matchprobes: a Bioconductor package for the sequence-matching of microarray probe elements

SUMMARY: The nucleotide sequences of the probes on a microarray can be used for a variety of purposes in the analysis of microarray experiments. We describe software and a paradigm for the creation of data packages for curating, distributing and working with probe sequence data in a uniform, across-types-of-microarrays manner. While the implementation is specific to the Bioconductor project, the ideas and general strategies are more general and could be easily adopted by other projects. AVAILABILITY: The R package matchprobes is available under LGPL at http://www.bioconductor.org SUPPLEMENTARY INFORMATION: The package contains documentation in the form of a vignette and manual pages.     

CALIB: a Bioconductor package for estimating absolute expression levels from two-color microarray data

In this article we describe a new Bioconductor package 'CALIB' for normalization of two-color microarray data. This approach is based on the measurements of external controls and estimates an absolute target level for each gene and condition pair, as opposed to working with log-ratios as a relative measure of expression. Moreover, this method makes no assumptions regarding the distribution of gene expression divergence. AVAILABILITY: http://bioconductor.org/packages/2.0/bioc Open Source.     

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data

Background  

Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models.     

flowCore: a Bioconductor package for high throughput flow cytometry

Background  

Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates.     

flowClust: a Bioconductor package for automated gating of flow cytometry data

Background  

As a high-throughput technology that offers rapid quantification of multidimensional characteristics for millions of cells, flow cytometry (FCM) is widely used in health research, medical diagnosis and treatment, and vaccine development. Nevertheless, there is an increasing concern about the lack of appropriate software tools to provide an automated analysis platform to parallelize the high-throughput data-generation platform. Currently, to a large extent, FCM data analysis relies on the manual selection of sequential regions in 2-D graphical projections to extract the cell populations of interest. This is a time-consuming task that ignores the high-dimensionality of FCM data.     

supraHex: An R/Bioconductor package for tabular omics data analysis using a supra-hexagonal map

Biologists are increasingly confronted with the challenge of quickly understanding genome-wide biological data, which usually involve a large number of genomic coordinates (e.g. genes) but a much smaller number of samples. To meet the need for data of this shape, we present an open-source package called ‘supraHex’ for training, analysing and visualising omics data. This package devises a supra-hexagonal map to self-organise the input data, offers scalable functionalities for post-analysing the map, and more importantly, allows for overlaying additional data for multilayer omics data comparisons. Via applying to DNA replication timing data of mouse embryogenesis, we demonstrate that supraHex is capable of simultaneously carrying out gene clustering and sample correlation, providing intuitive visualisation at each step of the analysis. By overlaying CpG and expression data onto the trained replication-timing map, we also show that supraHex is able to intuitively capture an inherent relationship between late replication, low CpG density promoters and low expression levels. As part of the Bioconductor project, supraHex makes accessible to a wide community in a simple way, what would otherwise be a complex framework for the ultrafast understanding of any tabular omics data, both scientifically and artistically. This package can run on Windows, Mac and Linux, and is freely available together with many tutorials on featuring real examples at http://supfam.org/supraHex.     

MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis

We present MeV+R, an integration of the JAVA MultiExperiment Viewer program with Bioconductor packages. This integration of MultiExperiment Viewer and R is easily extensible to other R packages and provides users with point and click access to traditionally command line driven tools written in R. We demonstrate the ability to use MultiExperiment Viewer as a graphical user interface for Bioconductor applications in microarray data analysis by incorporating three Bioconductor packages, RAMA, BRIDGE and iterativeBMA.     

BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis

biomaRt is a new Bioconductor package that integrates BioMart data resources with data analysis software in Bioconductor. It can annotate a wide range of gene or gene product identifiers (e.g. Entrez-Gene and Affymetrix probe identifiers) with information such as gene symbol, chromosomal coordinates, Gene Ontology and OMIM annotation. Furthermore biomaRt enables retrieval of genomic sequences and single nucleotide polymorphism information, which can be used in data analysis. Fast and up-to-date data retrieval is possible as the package executes direct SQL queries to the BioMart databases (e.g. Ensembl). The biomaRt package provides a tight integration of large, public or locally installed BioMart databases with data analysis in Bioconductor creating a powerful environment for biological data mining.     

A friendly statistics package for microarray analysis

SUMMARY: The friendly statistics package for microarray analysis (FSPMA) is a tool that aims to fill the gap between simple to use and powerful analysis. FSPMA is a platform-independent R-package that allows efficient exploration of microarray data without the need for computer programming. Analysis is based on a mixed model ANOVA library (YASMA) that was extended to allow more flexible comparisons and other useful operations like k nearest neighbour imputing and spike-based normalization. Processing is controlled by a definition file that specifies all the steps necessary to derive analysis results from quantified microarray data. In addition to providing analysis without programming, the definition file also serves as exact documentation of all the analysis steps. AVAILABILITY: The library is available under GPL 2 license and, together with additional information, provided at http://www.ccbi.cam.ac.uk/software/psyk/software.html#fspma     

ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data

Background  

Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) or ChIP followed by genome tiling array analysis (ChIP-chip) have become standard technologies for genome-wide identification of DNA-binding protein target sites. A number of algorithms have been developed in parallel that allow identification of binding sites from ChIP-seq or ChIP-chip datasets and subsequent visualization in the University of California Santa Cruz (UCSC) Genome Browser as custom annotation tracks. However, summarizing these tracks can be a daunting task, particularly if there are a large number of binding sites or the binding sites are distributed widely across the genome.     

ReQON: a Bioconductor package for recalibrating quality scores from next-generation sequencing data

ABSTRACT: BACKGROUND: Next-generation sequencing technologies have become important tools for genome-wide studies. However, the quality scores that are assigned to each base have been shown to be inaccurate. If the quality scores are used in downstream analyses, these inaccuracies can have a significant impact on the results. RESULTS: Here we present ReQON, a tool that recalibrates the base quality scores from an input BAM file of aligned sequencing data using logistic regression. ReQON also generates diagnostic plots showing the effectiveness of the recalibration. We show that ReQON produces quality scores that are both more accurate, in the sense that they more closely correspond to the probability of a sequencing error, and do a better job of discriminating between sequencing errors and non-errors than the original quality scores. We also compare ReQON to other available recalibration tools and show that ReQON is less biased and performs favorably in terms of quality score accuracy. CONCLUSION: ReQON is an open source software package, written in R and available through Bioconductor, for recalibrating base quality scores for next-generation sequencing data. ReQON produces a new BAM file with more accurate quality scores, which can improve the results of downstream analysis, and produces several diagnostic plots showing the effectiveness of the recalibration.     

twilight; a Bioconductor package for estimating the local false discovery rate

SUMMARY: twilight is a Bioconductor compatible package for analysing the statistical significance of differentially expressed genes. It is based on the concept of the local false discovery rate (FDR), a generalization of the frequently used global FDR. twilight implements the heuristic search algorithm for estimating the local FDR introduced in our earlier work. In addition to the raw significance measures, it produces diagnostic plots, which provide insight into the extent of differential expression across genes. AVAILABILITY: http://www.bioconductor.org CONTACT: stefanie.scheid@molgen.mpg.de SUPPLEMENTARY INFORMATION: Please visit our software webpage on http://compdiag.molgen.mpg.de/software.     

Propagating uncertainty in microarray data analysis

Microarray technology is associated with many sources of experimentaluncertainty. In this review we discuss a number of approachesfor dealing with this uncertainty in the processing of datafrom microarray experiments. We focus here on the analysis ofhigh-density oligonucleotide arrays, such as the popular AffymetrixGeneChip® array, which contain multiple probes for eachtarget. This set of probes can be used to determine an estimatefor the target concentration and can also be used to determinethe experimental uncertainty associated with this measurement.This measurement uncertainty can then be propagated throughthe downstream analysis using probabilistic methods. We giveexamples showing how these credibility intervals can be usedto help identify differential expression, to combine informationfrom replicated experiments and to improve the performance ofprincipal component analysis.      

NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data

A key benefit of long-read nanopore sequencing technology is the ability to detect modified DNA bases, such as 5-methylcytosine. The lack of R/Bioconductor tools for the effective visualization of nanopore methylation profiles between samples from different experimental groups led us to develop the NanoMethViz R package. Our software can handle methylation output generated from a range of different methylation callers and manages large datasets using a compressed data format. To fully explore the methylation patterns in a dataset, NanoMethViz allows plotting of data at various resolutions. At the sample-level, we use dimensionality reduction to look at the relationships between methylation profiles in an unsupervised way. We visualize methylation profiles of classes of features such as genes or CpG islands by scaling them to relative positions and aggregating their profiles. At the finest resolution, we visualize methylation patterns across individual reads along the genome using the spaghetti plot and heatmaps, allowing users to explore particular genes or genomic regions of interest. In summary, our software makes the handling of methylation signal more convenient, expands upon the visualization options for nanopore data and works seamlessly with existing methylation analysis tools available in the Bioconductor project. Our software is available at https://bioconductor.org/packages/NanoMethViz.     

A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R

This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.     

TargetSearch - a Bioconductor package for the efficient preprocessing of GC-MS metabolite profiling data

Background  

Metabolite profiling, the simultaneous quantification of multiple metabolites in an experiment, is becoming increasingly popular, particularly with the rise of systems-level biology. The workhorse in this field is gas-chromatography hyphenated with mass spectrometry (GC-MS). The high-throughput of this technology coupled with a demand for large experiments has led to data pre-processing, i.e. the quantification of metabolites across samples, becoming a major bottleneck. Existing software has several limitations, including restricted maximum sample size, systematic errors and low flexibility. However, the biggest limitation is that the resulting data usually require extensive hand-curation, which is subjective and can typically take several days to weeks.