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At least 199 records · Page 11Linked to original sources

Whole-genome automated assembly pipeline for Chlamydia trachomatis strains from reference, in vitro and clinical samples using the integrated CtGAP pipeline.

Whole genome sequencing (WGS) is pivotal for the molecular characterization of Chlamydia trachomatis (Ct)-the leading bacterial cause of sexually transmitted infections and infectious blindness worldwide. Ct WGS can inform epidemiologic, public health and outbreak investigations of these human-restricted pathogens. However, challenges persist in generating high-quality genomes for downstream analyses given its obligate intracellular nature and difficulty with in vitro propagation. No single tool exists for the entirety of Ct genome assembly, necessitating the adaptation of multiple programs with varying success. Compounding this issue is the absence of reliable Ct reference strain genomes. We, therefore, developed CtGAP-Chlamydia trachomatisGenome Assembly Pipeline-as an integrated 'one-stop-shop' pipeline for assembly and characterization of Ct genome sequencing data from various sources including isolates, in vitro samples, clinical swabs and urine. CtGAP, written in Snakemake, enables read quality statistics output, adapter and quality trimming, host read removal, de novo and reference-guided assembly, contig scaffolding, selective ompA, multi-locus-sequence and plasmid typing, phylogenetic tree construction, and recombinant genome identification. Twenty Ct reference genomes were also generated. Successfully validated on a diverse collection of 363 samples containing Ct, CtGAP represents a novel pipeline requiring minimal bioinformatics expertise with easy adaptation for use with other bacterial species.

Chlamydia trachomatis↗

How well do we understand the clusters found in microarray data?

We wished to quantify the state-of-the-art of our understanding of clusters in microarray data. To do this we systematically compared the clusters produced on sets of microarray data using a representative set of clustering algorithms (hierarchical, k-means, and a modified version of QT_CLUST) with the annotation schemes MIPS, GeneOntology and GenProtEC. We assumed that if a cluster reflected known biology its members would share related ontological annotations. This assumption is the basis of "guilt-by-association" and is commonly used to assign the putative function of proteins. To statistically measure the relationship between cluster and annotation we developed a new predictive discriminatory measure. We found that the clusters found in microarray data do not in general agree with functional annotation classes. Although many statistically significant relationships can be found, the majority of clusters are not related to known biology (as described in annotation ontologies). This implies that use of guilt-by-association is not supported by annotation ontologies. Depending on the estimate of the amount of noise in the data, our results suggest that bioinformatics has only codified a small proportion of the biological knowledge required to understand microarray data.

Algorithms↗

Extending SEE for large-scale phenotyping of mouse open-field behavior.

SEE (Software for the Exploration of Exploration) is a visualization and analysis tool designed for the study of open-field behavior in rodents. In this paper, I present new extensions of SEE that were designed to facilitate its use for mouse behavioral phenotyping and, especially, for the problems of discrimination of genotypes and the replication of results across laboratories and experimental conditions. These extensions were specifically designed to promote a new approach in behavioral phenotyping, reminiscent of the approach that has been successfully employed in bioinformatics during recent years. The path coordinates of all animals from many experiments are stored in a database. SEE can be used to query, visualize, and analyze any desirable subsection of this database and to design new measures (endpoints) with increasingly better discriminative power and replicability. The use of the new extensions is demonstrated here in the analysis of results from several experiments and laboratories, with an emphasis on this approach.

Animals↗

Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation.

MOTIVATION: Many bioinformatics data resources not only hold data in the form of sequences, but also as annotation. In the majority of cases, annotation is written as scientific natural language: this is suitable for humans, but not particularly useful for machine processing. Ontologies offer a mechanism by which knowledge can be represented in a form capable of such processing. In this paper we investigate the use of ontological annotation to measure the similarities in knowledge content or 'semantic similarity' between entries in a data resource. These allow a bioinformatician to perform a similarity measure over annotation in an analogous manner to those performed over sequences. A measure of semantic similarity for the knowledge component of bioinformatics resources should afford a biologist a new tool in their repertoire of analyses. RESULTS: We present the results from experiments that investigate the validity of using semantic similarity by comparison with sequence similarity. We show a simple extension that enables a semantic search of the knowledge held within sequence databases. AVAILABILITY: Software available from http://www.russet.org.uk.

Artificial Intelligence↗

Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer.

BACKGROUND: Surface-enhanced laser desorption/ionization (SELDI) is an affinity-based mass spectrometric method in which proteins of interest are selectively adsorbed to a chemically modified surface on a biochip, whereas impurities are removed by washing with buffer. This technology allows sensitive and high-throughput protein profiling of complex biological specimens. METHODS: We screened for potential tumor biomarkers in 169 serum samples, including samples from a cancer group of 103 breast cancer patients at different clinical stages [stage 0 (n = 4), stage I (n = 38), stage II (n = 37), and stage III (n = 24)], from a control group of 41 healthy women, and from 25 patients with benign breast diseases. Diluted serum samples were applied to immobilized metal affinity capture Ciphergen ProteinChip Arrays previously activated with Ni2+. Proteins bound to the chelated metal were analyzed on a ProteinChip Reader Model PBS II. Complex protein profiles of different diagnostic groups were compared and analyzed using the ProPeak software package. RESULTS: A panel of three biomarkers was selected based on their collective contribution to the optimal separation between stage 0-I breast cancer patients and noncancer controls. The same separation was observed using independent test data from stage II-III breast cancer patients. Bootstrap cross-validation demonstrated that a sensitivity of 93% for all cancer patients and a specificity of 91% for all controls were achieved by a composite index derived by multivariate logistic regression using the three selected biomarkers. CONCLUSIONS: Proteomics approaches such as SELDI mass spectrometry, in conjunction with bioinformatics tools, could greatly facilitate the discovery of new and better biomarkers. The high sensitivity and specificity achieved by the combined use of the selected biomarkers show great potential for the early detection of breast cancer.

Adult↗

Depositing electron microscopy maps.

A meeting was held at the European Bioinformatics Institute (EBI) in Hinxton, United Kingdom to discuss recent progress in the development of EMD, a database for maps determined by electron microscopy that is now integrated with MSD, the macromolecular structure database at EBI. This meeting of representatives of many of the major image processing groups in electron microscopy also discussed possible software developments that would ease the documentation and deposition of such datasets. The meeting concluded with a strong endorsement of map deposition in electron microscopy and its linkage with the family of archival databases in biomedical research.

Computational Biology↗

Multiple sequence alignment with the Clustal series of programs.

The Clustal series of programs are widely used in molecular biology for the multiple alignment of both nucleic acid and protein sequences and for preparing phylogenetic trees. The popularity of the programs depends on a number of factors, including not only the accuracy of the results, but also the robustness, portability and user-friendliness of the programs. New features include NEXUS and FASTA format output, printing range numbers and faster tree calculation. Although, Clustal was originally developed to run on a local computer, numerous Web servers have been set up, notably at the EBI (European Bioinformatics Institute) (http://www.ebi.ac.uk/clustalw/).

Algorithms↗

The Ensembl genome database project.

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements from sequence analysis to data storage and visualisation. The Ensembl site is one of the leading sources of human genome sequence annotation and provided much of the analysis for publication by the international human genome project of the draft genome. The Ensembl system is being installed around the world in both companies and academic sites on machines ranging from supercomputers to laptops.

Computational Biology↗

Plant metabolomics: large-scale phytochemistry in the functional genomics era.

Metabolomics or the large-scale phytochemical analysis of plants is reviewed in relation to functional genomics and systems biology. A historical account of the introduction and evolution of metabolite profiling into today's modern comprehensive metabolomics approach is provided. Many of the technologies used in metabolomics, including optical spectroscopy, nuclear magnetic resonance, and mass spectrometry are surveyed. The critical role of bioinformatics and various methods of data visualization are summarized and the future role of metabolomics in plant science assessed.

Computational Biology↗

TFinder: A Python Web Tool for Predicting Transcription Factor Binding Sites.

Transcription is a key cell process that consists of synthesizing several copies of RNA from a gene DNA sequence. This process is highly regulated and closely linked to the ability of transcription factors to bind specifically to DNA. TFinder is an easy-to-use Python web portal allowing the identification of Individual Motifs (IM) such as Transcription Factor Binding Sites (TFBS). Using the NCBI API, TFinder extracts either promoter or gene terminal regulatory regions, through a simple query of NCBI gene name or ID. It enables simultaneous analysis across five different species for an unlimited number of genes. TFinder searches for Individual Motifs in different formats, including IUPAC codes and JASPAR entries. Moreover, TFinder also allows de novo generations of a Position Weight Matrix (PWM) and the use of already established PWM. Finally, the data are provided in a tabular and a graph format showing the relevance and the P-value of the Individual Motifs found as well as their location relative to the Transcription Start Site (TSS) or the terminal region of the gene. The results are then sent by email to users facilitating the subsequent data analysis and sharing. TFinder is written in Python and freely available on GitHub under the MIT license: https://github.com/Jumitti/TFinder. It can be accessed as a web application implemented in Streamlit at https://tfinder-ipmc.streamlit.app. Resources are available on Streamlit "Resources" tab. TFINDER strength is that it relies on an all-in-one intuitive tool allowing users inexperienced with bioinformatics tools to retrieve gene regulatory regions sequences in multiple species and to search for individual motifs in a huge number of genes.

Transcription Factors↗

Developing an energy landscape for the novel function of a (beta/alpha)8 barrel: ammonia conduction through HisF.

HisH-hisF is a multidomain globular protein complex; hisH is a class I glutamine amidotransferase that hydrolyzes glutamine to form ammonia, and hisF is a (beta/alpha)8 barrel cyclase that completes the ring formation of imidizole glycerol phosphate synthase. Together, hisH and hisF form a glutamine amidotransferase that carries out the fifth step of the histidine biosynthetic pathway. Recently, it has been suggested that the (beta/alpha)8 barrel participates in a novel function: to channel ammonia from the active site of hisH to the active site of hisF. The present study presents a series of molecular dynamic simulations that investigate the channeling function of hisF. This article reconstructs potentials of mean force for the conduction of ammonia through the channel, and the entrance of ammonia through the strictly conserved channel gate, in both a closed and a hypothetical open conformation. The resulting energy landscape within the channel supports the idea that ammonia does indeed pass through the barrel, interacting with conserved hydrophilic residues along the way. The proposed open conformation, which involves an alternate rotamer state of one of the gate residues, presents only an approximately 2.5-kcal energy barrier to ammonia entry. Another alternate open-gate conformation, which may play a role in non-nitrogen-fixing organisms, is deduced through bioinformatics.

Algorithms↗

The European Bioinformatics Institute (EBI) databases.

This paper describes the databases and services of the European Bioinformatics Institute (EBI). In collaboration with DDBJ and GenBank/NCBI, the EBI maintains and distributes the EMBL Nucleotide Sequence Database, Europe's primary nucleotide sequence data resource. The EBI also maintains and distributes the SWISS-PROT Protein Sequence Database, in collaboration with Amos Bairoch of the University of Geneva. Over thirty additional specialist molecular biology databases, as well as software and documentation of interest to molecular biologists, are also available. The EBI network services include database searching, entry retrieval, and sequence similarity searching facilities.

Amino Acid Sequence↗

Computational methods for gene annotation: the Arabidopsis genome.

Since the structure of the DNA molecule was identified half a century ago, the complete genome sequence has been determined for 37 prokaryotes and several eukaryotes. With the exponential growth of genetic information, bioinformatics has attempted to predict gene locations and functions in cyberspace prior to experimental confirmation at the bench.

Arabidopsis↗

Storing biological sequence databases in relational form.

SUMMARY: We have created a set of applications using Perl and Java in combination with XML technology to install biological sequence databases into an Oracle RDBMS. An easy-to-use interface using Java has been created for database query and other tools developed to integrate with our in-house bioinformatics applications. AVAILIBILITY: The database schema, DTD file, and source codes are available from the authors via email. CONTACT: guochun_ xie@merck. com

Amino Acid Sequence↗

Representation and processing of complex DNA spatial architecture and its annotated genomic content.

This paper presents a new general approach for the spatial representation and visualization of DNA molecule and its annotated information. This approach is based on a biological 3D model that predicts the complex spatial trajectory of huge naked DNA. With such modeling, a global vision of the sequence is possible, which is different and complementary to other representations as textual, linguistics or syntactic ones. The DNA is well known as a three-dimensional structure. Whereas, the spatial information plays a great part during its evolution and its interaction with the other biological elements This work will motivate investigations in order to launch new bioinformatics studies for the analysis of the spatial architecture of the genome. Besides, in order to obtain a friendly interactive visualization, a powerful graphic modeling is proposed including DNA complex trajectory management and its annotated-based content structuring. The paper describes spatial architecture modeling, with consideration of both biological and computational constraints. This work is implemented through a powerful graphic software tool, named ADN-Viewer. Several examples of visualization are shown for various organisms and biological elements.

DNA↗

Poxvirus orthologous clusters: toward defining the minimum essential poxvirus genome.

Increasingly complex bioinformatic analysis is necessitated by the plethora of sequence information currently available. A total of 21 poxvirus genomes have now been completely sequenced and annotated, and many more genomes will be available in the next few years. First, we describe the creation of a database of continuously corrected and updated genome sequences and an easy-to-use and extremely powerful suite of software tools for the analysis of genomes, genes, and proteins. These tools are available free to all researchers and, in most cases, alleviate the need for using multiple Internet sites for analysis. Further, we describe the use of these programs to identify conserved families of genes (poxvirus orthologous clusters) and have named the software suite POCs, which is available at www.poxvirus.org. Using POCs, we have identified a set of 49 absolutely conserved gene families-those which are conserved between the highly diverged families of insect-infecting entomopoxviruses and vertebrate-infecting chordopoxviruses. An additional set of 41 gene families conserved in chordopoxviruses was also identified. Thus, 90 genes are completely conserved in chordopoxviruses and comprise the minimum essential genome, and these will make excellent drug, antibody, vaccine, and detection targets. Finally, we describe the use of these tools to identify necessary annotation and sequencing updates in poxvirus genomes. For example, using POCs, we identified 19 genes that were widely conserved in poxviruses but missing from the vaccinia virus strain Tian Tan 1998 GenBank file. We have reannotated and resequenced fragments of this genome and verified that these genes are conserved in Tian Tan. The results for poxvirus genes and genomes are discussed in light of evolutionary processes.

Amino Acid Sequence↗

Ensembl 2002: accommodating comparative genomics.

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of human, mouse and other genome sequences, available as either an interactive web site or as flat files. Ensembl also integrates manually annotated gene structures from external sources where available. As well as being one of the leading sources of genome annotation, Ensembl is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. These range from sequence analysis to data storage and visualisation and installations exist around the world in both companies and at academic sites. With both human and mouse genome sequences available and more vertebrate sequences to follow, many of the recent developments in Ensembl have focusing on developing automatic comparative genome analysis and visualisation.

Animals↗

PhyloNaP: a user-friendly database of phylogeny for natural product-producing enzymes.

SUMMARY: Phylogenetic analysis is widely used to predict enzyme function, yet building annotated and reusable trees is labor-intensive and requires extensive knowledge about the specific enzymes. Existing resources rarely cover biosynthetic enzymes and lack the context needed for meaningful analysis. We present PhyloNaP, the first large-scale resource dedicated to phylogenies of biosynthetic enzymes. PhyloNaP provides ∼51 000 annotated and interactive trees enriched with chemical, functional, and taxonomic information. Users can classify their own sequences via phylogenetic placement, enabling functional inference in an evolutionary context. A contribution portal allows the community to submit curated trees. By combining scale, breadth of annotation, and interactive functionality, PhyloNaP fills a major gap in bioinformatics resources for enzyme discovery and annotation, with immediate applications to secondary metabolism and beyond. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at https://phylonap.cs.uni-tuebingen.de.

Phylogeny↗