Daniel Dalevi

Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.

ABSTRACT A 16S rRNA gene database (http://greengenes.lbl.gov ) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria.

Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite

From the standpoints of both basic research and biotechnology, there is considerable interest in reaching a clearer understanding of the diversity of biological mechanisms employed during lignocellulose degradation. Globally, termites are an extremely successful group of wood-degrading organisms and are therefore important both for their roles in carbon turnover in the environment and as potential sources of biochemical catalysts for efforts aimed at converting wood into biofuels. Only recently have data supported any direct role for the symbiotic bacteria in the gut of the termite in cellulose and xylan hydrolysis. Here we use a metagenomic analysis of the bacterial community resident in the hindgut paunch of a wood-feeding ‘higher’ Nasutitermes species (which do not contain cellulose-fermenting protozoa) to show the presence of a large, diverse set of bacterial genes for cellulose and xylan hydrolysis. Many of these genes were expressed in vivo or had cellulase activity in vitro, and further analyses implicate spirochete and fibrobacter species in gut lignocellulose degradation. New insights into other important symbiotic functions including H2 metabolism, CO2-reductive acetogenesis and N2 fixation are also provided by this first system-wide gene analysis of a microbial community specialized towards plant lignocellulose degradation. Our results underscore how complex even a 1-μl environment can be.

ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes

UNLABELLED Microorganisms are ubiquitous in nature and constitute intrinsic parts of almost every ecosystem. A culture-independent and powerful way to study microbial communities is metagenomics. In such studies, functional analysis is performed on fragmented genetic material from multiple species in the community. The recent advances in high-throughput sequencing have greatly increased the amount of data in metagenomic projects. At present, there is an urgent need for efficient statistical tools to analyse these data. We have created ShotgunFunctionalizeR, an R-package for functional comparison of metagenomes. The package contains tools for importing, annotating and visualizing metagenomic data produced by shotgun high-throughput sequencing. ShotgunFunctionalizeR contains several statistical procedures for assessing functional differences between samples, both for individual genes and for entire pathways. In addition to standard and previously published methods, we have developed and implemented a novel approach based on a Poisson model. This procedure is highly flexible and thus applicable to a wide range of different experimental designs. We demonstrate the potential of ShotgunFunctionalizeR by performing a regression analysis on metagenomes sampled at multiple depths in the Pacific Ocean. AVAILABILITY http://shotgun.zool.gu.se

Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting

UNLABELLED A set of new algorithms and software tools for automatic protein identification using peptide mass fingerprinting is presented. The software is automatic, fast and modular to suit different laboratory needs, and it can be operated either via a Java user interface or called from within scripts. The software modules do peak extraction, peak filtering and protein database matching, and communicate via XML. Individual modules can therefore easily be replaced with other software if desired, and all intermediate results are available to the user. The algorithms are designed to operate without human intervention and contain several novel approaches. The performance and capabilities of the software is illustrated on spectra from different mass spectrometer manufacturers, and the factors influencing successful identification are discussed and quantified. MOTIVATION Protein identification with mass spectrometric methods is a key step in modern proteomics studies. Some tools are available today for doing different steps in the analysis. Only a few commercial systems integrate all the steps in the analysis, often for only one vendors hardware, and the details of these systems are not public. RESULTS A complete system for doing protein identification with peptide mass fingerprints is presented, including everything from peak picking to matching the database protein. The details of the different algorithms are disclosed so that academic researchers can have full control of their tools. AVAILABILITY The described software tools are available from the Halmstad University website www.hh.se/staff/bioinf/ SUPPLEMENTARY INFORMATION Details of the algorithms are described in supporting information available from the Halmstad University website www.hh.se/staff/bioinf/

Bridging the gap between systems biology and medicine

Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.

A multiple-outgroup approach to resolving division-level phylogenetic relationships using 16S rDNA data

The 16S rRNA gene (16S rDNA) is currently the most widely used gene for estimating the evolutionary history of prokaryotes. To date, there are more than 30,000 16S rDNA sequences available from the core databases, GenBank, EMBL and DDBJ. This great number may cause a dilemma when composing datasets for phylogenetic analysis, since the choice and number of reference organisms are known to affect the resulting tree topology. A group of sequences appearing monophyletic in one dataset may not be so in another. This can be especially problematic when establishing the relationships of distantly related sequences at the division (phylum) level. In this study, a multiple-outgroup approach to resolving division-level phylogenetic relationships is suggested using 16S rDNA data. The approach is illustrated by two case studies concerning the monophyly of two recently proposed bacterial divisions, OP9 and OP10.

Identification of putative regulatory upstream ORFs in the yeast genome using heuristics and evolutionary conservation

BackgroundThe translational efficiency of an mRNA can be modulated by upstream open reading frames (uORFs) present in certain genes. A uORF can attenuate translation of the main ORF by interfering with translational reinitiation at the main start codon. uORFs also occur by chance in the genome, in which case they do not have a regulatory role. Since the sequence determinants for functional uORFs are not understood, it is difficult to discriminate functional from spurious uORFs by sequence analysis.ResultsWe have used comparative genomics to identify novel uORFs in yeast with a high likelihood of having a translational regulatory role. We examined uORFs, previously shown to play a role in regulation of translation in Saccharomyces cerevisiae, for evolutionary conservation within seven Saccharomyces species. Inspection of the set of conserved uORFs yielded the following three characteristics useful for discrimination of functional from spurious uORFs: a length between 4 and 6 codons, a distance from the start of the main ORF between 50 and 150 nucleotides, and finally a lack of overlap with, and clear separation from, neighbouring uORFs. These derived rules are inherently associated with uORFs with properties similar to the GCN4 locus, and may not detect most uORFs of other types. uORFs with high scores based on these rules showed a much higher evolutionary conservation than randomly selected uORFs. In a genome-wide scan in S. cerevisiae, we found 34 conserved uORFs from 32 genes that we predict to be functional; subsequent analysis showed the majority of these to be located within transcripts. A total of 252 genes were found containing conserved uORFs with properties indicative of a functional role; all but 7 are novel. Functional content analysis of this set identified an overrepresentation of genes involved in transcriptional control and development.ConclusionEvolutionary conservation of uORFs in yeasts can be traced up to 100 million years of separation. The conserved uORFs have certain characteristics with respect to length, distance from each other and from the main start codon, and folding energy of the sequence. These newly found characteristics can be used to facilitate detection of other conserved uORFs.

Bayesian classifiers for detecting HGT using fixed and variable order markov models of genomic signatures

MOTIVATION Analyses of genomic signatures are gaining attention as they allow studies of species-specific relationships without involving alignments of homologous sequences. A naïve Bayesian classifier was built to discriminate between different bacterial compositions of short oligomers, also known as DNA words. The classifier has proven successful in identifying foreign genes in Neisseria meningitis. In this study we extend the classifier approach using either a fixed higher order Markov model (Mk) or a variable length Markov model (VLMk). RESULTS We propose a simple algorithm to lock a variable length Markov model to a certain number of parameters and show that the use of Markov models greatly increases the flexibility and accuracy in prediction to that of a naïve model. We also test the integrity of classifiers in terms of false-negatives and give estimates of the minimal sizes of training data. We end the report by proposing a method to reject a false hypothesis of horizontal gene transfer. AVAILABILITY Software and Supplementary information available at www.cs.chalmers.se/~dalevi/genetic_sign_classifiers/.

A 6-gene signature identifies four molecular subgroups of neuroblastoma

BackgroundThere are currently three postulated genomic subtypes of the childhood tumour neuroblastoma (NB); Type 1, Type 2A, and Type 2B. The most aggressive forms of NB are characterized by amplification of the oncogene MYCN (MNA) and low expression of the favourable marker NTRK1. Recently, mutations or high expression of the familial predisposition gene Anaplastic Lymphoma Kinase (ALK) was associated to unfavourable biology of sporadic NB. Also, various other genes have been linked to NB pathogenesis.ResultsThe present study explores subgroup discrimination by gene expression profiling using three published microarray studies on NB (47 samples). Four distinct clusters were identified by Principal Components Analysis (PCA) in two separate data sets, which could be verified by an unsupervised hierarchical clustering in a third independent data set (101 NB samples) using a set of 74 discriminative genes. The expression signature of six NB-associated genes ALK, BIRC5, CCND1, MYCN, NTRK1, and PHOX2B, significantly discriminated the four clusters (p < 0.05, one-way ANOVA test). PCA clusters p1, p2, and p3 were found to correspond well to the postulated subtypes 1, 2A, and 2B, respectively. Remarkably, a fourth novel cluster was detected in all three independent data sets. This cluster comprised mainly 11q-deleted MNA-negative tumours with low expression of ALK, BIRC5, and PHOX2B, and was significantly associated with higher tumour stage, poor outcome and poor survival compared to the Type 1-corresponding favourable group (INSS stage 4 and/or dead of disease, p < 0.05, Fishers exact test).ConclusionsBased on expression profiling we have identified four molecular subgroups of neuroblastoma, which can be distinguished by a 6-gene signature. The fourth subgroup has not been described elsewhere, and efforts are currently made to further investigate this groups specific characteristics.

A New Order Estimator for Fixed and Variable Length Markov Models with Applications to DNA Sequence Similarity

Recently Peres and Shields discovered a new method for estimating the order of a stationary fixed order Markov chain. They showed that the estimator is consistent by proving a threshold result. While this threshold is valid asymptotically in the limit, it is not very useful for DNA sequence analysis where data sizes are moderate. In this paper we give a novel interpretation of the Peres-Shields estimator as a sharp transition phenomenon. This yields a precise and powerful estimator that quickly identifies the core dependencies in data. We show that it compares favorably to other estimators, especially in the presence of variable dependencies. Motivated by this last point, we extend the Peres-Shields estimator to Variable Length Markov Chains. We compare it to a well-established estimator and show that it is superior in terms of the predictive likelihood. We give an application to the problem of detecting DNA sequence similarity in plasmids.