R-SAP: a multi-threading computational pipeline for the characterization of high-throughput RNA-sequencing data.

The rapid expansion in the quantity and quality of RNA-Seq data requires the development of sophisticated high-performance bioinformatics tools capable of rapidly transforming this data into meaningful information that is easily interpretable by biologists. Currently available analysis tools are often not easily installed by the general biologist and most of them lack inherent parallel processing capabilities widely recognized as an essential feature of next-generation bioinformatics tools. We present here a user-friendly and fully automated RNA-Seq analysis pipeline (R-SAP) with built-in multi-threading capability to analyze and quantitate high-throughput RNA-Seq datasets. R-SAP follows a hierarchical decision making procedure to accurately characterize various classes of transcripts and achieves a near linear decrease in data processing time as a result of increased multi-threading. In addition, RNA expression level estimates obtained using R-SAP display high concordance with levels measured by microarrays.

Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples.

BACKGROUND: Clinical laboratories routinely use formalin-fixed paraffin-embedded (FFPE) tissue or cell block cytology samples in oncology panel sequencing to identify mutations that can predict patient response to targeted therapy. To understand the technical error due to FFPE processing, a robustly characterized diploid cell line was used to create FFPE samples with four different pre-tissue processing formalin fixation times. A total of 96 FFPE sections were then distributed to different laboratories for targeted sequencing analysis by four oncopanels, and variants resulting from technical error were identified. RESULTS: Tissue sections that fail more frequently show low cellularity, lower than recommended library preparation DNA input, or target sequencing depth. Importantly, sections from block surfaces are more likely to show FFPE-specific errors, akin to "edge effects" seen in histology, while the inner samples display no quality degradation related to fixation time. CONCLUSIONS: To assure reliable results, we recommend avoiding the block surface portion and restricting mutation detection to genomic regions of high confidence.

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions.

BACKGROUND: Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. RESULTS: All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. CONCLUSION: This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

BACKGROUND: Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. RESULTS: In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. CONCLUSION: These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

De novo assembly and characterization of breast cancer transcriptomes identifies large numbers of novel fusion-gene transcripts of potential functional significance.

BACKGROUND: Gene-fusion or chimeric transcripts have been implicated in the onset and progression of a variety of cancers. Massively parallel RNA sequencing (RNA-Seq) of the cellular transcriptome is a promising approach for the identification of chimeric transcripts of potential functional significance. We report here the development and use of an integrated computational pipeline for the de novo assembly and characterization of chimeric transcripts in 55 primary breast cancer and normal tissue samples. METHODS: An integrated computational pipeline was employed to screen the transcriptome of breast cancer and control tissues for high-quality RNA-sequencing reads. Reads were de novo assembled into contigs followed by reference genome mapping. Chimeric transcripts were detected, filtered and characterized using our R-SAP algorithm. The relative abundance of reads was used to estimate levels of gene expression. RESULTS: De novo assembly allowed for the accurate detection of 1959 chimeric transcripts to nucleotide level resolution and facilitated detailed molecular characterization and quantitative analysis. A number of the chimeric transcripts are of potential functional significance including 79 novel fusion-protein transcripts and many chimeric transcripts with alterations in their un-translated leader regions. A number of chimeric transcripts in the cancer samples mapped to genomic regions devoid of any known genes. Several 'pro-neoplastic' fusions comprised of genes previously implicated in cancer are expressed at low levels in normal tissues but at high levels in cancer tissues. CONCLUSIONS: Collectively, our results underscore the utility of deep sequencing technologies and improved bioinformatics workflows to uncover novel and potentially significant chimeric transcripts in cancer and normal somatic tissues.

Integrated sequence and expression analysis of ovarian cancer structural variants underscores the importance of gene fusion regulation.

BACKGROUND: Genomic rearrangements or structural variants (SVs) are one of the most common classes of mutations in cancer. METHODS: An integrated DNA sequencing and transcriptional profiling (RNA sequence and microarray gene expression data) analysis was performed on six ovarian cancer patient samples. Matched sets of control (whole blood) samples from these same patients were used to distinguish cancer SVs of germline origin from those arising somatically in the cancer cell lineage. RESULTS: We detected 10,034 ovarian cancer SVs (5518 germline derived; 4516 somatically derived) at base-pair level resolution. Only 11 % of these variants were shown to have the potential to form gene fusions and, of these, less than 20 % were detected at the transcriptional level. CONCLUSIONS: Collectively our results are consistent with the view that gene fusions and other SVs can be significant factors in the onset and progression of ovarian cancer. The results further indicate that it may not only be the occurrence of these variants in cancer but their regulation that contributes to their biological and clinical significance.

Characterization and potential functional significance of human-chimpanzee large INDEL variation.

BACKGROUND: Although humans and chimpanzees have accumulated significant differences in a number of phenotypic traits since diverging from a common ancestor about six million years ago, their genomes are more than 98.5% identical at protein-coding loci. This modest degree of nucleotide divergence is not sufficient to explain the extensive phenotypic differences between the two species. It has been hypothesized that the genetic basis of the phenotypic differences lies at the level of gene regulation and is associated with the extensive insertion and deletion (INDEL) variation between the two species. To test the hypothesis that large INDELs (80 to 12,000 bp) may have contributed significantly to differences in gene regulation between the two species, we categorized human-chimpanzee INDEL variation mapping in or around genes and determined whether this variation is significantly correlated with previously determined differences in gene expression. RESULTS: Extensive, large INDEL variation exists between the human and chimpanzee genomes. This variation is primarily attributable to retrotransposon insertions within the human lineage. There is a significant correlation between differences in gene expression and large human-chimpanzee INDEL variation mapping in genes or in proximity to them. CONCLUSIONS: The results presented herein are consistent with the hypothesis that large INDELs, particularly those associated with retrotransposons, have played a significant role in human-chimpanzee regulatory evolution.