Pan-cancer analysis reveals multifaceted roles of retrotransposon-fusion RNAs.

Transposon-derived transcripts are abundant in RNA sequences, yet their landscape and function, especially for fusion transcripts derived from unannotated or somatically acquired transposons, remains underexplored. Here, we developed a new bioinformatic tool to detect transposon-fusion transcripts in RNA-sequencing data and performed a pan-cancer analysis of 10,257 cancer samples across 34 cancer types as well as 3,088 normal tissue samples. We identified 52,277 cancer-specific fusions with ~30 events per cancer and hotspot loci within transposons vulnerable to fusion formation. Exonization of intronic transposons was the most prevalent genic fusions, while somatic L1 insertions constituted a small fraction of cancer-specific fusions. Source L1s and HERVs, but not Alus showed decreased DNA methylation in cancer upon fusion formation. Overall cancer-specific L1 fusions were enriched in tumor suppressors while Alu fusions were enriched in oncogenes, including recurrent Alu fusions in EZH2 predictive of patient survival. We also demonstrated that transposon-derived peptides triggered CD8+ T-cell activation to the extent comparable to EBV viruses. Our findings reveal distinct epigenetic and tumorigenic mechanisms underlying transposon fusions across different families and highlight transposons as novel therapeutic targets and the source of potent neoantigens.

Deconvoluting essential gene signatures for cancer growth from genomic expression in compound-treated cells.

MOTIVATION: Essential gene signatures for cancer growth have been typically identified via RNAi or CRISPR-Cas9. Here, we propose an alternative method that reveals the essential gene signatures by analysing genomic expression profiles in compound-treated cells. With a large amount of the existing compound-induced data, essential gene signatures at genomic scale are efficiently characterized without technical challenges in the previous techniques. RESULTS: An essential gene is characterized as a gene presenting positive correlation between its down-regulation and cell growth inhibition induced by diverse compounds, which were collected from LINCS and CGP. Among 12 741 genes, 1092, 1 228 827 962, 1 664 580 and 829 essential genes are characterized for each of A375, A549, BT20, LNCAP, MCF7, MDAMB231 and PC3 cell lines (P-value ≤ 1.0E-05). Comparisons to the previously identified essential genes yield significant overlaps in A375 and A549 (P-value ≤ 5.0E-05) and the 103 common essential genes are enriched in crucial processes for cancer growth. In most comparisons in A375, MCF7, BT20 and A549, the characterized essential genes yield more essential characteristics than those of the previous techniques, i.e. high gene expression, high degrees of protein-protein interactions, many homologs and few paralogs. Remarkably, the essential genes commonly characterized by both the previous and proposed techniques show more significant essential characteristics than those solely relied on the previous techniques. We expect that this work provides new aspects in essential gene signatures. AVAILABILITY AND IMPLEMENTATION: The Python implementations are available at https://github.com/jmjung83/deconvolution_of_essential_gene_signitures. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeviCNV: detection and visualization of exon-level copy number variants in targeted next-generation sequencing data.

BACKGROUND: Targeted next-generation sequencing (NGS) is increasingly being adopted in clinical laboratories for genomic diagnostic tests. RESULTS: We developed a new computational method, DeviCNV, intended for the detection of exon-level copy number variants (CNVs) in targeted NGS data. DeviCNV builds linear regression models with bootstrapping for every probe to capture the relationship between read depth of an individual probe and the median of read depth values of all probes in the sample. From the regression models, it estimates the read depth ratio of the observed and predicted read depth with confidence interval for each probe which is applied to a circular binary segmentation (CBS) algorithm to obtain CNV candidates. Then, it assigns confidence scores to those candidates based on the reliability and strength of the CNV signals inferred from the read depth ratios of the probes within them. Finally, it also provides gene-centric plots with confidence levels of CNV candidates for visual inspection. We applied DeviCNV to targeted NGS data generated for newborn screening and demonstrated its ability to detect novel pathogenic CNVs from clinical samples. CONCLUSIONS: We propose a new pragmatic method for detecting CNVs in targeted NGS data with an intuitive visualization and a systematic method to assign confidence scores for candidate CNVs. Since DeviCNV was developed for use in clinical diagnosis, sensitivity is increased by the detection of exon-level CNVs.

A New Integrated Newborn Screening Workflow Can Provide a Shortcut to Differential Diagnosis and Confirmation of Inherited Metabolic Diseases.

PURPOSE: We developed a new workflow design which included results from both biochemical and targeted gene sequencing analysis interpreted comprehensively. We then conducted a pilot study to evaluate the benefit of this new approach in newborn screening (NBS) and demonstrated the efficiency of this workflow in detecting causative genetic variants. MATERIALS AND METHODS: Ten patients in Group 1 were diagnosed clinically using biochemical assays only, and 10 newborns in Group 2 were diagnosed with suspected inherited metabolic disease (IMD) in NBS. We applied NewbornDiscovery (SD Genomics), an integrated workflow design that encompasses analyte-phenotype-gene, single nucleotide variant/small insertion and deletion/copy number variation analyses along with clinical interpretation of genetic variants related to each participant's condition. RESULTS: A molecular genetic diagnosis was established in 95% (19/20) of individuals. In Group 1, 13 and 7 of 20 alleles were classified as pathogenic and likely pathogenic, respectively. In Group 2, 11 and 6 of 17 alleles with identified causative variants were pathogenic and likely pathogenic, respectively. There were no variants of uncertain significance. For each individual, the NewbornDiscovery and biochemical analysis results reached 100% concordance, since the single newborn testing negative for causative genetic variant in Group 2 showed a benign clinical course. CONCLUSION: This integrated diagnostic workflow resulted in a high yield. This approach not only enabled early confirmation of specific IMD, but also detected conditions not included in the current NBS.

Publisher Correction: CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Author Correction: CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects.

In silico network-based methods have shown promising results in the field of drug development. Yet, most of networks used in the previous research have not included context information even though biological associations actually do appear in the specific contexts. Here, we reconstruct an anatomical context-specific network by assigning contexts to biological associations using protein expression data and scientific literature. Furthermore, we employ the context-specific network for the analysis of drug effects with a proximity measure between drug targets and diseases. Distinct from previous context-specific networks, intercellular associations and phenomic level entities such as biological processes are included in our network to represent the human body. It is observed that performances in inferring drug-disease associations are increased by adding context information and phenomic level entities. In particular, hypertension, a disease related to multiple organs and associated with several phenomic level entities, is analyzed in detail to investigate how our network facilitates the inference of drug-disease associations. Our results indicate that the inclusion of context information, intercellular associations, and phenomic level entities can contribute towards a better prediction of drug-disease associations and provide detailed insight into understanding of how drugs affect diseases in the human body.

Prediction of drugs having opposite effects on disease genes in a directed network.

BACKGROUND: Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information. METHODS: We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with 'effect type', an integrated directed molecular network with 'effect type' and 'effect direction', and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations. RESULTS: We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main . CONCLUSIONS: Our results showed that 'effect type' and 'effect direction' information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.