realistic models allow several copies of a gene in a genome. One speaks of gene families, and comparative genomic methods that allow this kind of input are called gene family-based. The most powerful-but also most complex-models avoid this preprocessing of the input data and instead integrate the family assignment within the comparative analysis. Such methods are called gene family-free. In this article, we study an intermediate approach between family-based and family-free genomic similarity measures. Introducing this simpler model, called gene connections, we focus on the combinatorial aspects of gene family-free genome comparison. While in most cases, the computational costs to the general family-free case are the same, we also find an instance where the gene connections model has lower complexity. Within the gene connections model, we define three variants of genomic similarity measures that have different expression powers. We give polynomial-time algorithms for two of them, while we show NP-hardness for the third, most powerful one. We also generalize the measures and algorithms to make them more robust against recent local disruptions in gene order. Our theoretical findings are supported by experimental results, proving the applicability and performance of our newly defined similarity measures.

Gene clusters are sets of genes in a genome with associated functionality. Often, they exhibit close proximity to each other on the chromosome which can be beneficial for their common regulation. A popular strategy for finding gene clusters is to exploit the close proximity by identifying sets of genes that are consistently close to each other on their respective chromosomal sequences across several related species.

Metagenomics aims at exploring microbial communities concerning their composition and functioning. Application of high-throughput sequencing technologies for the analysis of environmental DNA-preparations can generate large sets of metagenome sequence data which have to be analyzed by means of bioinformatics tools to unveil the taxonomic composition of the analyzed community as well as the repertoire of genes and gene functions. A bioinformatics software platform is required that allows the automated taxonomic and functional analysis and interpretation of metagenome datasets without manual effort. To address current demands in metagenome data analyses, the novel platform MetaSAMS was developed. MetaSAMS automatically accomplishes the tasks necessary for analyzing the composition and functional repertoire of a given microbial community from metagenome sequence data by implementing two software pipelines: (i) the first pipeline consists of three different classifiers performing the taxonomic profiling of metagenome sequences and (ii) the second functional pipeline accomplishes region predictions on assembled contigs and assigns functional information to predicted coding sequences. Moreover, MetaSAMS provides tools for statistical and comparative analyses based on the taxonomic and functional annotations. The capabilities of MetaSAMS are demonstrated for two metagenome datasets obtained from a biogas-producing microbial community of a production-scale biogas plant. The MetaSAMS web interface is available at https://metasams.cebitec.uni-bielefeld.de.

Gene-order-based comparison of multiple genomes provides signals for functional analysis of genes and the evolutionary process of genome organization. Gene clusters are regions of co-localized genes on genomes of different species. The rapid increase in sequenced genomes necessitates bioinformatics tools for finding gene clusters in hundreds of genomes. Existing tools are often restricted to few (in many cases, only two) genomes, and often make restrictive assumptions such as short perfect conservation, conserved gene order or monophyletic gene clusters. We present Gecko 3, an open-source software for finding gene clusters in hundreds of bacterial genomes, that comes with an easy-to-use graphical user interface. The underlying gene cluster model is intuitive, can cope with low degrees of conservation as well as misannotations and is complemented by a sound statistical evaluation. To evaluate the biological benefit of Gecko 3 and to exemplify our method, we search for gene clusters in a dataset of 678 bacterial genomes using Synechocystis sp. PCC 6803 as a reference. We confirm detected gene clusters reviewing the literature and comparing them to a database of operons; we detect two novel clusters, which were confirmed by publicly available experimental RNA-Seq data. The computational analysis is carried out on a laptop computer in, Nucleic Acids Research, 44 (20), ISSN:1362-4962, ISSN:0301-5610

Many important questions in molecular biology, evolution and biomedicine can be addressed by comparative genomics approaches. One of the basic tasks when comparing genomes is the definition of measures of similarity (or dissimilarity) between two genomes, for example to elucidate the phylogenetic relationships between species.

Next generation pyrosequencing of high G+C content genomes still poses problems to automated sequencing and assembly processes which necessitates cost and time intensive manual work in order to finish such genomes completely. The sequencing of the high G+C actinomycete Actinoplanes sp. SE50/110 was performed with standard pyrosequencing technology (454 Life Sciences) and revealed a high number of gaps. The reasons for the introduction of gaps were analyzed on a previously known 41kb long DNA reference sequence from Actinoplanes sp. SE50/110, hosting the acarbose biosynthesis gene cluster. Mapping of the sequencing results on the reference gene cluster sequence revealed a fragmentation into 30 contiguous sequences of different lengths. The gaps between these sequences were characterized by extremely low read coverage which strongly correlated with the G+C content in the gap regions in a negative manner. Furthermore, the gap-sequences contained strong stem-loop structures which hindered the amplification of these sequences during the emulsion PCR. Being significantly underrepresented or absent in the subsequent sequencing process, these sequences lead to weakly or uncovered genomic regions which forces the assembly algorithm to output multiple contiguous sequences instead of one finished genome. However, by applying a different pyrosequencing protocol, it was possible to sequence the complete acarbose biosynthesis gene cluster. The changes to the protocol include longer read length and addition of chemicals to the amplification chemistry, which reduces the self-annealing of DNA fragments during the amplification process and enables the complete reconstruction of high G+C content genomes without manual intervention. Copyright 2011 Elsevier B.V. All rights reserved.

A total community DNA sample from an agricultural biogas reactor continuously fed with maize silage, green rye, and small proportions of chicken manure has recently been sequenced using massively parallel pyrosequencing. In this Study, the sample was computationally characterized without a prior assembly step, providing quantitative insights into the taxonomic composition and gene content of the underlying microbial community. Clostridiales from the phylum Firmicutes is the most prevalent phylogenetic order, Methanomicrobiales are dominant among methanogenic archaea. An analysis of Operational Taxonomic Units (OTUs) revealed that the entire microbial community is only partially covered by the sequenced sample, despite that estimates suggest only a moderate overall diversity of the community. Furthermore, the results strongly indicate that archaea related to the genus Methanoculleus, using CO2 as electron acceptor and H-2 as electron donor, are the main producers of methane in the analyzed biogas reactor sample. A phylogenetic analysis of glycosyl hydrolase protein families suggests that Clostridia play an important role in the digestion of polysaccharides and oligosaccharides. Finally, the results unveiled that most of the organisms constituting the sample are still unexplored. (C) 2008 Elsevier B.V. All rights reserved.

Copyright @ 2008 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. In order to understand the phenotype of any living system, it is essential to not only investigate its genes, but also the specific metabolic pathway variant of the organism of interest, ideally in comparison with other organisms. The Comparative Pathway Analyzer, CPA, calculates and displays the differences in metabolic reaction content between two sets of organisms. Because results are highly dependent on the distribution of organisms into these two sets and the appropriate definition of these sets often is not easy, we provide hierarchical clustering methods for the identification of significant groupings. CPA also visualizes the reaction content of several organisms simultaneously allowing easy comparison. Reaction annotation data and maps for visualizing the results are taken from the KEGG database. Additionally, users can upload their own annotation data. This website is free and open to all users and there is no login requirement. It is available at https://www.cebitec.uni-bielefeld.de/groups/brf/software/cpa/index.html. Bundesministerium fur Bildung und Forschung, Deutsche Forschungsgemeinschaft, International Graduate School in Bioinformatics and Genome Research, and University of Glasgow.

Summary This study describes the esophageal cancer methylation landscape and its impact on gene expression. Genes aberrantly methylated suggest a mechanism that could lead to genomic instability and chromothripsis. A CpG island methylator phenotype-like subtype with potentially worse clinical outcome was also identified., The incidence of esophageal adenocarcinoma (EAC) has risen significantly over recent decades. Although survival has improved, cure rates remain poor, with

Rearrangements are mutations that can change the organization of a genome, but not its content. Examples are inversions of DNA segments, translocations of chromosome ends, fusions and fissions of chromosomes. All mentioned rearrangements can be represented by the generic Double Cut and Join (DCJ) operation. However, the DCJ operation also allows circular chromosomes to be created at intermediate steps, even if the compared genomes are linear. In this case it is more plausible to consider a restriction in which the reincorporation of a circular chromosome has to be done immediately after its creation. We call these two consecutive operations an $ER$ composition. It has been shown that an $ER$ composition mimics either an internal block interchange (when two segments in the same chromosome exchange their positions), or an internal transposition (the special case of a block interchange when the two segments are adjacent). The DCJ distance of two genomes is the same, regardless of this restriction, and can be computed in linear time. For comparing two genomes with unequal contents, in addition to rearrangements we have to allow insertions and deletions of DNA segments—named indels . It is already known that the distance in the model combining DCJ and indel operations can be exactly computed. Again, for linear genomes it would be more plausible to adopt a restricted version with $ER$ compositions. This model was studied recently by da Silva et al. ( BMC Bioinformatics 13, Suppl. 19, S14, 2012), but only an upper bound for the restricted DCJ-indel distance was provided. Here we first solve an open problem posed in that paper and present a very simple proof showing that the distance, which can be computed in linear time, is the same for both the unrestricted and the restricted DCJ-indel models. We then give a simpler algorithm for computing an optimal restricted DCJ-indel sorting scenario in $O(n \;\log\; n)$ time. We also relate the DCJ-indel distance to the restricted DCJ-substitution distance, which instead of indels considers a more powerful operation that allows the substitution of a DNA segment by another DNA segment. We show that the DCJ-indel distance is a 2-approximation for the restricted DCJ-substitution distance.

We present the novel prokaryotic gene finder GISMO, which combines searches for protein family domains with composition-based classification based on a support vector machine. GISMO is highly accurate; exhibiting high sensitivity and specificity in gene identification. We found that it performs well for complete prokaryotic chromosomes, irrespective of their GC content, and also for plasmids as short as 10 kb, short genes and for genes with atypical sequence composition. Using GISMO, we found several thousand new predictions for the published genomes that are supported by extrinsic evidence, which strongly suggest that these are very likely biologically active genes. The source code for GISMO is freely available under the GPL license.

Fast and exact comparison of large genomic sequences remains a challenging task in biosequence analysis. We consider the problem of finding all epsilon-matches between two sequences, i.e., all local alignments over a given length with an error rate of at most epsilon. We study this problem theoretically, giving an efficient q-gram filter for solving it. Two applications of the filter are also discussed, in particular genomic sequence assembly and BLAST-like sequence comparison. Our results show that the method is 25 times faster than BLAST, while not being heuristic.

Motivation: Fast algorithms and well-arranged visualizations are required for the comprehensive analysis of the ever-growing size of genomic and transcriptomic next-generation sequencing data. Results: ReadXplorer is a software offering straightforward visualization and extensive analysis functions for genomic and transcriptomic DNA sequences mapped on a reference. A unique specialty of ReadXplorer is the quality classification of the read mappings. It is incorporated in all analysis functions and displayed in ReadXplorer's various synchronized data viewers for (i) the reference sequence, its base coverage as (ii) normalizable plot and (iii) histogram, (iv) read alignments and (v) read pairs. ReadXplorer's analysis capability covers RNA secondary structure prediction, single nucleotide polymorphism and deletion–insertion polymorphism detection, genomic feature and general coverage analysis. Especially for RNA-Seq data, it offers differential gene expression analysis, transcription start site and operon detection as well as RPKM value and read count calculations. Furthermore, ReadXplorer can combine or superimpose coverage of different datasets. Availability and implementation: ReadXplorer is available as open-source software at http://www.readxplorer.org along with a detailed manual. Contact: rhilker@mikrobio.med.uni-giessen.de Supplementary information: Supplementary Data are available at Bioinformatics online.

Axillary malodor is formed in a biotransformation of initially odorless sweat components by members of the human skin microbiota. Metatranscriptomics of the human armpit microbiota allows for the analysis of the active microbial community on taxonomical and functional levels. Taxonomic profiles of the active axillary microbiota of three males revealed an unexpected diversity of staphylococci and corynebacteria probably contributing to body odor formation.

De novo genome assembly is the process of reconstructing a complete genomic sequence from countless small sequencing reads. Due to the complexity of this task, numerous genome assemblers have been developed to cope with different requirements and the different kinds of data provided by sequencers within the fast evolving field of next-generation sequencing technologies. In particular, the recently introduced generation of benchtop sequencers, like Illumina's MiSeq and Ion Torrent's Personal Genome Machine (PGM), popularized the easy, fast, and cheap sequencing of bacterial organisms to a broad range of academic and clinical institutions. With a strong pragmatic focus, here, we give a novel insight into the line of assembly evaluation surveys as we benchmark popular de novo genome assemblers based on bacterial data generated by benchtop sequencers. Therefore, single-library assemblies were generated, assembled, and compared to each other by metrics describing assembly contiguity and accuracy, and also by practice-oriented criteria as for instance computing time. In addition, we extensively analyzed the effect of the depth of coverage on the genome assemblies within reasonable ranges and the k-mer optimization problem of de Bruijn Graph assemblers. Our results show that, although both MiSeq and PGM allow for good genome assemblies, they require different approaches. They not only pair with different assembler types, but also affect assemblies differently regarding the depth of coverage where oversampling can become problematic. Assemblies vary greatly with respect to contiguity and accuracy but also by the requirement on the computing power. Consequently, no assembler can be rated best for all preconditions. Instead, the given kind of data, the demands on assembly quality, and the available computing infrastructure determines which assembler suits best. The data sets, scripts and all additional information needed to replicate our results are freely available at ftp://ftp.cebitec.uni-bielefeld.de/pub/GABenchToB.

Chinese hamster ovary (CHO) cell lines are one of the major production tools for monoclonal antibodies, recombinant proteins, and therapeutics. Although many efforts have significantly improved the availability of sequence information for CHO cells in the last years, forthcoming draft genomes still lack the information depth known from the mouse or human genomes. Many genes annotated for CHO cells and the Chinese hamster reference genome still are in silico predictions, only insufficiently verified by biological experiments. The correct annotation of transcription start sites (TSSs) is of special interest for CHO cells, as these directly define the location of the eukaryotic core promoter. Our study aims to elucidate these largely unexplored regions, trying to shed light on promoter landscapes in the Chinese hamster genome. Based on a 5' enriched dual library RNA sequencing approach 6547 TSSs were identified, of which over 90% were assigned to known genes. These TSSs were used to perform extensive promoter studies using a novel, modular bioinformatics pipeline, incorporating analyses of important regulatory elements of the eukaryotic core promoter on per-gene level and on genomic scale. Copyright 2014 Elsevier B.V. All rights reserved.

Actinoplanes sp. SE50/110 is the producer of the alpha-glucosidase inhibitor acarbose, which is an economically relevant and potent drug in the treatment of type-2 diabetes mellitus. In this study, we present the detection of transcription start sites on this genome by sequencing enriched 5'-ends of primary transcripts. Altogether, 1,427 putative transcription start sites were initially identified. With help of the annotated genome sequence, 661 transcription start sites were found to belong to the leader region of protein-coding genes with the surprising result that roughly 20% of these genes rank among the class of leaderless transcripts. Next, conserved promoter motifs were identified for protein-coding genes with and without leader sequences. The mapped transcription start sites were finally used to improve the annotation of the Actinoplanes sp. SE50/110 genome sequence. Concerning protein-coding genes, 41 translation start sites were corrected and 9 novel protein-coding genes could be identified. In addition to this, 122 previously undetermined non-coding RNA (ncRNA) genes of Actinoplanes sp. SE50/110 were defined. Focusing on antisense transcription start sites located within coding genes or their leader sequences, it was discovered that 96 of those ncRNA genes belong to the class of antisense RNA (asRNA) genes. The remaining 26 ncRNA genes were found outside of known protein-coding genes. Four chosen examples of prominent ncRNA genes, namely the transfer messenger RNA gene ssrA, the ribonuclease P class A RNA gene rnpB, the cobalamin riboswitch RNA gene cobRS, and the selenocysteine-specific tRNA gene selC, are presented in more detail. This study demonstrates that sequencing of enriched 5'-ends of primary transcripts and the identification of transcription start sites are valuable tools for advanced genome annotation of Actinoplanes sp. SE50/110 and most probably also for other bacteria.

Background Genes occurring co-localized in multiple genomes can be strong indicators for either functional constraints on the genome organization or remnant ancestral gene order. The computational detection of these patterns, which are usually referred to as gene clusters, has become increasingly sensitive over the past decade. The most powerful approaches allow for various types of imperfect cluster conservation: Cluster locations may be internally rearranged. The individual cluster locations may contain only a subset of the cluster genes and may be disrupted by uninvolved genes. Moreover cluster locations may not at all occur in some or even most of the studied genomes. The detection of such low quality clusters increases the risk of mistaking faint patterns that occur merely by chance for genuine findings. Therefore, it is crucial to estimate the significance of computational gene cluster predictions and discriminate between true conservation and coincidental clustering. Results In this paper, we present an efficient and accurate approach to estimate the significance of gene cluster predictions under the approximate common intervals model. Given a single gene cluster prediction, we calculate the probability to observe it with the same or a higher degree of conservation under the null hypothesis of random gene order, and add a correction factor to account for multiple testing. Our approach considers all parameters that define the quality of gene cluster conservation: the number of genomes in which the cluster occurs, the number of involved genes, the degree of conservation in the different genomes, as well as the frequency of the clustered genes within each genome. We apply our approach to evaluate gene cluster predictions in a large set of well annotated genomes.

Mycoplasma salivarium belongs to the class of the smallest self-replicating Tenericutes and is predominantly found in the oral cavity of humans. In general it is considered as a non-pathogenic commensal. However, some reports point to an association with human diseases. M. salivarium was found e.g. as causative agent of a submasseteric abscess, in necrotic dental pulp, in brain abscess and clogged biliary stent. Here we describe the detection of M. salivarium on the surface of a squamous cell carcinoma of the tongue of a patient with Fanconi anaemia (FA). FA is an inherited bone marrow failure syndrome based on defective DNA-repair that increases the risk of carcinomas especially oral squamous cell carcinoma. Employing high coverage, massive parallel Roche/454-next-generation-sequencing of 16S rRNA gene amplicons we analysed the oral microbiome of this FA patient in comparison to that of an FA patient with a benign leukoplakia and five healthy individuals. The microbiota of the FA patient with leukoplakia correlated well with that of the healthy controls. A dominance of Streptococcus, Veillonella and Neisseria species was typically observed. In contrast, the microbiome of the cancer bearing FA patient was dominated by Pseudomonas aeruginosa at the healthy sites, which changed to a predominance of 98% M. salivarium on the tumour surface. Quantification of the mycoplasma load in five healthy, two tumour- and two leukoplakia-FA patients by TaqMan-PCR confirmed the prevalence of M. salivarium at the tumour sites. These new findings suggest that this mycoplasma species with its reduced coding capacity found ideal breeding grounds at the tumour sites. Interestingly, the oral cavity of all FA patients and especially samples at the tumour sites were in addition positive for Candida albicans. It remains to be elucidated in further studies whether M. salivarium can be used as a predictive biomarker for tumour development in these patients.

Current diagnostic approaches that characterize T-cell deficiency by analyzing diversity of T-cell receptor sequences effectuate limited informational gain about the actual restrictiveness. For deeper insight into T-cell receptor repertoires we developed next-generation-sequencing-spectratyping, which employs high coverage Roche/454 sequencing of T-cell receptor (β)-chain amplicons. For automated analysis of high-throughput-sequencing data, we developed a freely available software, the TCR profiler. Gene usage, length, encoded amino acid sequence and sequence diversity of the complementarity determining region 3 were determined and comprehensively integrated into a novel complexity score. Repertoires of CD8(+) T cells from children with idiopathic or hepatitis-induced very severe aplastic anemia (n=7), children two months after bone marrow transplantation (n=7) and healthy controls (children n=5, adults n=5) were analyzed. Complexity scores clearly distinguished between healthy and diseased, and even between different immune deficiency states. The repertoire of aplastic anemia patients was dominated by public (i.e. present in more than one person) T-cell receptor clonotypes, whereas only 0.2% or 1.9% were public in normal children and adults, respectively. The CDR3 sequence ASSGVGFSGANVLT was highly prevalent in 3 cases of hepatitis-induced anemia (15-32% of all sequences), but was only low expressed in idiopathic aplastic anemia (2-5%, n=4) or healthy controls (1%). Fifteen high frequent sequences were present exclusively in aplastic anemia patients. Next-generation-sequencing-spectratyping allows in-depth analysis of T-cell receptor repertoires and their restriction in clinical samples. A dominating clonotype was identified in hepatitis-induced anemia that may be associated with disease pathogenesis and several aplastic-anemia-associated, putatively autoreactive clonotypes were sequenced.

Background Modern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features. Results In this paper we introduce two algorithms for retention time alignment of multiple GC-MS datasets: multiple alignment by bidirectional best hits peak assignment and cluster extension (BIPACE) and center-star multiple alignment by pairwise partitioned dynamic time warping (CeMAPP-DTW). We show how the similarity-based peak group matching method BIPACE may be used for multiple alignment calculation individually and how it can be used as a preprocessing step for the pairwise alignments performed by CeMAPP-DTW. We evaluate the algorithms individually and in combination on a previously published small GC-MS dataset studying the Leishmania parasite and on a larger GC-MS dataset studying grains of wheat (Triticum aestivum). Conclusions We have shown that BIPACE achieves very high precision and recall and a very low number of false positive peak assignments on both evaluation datasets. CeMAPP-DTW finds a high number of true positives when executed on its own, but achieves even better results when BIPACE is used to constrain its search space. The source code of both algorithms is included in the OpenSource software framework Maltcms, which is available from http://maltcms.sf.net. The evaluation scripts of the present study are available from the same source.

Actinoplanes sp. SE50/110 is known as the producer of the alpha-glucosidase inhibitor acarbose, a potent drug in the treatment of type-2 diabetes mellitus. We conducted the first whole transcriptome analysis of Actinoplanes sp. SE50/110, using RNA-sequencing technology for comparative gene expression studies between cells grown in maltose minimal medium, maltose minimal medium with trace elements, and glucose complex medium. We first studied the behavior of Actinoplanes sp. SE50/110 cultivations in these three media and found that the different media had significant impact on growth rate and in particular on acarbose production. It was demonstrated that Actinoplanes sp. SE50/110 grew well in all three media, but acarbose biosynthesis was only observed in cultures grown in maltose minimal medium with and without trace elements. When comparing the expression profiles between the maltose minimal media with and without trace elements, only few significantly differentially expressed genes were found, which mainly code for uptake systems of metal ions provided in the trace element solution. In contrast, the comparison of expression profiles from maltose minimal medium and glucose complex medium revealed a large number of differentially expressed genes, of which the most conspicuous genes account for iron storage and uptake. Furthermore, the acarbose gene cluster was found to be highly expressed in maltose-containing media and almost silent in the glucose-containing medium. In addition, a putative antibiotic biosynthesis gene cluster was found to be similarly expressed as the acarbose cluster.

Background Mancheron, Uricaru and Rivals (Nucleic Acids Res. 39:e101, 2011) recently introduced a new approach in the context of multiple genome comparison that allows to detect regions of strong overlaps in a set of pairwise local alignments between several reference genomes and one target genome. Such overlap regions are an important source of information in genome annotation. Results In this paper we introduce a series of algorithms that improve over the approach of Mancheron et al., both in terms of computational complexity and in practical runtime. We also extend the problem definition such that overlaps to different reference genomes can be rated differently and regions overlapping only a subset of the reference genomes are detected.

Background The distance between two genomes is often computed by comparing only the common markers between them. Some approaches are also able to deal with non-common markers, allowing the insertion or the deletion of such markers. In these models, a deletion and a subsequent insertion that occur at the same position of the genome count for two sorting steps. Results Here we propose a new model that sorts non-common markers with substitutions, which are more powerful operations that comprehend insertions and deletions. A deletion and an insertion that occur at the same position of the genome can be modeled as a substitution, counting for a single sorting step. Conclusions Comparing genomes with unequal content, but without duplicated markers, we give a linear time algorithm to compute the genomic distance considering substitutions and double-cut-and-join (DCJ) operations. This model provides a parsimonious genomic distance to handle genomes free of duplicated markers, that is in practice a lower bound to the real genomic distances. The method could also be used to refine orthology assignments, since in some cases a substitution could actually correspond to an unannotated orthology.

Background Actinoplanes sp. SE50/110 is known as the wild type producer of the alpha-glucosidase inhibitor acarbose, a potent drug used worldwide in the treatment of type-2 diabetes mellitus. As the incidence of diabetes is rapidly rising worldwide, an ever increasing demand for diabetes drugs, such as acarbose, needs to be anticipated. Consequently, derived Actinoplanes strains with increased acarbose yields are being used in large scale industrial batch fermentation since 1990 and were continuously optimized by conventional mutagenesis and screening experiments. This strategy reached its limits and is generally superseded by modern genetic engineering approaches. As a prerequisite for targeted genetic modifications, the complete genome sequence of the organism has to be known. Results Here, we present the complete genome sequence of Actinoplanes sp. SE50/110 [GenBank:CP003170], the first publicly available genome of the genus Actinoplanes, comprising various producers of pharmaceutically and economically important secondary metabolites. The genome features a high mean G + C content of 71.32% and consists of one circular chromosome with a size of 9,239,851 bp hosting 8,270 predicted protein coding sequences. Phylogenetic analysis of the core genome revealed a rather distant relation to other sequenced species of the family Micromonosporaceae whereas Actinoplanes utahensis was found to be the closest species based on 16S rRNA gene sequence comparison. Besides the already published acarbose biosynthetic gene cluster sequence, several new non-ribosomal peptide synthetase-, polyketide synthase- and hybrid-clusters were identified on the Actinoplanes genome. Another key feature of the genome represents the discovery of a functional actinomycete integrative and conjugative element. Conclusions The complete genome sequence of Actinoplanes sp. SE50/110 marks an important step towards the rational genetic optimization of the acarbose production. In this regard, the identified actinomycete integrative and conjugative element could play a central role by providing the basis for the development of a genetic transformation system for Actinoplanes sp. SE50/110 and other Actinoplanes spp. Furthermore, the identified non-ribosomal peptide synthetase- and polyketide synthase-clusters potentially encode new antibiotics and/or other bioactive compounds, which might be of pharmacologic interest.

Background Classical approaches to compute the genomic distance are usually limited to genomes with the same content, without duplicated markers. However, differences in the gene content are frequently observed and can reflect important evolutionary aspects. A few polynomial time algorithms that include genome rearrangements, insertions and deletions (or substitutions) were already proposed. These methods often allow a block of contiguous markers to be inserted, deleted or substituted at once but result in distance functions that do not respect the triangular inequality and hence do not constitute metrics. Results In the present study we discuss the disruption of the triangular inequality in some of the available methods and give a framework to establish an efficient correction for two models recently proposed, one that includes insertions, deletions and double cut and join (DCJ) operations, and one that includes substitutions and DCJ operations. Conclusions We show that the proposed framework establishes the triangular inequality in both distances, by summing a surcharge on indel operations and on substitutions that depends only on the number of markers affected by these operations. This correction can be applied a posteriori, without interfering with the already available formulas to compute these distances. We claim that this correction leads to distances that are biologically more plausible.

Background Corynebacterium pseudotuberculosis is generally regarded as an important animal pathogen that rarely infects humans. Clinical strains are occasionally recovered from human cases of lymphadenitis, such as C. pseudotuberculosis FRC41 that was isolated from the inguinal lymph node of a 12-year-old girl with necrotizing lymphadenitis. To detect potential virulence factors and corresponding gene-regulatory networks in this human isolate, the genome sequence of C. pseudotuberculosis FCR41 was determined by pyrosequencing and functionally annotated. Results Sequencing and assembly of the C. pseudotuberculosis FRC41 genome yielded a circular chromosome with a size of 2,337,913 bp and a mean G+C content of 52.2%. Specific gene sets associated with iron and zinc homeostasis were detected among the 2,110 predicted protein-coding regions and integrated into a gene-regulatory network that is linked with both the central metabolism and the oxidative stress response of FRC41. Two gene clusters encode proteins involved in the sortase-mediated polymerization of adhesive pili that can probably mediate the adherence to host tissue to facilitate additional ligand-receptor interactions and the delivery of virulence factors. The prominent virulence factors phospholipase D (Pld) and corynebacterial protease CP40 are encoded in the genome of this human isolate. The genome annotation revealed additional serine proteases, neuraminidase H, nitric oxide reductase, an invasion-associated protein, and acyl-CoA carboxylase subunits involved in mycolic acid biosynthesis as potential virulence factors. The cAMP-sensing transcription regulator GlxR plays a key role in controlling the expression of several genes contributing to virulence. Conclusion The functional data deduced from the genome sequencing and the extended knowledge of virulence factors indicate that the human isolate C. pseudotuberculosis FRC41 is equipped with a distinct gene set promoting its survival under unfavorable environmental conditions encountered in the mammalian host.

Background Recent high throughput sequencing technologies are capable of generating a huge amount of data for bacterial genome sequencing projects. Although current sequence assemblers successfully merge the overlapping reads, often several contigs remain which cannot be assembled any further. It is still costly and time consuming to close all the gaps in order to acquire the whole genomic sequence. Results Here we propose an algorithm that takes several related genomes and their phylogenetic relationships into account to create a graph that contains the likelihood for each pair of contigs to be adjacent. Subsequently, this graph can be used to compute a layout graph that shows the most promising contig adjacencies in order to aid biologists in finishing the complete genomic sequence. The layout graph shows unique contig orderings where possible, and the best alternatives where necessary. Conclusions Our new algorithm for contig ordering uses sequence similarity as well as phylogenetic information to estimate adjacencies of contigs. An evaluation of our implementation shows that it performs better than recent approaches while being much faster at the same time.

The double cut and join (DCJ) operation, introduced by Yancopoulos, Attie and Friedberg in 2005, allows one to represent most rearrangement events in genomes. However, a DCJ cannot perform an insertion or a deletion and most approaches under this model consider only genomes with the same content and without duplications, including the linear time algorithms to compute the DCJ distance and to find an optimal DCJ sorting sequence. In this work, we compare two genomes with unequal content, but still without duplications, and present a new linear time algorithm to compute the genomic distance, considering DCJ and indel operations. With this method we find preliminary evidence of the occurrence of clusters of deletions in the Rickettsia bacterium.

Summary: Recent parallel pyrosequencing methods and the increasing number of finished genomes encourage the sequencing and investigation of closely related strains. Although the sequencing itself becomes easier and cheaper with each machine generation, the finishing of the genomes remains difficult. Instead of the desired whole genomic sequence, a set of contigs is the result of the assembly. In this applications note, we present the tool r2cat (related reference contig arrangement tool) that helps in the task of comparative assembly and also provides an interactive visualization for synteny inspection. Availability: http://bibiserv.techfak.uni-bielefeld.de/r2cat Contact: peter.husemann@cebitec.uni-bielefeld.de

The order of genes in genomes provides extensive information. In comparative genomics, differences or similarities of gene orders are determined to predict functional relations of genes or phylogenetic relations of genomes. For this purpose, various combinatorial models can be used to identify gene clusters--groups of genes that are colocated in a set of genomes. We introduce a unified approach to model gene clusters and define the problem of labeling the inner nodes of a given phylogenetic tree with sets of gene clusters. Our optimization criterion in this context combines two properties: parsimony, i.e., the number of gains and losses of gene clusters has to be minimal, and consistency, i.e., for each ancestral node, there must exist at least one potential gene order that contains all the reconstructed clusters. We present and evaluate an exact algorithm to solve this problem. Despite its exponential worst-case time complexity, our method is suitable even for large-scale data. We show the effectiveness and efficiency on both simulated and real data.

Whole genome comparison based on gene order has become a popular approach in comparative genomics. An important task in this field is the detection of gene clusters, i.e., sets of genes that occur co-localized in several genomes. For most applications, it is preferable to extend this definition to allow for small deviations in the gene content of the cluster occurrences. However, relaxing the equality constraint increases the computational complexity of gene cluster detection drastically. Existing approaches deal with this problem by using simplifying constraints on the cluster definition and/or allowing only pairwise genome comparison. In this article, we introduce a cluster concept named median gene clusters that improves over existing models, present efficient algorithms for their computation and show experimental results on the detection of approximate gene clusters in multiple genomes.

Metagenomics is providing striking insights into the ecology of microbial communities. The recently developed massively parallel 454 pyrosequencing technique gives the opportunity to rapidly obtain metagenomic sequences at a low cost and without cloning bias. However, the phylogenetic analysis of the short reads produced represents a significant computational challenge. The phylogenetic algorithm CARMA for predicting the source organisms of environmental 454 reads is described. The algorithm searches for conserved Pfam domain and protein families in the unassembled reads of a sample. These gene fragments (environmental gene tags, EGTs), are classified into a higher-order taxonomy based on the reconstruction of a phylogenetic tree of each matching Pfam family. The method exhibits high accuracy for a wide range of taxonomic groups, and EGTs as short as 27 amino acids can be phylogenetically classified up to the rank of genus. The algorithm was applied in a comparative study of three aquatic microbial samples obtained by 454 pyrosequencing. Profound differences in the taxonomic composition of these samples could be clearly revealed.

In the past years, many combinatorial arguments have been made to support the theory that mammalian genome rearrangement scenarios rely heavily on breakpoint reuse. Different models of genome rearrangements have been suggested, from the classical set of operations that include inversions, translocations, fusions and fissions, to more elaborate models that include transpositions. Here we show that the current definition of breakpoint reuse rateis based on assumptions that are seldom true for mammalian genomes, and propose a new approach to compute this parameter. We explore the formal properties of this new measure and apply these results to the human-mouse genome comparison. We show that the reuse rate is intimately linked to a particular rearrangement scenario, and that the reuse rate can vary from 0.89 to 1.51 for scenarios of the same length that transform the mouse genome into the human genome, where a rate of 1 indicates no reuse at all.

Repeats form a major class of sequence in genomes with implications for functional genomics and practical problems. Their detection and analysis pose a number of challenges in genomic sequence analysis, especially if the genome is not completely sequenced. The most abundant and evolutionary active forms of repeats are found in the form of familiesof long similar sequences. We present a novel method for repeat family detection and characterization in cases where the target genome sequence is not completely known. Therefore we first establish the sequence graph, a compacted version of sparse de Bruijn graphs. Using appropriate analysis of the structure of this graph and its connected components after local modifications, we are able to devise two algorithms for repeat family detection. The applicability of the methods is shown for both simulated and real genomic data sets.

Background For typing of Staphylococcus aureus, DNA sequencing of the repeat region of the protein A (spa) gene is a well established discriminatory method for outbreak investigations. Recently, it was hypothesized that this region also reflects long-term epidemiology. However, no automated and objective algorithm existed to cluster different repeat regions. In this study, the Based Upon Repeat Pattern (BURP) implementation that is a heuristic variant of the newly described EDSI algorithm was investigated to infer the clonal relatedness of different spa types. For calibration of BURP parameters, 400 representative S. aureus strains with different spa types were characterized by MLST and clustered using eBURST as "gold standard" for their phylogeny. Typing concordance analysis between eBURST and BURP clustering (spa-CC) were performed using all possible BURP parameters to determine their optimal combination. BURP was subsequently evaluated with a strain collection reflecting the breadth of diversity of S. aureus (JCM 2002; 40:4544). Results In total, the 400 strains exhibited 122 different MLST types. eBURST grouped them into 23 clonal complexes (CC; 354 isolates) and 33 singletons (46 isolates). BURP clustering of spa types using all possible parameter combinations and subsequent comparison with eBURST CCs resulted in concordances ranging from 8.2 to 96.2%. However, 96.2% concordance was reached only if spa types shorter than 8 repeats were excluded, which resulted in 37% excluded spa types. Therefore, the optimal combination of the BURP parameters was "exclude spa types shorter than 5 repeats" and "cluster spa types into spa-CC if cost distances are less than 4" exhibiting 95.3% concordance to eBURST. This algorithm identified 24 spa-CCs, 40 singletons, and excluded only 7.8% spa types. Analyzing the natural population with these parameters, the comparison of whole-genome micro-array groupings (at the level of 0.31 Pearson correlation index) and spa-CCs gave a concordance of 87.1%; BURP spa-CCs vs. manually grouped spa types resulted in 95.7% concordance. Conclusion BURP is the first automated and objective tool to infer clonal relatedness from spa repeat regions. It is able to extract an evolutionary signal rather congruent to MLST and micro-array data.

The study of genome rearrangements is an important tool in comparative genomics. This paper revisits the problem of sorting a multichromosomal genome by translocations, i.e., exchanges of chromosome ends. We give an elementary proof of the formula for computing the translocation distance in linear time, and we give a new algorithm for sorting by translocations, correcting an error in a previous algorithm by Hannenhalli.

Motivation: Novel sequencing techniques can give access to organisms that are difficult to cultivate using conventional methods. When applied to environmental samples, the data generated has some drawbacks, e.g. short length of assembled contigs, in-frame stop codons and frame shifts. Unfortunately, current gene finders cannot circumvent these difficulties. At the same time, the automated prediction of genes is a prerequisite for the increasing amount of genomic sequences to ensure progress in metagenomics. Results: We introduce a novel gene finding algorithm that incorporates features overcoming the short length of the assembled contigs from environmental data, in-frame stop codons as well as frame shifts contained in bacterial sequences. The results show that by searching for sequence similarities in an environmental sample our algorithm is capable of detecting a high fraction of its gene content, depending on the species composition and the overall size of the sample. The method is valuable for hunting novel unknown genes that may be specific for the habitat where the sample is taken. Finally, we show that our algorithm can even exploit the limited information contained in the short reads generated by 454 technology for the prediction of protein coding genes. Availability: The program is freely available upon request. Contact: Lutz.Krause@CeBiTec.Uni-Bielefeld.DE

Traditional sequence comparison by alignment employs a mutation model comprised of two events, substitutions and indels (insertions or deletions) of single positions. However, modern genetic analysis knows a variety of more complex mutation events (e. g., duplications, excisions, and rearrangements), especially regarding DNA. With ever more DNA sequence data becoming available, the need to accurately compare sequences which have clearly undergone more complicated types of mutational processes is becoming critical. Herein we introduce a new method for pairwise alignment and comparison of sequences with respect to the special evolution of tandem repeats: substitutions and indels of single positions and, additionally, duplications and excisions of variable degree (i.e., of one or more repeat copies simultaneously) are taken into account. To evaluate our method, we apply it to the spa VNTR (variable number of tandem repeats) cluster of Staphylococcus aureus, a bacterium of high medical importance.

Summary: We provide the graphical tool BACCardI for the construction of virtual clone maps from standard assembler output files or BLAST based sequence comparisons. This new tool has been applied to numerous genome projects to solve various problems including (a) validation of whole genome shotgun assemblies, (b) support for contig ordering in the finishing phase of a genome project, and (c) intergenome comparison between related strains when only one of the strains has been sequenced and a large insert library is available for the other. The BACCardI software can seamlessly interact with various sequence assembly packages. Motivation: Genomic assemblies generated from sequence information need to be validated by independent methods such as physical maps. The time-consuming task of building physical maps can be circumvented by virtual clone maps derived from read pair information of large insert libraries. Availability: The BACCardI tool is freely available at http://www.cebitec.uni-bielefeld.de/groups/brf/software/baccardi/ Contact: fm@Cebitec.Uni-Bielefeld.DE

The repetitive structure of genomic DNA holds many secrets to be discovered. A systematic study of repetitive DNA on a genomic or inter-genomic scale requires extensive algorithmic support. The REPuter program described herein was designed to serve as a fundamental tool in such studies. Efficient and complete detection of various types of repeats is provided together with an evaluation of significance and interactive visualization. This article circumscribes the wide scope of repeat analysis using applications in five different areas of sequence analysis: checking fragment assemblies, searching for low copy repeats, finding unique sequences, comparing gene structures and mapping of cDNA/EST sequences.

In physical mapping one orders a set of genetic landmarks or a library of cloned fragments of DNA according to their position in the genome. This is a preparatory step for efficient sequencing.Our approach to physical mapping divides the problem into smaller and easier subproblems by partitioning the probe set into independent parts (contigs). The focus is on the selection of probe sets which can be grouped together into contigs. We introduce a new distance function between probes, the averaged rank distance (ARD). The ARD measures the reliability of certain probe configurations in physical maps which are generated by bootstrap resampling of the raw data. This mimics an independent experiment repetition in silico. The ARD measures the distances of probes within a contig and smoothes the distances of probes in different contigs. It shows distinct jumps at contig borders. This makes it appropriate for contig selection by clustering. We designed a physical mapping algorithm that makes use of these observations and seems to be particularly well suited to the delineation of reliable contigs.We evaluated our method on data sets from two physical mapping projects. In comparison to a physical map of Pasteurella haemolytica that was computed using simulated annealing, the newly computed map is considerably cleaner. On data from Xylella fastidiosa the contigs produced by the new method could be compared to a map produced by a group of experts and the two maps largely agree in the definition of the contigs. The results of our method have already proven helpful for the design of experiments aiming at further improving the quality of a map.

The SYSTERS (short for SYSTEmatic Re-Searching) protein sequence cluster set consists of the classification of all sequences from SWISS-PROT and PIR into disjoint protein family clusters and hierarchically into superfamily and subfamily clusters. The cluster set can be searched with a sequence using the SSMAL search tool or a traditional database search tool like BLAST or FASTA. Additionally a multiple alignment is generated for each cluster and annotated with domain information from the Pfam database of protein domain families. A taxonomic overview of the organisms covered by a cluster is given based on the NCBI taxonomy. The cluster set is available for querying and browsing at http://www.dkfz-heidelberg. de/tbi/services/cluster/systersform