DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging

Methylation of DNA is known to be essential to development and dramatically altered in cancers. The Illumina HumanMethylation450 BeadChip has been used extensively as a cost-effective way to profile nearly half a million CpG sites across the human genome. Here we present DiffVar, a novel method to test for differential variability between sample groups. DiffVar employs an empirical Bayes model framework that can take into account any experimental design and is robust to outliers. We applied DiffVar to several datasets from The Cancer Genome Atlas, as well as an aging dataset. DiffVar is available in the missMethyl Bioconductor R package

Transcript length bias in RNA-seq data confounds systems biology

BACKGROUND: Several recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data. RESULTS: We investigated the effect of transcript length bias in RNA-seq data using three different published data sets. For standard analyses using aggregated tag counts for each gene, the ability to call differentially expressed genes between samples is strongly associated with the length of the transcript. CONCLUSION: Transcript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses. REVIEWERS: This article was reviewed by Rohan Williams (nominated by Gavin Huttley), Nicole Cloonan (nominated by Mark Ragan) and James Bullard (nominated by Sandrine Dudoit)

A scaling normalization method for differential expression analysis of RNA-seq data

A novel and empirical method for normalization of RNA-seq data is presente

From RNA-seq reads to differential expression results

Many methods and tools are available for preprocessing high-throughput RNA sequencing data and detecting differential expression

Unravelling Active Galactic Nuclei

A complete flat-spectrum radio-loud sample of AGN includes a significant fraction of Seyfert-like AGN including a NLS1. Analysis of their optical spectra suggests that the reddest continuum colours are either associated with AGN in nearby resolved galaxies, or distant quasars showing relatively narrow permitted emission lines.Comment: Poster contribution presented at the Joint MPE,AIP,ESO workshop on NLS1s, Bad Honnef, Dec. 1999, to appear in New Astronomy Reviews; also available at http://wave.xray.mpe.mpg.de/conferences/nls1-worksho

Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes

Normalization is critical for removing systematic variation from microarray data. For two-color microarray platforms, intensity-dependent lowess normalization is commonly used to correct relative gene expression values for biases. Here we outline a normalization method for use when the assumptions of lowess normalization fail. Specifically, this can occur when specialized boutique arrays are constructed that contain a subset of genes selected to test particular biological functions

Black Hole Mass Estimates of Radio Selected Quasars

The black hole (BH) mass in the centre of AGN has been estimated for a sample of radio-selected flat-spectrum quasars to investigate the relationship between BH mass and radio properties of quasars. We have used the virial assumption with measurements of the H$\beta$ FWHM and luminosity to estimate the central BH mass. In contrast to previous studies we find no correlation between BH mass and radio power in these AGN. We find a range in BH mass similar to that seen in radio-quiet quasars from previous studies. We believe the reason that the low BH mass radio-loud quasars have not been measured in previous studies is due to optical selection effects which tend to miss the less optically luminous radio-loud sources.Comment: 22 pages, 7 figures, accepted for publication in Ap

SuperFreq: Integrated mutation detection and clonal tracking in cancer.

Analysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples

Gene ontology analysis for RNA-seq: accounting for selection bias

GOseq is a method for GO analysis of RNA-seq data that takes into account the length bias inherent in RNA-se