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.     

OTUbase: an R infrastructure package for operational taxonomic unit data

SUMMARY: OTUbase is an R package designed to facilitate the analysis of operational taxonomic unit (OTU) data and sequence classification (taxonomic) data. Currently there are programs that will cluster sequence data into OTUs and/or classify sequence data into known taxonomies. However, there is a need for software that can take the summarized output of these programs and organize it into easily accessed and manipulated formats. OTUbase provides this structure and organization within R, to allow researchers to easily manipulate the data with the rich library of R packages currently available for additional analysis. AVAILABILITY: OTUbase is an R package available through Bioconductor. It can be found at http://www.bioconductor.org/packages/release/bioc/html/OTUbase.html.     

FELLA: an R package to enrich metabolomics data

Background

Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.

Results

We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets –three from Homo sapiens, two from Danio rerio and one from Mus musculus– and has reproduced findings from the original studies and from independent literature.

Conclusions

The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.

     

AStream: an R package for annotating LC/MS metabolomic data

AStream, an R-statistical software package for the curation and identification of feature peaks extracted from liquid chromatography mass spectrometry (LC/MS) metabolomics data, is described. AStream detects isotopic, fragment and adduct patterns by identifying feature pairs that fulfill expected relational patterns. Data reduction by AStream allows compounds to be identified reliably and subsequently linked to metabolite databases. AStream provides researchers with a fast, reliable tool for summarizing metabolomic data, notably reducing curation time and increasing consistency of results. AVAILABILITY: The AStream R package and a study example can be freely accessed at http://www.urr.cat/AStream/AStream.html.     

下一代测序中ChIP-seq数据的处理与分析

将染色质免疫共沉淀技术(ChIP)与下一代高通量测序技术相结合的染色质免疫共沉淀测序(ChIP-seq),已成为功能基因组学、特别是基因表达调控领域研究的关键技术。ChIP-seq实验带来的海量数据向生物信息学研究人员提出了新的挑战。由于此领域数据处理技术的发展大大滞后于实验技术进步,有必要系统地介绍和回顾ChIP-seq数据处理的各个方面,以便更多研究人员进入此领域设计或改进相应的算法。文章结合实例详细介绍了ChIP-seq数据整个流程,并重点讨论了其中的主要问题和关键环节,为这一研究领域的科研人员提供一个快速而深入的认识。     

ggbio: an R package for extending the grammar of graphics for genomic data

ABSTRACT: We introduce ggbio{}, a new methodology to visualize and explore genomics annotations and high-throughput data. The plots provide detailed views of genomic regions, summary views of sequence alignments and splicing patterns, and genome-wide overviews with karyogram, circular and grand linear layouts. The methods leverage the statistical functionality available in R{}, the grammar of graphics and the data handling capabilities of the Bioconductor project. The plots are specified within a modular framework that enables users to construct plots in a systematic way, and are generated directly from Bioconductor data structures. The ggbio{} R{} package is available at url{http://tengfei.github.com/ggbio/}.     

MADE4: an R package for multivariate analysis of gene expression data

SUMMARY: MADE4, microarray ade4, is a software package that facilitates multivariate analysis of microarray gene-expression data. MADE4 accepts a wide variety of gene-expression data formats. MADE4 takes advantage of the extensive multivariate statistical and graphical functions in the R package ade4, extending these for application to microarray data. In addition, MADE4 provides new graphical and visualization tools that aid in interpretation of multivariate analysis of microarray data.     

Rseg--an R package to optimize segmentation of SNP array data

The use of high-density SNP arrays for investigating copy number alterations in clinical tumor samples, with intra tumor heterogeneity and varying degrees of normal cell contamination, imposes several problems for commonly used segmentation algorithms. This calls for flexibility when setting thresholds for calling gains and losses. In addition, sample normalization can induce artifacts in the copy-number ratios for the non-changed genomic elements in the tumor samples. RESULTS: We present an open source R package, Rseg, which allows the user to define sample-specific thresholds to call gains and losses. It also allows the user to correct for normalization artifacts. AVAILABILITY: The R package, Rseg, is available at: http://www.cs.au.dk/~plamy/Rseg/ and runs on Linux and MS-Windows.     

Graphical method for analysing stathmokinetic data

The accumulation of cells in G2M measured flow cytometrically follows a straight line when plotted against time in colcemid with the transform ln (1 + fG2M), where fG2M is the fraction of cells in the G2M peak. Extending this function to the value, ln 2, will locate a point B. Drawing a line from point B to ln 1 perpendicular to the abscissa, will locate point A at the initial value of ln (1 + fG2M). A line from ln 2 at t = 0 through point A will intersect the abscissa at the cell doubling time. Application of this method to the depletion of G1 cells is also possible.     

R453Plus1Toolbox: an R/Bioconductor package for analyzing Roche 454 Sequencing data

The R453Plus1Toolbox is an R/Bioconductor package for the analysis of 454 Sequencing data. Projects generated with Roche's data analysis software can be imported into R allowing advanced and customized analyses within the R/Bioconductor environment for sequencing data. Several methods were implemented extending the current functionality of Roche's software. These extensions include methods for quality assurance and annotation of detected variants. Further, a pipeline for the detection of structural variants, e.g. balanced chromosomal translocations, is provided. AVAILABILITY: The R453Plus1Toolbox is implemented in R and available at http://www.bioconductor.org/. A vignette outlining typical workflows is included in the package. CONTACT: h.klein@uni-muenster.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

BioFTF: An R package for biodiversity assessment with the functional data analysis approach

This paper presents the R package BioFTF, which is a tool for statistical biodiversity assessment in the functional data analysis framework. Diversity is a key topic in many research fields; however, in the literature, it is demonstrated that the existing indices do not capture the different aspects of this concept. Thus, a main drawback is that different indicators may lead to different orderings among communities according to their biodiversity. A possible method to evaluate biodiversity consists in using diversity profiles that are curves depending on a specific parameter. In this setting, it is possible to adopt some functional instruments proposed in the literature, such as the first and second derivatives, the curvature, the radius of curvature and the arc length. Specifically, the derivatives and the curvature (or the radius of curvature) highlight any peculiar behaviour of the profiles, whereas the arc length helps in ranking curves, given the richness. Because these instruments do not solve the issue of ranking communities with different numbers of species, we propose an important methodological contribution that introduces the surface area. Indeed, this tool is a scalar measure that reflects the information provided by the biodiversity profile and allows for ordering communities with different richness. However, this approach requires mathematical skills that the average user may not have; thus, our idea is to provide a user-friendly tool for both non-statistician and statistician practitioners to measure biodiversity in a functional context.