DiscoverySpace: an interactive data analysis application

DiscoverySpace is a graphical application for bioinformatics data analysis. Users can seamlessly traverse references between biological databases and draw together annotations in an intuitive tabular interface. Datasets can be compared using a suite of novel tools to aid in the identification of significant patterns. DiscoverySpace is of broad utility and its particular strength is in the analysis of serial analysis of gene expression (SAGE) data. The application is freely available online

FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology

Summary: Next-generation sequencing can provide insight into protein–DNA association events on a genome-wide scale, and is being applied in an increasing number of applications in genomics and meta-genomics research. However, few software applications are available for interpreting these experiments. We present here an efficient application for use with chromatin-immunoprecipitation (ChIP-Seq) experimental data that includes novel functionality for identifying areas of gene enrichment and transcription factor binding site locations, as well as for estimating DNA fragment size distributions in enriched areas. The FindPeaks application can generate UCSC compatible custom ‘WIG’ track files from aligned-read files for short-read sequencing technology. The software application can be executed on any platform capable of running a Java Runtime Environment. Memory requirements are proportional to the number of sequencing reads analyzed; typically 4 GB permits processing of up to 40 million reads

Comprehensive molecular portraits of human breast tumours

We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at.10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer. © 2012 Macmillan Publishers Limited. All rights reserved

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies

The expression level of small non-coding RNAs derived from the first exon of protein-coding genes is predictive of cancer status

Small non-coding RNAs (smRNAs) are known to be significantly enriched near the transcriptional start sites of genes. However, the functional relevance of these smRNAs remains unclear, and they have not been associated with human disease. Within the cancer genome atlas project (TCGA), we have generated small RNA datasets for many tumor types. In prior cancer studies, these RNAs have been regarded as transcriptional "noise," due to their apparent chaotic distribution. In contrast, we demonstrate their striking potential to distinguish efficiently between cancer and normal tissues and classify patients with cancer to subgroups of distinct survival outcomes. This potential to predict cancer status is restricted to a subset of these smRNAs, which is encoded within the first exon of genes, highly enriched within CpG islands and negatively correlated with DNA methylation levels. Thus, our data show that genome-wide changes in the expression levels of small non-coding RNAs within first exons are associated with cancer. Synopsis The expression of small non-coding RNAs encoded within the first exon of genes can be used to efficiently identify cancer samples and classify patients into subgroups of different survival. Such pan-cancer association is the first link between these RNAs and disease. Exon 1 small non-coding RNAs (smRNAs) can distinguish between cancer and normal tissues. The prediction potential of exon 1 smRNAs differs from that of other smRNAs around transcriptional start sites (TSS). smRNA locations around TSS are conserved between different individuals. smRNA locations are enriched within CpG islands and their levels negatively correlated with DNA methylation. The expression of small non-coding RNAs encoded within the first exon of genes can be used to efficiently identify cancer samples and classify patients into subgroups of different survival. Such pan-cancer association is the first link between these RNAs and disease. © 2014 The Authors

De novo transcriptome assembly with ABySS

Motivation: Whole transcriptome shotgun sequencing data from non-normalized samples offer unique opportunities to study the metabolic states of organisms. One can deduce gene expression levels using sequence coverage as a surrogate, identify coding changes or discover novel isoforms or transcripts. Especially for discovery of novel events, de novo assembly of transcriptomes is desirable. Results: Transcriptome from tumor tissue of a patient with follicular lymphoma was sequenced with 36 base pair (bp) single- and paired-end reads on the Illumina Genome Analyzer II platform. We assembled ~194 million reads using ABySS into 66 921 contigs 100 bp or longer, with a maximum contig length of 10 951 bp, representing over 30 million base pairs of unique transcriptome sequence, or roughly 1% of the genome. © The Author 2009. Published by Oxford University Press. All rights reserved

Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing

Sequence-based methods for transcriptome characterization have typically relied on generation of either serial analysis of gene expression tags or expressed sequence tags. Although such approaches have the potential to enumerate transcripts by counting sequence tags derived from them, they typically do not robustly survey the majority of transcripts along their entire length. Here we show that massively parallel sequencing of randomly primed cDNAs, using a next-generation sequencing-by-synthesis technology, offers the potential to generate relative measures of mRNA and individual exon abundance while simultaneously profiling the prevalence of both annotated and novel exons and exon-splicing events. This technique identifies known single nucleotide polymorphisms (SNPs) as well as novel single-base variants. Analysis of these variants, and previously unannotated splicing events in the HeLa S3 cell line, reveals an overrepresentation of gene categories including those previously implicated in cancer