J, Sawitzki G, Smith C, Smyth G, Tierney L, Yang J, Y, Zhang J. Bioconductor: open software development for computational biology and bioinformatics.

Department of Biostatistical Science, Dana-Farber Cancer Institute, 44 Binney St, Boston, MA 02115, USA.
Genome biology (Impact Factor: 10.81). 02/2004; 5(10):R80. DOI: 10.1186/gb-2004-5-10-r80
Source: PubMed

ABSTRACT The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

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Available from: Yongchao Ge, Sep 28, 2015
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    • "All packages are either available from CRAN or Bioconductor (Gentleman et al., 2004) and can be freely combined in a plugin-like architecture. R is an open, operating system-independent platform for a broad spectrum of calculation options. "
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    ABSTRACT: There is an ever-increasing number of applications, which use quantitative PCR (qPCR) or digital PCR (dPCR) to elicit fundamentals of biological processes. Moreover, quantitative isothermal amplification (qIA) methods have become more prominent in life sciences and point-of-care- diagnostics. Additionally, the analysis of melting data is essential during many experiments. Several software packages have been developed for the analysis of such datasets. In most cases, the software is either distributed as closed source software or as monolithic block with little freedom to perform highly customized analysis procedures. We argue, among others, that R is an excellent foundation for reproducible and transparent data analysis in a highly customizable cross-platform environment. However, for novices it is often challenging to master R or learn capabilities of the vast number of packages available. In the paper, we describe exemplary workflows for the analysis of qPCR, qIA or dPCR experiments including the analysis of melting curve data. Our analysis relies entirely on R packages available from public repositories. Additionally, we provide information related to standardized and reproducible research.
    The R Journal 06/2015; 7(1):127-150. · 1.04 Impact Factor
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    • "Differentially expressed genes were determined using the Bio - conductor package " limma " ( Gentleman et al . , 2004 ; Smyth , 2005 ) of the statistical programming language R . Limma fits linear models to the expression values of each gene and deter - mines differential expression using moderated t - statistics . P - values were adjusted according to the method of Benjamini and Hochberg ( 1995 ) . Genes with an adjusted p < 0 . 05 and a log 2 - fold "
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    ABSTRACT: Sepsis is a clinical syndrome that can be caused by bacteria or fungi. Early knowledge on the nature of the causative agent is a prerequisite for targeted anti-microbial therapy. Besides currently used detection methods like blood culture and PCR-based assays, the analysis of the transcriptional response of the host to infecting organisms holds great promise. In this study, we aim to examine the transcriptional footprint of infections caused by the bacterial pathogens Staphylococcus aureus and Escherichia coli and the fungal pathogens Candida albicans and Aspergillus fumigatus in a human whole-blood model. Moreover, we use the expression information to build a random forest classifier to classify if a sample contains a bacterial, fungal, or mock-infection. After normalizing the transcription intensities using stably expressed reference genes, we filtered the gene set for biomarkers of bacterial or fungal blood infections. This selection is based on differential expression and an additional gene relevance measure. In this way, we identified 38 biomarker genes, including IL6, SOCS3, and IRG1 which were already associated to sepsis by other studies. Using these genes, we trained the classifier and assessed its performance. It yielded a 96% accuracy (sensitivities >93%, specificities >97%) for a 10-fold stratified cross-validation and a 92% accuracy (sensitivities and specificities >83%) for an additional test dataset comprising Cryptococcus neoformans infections. Furthermore, the classifier is robust to Gaussian noise, indicating correct class predictions on datasets of new species. In conclusion, this genome-wide approach demonstrates an effective feature selection process in combination with the construction of a well-performing classification model. Further analyses of genes with pathogen-dependent expression patterns can provide insights into the systemic host responses, which may lead to new anti-microbial therapeutic advances.
    Frontiers in Microbiology 02/2015; 6(171). DOI:10.3389/fmicb.2015.00171 · 3.99 Impact Factor
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    • "The data were collected using ILLUMINA GENOMESTUDIO software. Analysis of the microarray output data was conducted using the R statistical language (R Core Team, 2010) and the LIMMA and Combat (batch correction) statistical packages from Bioconductor (Gentleman et al., 2004). Microarrays displaying unusually low median intensity, low variability, or low correlation relative to the bulk of the arrays were discarded from the rest of the analysis. "
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    ABSTRACT: Aging leads to dysregulation of multiple components of the immune system that results in increased susceptibility to infections and poor response to vaccines in the aging population. The dysfunctions of adaptive B and T cells are well documented, but the effect of aging on innate immunity remains incompletely understood. Using a heterogeneous population of peripheral blood mononuclear cells (PBMCs), we first undertook transcriptional profiling and found that PBMCs isolated from old individuals (≥ 65 years) exhibited a delayed and altered response to stimulation with TLR4, TLR7/8, and RIG-I agonists compared to cells obtained from adults (≤ 40 years). This delayed response to innate immune agonists resulted in the reduced production of pro-inflammatory and antiviral cytokines and chemokines including TNFα, IL-6, IL-1β, IFNα, IFNγ, CCL2, and CCL7. While the major monocyte and dendritic cell subsets did not change numerically with aging, activation of specific cell types was altered. PBMCs from old subjects also had a lower frequency of CD40+ monocytes, impaired up-regulation of PD-L1 on monocytes and T cells, and increased expression of PD-L2 and B7-H4 on B cells. The defective immune response to innate agonists adversely affected adaptive immunity as TLR-stimulated PBMCs (minus CD3 T cells) from old subjects elicited significantly lower levels of adult T-cell proliferation than those from adult subjects in an allogeneic mixed lymphocyte reaction (MLR). Collectively, these age-associated changes in cytokine, chemokine and interferon production, as well as co-stimulatory protein expression could contribute to the blunted memory B- and T-cell immune responses to vaccines and infections. © 2015 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
    Aging cell 02/2015; 14(3). DOI:10.1111/acel.12320 · 6.34 Impact Factor
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