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A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication.

ABSTRACT: We propose a freely accessible web-based pipeline, which processes raw microarray scan data to obtain experimentally consolidated gene expression values. The tool MADSCAN, which stands for MicroArray Data Suites of Computed ANalysis, makes a practical choice among the numerous methods available for filtering, normalizing and scaling of raw microarray expression data in a dynamic and automatic way. Different statistical methods have been adapted to extract reliable information from replicate gene spots as well as from replicate microarrays for each biological situation under study. A carefully constructed experimental design thus allows to detect outlying expression values and to identify statistically significant expression values, together with a list of quality controls with proposed threshold values. The integrated processing procedure described here, based on multiple measurements per gene, is decisive for reliably monitoring subtle gene expression changes typical for most biological events.

SUBMITTER: Le Meur N

PROVIDER: S-EPMC524282 | biostudies-literature | 2004

REPOSITORIES: biostudies-literature

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A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication.

Le Meur Nolwenn N Lamirault Guillaume G Bihouée Audrey A Steenman Marja M Bédrine-Ferran Hélène H Teusan Raluca R Ramstein Gérard G Léger Jean J JJ

Nucleic acids research 20041008 18

We propose a freely accessible web-based pipeline, which processes raw microarray scan data to obtain experimentally consolidated gene expression values. The tool MADSCAN, which stands for MicroArray Data Suites of Computed ANalysis, makes a practical choice among the numerous methods available for filtering, normalizing and scaling of raw microarray expression data in a dynamic and automatic way. Different statistical methods have been adapted to extract reliable information from replicate gene ...[more]

PMID: 15475389

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Project description:BackgroundEnvironmental metagenomics is a challenging approach that is exponentially spreading in the scientific community to investigate taxonomic diversity and possible functions of the biological components. The massive amount of sequence data produced, often endowed with rich environmental metadata, needs suitable computational tools to fully explore the embedded information. Bioinformatics plays a key role in providing methodologies to manage, process and mine molecular data, integrated with environmental metagenomics collections. One such relevant example is represented by the Tara Ocean Project.ResultsWe considered the Tara 16S miTAGs released by the consortium, representing raw sequences from a shotgun metagenomics approach with similarities to 16S rRNA genes. We generated assembled 16S rDNA sequences, which were classified according to their lengths, the possible presence of chimeric reads, the putative taxonomic affiliation. The dataset was included in GLOSSary (the GLobal Ocean 16S Subunit web accessible resource), a bioinformatics platform to organize environmental metagenomics data. The aims of this work were: i) to present alternative computational approaches to manage challenging metagenomics data; ii) to set up user friendly web-based platforms to allow the integration of environmental metagenomics sequences and of the associated metadata; iii) to implement an appropriate bioinformatics platform supporting the analysis of 16S rDNA sequences exploiting reference datasets, such as the SILVA database. We organized the data in a next-generation NoSQL "schema-less" database, allowing flexible organization of large amounts of data and supporting native geospatial queries. A web interface was developed to permit an interactive exploration and a visual geographical localization of the data, either raw miTAG reads or 16S contigs, from our processing pipeline. Information on unassembled sequences is also available. The taxonomic affiliations of contigs and miTAGs, and the spatial distribution of the sampling sites and their associated sequence libraries, as they are contained in the Tara metadata, can be explored by a query interface, which allows both textual and visual investigations. In addition, all the sequence data were made available for a dedicated BLAST-based web application alongside the SILVA collection.ConclusionsGLOSSary provides an expandable bioinformatics environment, able to support the scientific community in current and forthcoming environmental metagenomics analyses.

Project description:BackgroundAlthough the biocatalytic conversion of cellulosic biomass could replace fossil oil for the production of various compounds, it is often not economically viable due to the high costs of cellulolytic enzymes. One possibility to reduce costs is consolidated bioprocessing (CBP), integrating cellulase production, hydrolysis of cellulose, and the fermentation of the released sugars to the desired product into one process step. To establish such a process, the most suitable cellulase-producing organism has to be identified. Thereby, it is crucial to evaluate the candidates under target process conditions. In this work, the chosen model process was the conversion of cellulose to the platform chemical itaconic acid by a mixed culture of a cellulolytic fungus with Aspergillus terreus as itaconic acid producer. Various cellulase producers were analyzed by the introduced freeze assay that measures the initial carbon release rate, quantifying initial cellulase activity under target process conditions. Promising candidates were then characterized online by monitoring their respiration activity metabolizing cellulose to assess the growth and enzyme production dynamics.ResultsThe screening of five different cellulase producers with the freeze assay identified Trichoderma reesei and Penicillium verruculosum as most promising. The measurement of the respiration activity revealed a retarded induction of cellulase production for P. verruculosum but a similar cellulase production rate afterwards, compared to T. reesei. The freeze assay measurement depicted that P. verruculosum reaches the highest initial carbon release rate among all investigated cellulase producers. After a modification of the cultivation procedure, these results were confirmed by the respiration activity measurement. To compare both methods, a correlation between the measured respiration activity and the initial carbon release rate of the freeze assay was introduced. The analysis revealed that the different initial enzyme/cellulose ratios as well as a discrepancy in cellulose digestibility are the main differences between the two approaches.ConclusionsWith two complementary methods to quantify cellulase activity and the dynamics of cellulase production for CBP applications, T. reesei and P. verruculosum were identified as compatible candidates for the chosen model process. The presented methods can easily be adapted to screen for suitable cellulose degrading organisms for various other applications.

Project description:For nearly 100 years serotyping has been the gold standard for the identification of Salmonella serovars. Despite the increasing adoption of DNA-based subtyping approaches, serotype information remains a cornerstone in food safety and public health activities aimed at reducing the burden of salmonellosis. At the same time, recent advances in whole-genome sequencing (WGS) promise to revolutionize our ability to perform advanced pathogen characterization in support of improved source attribution and outbreak analysis. We present the Salmonella In Silico Typing Resource (SISTR), a bioinformatics platform for rapidly performing simultaneous in silico analyses for several leading subtyping methods on draft Salmonella genome assemblies. In addition to performing serovar prediction by genoserotyping, this resource integrates sequence-based typing analyses for: Multi-Locus Sequence Typing (MLST), ribosomal MLST (rMLST), and core genome MLST (cgMLST). We show how phylogenetic context from cgMLST analysis can supplement the genoserotyping analysis and increase the accuracy of in silico serovar prediction to over 94.6% on a dataset comprised of 4,188 finished genomes and WGS draft assemblies. In addition to allowing analysis of user-uploaded whole-genome assemblies, the SISTR platform incorporates a database comprising over 4,000 publicly available genomes, allowing users to place their isolates in a broader phylogenetic and epidemiological context. The resource incorporates several metadata driven visualizations to examine the phylogenetic, geospatial and temporal distribution of genome-sequenced isolates. As sequencing of Salmonella isolates at public health laboratories around the world becomes increasingly common, rapid in silico analysis of minimally processed draft genome assemblies provides a powerful approach for molecular epidemiology in support of public health investigations. Moreover, this type of integrated analysis using multiple sequence-based methods of sub-typing allows for continuity with historical serotyping data as we transition towards the increasing adoption of genomic analyses in epidemiology. The SISTR platform is freely available on the web at https://lfz.corefacility.ca/sistr-app/.

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A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication.

Publications

A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication.

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