Motivation: Rapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. We introduce a computational framework, ProTIE, to integratively analyze all three types of omics data for a complete molecular profile of a tissue sample. Our framework features MiStrVar, a novel algorithmic method to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can accurately profile structurally aberrant transcripts in tumors. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures. Observing structural aberrations in all three types of omics data validates their presence in the tumor samples. Results: We have applied our framework to all The Cancer Genome Atlas (TCGA) breast cancer Whole Genome Sequencing (WGS) and/or RNA-Seq datasets, spanning all four major subtypes, for which proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been released. A recent study on this dataset focusing on SNVs has reported many that lead to novel peptides. Complementing and significantly broadening this study, we detected 244 novel peptides from 432 candidate genomic or transcriptomic sequence aberrations. Many of the fusions and microSVs we discovered have not been reported in the literature. Interestingly, the vast majority of these translated aberrations, fusions in particular, were private, demonstrating the extensive inter-genomic heterogeneity present in breast cancer. Many of these aberrations also have matching out-of-frame downstream peptides, potentially indicating novel protein sequence and structure. Availability and implementation: MiStrVar is available for download at https://bitbucket.org/compbio/mistrvar, and ProTIE is available at https://bitbucket.org/compbio/protie. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Successful development and application of precision oncology approaches require robust elucidation of the genomic landscape of a patient's cancer and, ideally, the ability to monitor therapy-induced genomic changes in the tumour in an inexpensive and minimally invasive manner. Thanks to recent advances in sequencing technologies, 'liquid biopsy', the sampling of patient's bodily fluids such as blood and urine, is considered as one of the most promising approaches to achieve this goal. In many cancer patients, and especially those with advanced metastatic disease, deep sequencing of circulating cell free DNA (cfDNA) obtained from patient's blood yields a mixture of reads originating from the normal DNA and from multiple tumour subclones-called circulating tumour DNA or ctDNA. The ctDNA/cfDNA ratio as well as the proportion of ctDNA originating from specific tumour subclones depend on multiple factors, making comprehensive detection of mutations difficult, especially at early stages of cancer. Furthermore, sensitive and accurate detection of single nucleotide variants (SNVs) and indels from cfDNA is constrained by several factors such as the sequencing errors and PCR artifacts, and mapping errors related to repeat regions within the genome. In this article, we introduce SiNVICT, a computational method that increases the sensitivity and specificity of SNV and indel detection at very low variant allele frequencies. SiNVICT has the capability to handle multiple sequencing platforms with different error properties; it minimizes false positives resulting from mapping errors and other technology specific artifacts including strand bias and low base quality at read ends. SiNVICT also has the capability to perform time-series analysis, where samples from a patient sequenced at multiple time points are jointly examined to report locations of interest where there is a possibility that certain clones were wiped out by some treatment while some subclones gained selective advantage. RESULTS: We tested SiNVICT on simulated data as well as prostate cancer cell lines and cfDNA obtained from castration-resistant prostate cancer patients. On both simulated and biological data, SiNVICT was able to detect SNVs and indels with variant allele percentages as low as 0.5%. The lowest amounts of total DNA used for the biological data where SNVs and indels could be detected with very high sensitivity were 2.5 ng on the Ion Torrent platform and 10 ng on Illumina. With increased sequencing and mapping accuracy, SiNVICT might be utilized in clinical settings, making it possible to track the progress of point mutations and indels that are associated with resistance to cancer therapies and provide patients personalized treatment. We also compared SiNVICT with other popular SNV callers such as MuTect, VarScan2 and Freebayes. Our results show that SiNVICT performs better than these tools in most cases and allows further data exploration such as time-series analysis on cfDNA sequencing data. AVAILABILITY AND IMPLEMENTATION: SiNVICT is available at: https://sfu-compbio.github.io/sinvictSupplementary information: Supplementary data are available at Bioinformatics online. CONTACT: [email protected].