Exploration of the gene fusion landscape of glioblastoma using transcriptome sequencing and copy number data

Background

RNA-seq has spurred important gene fusion discoveries in a number of different cancers, including lung, prostate, breast, brain, thyroid and bladder carcinomas. Gene fusion discovery can potentially lead to the development of novel treatments that target the underlying genetic abnormalities.

Results

In this study, we provide comprehensive view of gene fusion landscape in 185 glioblastoma multiforme patients from two independent cohorts. Fusions occur in approximately 30-50% of GBM patient samples. In the Ivy Center cohort of 24 patients, 33% of samples harbored fusions that were validated by qPCR and Sanger sequencing. We were able to identify high-confidence gene fusions from RNA-seq data in 53% of the samples in a TCGA cohort of 161 patients. We identified 13 cases (8%) with fusions retaining a tyrosine kinase domain in the TCGA cohort and one case in the Ivy Center cohort. Ours is the first study to describe recurrent fusions involving non-coding genes. Genomic locations 7p11 and 12q14-15 harbor majority of the fusions. Fusions on 7p11 are formed in focally amplified EGFR locus whereas 12q14-15 fusions are formed by complex genomic rearrangements. All the fusions detected in this study can be further visualized and analyzed using our website: http://ivygap.swedish.org/fusions.

Conclusions

Our study highlights the prevalence of gene fusions as one of the major genomic abnormalities in GBM. The majority of the fusions are private fusions, and a minority of these recur with low frequency. A small subset of patients with fusions of receptor tyrosine kinases can benefit from existing FDA approved drugs and drugs available in various clinical trials. Due to the low frequency and rarity of clinically relevant fusions, RNA-seq of GBM patient samples will be a vital tool for the identification of patient-specific fusions that can drive personalized therapy.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-818) contains supplementary material, which is available to authorized users.     

Allelic mapping bias in RNA-sequencing is not a major confounder in eQTL studies

Background

RNA sequencing (RNA-seq) is the current gold-standard method to quantify gene expression for expression quantitative trait locus (eQTL) studies. However, a potential caveat in these studies is that RNA-seq reads carrying the non-reference allele of variant loci can have lower probability to map correctly to the reference genome, which could bias gene quantifications and cause false positive eQTL associations. In this study, we analyze the effect of this allelic mapping bias in eQTL discovery.

Results

We simulate RNA-seq read mapping over 9.5 M common SNPs and indels, with 15.6% of variants showing biased mapping rate for reference versus non-reference reads. However, removing potentially biased RNA-seq reads from an eQTL dataset of 185 individuals has a very small effect on gene and exon quantifications and eQTL discovery. We detect only a handful of likely false positive eQTLs, and overall eQTL SNPs show no significant enrichment for high mapping bias.

Conclusion

Our results suggest that RNA-seq quantifications are generally robust against allelic mapping bias, and that this does not have a severe effect on eQTL discovery. Nevertheless, we provide our catalog of putatively biased loci to allow better controlling for mapping bias to obtain more accurate results in future RNA-seq studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0467-2) contains supplementary material, which is available to authorized users.     

An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era

Background

Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment?

Results

We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined.

Conclusions

Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0523-y) contains supplementary material, which is available to authorized users.     

Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq

Background

Massively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples.

Results

False-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive predictive values.

Conclusions

Our results highlighted the need for combined use of more sensitive DEG analysis methods and high-throughput validation of identified DEGs in future RNA-seq experiments. They indicated limited utility of sample pooling strategies for RNA-seq in similar setups and supported increasing the number of biological replicate samples.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1767-y) contains supplementary material, which is available to authorized users.     

Assessing structural variation in a personal genome—towards a human reference diploid genome

Background

Characterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods.

Results

We demonstrate Parliament’s efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus.

Conclusions

HS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, the HS1011 data constitute a public resource for novel SV discovery, software calibration, and personal genome structural variation analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1479-3) contains supplementary material, which is available to authorized users.     

Evaluation of variant identification methods for whole genome sequencing data in dairy cattle

Background

Advances in human genomics have allowed unprecedented productivity in terms of algorithms, software, and literature available for translating raw next-generation sequence data into high-quality information. The challenges of variant identification in organisms with lower quality reference genomes are less well documented. We explored the consequences of commonly recommended preparatory steps and the effects of single and multi sample variant identification methods using four publicly available software applications (Platypus, HaplotypeCaller, Samtools and UnifiedGenotyper) on whole genome sequence data of 65 key ancestors of Swiss dairy cattle populations. Accuracy of calling next-generation sequence variants was assessed by comparison to the same loci from medium and high-density single nucleotide variant (SNV) arrays.

Results

The total number of SNVs identified varied by software and method, with single (multi) sample results ranging from 17.7 to 22.0 (16.9 to 22.0) million variants. Computing time varied considerably between software. Preparatory realignment of insertions and deletions and subsequent base quality score recalibration had only minor effects on the number and quality of SNVs identified by different software, but increased computing time considerably. Average concordance for single (multi) sample results with high-density chip data was 58.3% (87.0%) and average genotype concordance in correctly identified SNVs was 99.2% (99.2%) across software. The average quality of SNVs identified, measured as the ratio of transitions to transversions, was higher using single sample methods than multi sample methods. A consensus approach using results of different software generally provided the highest variant quality in terms of transition/transversion ratio.

Conclusions

Our findings serve as a reference for variant identification pipeline development in non-human organisms and help assess the implication of preparatory steps in next-generation sequencing pipelines for organisms with incomplete reference genomes (pipeline code is included). Benchmarking this information should prove particularly useful in processing next-generation sequencing data for use in genome-wide association studies and genomic selection.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-948) contains supplementary material, which is available to authorized users.     

Simultaneous analysis of large-scale RNAi screens for pathogen entry

Background

Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries.

Results

We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied.

Conclusions

Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1162) contains supplementary material, which is available to authorized users.     

Validation of multiple single nucleotide variation calls by additional exome analysis with a semiconductor sequencer to supplement data of whole-genome sequencing of a human population

Background

Validation of single nucleotide variations in whole-genome sequencing is critical for studying disease-related variations in large populations. A combination of different types of next-generation sequencers for analyzing individual genomes may be an efficient means of validating multiple single nucleotide variations calls simultaneously.

Results

Here, we analyzed 12 independent Japanese genomes using two next-generation sequencing platforms: the Illumina HiSeq 2500 platform for whole-genome sequencing (average depth 32.4×), and the Ion Proton semiconductor sequencer for whole exome sequencing (average depth 109×). Single nucleotide polymorphism (SNP) calls based on the Illumina Human Omni 2.5-8 SNP chip data were used as the reference. We compared the variant calls for the 12 samples, and found that the concordance between the two next-generation sequencing platforms varied between 83% and 97%.

Conclusions

Our results show the versatility and usefulness of the combination of exome sequencing with whole-genome sequencing in studies of human population genetics and demonstrate that combining data from multiple sequencing platforms is an efficient approach to validate and supplement SNP calls.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-673) contains supplementary material, which is available to authorized users.     

Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data

Background

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.

Methodology/Results

We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.

Conclusions

Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.