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The accurate prediction of cancer drug sensitivity according to the multiomics profiles of individual patients is crucial for precision cancer medicine. However, the development of prediction models has been challenged by the complex crosstalk of input features and the resistance-dominant drug response information contained in public databases. In this study, we propose a novel multidrug response prediction framework, response-aware multitask prediction (RAMP), via a Bayesian neural network and restrict it by soft-supervised contrastive regularization. To utilize network embedding vectors as representation learning features for heterogeneous networks, we harness response-aware negative sampling, which applies cell line–drug response information to the training of network embeddings. RAMP overcomes the prediction accuracy limitation induced by the imbalance of trained response data based on the comprehensive selection and utilization of drug response features. When trained on the Genomics of Drug Sensitivity in Cancer dataset, RAMP achieved an area under the receiver operating characteristic curve > 89%, an area under the precision-recall curve > 59% and an $\textrm{F}_1$ score > 52% and outperformed previously developed methods on both balanced and imbalanced datasets. Furthermore, RAMP predicted many missing drug responses that were not included in the public databases. Our results showed that RAMP will be suitable for the high-throughput prediction of cancer drug sensitivity and will be useful for guiding cancer drug selection processes. The Python implementation for RAMP is available at https://github.com/hvcl/RAMP.

DNA polymerase theta (POLQ)-mediated end joining (TMEJ) is a distinct pathway for mediating DNA double-strand break (DSB) repair. TMEJ is required for the viability of BRCA-mutated cancer cells. It is crucial to identify tumors that rely on POLQ activity for DSB repair, because such tumors are defective in other DSB repair pathways and have predicted sensitivity to POLQ inhibition and to cancer therapies that produce DSBs. We define here the POLQ-associated mutation signatures in human cancers, characterized by short insertions and deletions in a specific range of microhomologies. By analyzing 82 COSMIC (Catalogue of Somatic Mutations in Cancer) signatures, we found that BRCA-mutated cancers with a higher level of POLQ expression have a greatly enhanced representation of the small insertion and deletion signature 6, as well as single base substitution signature 3. Using human cancer cells with disruptions of POLQ, we further show that TMEJ dominates end joining of two separated DSBs (distal EJ). Templated insertions with microhomology are enriched in POLQ-dependent distal EJ. The use of this signature analysis will aid in identifying tumors relying on POLQ activity.

R-loops are formed when replicative forks collide with the transcriptional machinery and can cause genomic instability. However, it is unclear how R-loops are regulated at transcription-replication conflict (TRC) sites and how replisome proteins are regulated to prevent R-loop formation or mediate R-loop tolerance. Here, we report that ATAD5, a PCNA unloader, plays dual functions to reduce R-loops both under normal and replication stress conditions. ATAD5 interacts with RNA helicases such as DDX1, DDX5, DDX21 and DHX9 and increases the abundance of these helicases at replication forks to facilitate R-loop resolution. Depletion of ATAD5 or ATAD5-interacting RNA helicases consistently increases R-loops during the S phase and reduces the replication rate, both of which are enhanced by replication stress. In addition to R-loop resolution, ATAD5 prevents the generation of new R-loops behind the replication forks by unloading PCNA which, otherwise, accumulates and persists on DNA, causing a collision with the transcription machinery. Depletion of ATAD5 reduces transcription rates due to PCNA accumulation. Consistent with the role of ATAD5 and RNA helicases in maintaining genomic integrity by regulating R-loops, the corresponding genes were mutated or downregulated in several human tumors.

Background Long DNA reads produced by single-molecule and pore-based sequencers are more suitable for assembly and structural variation discovery than short-read DNA fragments. For de novo assembly, Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are the favorite options. However, PacBio's SMRT sequencing is expensive for a full human genome assembly and costs more than $40,000 US for 30× coverage as of 2019. ONT PromethION sequencing, on the other hand, is 1/12 the price of PacBio for the same coverage. This study aimed to compare the cost-effectiveness of ONT PromethION and PacBio's SMRT sequencing in relation to the quality. Findings We performed whole-genome de novo assemblies and comparison to construct an improved version of KOREF, the Korean reference genome, using sequencing data produced by PromethION and PacBio. With PromethION, an assembly using sequenced reads with 64× coverage (193 Gb, 3 flowcell sequencing) resulted in 3,725 contigs with N50s of 16.7 Mb and a total genome length of 2.8 Gb. It was comparable to a KOREF assembly constructed using PacBio at 62× coverage (188 Gb, 2,695 contigs, and N50s of 17.9 Mb). When we applied Hi-C–derived long-range mapping data, an even higher quality assembly for the 64× coverage was achieved, resulting in 3,179 scaffolds with an N50 of 56.4 Mb. Conclusion The pore-based PromethION approach provided a high-quality chromosome-scale human genome assembly at a low cost with long maximum contig and scaffold lengths and was more cost-effective than PacBio at comparable quality measurements.

Whole-exome sequencing (WES) has become a standard method for detecting genetic variants in human diseases. Although the primary use of WES data has been the identification of single nucleotide variations and indels, these data also offer a possibility of detecting copy number variations (CNVs) at high resolution. However, WES data have uneven read coverage along the genome owing to the target capture step, and the development of a robust WES-based CNV tool is challenging. Here, we evaluate six WES somatic CNV detection tools: ADTEx, CONTRA, Control-FREEC, EXCAVATOR, ExomeCNV and Varscan2. Using WES data from 50 kidney chromophobe, 50 bladder urothelial carcinoma, and 50 stomach adenocarcinoma patients from The Cancer Genome Atlas, we compared the CNV calls from the six tools with a reference CNV set that was identified by both single nucleotide polymorphism array 6.0 and whole-genome sequencing data. We found that these algorithms gave highly variable results: visual inspection reveals significant differences between the WES-based segmentation profiles and the reference profile, as well as among the WES-based profiles. Using a 50% overlap criterion, 13-77% of WES CNV calls were covered by CNVs from the reference set, up to 21% of the copy gains were called as losses or vice versa, and dramatic differences in CNV sizes and CNV numbers were observed. Overall, ADTEx and EXCAVATOR had the best performance with relatively high precision and sensitivity. We suggest that the current algorithms for somatic CNV detection from WES data are limited in their performance and that more robust algorithms are needed.

Motivation: Many genomes have been completely sequenced. However, detecting and analyzing their protein–protein interactions by experimental methods such as co-immunoprecipitation, tandem affinity purification and Y2H is not as fast as genome sequencing. Therefore, a computational prediction method based on the known protein structural interactions will be useful to analyze large-scale protein–protein interaction rules within and among complete genomes. Results: We confirmed that all the predicted protein family interactomes (the full set of protein family interactions within a proteome) of 146 species are scale-free networks, and they share a small core network comprising 36 protein families related to indispensable cellular functions. We found two fundamental differences among prokaryotic and eukaryotic interactomes: (1) eukarya had significantly more hub families than archaea and bacteria and (2) certain special hub families determined the topology of the eukaryotic interactomes. Our comparative analysis suggests that a very small number of expansive protein families led to the evolution of interactomes and seemed tohave played a key role in species diversification. Contact: jong@kribb.re.kr Supplementary information: http://interactomics.org

Summary:BIPA is a database for protein–nucleic acid interactions in 3D structures. The database provides various physicochemical features of protein–nucleic acid interface such as size, shape, residue propensity, secondary structure composition and intermolecular interactions. The database also contains multiple structural alignments of nucleic acid-binding protein families with annotations of local environments in order to allow definition of features that influence acceptability of mutations at a particular position in a protein family. A web interface has been designed to present the results of these analyses and facilitate navigation of protein–nucleic acid interfaces. Availability: http://www-cryst.bioc.cam.ac.uk/bipa Contact: semin@cryst.bioc.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.