Analysis and Management of HIV Peptide Microarray Experiments

López-Campos, Guillermo; García-Albert, Luis; Martín-Sánchez, Fernando; García-Sáiz, Alfredo

doi:10.1055/s-0038-1634060

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…Synthesized peptides are spotted in a grid-layout on glass slides which allow the screening of thousands of peptides within a single experiment with requiring only a small quantity of sample. Applications range from studying the humoral response to HIV [1] or food allergens [2] to the detection of cancer biomarkers [3] and antibody signatures [4] to the characterization of protein-protein interactions [5] and of kinase substrates [6,7]. …”

Section: Introductionmentioning

confidence: 99%

“…For differential analysis, several approaches have adapted differential expression detection schemes to peptide microarrays, including a support vector machine driven webtool for distinguishing peptide binding intensities of two experimental groups [14] and adapted statistical tests for differentiating measured intensities for two populations [1,15]. …”

Section: Introductionmentioning

confidence: 99%

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rapmad: Robust analysis of peptide microarray data

et al. 2011

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BackgroundPeptide microarrays offer an enormous potential as a screening tool for peptidomics experiments and have recently seen an increased field of application ranging from immunological studies to systems biology. By allowing the parallel analysis of thousands of peptides in a single run they are suitable for high-throughput settings. Since data characteristics of peptide microarrays differ from DNA oligonucleotide microarrays, computational methods need to be tailored to these specifications to allow a robust and automated data analysis. While follow-up experiments can ensure the specificity of results, sensitivity cannot be recovered in later steps. Providing sensitivity is thus a primary goal of data analysis procedures. To this end we created rapmad (Robust Alignment of Peptide MicroArray Data), a novel computational tool implemented in R.ResultsWe evaluated rapmad in antibody reactivity experiments for several thousand peptide spots and compared it to two existing algorithms for the analysis of peptide microarrays. rapmad displays competitive and superior behavior to existing software solutions. Particularly, it shows substantially improved sensitivity for low intensity settings without sacrificing specificity. It thereby contributes to increasing the effectiveness of high throughput screening experiments.Conclusionsrapmad allows the robust and sensitive, automated analysis of high-throughput peptide array data. The rapmad R-package as well as the data sets are available from http://www.tron-mz.de/compmed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

rapmad: Robust analysis of peptide microarray data

et al. 2011

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“…The signal intensity of the two fluors was determined using ImaGene software (BioDiscovery, El Segundo, CA, USA). Microarray data were analysed using ImaGene software, Microsoft Excel and an in-house designed and built Microsoft Access database [21]. Gene calling was based on a signal-to-noise ratio (SNR) >3 for each spot.…”

Section: Methodsmentioning

confidence: 99%

Analysis of the genome content of Lactococcus garvieae by genomic interspecies microarray hybridization

et al. 2010

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BackgroundLactococcus garvieae is a bacterial pathogen that affects different animal species in addition to humans. Despite the widespread distribution and emerging clinical significance of L. garvieae in both veterinary and human medicine, there is almost a complete lack of knowledge about the genetic content of this microorganism. In the present study, the genomic content of L. garvieae CECT 4531 was analysed using bioinformatics tools and microarray-based comparative genomic hybridization (CGH) experiments. Lactococcus lactis subsp. lactis IL1403 and Streptococcus pneumoniae TIGR4 were used as reference microorganisms.ResultsThe combination and integration of in silico analyses and in vitro CGH experiments, performed in comparison with the reference microorganisms, allowed establishment of an inter-species hybridization framework with a detection threshold based on a sequence similarity of ≥ 70%. With this threshold value, 267 genes were identified as having an analogue in L. garvieae, most of which (n = 258) have been documented for the first time in this pathogen. Most of the genes are related to ribosomal, sugar metabolism or energy conversion systems. Some of the identified genes, such as als and mycA, could be involved in the pathogenesis of L. garvieae infections.ConclusionsIn this study, we identified 267 genes that were potentially present in L. garvieae CECT 4531. Some of the identified genes could be involved in the pathogenesis of L. garvieae infections. These results provide the first insight into the genome content of L. garvieae.

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“…This annotation includes information related with a variety of data types, such as concentration of different reagents, photo-spectrometry data related with efficiency in labeling processes and other experimental parameters, as well as a curated annotation of the probes on the microarray. This information was managed using a "in-house" built MIAME compliant system [ 42 ] that facilitated data analyses, results interpretation and data export to public repositories.…”

Section: Reviewmentioning

confidence: 99%

Lactococcus garvieae: a small bacteria and a big data world

López-Campos

Aguado-Urda

Blanco

et al. 2015

Health Inf Sci Syst

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ObjectiveTo describe the importance of bioinformatics tools to analyze the big data yielded from new "omics" generation-methods, with the aim of unraveling the biology of the pathogen bacteria Lactococcus garvieae.MethodsThe paper provides the vision of the large volume of data generated from genome sequences, gene expression profiles by microarrays and other experimental methods that require biomedical informatics methods for management and analysis.ResultsThe use of biomedical informatics methods improves the analysis of big data in order to obtain a comprehensive characterization and understanding of the biology of pathogenic organisms, such as L. garvieae.ConclusionsThe "Big Data" concepts of high volume, veracity and variety are nowadays part of the research in microbiology associated with the use of multiple methods in the "omic" era. The use of biomedical informatics methods is a requisite necessary to improve the analysis of these data.

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Analysis and Management of HIV Peptide Microarray Experiments

Cited by 9 publications

References 10 publications

rapmad: Robust analysis of peptide microarray data

rapmad: Robust analysis of peptide microarray data

Analysis of the genome content of Lactococcus garvieae by genomic interspecies microarray hybridization

Lactococcus garvieae: a small bacteria and a big data world

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