Supplementary data are available at Bioinformatics online.

MotivationThe emergence of high-throughput sequencing technologies revolutionized genomics in early 2000s. The next revolution came with the era of long-read sequencing. These technological advances along with novel computational approaches became the next step towards the automatic pipelines capable to assemble nearly complete mammalian-size genomes.ResultsIn this manuscript, we demonstrate performance of the state-of-the-art genome assembly software on six eukaryotic datasets sequenced using different technologies. To evaluate the results, we developed QUAST-LG—a tool that compares large genomic de novo assemblies against reference sequences and computes relevant quality metrics. Since genomes generally cannot be reconstructed completely due to complex repeat patterns and low coverage regions, we introduce a concept of upper bound assembly for a given genome and set of reads, and compute theoretical limits on assembly correctness and completeness. Using QUAST-LG, we show how close the assemblies are to the theoretical optimum, and how far this optimum is from the finished reference.Availability and implementation http://cab.spbu.ru/software/quast-lg Supplementary information Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

http://cab.spbu.ru/software/icarus CONTACT: aleksey.gurevich@spbu.ruSupplementary information: Supplementary data are available at Bioinformatics online.

Current understanding of the mutation spectrum of relapsed/refractory (RR) tumors is limited. We performed whole exome sequencing (WES) on 47 diffuse large B cell lymphoma (DLBCL) tumors that persisted after R-CHOP treatment, 8 matched to primary biopsies. We compared genomic alterations from the RR cohort against two treatment-naïve DLBCL cohorts (n=112). While the overall number and types of mutations did not differ significantly, we identified frequency changes in DLBCL driver genes. The overall frequency of MYD88 mutant samples increased (12% to 19%), but we noted a decrease in p.L265P (8% to 4%) and increase in p.S219C mutations (2% to 6%). CARD11 p.D230N, PIM1 p.K115N and CD79B p.Y196C mutations were not observed in the RR cohort, although these mutations were prominent in the primary DLBCL samples. We observed an increase in BCL2 mutations (21% to 38% of samples), BCL2 amplifications (3% to 6% of samples) and CREBBP mutations (31% to 42% of samples) in the RR cohort, supported by acquisition of mutations in these genes in relapsed compared to diagnostic biopsies from the same patient. These increases may reflect the genetic characteristics of R-CHOP RR tumors expected to be enriched for during clinical trial enrollment. These findings hold significance for a number of emerging targeted therapies aligned to genetic targets and biomarkers in DLBCL, reinforcing the importance of time-of-treatment biomarker screening during DLBCL therapy selection.

Motivation: While somatic mutagenesis is the driving force of most human cancers, the germline genome is of significant clinical value in several tumor types. Cancer predisposition variants are important for risk management and surveillance, and can also have major implications for treatment strategies since many are in DNA repair genes. Following the incorporation of high-throughput DNA sequencing in cancer clinics and research, there is thus a need to provide clinically oriented sequencing reports for risk-associated germline variants and their potential therapeutic relevance on a per patient basis. Results: We have developed the Cancer Predisposition Sequencing Reporter (CPSR), an open-source computational workflow that provides a structured report of germline variants identified in known cancer predisposition genes. Building upon existing knowledge sources and variant databases relevant for cancer susceptibility, CPSR combines a transparent and cancer-dedicated scoring scheme for variant pathogenicity ( American College of Medical Genetics and Genomics, ACMG) with existing variant classifications from ClinVar in order to derive a structured and prioritised list of variant findings. The workflow outputs a comprehensive andinteractive HTML report that highlights putative markers of therapeutic, prognostic and diagnostic relevance. Importantly, the set of cancer predisposition genes profiled in the report can be flexibly chosen from nearly 40 virtual gene panels established by scientific experts, enabling a customization of the report for different screening purposes. The report can be configured to also list potential incidental variant findings as recommended by ACMG, as well as the status of low-risk variants from genome-wide association studies in cancer. Availability and Implementation:The software is implemented in Python/R, and is freely available through Docker technology. Documentation, example reports, and installation instructions are accessible via the project GitHub page: https://github.com/sigven/cpsr Contact: sigven@ifi.uio.no

The value of high-throughput germline genetic testing is increasingly recognized in clinical cancer care. Disease-associated germline variants in cancer patients are important for risk management and surveillance, surgical decisions and can also have major implications for treatment strategies since many are in DNA repair genes. With the increasing availability of high-throughput DNA sequencing in cancer clinics and research, there is thus a need to provide clinically oriented sequencing reports for germline variants and their potential therapeutic relevance on a per-patient basis. To meet this need, we have developed the Cancer Predisposition Sequencing Reporter (CPSR), an open-source computational workflow that generates a structured report of germline variants identified in known cancer predisposition genes, highlighting markers of therapeutic, prognostic and diagnostic relevance. A fully automated variant classification procedure based on more than 30 refined American College of Medical Genetics and Genomics (ACMG) criteria represents an integral part of the workflow. Importantly, the set of cancer predisposition genes profiled in the report can be flexibly chosen from more than 40 virtual gene panels established by scientific experts, enabling customization of the report for different screening purposes and clinical contexts. The report can be configured to also list actionable secondary variant findings, as recommended by ACMG. CPSR demonstrates comparable sensitivity and specificity for the detection of pathogenic variants when compared to other algorithms in the field. Technically, the tool is implemented in Python/R, and is freely available through Docker technology. Source code, documentation, example reports and installation instructions are accessible via the project GitHub

Selecting proper genome assembly is key for downstream analysis in genomics studies. However, the availability of many genome assembly tools and the huge variety of their running parameters challenge this task. The existing online evaluation tools are limited to specific taxa or provide just a one-sided view on the assembly quality. We present WebQUAST, a web server for multifaceted quality assessment and comparison of genome assemblies based on the state-of-the-art QUAST tool. The server is freely available at https://www.ccb.uni-saarland.de/quast/. WebQUAST can handle an unlimited number of genome assemblies and evaluate them against a user-provided or pre-loaded reference genome or in a completely reference-free fashion. We demonstrate key WebQUAST features in three common evaluation scenarios: assembly of an unknown species, a model organism, and a close variant of it.