UnCoVar: a reproducible and scalable workflow for transparent and robust virus variant calling and lineage assignment using SARS-CoV-2 as an example.

BACKGROUND: At a global scale, the SARS-CoV-2 virus did not remain in its initial genotype for a long period of time, with the first global reports of variants of concern (VOCs) in late 2020. Subsequently, genome sequencing has become an indispensable tool for characterizing the ongoing pandemic, particularly for typing SARS-CoV-2 samples obtained from patients or environmental surveillance. For such SARS-CoV-2 typing, various in vitro and in silico workflows exist, yet to date, no systematic cross-platform validation has been reported. RESULTS: In this work, we present the first comprehensive cross-platform evaluation and validation of in silico SARS-CoV-2 typing workflows. The evaluation relies on a dataset of 54 patient-derived samples sequenced with several different in vitro approaches on all relevant state-of-the-art sequencing platforms. Moreover, we present UnCoVar, a robust, production-grade reproducible SARS-CoV-2 typing workflow that outperforms all other tested approaches in terms of precision and recall. CONCLUSIONS: In many ways, the SARS-CoV-2 pandemic has accelerated the development of techniques and analytical approaches. We believe that this can serve as a blueprint for dealing with future pandemics. Accordingly, UnCoVar is easily generalizable towards other viral pathogens and future pandemics. The fully automated workflow assembles virus genomes from patient samples, identifies existing lineages, and provides high-resolution insights into individual mutations. UnCoVar includes extensive quality control and automatically generates interactive visual reports. UnCoVar is implemented as a Snakemake workflow. The open-source code is available under a BSD 2-clause license at github.com/IKIM-Essen/uncovar.

Insane in the vembrane: filtering and transforming VCF/BCF files.

SUMMARY: We present vembrane as a command line variant call format (VCF)/binary call format (BCF) filtering tool that consolidates and extends the filtering functionality of previous software to meet any imaginable filtering use case. Vembrane exposes the VCF/BCF file type specification and its inofficial extensions by the annotation tools VEP and SnpEff as Python data structures. vembrane filter enables filtration by Python expressions, requiring only basic knowledge of the Python programming language. vembrane table allows users to generate tables from subsets of annotations or functions thereof. Finally, it is fast, by using pysam and relying on lazy evaluation. AVAILABILITY AND IMPLEMENTATION: Source code and installation instructions are available at github.com/vembrane/vembrane (doi: 10.5281/zenodo.7003981). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

VISPR-online: a web-based interactive tool to visualize CRISPR screening experiments.

BACKGROUND: VISPR is an interactive visualization and analysis framework for CRISPR screening experiments. However, it only supports the output of MAGeCK, and requires installation and manual configuration. Furthermore, VISPR is designed to run on a single computer, and data sharing between collaborators is challenging. RESULTS: To make the tool easily accessible to the community, we present VISPR-online, a web-based general application allowing users to visualize, explore, and share CRISPR screening data online with a few simple steps. VISPR-online provides an exploration of screening results and visualization of read count changes. Apart from MAGeCK, VISPR-online supports two more popular CRISPR screening analysis tools: BAGEL and JACKS. It provides an interactive environment for exploring gene essentiality, viewing guide RNA (gRNA) locations, and allowing users to resume and share screening results. CONCLUSIONS: VISPR-online allows users to visualize, explore and share CRISPR screening data online. It is freely available at http://vispr-online.weililab.org , while the source code is available at https://github.com/lemoncyb/VISPR-online .

A Bayesian model for single cell transcript expression analysis on MERFISH data.

MOTIVATION: Multiplexed error-robust fluorescence in-situ hybridization (MERFISH) is a recent technology to obtain spatially resolved gene or transcript expression profiles in single cells for hundreds to thousands of genes in parallel. So far, no statistical framework to analyze MERFISH data is available. RESULTS: We present a Bayesian model for single cell transcript expression analysis on MERFISH data. We show that the model successfully captures uncertainty in MERFISH data and eliminates systematic biases that can occur in raw RNA molecule counts obtained with MERFISH. Our model accurately estimates transcript expression and additionally provides the full probability distribution and credible intervals for each transcript. We further show how this enables MERFISH to scale towards the whole genome while being able to control the uncertainty in obtained results. AVAILABILITY AND IMPLEMENTATION: The presented model is implemented on top of Rust-Bio (Köster, 2016) and available open-source as MERFISHtools (https://merfishtools.github.io). It can be easily installed via Bioconda (Grüning et al., 2018). The entire analysis performed in this paper is provided as a fully reproducible Snakemake (Köster and Rahmann, 2012) workflow via Zenodo (https://doi.org/10.5281/zenodo.752340). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Full-length de novo viral quasispecies assembly through variation graph construction.

MOTIVATION: Viruses populate their hosts as a viral quasispecies: a collection of genetically related mutant strains. Viral quasispecies assembly is the reconstruction of strain-specific haplotypes from read data, and predicting their relative abundances within the mix of strains is an important step for various treatment-related reasons. Reference genome independent ('de novo') approaches have yielded benefits over reference-guided approaches, because reference-induced biases can become overwhelming when dealing with divergent strains. While being very accurate, extant de novo methods only yield rather short contigs. The remaining challenge is to reconstruct full-length haplotypes together with their abundances from such contigs. RESULTS: We present Virus-VG as a de novo approach to viral haplotype reconstruction from preassembled contigs. Our method constructs a variation graph from the short input contigs without making use of a reference genome. Then, to obtain paths through the variation graph that reflect the original haplotypes, we solve a minimization problem that yields a selection of maximal-length paths that is, optimal in terms of being compatible with the read coverages computed for the nodes of the variation graph. We output the resulting selection of maximal length paths as the haplotypes, together with their abundances. Benchmarking experiments on challenging simulated and real datasets show significant improvements in assembly contiguity compared to the input contigs, while preserving low error rates compared to the state-of-the-art viral quasispecies assemblers. AVAILABILITY AND IMPLEMENTATION: Virus-VG is freely available at https://bitbucket.org/jbaaijens/virus-vg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis.

BACKGROUND: RNA sequencing has become a ubiquitous technology used throughout life sciences as an effective method of measuring RNA abundance quantitatively in tissues and cells. The increase in use of RNA-seq technology has led to the continuous development of new tools for every step of analysis from alignment to downstream pathway analysis. However, effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts. RESULTS: Using the workflow management system Snakemake we have developed a user friendly, fast, efficient, and comprehensive pipeline for RNA-seq analysis. VIPER (Visualization Pipeline for RNA-seq analysis) is an analysis workflow that combines some of the most popular tools to take RNA-seq analysis from raw sequencing data, through alignment and quality control, into downstream differential expression and pathway analysis. VIPER has been created in a modular fashion to allow for the rapid incorporation of new tools to expand the capabilities. This capacity has already been exploited to include very recently developed tools that explore immune infiltrate and T-cell CDR (Complementarity-Determining Regions) reconstruction abilities. The pipeline has been conveniently packaged such that minimal computational skills are required to download and install the dozens of software packages that VIPER uses. CONCLUSIONS: VIPER is a comprehensive solution that performs most standard RNA-seq analyses quickly and effectively with a built-in capacity for customization and expansion.

Cancer evolution, mutations, and clonal selection in relapse neuroblastoma.

The notion of cancer as a complex evolutionary system has been validated by in-depth molecular analyses of tumor progression over the last years. While a complex interplay of cell-autonomous programs and cell-cell interactions determines proliferation and differentiation during normal development, intrinsic and acquired plasticity of cancer cells allow for evasion of growth factor limitations, apoptotic signals, or attacks from the immune system. Treatment-induced molecular selection processes have been described by a number of studies already, but understanding of those events facilitating metastatic spread, organ-specific homing, and resistance to anoikis is still in its early days. In principle, somatic events giving rise to cancer progression should be easier to follow in childhood tumors bearing fewer mutations and genomic aberrations than their counterparts in adulthood. We have previously reported on the genetic events accompanying relapsing neuroblastoma, a solid tumor of early childhood. Our results indicated significantly higher single nucleotide variants in relapse tumors, gave hints for branched tumor evolution upon treatment and clonal selection as deduced from shifts in allelic frequencies between primary and relapsing neuroblastoma. Here, we will review these findings and give an outlook on dealing with intratumoral heterogeneity and sub-clonal diversity in neuroblastoma for future targeted treatments.

Rust-Bio: a fast and safe bioinformatics library.

SUMMARY: We present Rust-Bio, the first general purpose bioinformatics library for the innovative Rust programming language. Rust-Bio leverages the unique combination of speed, memory safety and high-level syntax offered by Rust to provide a fast and safe set of bioinformatics algorithms and data structures with a focus on sequence analysis. AVAILABILITY AND IMPLEMENTATION: Rust-Bio is available open source under the MIT license at https://rust-bio.github.io. CONTACT: koester@jimmy.harvard.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SimLoRD: Simulation of Long Read Data.

MOTIVATION: Third generation sequencing methods provide longer reads than second generation methods and have distinct error characteristics. While there exist many read simulators for second generation data, there is a very limited choice for third generation data. RESULTS: We analyzed public data from Pacific Biosciences (PacBio) SMRT sequencing, developed an error model and implemented it in a new read simulator called SimLoRD. It offers options to choose the read length distribution and to model error probabilities depending on the number of passes through the sequencer. The new error model makes SimLoRD the most realistic SMRT read simulator available. AVAILABILITY AND IMPLEMENTATION: SimLoRD is available open source at http://bitbucket.org/genomeinformatics/simlord/ and installable via Bioconda (http://bioconda.github.io). CONTACT: Bianca.Stoecker@uni-due.de or Sven.Rahmann@uni-due.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CRISPR-DO for genome-wide CRISPR design and optimization.

MOTIVATION: Despite the growing popularity in using CRISPR/Cas9 technology for genome editing and gene knockout, its performance still relies on well-designed single guide RNAs (sgRNA). In this study, we propose a web application for the Design and Optimization (CRISPR-DO) of guide sequences that target both coding and non-coding regions in spCas9 CRISPR system across human, mouse, zebrafish, fly and worm genomes. CRISPR-DO uses a computational sequence model to predict sgRNA efficiency, and employs a specificity scoring function to evaluate the potential of off-target effect. It also provides information on functional conservation of target sequences, as well as the overlaps with exons, putative regulatory sequences and single-nucleotide polymorphisms (SNPs). The web application has a user-friendly genome-browser interface to facilitate the selection of the best target DNA sequences for experimental design. AVAILABILITY AND IMPLEMENTATION: CRISPR-DO is available at http://cistrome.org/crispr/ CONTACT: qiliu@tongji.edu.cn or hanxu@jimmy.harvard.edu or xsliu@jimmy.harvard.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Identifying transcriptional miRNA biomarkers by integrating high-throughput sequencing and real-time PCR data.

Using both high-throughput sequencing and real-time PCR, the miRNA transcriptome can be analyzed in complementary ways. We describe the necessary bioinformatics pipeline, including software tools, and key methodological steps in the process, such as adapter removal, read mapping, normalization, and multiple testing issues for biomarker identification. The methods are exemplified by the analysis of five favorable (event-free survival) vs. five unfavorable (died of disease) neuroblastoma tumor samples with a total of over 188 million reads.

Pancreatic cancer acquires resistance to MAPK pathway inhibition by clonal expansion and adaptive DNA hypermethylation.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis. It is marked by extraordinary resistance to conventional therapies including chemotherapy and radiation, as well as to essentially all targeted therapies evaluated so far. More than 90% of PDAC cases harbor an activating KRAS mutation. As the most common KRAS variants in PDAC remain undruggable so far, it seemed promising to inhibit a downstream target in the MAPK pathway such as MEK1/2, but up to now preclinical and clinical evaluation of MEK inhibitors (MEKi) failed due to inherent and acquired resistance mechanisms. To gain insights into molecular changes during the formation of resistance to oncogenic MAPK pathway inhibition, we utilized short-term passaged primary tumor cells from ten PDACs of genetically engineered mice. We followed gain and loss of resistance upon MEKi exposure and withdrawal by longitudinal integrative analysis of whole genome sequencing, whole genome bisulfite sequencing, RNA-sequencing and mass spectrometry data. RESULTS: We found that resistant cell populations under increasing MEKi treatment evolved by the expansion of a single clone but were not a direct consequence of known resistance-conferring mutations. Rather, resistant cells showed adaptive DNA hypermethylation of 209 and hypomethylation of 8 genomic sites, most of which overlap with regulatory elements known to be active in murine PDAC cells. Both DNA methylation changes and MEKi resistance were transient and reversible upon drug withdrawal. Furthermore, MEKi resistance could be reversed by DNA methyltransferase inhibition with remarkable sensitivity exclusively in the resistant cells. CONCLUSION: Overall, the concept of acquired therapy resistance as a result of the expansion of a single cell clone with epigenetic plasticity sheds light on genetic, epigenetic and phenotypic patterns during evolvement of treatment resistance in a tumor with high adaptive capabilities and provides potential for reversion through epigenetic targeting.

Neoadjuvant nivolumab with or without relatlimab in resectable non-small-cell lung cancer: a randomized phase 2 trial.

Antibodies targeting the immune checkpoint molecules PD-1, PD-L1 and CTLA-4, administered alone or in combination with chemotherapy, are the standard of care in most patients with metastatic non-small-cell lung cancers. When given before curative surgery, tumor responses and improved event-free survival are achieved. New antibody combinations may be more efficacious and tolerable. In an ongoing, open-label phase 2 study, 60 biomarker-unselected, treatment-naive patients with resectable non-small-cell lung cancer were randomized to receive two preoperative doses of nivolumab (anti-PD-1) with or without relatlimab (anti-LAG-3) antibody therapy. The primary study endpoint was the feasibility of surgery within 43 days, which was met by all patients. Curative resection was achieved in 95% of patients. Secondary endpoints included pathological and radiographic response rates, pathologically complete resection rates, disease-free and overall survival rates, and safety. Major pathological (≤10% viable tumor cells) and objective radiographic responses were achieved in 27% and 10% (nivolumab) and in 30% and 27% (nivolumab and relatlimab) of patients, respectively. In 100% (nivolumab) and 90% (nivolumab and relatlimab) of patients, tumors and lymph nodes were pathologically completely resected. With 12 months median duration of follow-up, disease-free survival and overall survival rates at 12 months were 89% and 93% (nivolumab), and 93% and 100% (nivolumab and relatlimab). Both treatments were safe with grade ≥3 treatment-emergent adverse events reported in 10% and 13% of patients per study arm. Exploratory analyses provided insights into biological processes triggered by preoperative immunotherapy. This study establishes the feasibility and safety of dual targeting of PD-1 and LAG-3 before lung cancer surgery.ClinicalTrials.gov Indentifier: NCT04205552 .

MiR-137 functions as a tumor suppressor in neuroblastoma by downregulating KDM1A.

Neuroblastoma is the most common extracranial solid tumor of childhood, and accounts for ∼15% of all childhood cancer deaths. The histone demethylase, lysine-specific demethylase 1 (KDM1A, previously known as LSD1), is strongly expressed in neuroblastomas, and overexpression correlates with poor patient prognosis. Inducing differentiation in neuroblastoma cells has previously been shown to down regulate KDM1A, and siRNA-mediated KDM1A knockdown inhibited neuroblastoma cell viability. The microRNA, miR-137, has been reported to be downregulated in several human cancers, and KDM1A mRNA was reported as a putative target of miR-137 in colon cancer. We hypothesized that miR-137 might have a tumor-suppressive role in neuroblastoma mediated via downregulation of KDM1A. Indeed, low levels of miR-137 expression in primary neuroblastomas correlated with poor patient prognosis. Re-expressing miR-137 in neuroblastoma cell lines increased apoptosis and decreased cell viability and proliferation. KDM1A mRNA was repressed by miR-137 in neuroblastoma cells, and was validated as a direct target of miR-137 using reporter assays in SHEP and HEK293 cells. Furthermore, siRNA-mediated KDM1A knockdown phenocopied the miR-137 re-expression phenotype in neuroblastoma cells. We conclude that miR-137 directly targets KDM1A mRNA in neuroblastoma cells, and activates cell properties consistent with tumor suppression. Therapeutic strategies to re-express miR-137 in neuroblastomas could be useful to reduce tumor aggressiveness.

Next-generation RNA sequencing reveals differential expression of MYCN target genes and suggests the mTOR pathway as a promising therapy target in MYCN-amplified neuroblastoma.

In many cancer types, MYC proteins are known to be master regulators of the RNA-producing machinery. Neuroblastoma is a tumor of early childhood characterized by heterogeneous clinical courses. Amplification of the MYCN oncogene is a marker of poor patient outcome in this disease. Here, we investigated the MYCN-driven transcriptome of 20 primary neuroblastomas with and without MYCN amplification using next-generation RNA sequencing and compared the results to those from an in vitro cell model for inducible MYCN (SH-EP MYCN-ER). Transcriptome sequencing produced 30-90 million mappable reads for each dataset. The most abundant RNA species was mRNA, but snoRNAs, pseudogenes and processed transcripts were also recovered. A total of 223 genes were significantly differentially expressed between MYCN-amplified and single-copy tumors. Of those genes associated with MYCN both in vitro and in vivo, 32% of MYCN upregulated and 37% of MYCN downregulated genes were verified either as previously identified MYCN targets or as having MYCN-binding motifs. Pathway analyses suggested transcriptomal upregulation of mTOR-related genes by MYCN. MYCN-driven neuroblastomas in mice displayed activation of the mTOR pathway on the protein level and activation of MYCN in SH-EP MYCN-ER cells resulted in high sensitivity toward mTOR inhibition in vitro. We conclude that next-generation RNA sequencing allows for the identification of MYCN regulated transcripts in neuroblastoma. As our results suggest MYCN involvement in mTOR pathway activation on the transcriptional level, mTOR inhibitors should be further evaluated for the treatment of MYCN-amplified neuroblastoma.

Snakemake--a scalable bioinformatics workflow engine.

SUMMARY: Snakemake is a workflow engine that provides a readable Python-based workflow definition language and a powerful execution environment that scales from single-core workstations to compute clusters without modifying the workflow. It is the first system to support the use of automatically inferred multiple named wildcards (or variables) in input and output filenames. AVAILABILITY: http://snakemake.googlecode.com. CONTACT: johannes.koester@uni-due.de.

A virally encoded GPCR drives glioblastoma through feed-forward activation of the SK1-S1P1 signaling axis.

The G protein-coupled receptor (GPCR) US28 encoded by the human cytomegalovirus (HCMV) is associated with accelerated progression of glioblastomas, aggressive brain tumors with a generally poor prognosis. Here, we showed that US28 increased the malignancy of U251 glioblastoma cells by enhancing signaling mediated by sphingosine-1-phosphate (S1P), a bioactive lipid that stimulates oncogenic pathways in glioblastoma. US28 expression increased the abundance of the key components of the S1P signaling axis, including an enzyme that generates S1P [sphingosine kinase 1 (SK1)], an S1P receptor [S1P receptor 1 (S1P1)], and S1P itself. Enhanced S1P signaling promoted glioblastoma cell proliferation and survival by activating the kinases AKT and CHK1 and the transcriptional regulators cMYC and STAT3 and by increasing the abundance of cancerous inhibitor of PP2A (CIP2A), driving several feed-forward signaling loops. Inhibition of S1P signaling abrogated the proliferative and anti-apoptotic effects of US28. US28 also activated the S1P signaling axis in HCMV-infected cells. This study uncovers central roles for S1P and CIP2A in feed-forward signaling that contributes to the US28-mediated exacerbation of glioblastoma.