| 1. |
- Bengtsson-Palme, Johan, 1985, et al.
(författare)
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Strategies to improve usability and preserve accuracy in biological sequence databases
- 2016
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Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 16:18, s. 2454-2460
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Tidskriftsartikel (refereegranskat)abstract
- Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identifications of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate post-submission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.
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| 2. |
- Boulund, Fredrik, 1985, et al.
(författare)
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A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences
- 2012
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Broad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail. RESULTS: In this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature. CONCLUSIONS: The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/.
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| 3. |
- Boulund, Fredrik, et al.
(författare)
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Computational and Statistical Considerations in the Analysis of Metagenomic Data
- 2018
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Ingår i: Metagenomics: Perspectives, Methods, and Applications. - 9780081022689 ; , s. 81-102
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In shotgun metagenomics, microbial communities are studied by random DNA fragments sequenced directly from environmental and clinical samples. The resulting data is massive, potentially consisting of billions of sequence reads describing millions of microbial genes. The data interpretation is therefore nontrivial and dependent on dedicated computational and statistical methods. In this chapter we discuss the many challenges associated with the analysis of shotgun metagenomic data. First, we address computational issues related to the quantification of genes in metagenomes. We describe algorithms for efficient sequence comparisons, recommended practices for setting up data workflows and modern high-performance computer resources that can be used to perform the analysis. Next, we outline the statistical aspects, including removal of systematic errors and how to identify differences between microbial communities from different experimental conditions. We conclude by underlining the increasing importance of efficient and reliable computational and statistical solutions in the analysis of large metagenomic datasets.
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| 4. |
- Buongermino Pereira, Mariana, 1982, et al.
(författare)
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A comprehensive survey of integron-associated genes present in metagenomes
- 2020
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundIntegrons are genomic elements that mediate horizontal gene transfer by inserting and removing genetic material using site-specific recombination. Integrons are commonly found in bacterial genomes, where they maintain a large and diverse set of genes that plays an important role in adaptation and evolution. Previous studies have started to characterize the wide range of biological functions present in integrons. However, the efforts have so far mainly been limited to genomes from cultivable bacteria and amplicons generated by PCR, thus targeting only a small part of the total integron diversity. Metagenomic data, generated by direct sequencing of environmental and clinical samples, provides a more holistic and unbiased analysis of integron-associated genes. However, the fragmented nature of metagenomic data has previously made such analysis highly challenging.ResultsHere, we present a systematic survey of integron-associated genes in metagenomic data. The analysis was based on a newly developed computational method where integron-associated genes were identified by detecting their associated recombination sites. By processing contiguous sequences assembled from more than 10 terabases of metagenomic data, we were able to identify 13,397 unique integron-associated genes. Metagenomes from marine microbial communities had the highest occurrence of integron-associated genes with levels more than 100-fold higher than in the human microbiome. The identified genes had a large functional diversity spanning over several functional classes. Genes associated with defense mechanisms and mobility facilitators were most overrepresented and more than five times as common in integrons compared to other bacterial genes. As many as two thirds of the genes were found to encode proteins of unknown function. Less than 1% of the genes were associated with antibiotic resistance, of which several were novel, previously undescribed, resistance gene variants.ConclusionsOur results highlight the large functional diversity maintained by integrons present in unculturable bacteria and significantly expands the number of described integron-associated genes.
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| 5. |
- Buongermino Pereira, Mariana, 1982, et al.
(författare)
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Comparison of normalization methods for the analysis of metagenomic gene abundance data
- 2018
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 19:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: In shotgun metagenomics, microbial communities are studied through direct sequencing of DNA without any prior cultivation. By comparing gene abundances estimated from the generated sequencing reads, functional differences between the communities can be identified. However, gene abundance data is affected by high levels of systematic variability, which can greatly reduce the statistical power and introduce false positives. Normalization, which is the process where systematic variability is identified and removed, is therefore a vital part of the data analysis. A wide range of normalization methods for high-dimensional count data has been proposed but their performance on the analysis of shotgun metagenomic data has not been evaluated. Results: Here, we present a systematic evaluation of nine normalization methods for gene abundance data. The methods were evaluated through resampling of three comprehensive datasets, creating a realistic setting that preserved the unique characteristics of metagenomic data. Performance was measured in terms of the methods ability to identify differentially abundant genes (DAGs), correctly calculate unbiased p-values and control the false discovery rate (FDR). Our results showed that the choice of normalization method has a large impact on the end results. When the DAGs were asymmetrically present between the experimental conditions, many normalization methods had a reduced true positive rate (TPR) and a high false positive rate (FPR). The methods trimmed mean of M-values (TMM) and relative log expression (RLE) had the overall highest performance and are therefore recommended for the analysis of gene abundance data. For larger sample sizes, CSS also showed satisfactory performance. Conclusions: This study emphasizes the importance of selecting a suitable normalization methods in the analysis of data from shotgun metagenomics. Our results also demonstrate that improper methods may result in unacceptably high levels of false positives, which in turn may lead to incorrect or obfuscated biological interpretation.
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| 6. |
- Buongermino Pereira, Mariana, 1982, et al.
(författare)
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Differences in the Binding Affinities of ErbB Family: Heterogeneity in the Prediction of Resistance Mutants
- 2013
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203 .- 1932-6203. ; 8:10, s. e77054-
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Tidskriftsartikel (refereegranskat)abstract
- The pressure exerted by drugs targeted to a protein in any therapy inevitably leads to the emergence of drug resistance. One major mechanism of resistance involves the mutation of key residues in the target protein. Drugs that competitively replace a natural substrate are often made ineffective by mutations that reduce the drug’s affinity relative to that of the natural substrate. Hence atomic level understanding of the mechanisms that underlie this behavior is of utmost importance in efforts to design new drugs that can target such mutant proteins. Methods that can predict these mutations before they appear in clinic would be a major advance in the selection of the ppropriate treatment strategy in patients. The present computational approach aims to model this emergence in EGFR and ErbB2 after treatment with the drug lapatinib, by investigating the structural, dynamic and energetic effects on these kinases when bound to the natural substrate ATP andto lapatinib. The study reveals binding modes and subpopulations that are presumably normally cryptic and these have been analyzed extensively here with respect to sites that are predicted to be hotspots for resisting mutations. These positions are compared in the context of currently available data from laboratory-based experiments and mechanistic details, at the atomistic level, of the origin of resistance are developed. The prediction of novel mutations, if validated by their emergence in the clinic, will make these methods as a powerful predictive tool which can be used in the design of new kinase inhibitors.
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| 7. |
- Buongermino Pereira, Mariana, 1982, et al.
(författare)
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HattCI: Fast and Accurate attC site Identification Using Hidden Markov Models
- 2016
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Ingår i: Journal of Computational Biology. - : Mary Ann Liebert Inc. - 1066-5277 .- 1557-8666. ; 23:11, s. 891-902
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Tidskriftsartikel (refereegranskat)abstract
- Integrons are genetic elements that facilitate the horizontal gene transfer in bacteria and are known to harbor genes associated with antibiotic resistance. The gene mobility in the integrons is governed by the presence of attC sites, which are 55 to 141-nucleotide-long imperfect inverted repeats. Here we present HattCI, a new method for fast and accurate identification of attC sites in large DNA data sets. The method is based on a generalized hidden Markov model that describes each core component of an attC site individually. Using twofold cross-validation experiments on a manually curated reference data set of 231 attC sites from class 1 and 2 integrons, HattCI showed high sensitivities of up to 91.9% while maintaining satisfactory false-positive rates. When applied to a metagenomic data set of 35 microbial communities from different environments, HattCI found a substantially higher number of attC sites in the samples that are known to contain more horizontally transferred elements. HattCI will significantly increase the ability to identify attC sites and thus integron-mediated genes in genomic and metagenomic data. HattCI is implemented in C and is freely available at http://bioinformatics.math.chalmers.se/HattCI.
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| 8. |
- Buongermino Pereira, Mariana, 1982
(författare)
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Modeling of bacterial DNA patterns important in horizontal gene transfer using stochastic grammars
- 2015
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- DNA contains genes which carry the blueprints for all processes necessary to maintain life. In addition to genes, DNA also contains a wide range of functional patterns, which governs many of these processes. These functional patterns have typically a high variability, both within and between species, which makes them hard to detect. Stochastic models, such as hidden Markov models and conditional random fields, offer flexible frameworks that can be used to describe these patterns, their variability and dependencies. In this thesis, we describe two such models for identification of attC sites, patterns necessary for the sharing of genes between bacteria, in a process known as horizontal gene transfer. Acquired genes causing bacteria to become resistant to antibiotics are often associated with attC sites, which make their identification highly relevant. In the first paper we develop a stochastic regular grammar defined by an eight-state generalized hidden Markov model that describes the sequence conservation and length distribution of the different parts of an attC site. The different model assumptions were evaluated and improved using cross-validation experiments, which resulted in a high sensitivity in detecting attC sites. The model was applied to a real dataset in the form of a well-studied plasmid and was able to find the majority of the present attC sites. In addition, six metagenomic samples from polluted and pristine environments were analysed. The model predicted a 15-fold higher abundance of attC sites in the polluted environments compared to the pristine ones. The model implementation, HattCI, was done in R and is freely available at http://bioinformatics.math.chalmers.se/HattCI. AttC sites fold into a three-dimensional structure that is crucial for the horizontal transfer of genes. In the second paper, we extend our previous model to include specific information about this folding. We develop a stochastic context-free grammar, which is suited to describe the nested dependencies induced by the structure. The grammar includes features that describe thermodynamic properties of the folding. The model is formulated in the framework of conditional random fields, with parameter estimation done numerically using structured support vector machines. A first implementation of the model has been completed; further experiments, such as evaluation of the performance using cross-validation is planned. This thesis demonstrates the flexibility of stochastic grammars for modelling the variability and dependencies in DNA patterns. It also emphasizes the value of the use of stochastic methods in the field of microbiology and infectious diseases.
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| 9. |
- Buongermino Pereira, Mariana, 1982
(författare)
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Statistical modelling and analyses of DNA sequence data with applications to metagenomics
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Microorganisms are organised in complex communities and are ubiquitous in all ecosystems, including natural environments and inside the human gut. Metagenomics, which is the direct sequencing of DNA from a sample, enables studying the collective genomes of the organisms that are there present. However, the resulting data is highly variable, and statistical models are therefore necessary to assure correct biological interpretations. This thesis aims to develop statistical models that provide an increased understanding of metagenomics data. In Paper I, we develop, implement and evaluate HattCI, which is a high-performance generalised hidden Markov model for the identification of integron-associated attC sites in DNA sequence data. In Paper II, we implement HattCI and other bioinformatics tools into a computational method to identify and characterise the biological functions of integron-mediated genes. The method is used to identify 13,397 integron-mediated genes present in metagenomic data. In Paper III, we provide a conceptual overview of the computational and statistical challenges involved in analysing gene abundance data. In Paper IV, we perform a comprehensive evaluation of nine normalisation methods for metagenomic gene abundance data. Our results highlight the importance of using a suitable method to avoid introducing an unacceptably high rate of false positives. The methods presented in this thesis improve the analysis of metagenomic data and thereby the understanding of microbial communities. Specifically, this thesis highlights the importance of statistical modelling in addressing the large variability of high-dimensional biological data and ensuring its sound interpretation.
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| 10. |
- Johnning, Anna, 1985, et al.
(författare)
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The resistomes of six carbapenem-resistant pathogens - a critical genotype-phenotype analysis
- 2018
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Ingår i: Microbial Genomics. - : Microbiology Society. - 2057-5858. ; 4:11
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Tidskriftsartikel (refereegranskat)abstract
- Carbapenem resistance is a rapidly growing threat to our ability to treat refractory bacterial infections. To understand how carbapenem resistance is mobilized and spread between pathogens, it is important to study the genetic context of the underlying resistance mechanisms. In this study, the resistomes of six clinical carbapenem-resistant isolates of five different species - Acinetobacter baumannii, Escherichia colt, two Klebsiella pneumoniae, Proteus mirabilis and Pseudomonas aeruginosa - were characterized using whole genome sequencing. All Enterobacteriaceae isolates and the A. baumannii isolate had acquired a large number of antimicrobial resistance genes (7-18 different genes per isolate), including the following encoding carbapenemases: bla(KPC-2), bla(OXA-48), bla(OXA-72), bla(NDM-1), bla(NDm-7) and bla(VIM-1). In addition, a novel version of bla(SHv) was discovered. Four new resistance plasmids were identified and their fully assembled sequences were verified using optical DNA mapping. Most of the resistance genes were colocalized on these and other plasmids, suggesting a risk for coselection. In contrast, five out of six carbapenemase genes were present on plasmids with no or few other resistance genes. The expected level of resistance - based on acquired resistance determinants - was concordant with measured levels in most cases. There were, however, several important discrepancies for four of the six isolates concerning multiple classes of antibiotics. In conclusion, our results further elucidate the diversity of carbapenemases, their mechanisms of horizontal transfer and possible patterns of co-selection. The study also emphasizes the difficulty of using whole genome sequencing for antimicrobial susceptibility testing of pathogens with complex genotypes.
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