nf-core/nanostring: a pipeline for reproducible NanoString nCounter analysis.

MOTIVATION: The NanoString™ nCounter® technology platform is a widely used targeted quantification platform for the analysis of gene expression of up to ∼800 genes. Whereas the software tools by the manufacturer can perform the analysis in an interactive and GUI driven approach, there is no portable and user-friendly workflow available that can be used to perform reproducible analysis of multiple samples simultaneously in a scalable fashion on different computing infrastructures. RESULTS: Here, we present the nf-core/nanostring open-source pipeline to perform a comprehensive analysis including quality control and additional features such as expression visualization, annotation with additional metadata and input creation for differential gene expression analysis. The workflow features an easy installation, comprehensive documentation, open-source code with the possibility for further extensions, a strong portability across multiple computing environments and detailed quality metrics reporting covering all parts of the pipeline. nf-core/nanostring has been implemented in the Nextflow workflow language and supports Docker, Singularity, Podman container technologies as well as Conda environments, enabling easy deployment on any Nextflow supported compatible system, including most widely used cloud computing environments such as Google GCP or Amazon AWS. AVAILABILITY AND IMPLEMENTATION: The source code, documentation and installation instructions as well as results for continuous tests are freely available at https://github.com/nf-core/nanostring and https://nf-co.re/nanostring.

nf-core/airrflow: an adaptive immune receptor repertoire analysis workflow employing the Immcantation framework.

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets. nf-core/airrflow is available free of charge, under the MIT license on GitHub (https://github.com/nf-core/airrflow). Detailed documentation and example results are available on the nf-core website at (https://nf-co.re/airrflow).

89Zr-immuno-PET using the anti-LAG-3 tracer [89Zr]Zr-BI 754111: demonstrating target specific binding in NSCLC and HNSCC.

PURPOSE: Although lymphocyte activation gene-3 (LAG-3) directed therapies demonstrate promising clinical anti-cancer activity, only a subset of patients seems to benefit and predictive biomarkers are lacking. Here, we explored the potential use of the anti-LAG-3 antibody tracer [89Zr]Zr-BI 754111 as a predictive imaging biomarker and investigated its target specific uptake as well as the correlation of its tumor uptake and the tumor immune infiltration. METHODS: Patients with head and neck (N = 2) or lung cancer (N = 4) were included in an imaging substudy of a phase 1 trial with BI 754091 (anti-PD-1) and BI 754111 (anti-LAG-3). After baseline tumor biopsy and [18F]FDG-PET, patients were given 240 mg of BI 754091, followed 8 days later by administration of [89Zr]Zr-BI 754111 (37 MBq, 4 mg). PET scans were performed 2 h, 96 h, and 144 h post-injection. To investigate target specificity, a second tracer administration was given two weeks later, this time with pre-administration of 40 (N = 3) or 600 mg (N = 3) unlabeled BI 754111, followed by PET scans at 96 h and 144 h post-injection. Tumor immune cell infiltration was assessed by immunohistochemistry and RNA sequencing. RESULTS: Tracer uptake in tumors was clearly visible at the 4-mg mass dose (tumor-to-plasma ratio 1.63 [IQR 0.37-2.89]) and could be saturated by increasing mass doses (44 mg: 0.67 [IQR 0.50-0.85]; 604 mg: 0.56 [IQR 0.42-0.75]), demonstrating target specificity. Tumor uptake correlated to immune cell-derived RNA signatures. CONCLUSIONS: [89Zr]Zr-BI-754111 PET imaging shows favorable technical and biological characteristics for developing a potential predictive imaging biomarker for LAG-3-directed therapies. TRIAL REGISTRATION: ClinicalTrials.gov , NCT03780725. Registered 19 December 2018.

Genomic analyses of hair from Ludwig van Beethoven.

Ludwig van Beethoven (1770-1827) remains among the most influential and popular classical music composers. Health problems significantly impacted his career as a composer and pianist, including progressive hearing loss, recurring gastrointestinal complaints, and liver disease. In 1802, Beethoven requested that following his death, his disease be described and made public. Medical biographers have since proposed numerous hypotheses, including many substantially heritable conditions. Here we attempt a genomic analysis of Beethoven in order to elucidate potential underlying genetic and infectious causes of his illnesses. We incorporated improvements in ancient DNA methods into existing protocols for ancient hair samples, enabling the sequencing of high-coverage genomes from small quantities of historical hair. We analyzed eight independently sourced locks of hair attributed to Beethoven, five of which originated from a single European male. We deemed these matching samples to be almost certainly authentic and sequenced Beethoven's genome to 24-fold genomic coverage. Although we could not identify a genetic explanation for Beethoven's hearing disorder or gastrointestinal problems, we found that Beethoven had a genetic predisposition for liver disease. Metagenomic analyses revealed furthermore that Beethoven had a hepatitis B infection during at least the months prior to his death. Together with the genetic predisposition and his broadly accepted alcohol consumption, these present plausible explanations for Beethoven's severe liver disease, which culminated in his death. Unexpectedly, an analysis of Y chromosomes sequenced from five living members of the Van Beethoven patrilineage revealed the occurrence of an extra-pair paternity event in Ludwig van Beethoven's patrilineal ancestry.

Identification of Broadly Applicable Adeno-Associated Virus Vectors by Systematic Comparison of Commonly Used Capsid Variants In Vitro.

Adeno-associated viruses (AAVs) represent highly attractive gene therapy vectors and potent research tools for the modulation of gene expression in animal models or difficult-to-transfect cell cultures. Engineered variants, comprising chimeric, mutated, or peptide-inserted capsids, have strongly broadened the utility of AAVs by altering cellular tropism, enabling immune evasion, or increasing transduction efficiency. In this work, the performance of 50 of the most used, predominantly published, AAVs was compared on several primary cells, cell lines, and induced pluripotent stem cell-derived models from different organs, including the adipose tissue, liver, lung, brain, and eyes. To identify the most efficient capsids for each cell type, self-complementary AAVs were standardized by digital polymerase chain reaction, arrayed on 96-well plates, and screened using high-content imaging. To enable best use of the data, all results are also provided in a web app. The utility of one selected AAV variant is further exemplified in a liver fibrosis assay based on primary hepatic stellate cells, where it successfully reversed a small interfering RNA (siRNA)-induced phenotype. Most importantly, our comparative analysis revealed that a subselection of only five AAV variants (AAV2.NN, AAV9-SLRSPPS, AAV6.2, AAV6TM, and AAV1P5) enabled efficient transduction of all tested cell types and markedly outperformed other well-established capsids, such as AAV2-7m8. These findings suggest that a core panel comprising these five capsid variants is a universally applicable and sufficient tool to identify potent AAVs for gene expression modulation in cellular systems.

The origin and legacy of the Etruscans through a 2000-year archeogenomic time transect.

The origin, development, and legacy of the enigmatic Etruscan civilization from the central region of the Italian peninsula known as Etruria have been debated for centuries. Here we report a genomic time transect of 82 individuals spanning almost two millennia (800 BCE to 1000 CE) across Etruria and southern Italy. During the Iron Age, we detect a component of Indo-European­associated steppe ancestry and the lack of recent Anatolian-related admixture among the putative non­Indo-European­speaking Etruscans. Despite comprising diverse individuals of central European, northern African, and Near Eastern ancestry, the local gene pool is largely maintained across the first millennium BCE. This drastically changes during the Roman Imperial period where we report an abrupt population-wide shift to ~50% admixture with eastern Mediterranean ancestry. Last, we identify northern European components appearing in central Italy during the Early Middle Ages, which thus formed the genetic landscape of present-day Italian populations.

DamageProfiler: fast damage pattern calculation for ancient DNA.

MOTIVATION: In ancient DNA research, the authentication of ancient samples based on specific features remains a crucial step in data analysis. Because of this central importance, researchers lacking deeper programming knowledge should be able to run a basic damage authentication analysis. Such software should be user-friendly and easy to integrate into an analysis pipeline. RESULTS: DamageProfiler is a Java-based, stand-alone software to determine damage patterns in ancient DNA. The results are provided in various file formats and plots for further processing. DamageProfiler has an intuitive graphical as well as command line interface that allows the tool to be easily embedded into an analysis pipeline. AVAILABILITY AND IMPLEMENTATION: All of the source code is freely available on GitHub (https://github.com/Integrative-Transcriptomics/DamageProfiler). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reproducible, portable, and efficient ancient genome reconstruction with nf-core/eager.

The broadening utilisation of ancient DNA to address archaeological, palaeontological, and biological questions is resulting in a rising diversity in the size of laboratories and scale of analyses being performed. In the context of this heterogeneous landscape, we present an advanced, and entirely redesigned and extended version of the EAGER pipeline for the analysis of ancient genomic data. This Nextflow pipeline aims to address three main themes: accessibility and adaptability to different computing configurations, reproducibility to ensure robust analytical standards, and updating the pipeline to the latest routine ancient genomic practices. The new version of EAGER has been developed within the nf-core initiative to ensure high-quality software development and maintenance support; contributing to a long-term life-cycle for the pipeline. nf-core/eager will assist in ensuring that a wider range of ancient DNA analyses can be applied by a diverse range of research groups and fields.

Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline.

One of the major methods to identify microbial community composition, to unravel microbial population dynamics, and to explore microbial diversity in environmental samples is high-throughput DNA- or RNA-based 16S rRNA (gene) amplicon sequencing in combination with bioinformatics analyses. However, focusing on environmental samples from contrasting habitats, it was not systematically evaluated (i) which analysis methods provide results that reflect reality most accurately, (ii) how the interpretations of microbial community studies are biased by different analysis methods and (iii) if the most optimal analysis workflow can be implemented in an easy-to-use pipeline. Here, we compared the performance of 16S rRNA (gene) amplicon sequencing analysis tools (i.e., Mothur, QIIME1, QIIME2, and MEGAN) using three mock datasets with known microbial community composition that differed in sequencing quality, species number and abundance distribution (i.e., even or uneven), and phylogenetic diversity (i.e., closely related or well-separated amplicon sequences). Our results showed that QIIME2 outcompeted all other investigated tools in sequence recovery (>10 times fewer false positives), taxonomic assignments (>22% better F-score) and diversity estimates (>5% better assessment), suggesting that this approach is able to reflect the in situ microbial community most accurately. Further analysis of 24 environmental datasets obtained from four contrasting terrestrial and freshwater sites revealed dramatic differences in the resulting microbial community composition for all pipelines at genus level. For instance, at the investigated river water sites Sphaerotilus was only reported when using QIIME1 (8% abundance) and Agitococcus with QIIME1 or QIIME2 (2 or 3% abundance, respectively), but both genera remained undetected when analyzed with Mothur or MEGAN. Since these abundant taxa probably have implications for important biogeochemical cycles (e.g., nitrate and sulfate reduction) at these sites, their detection and semi-quantitative enumeration is crucial for valid interpretations. A high-performance computing conformant workflow was constructed to allow FAIR (Findable, Accessible, Interoperable, and Re-usable) 16S rRNA (gene) amplicon sequence analysis starting from raw sequence files, using the most optimal methods identified in our study. Our presented workflow should be considered for future studies, thereby facilitating the analysis of high-throughput 16S rRNA (gene) sequencing data substantially, while maximizing reliability and confidence in microbial community data analysis.

Ten simple rules for providing effective bioinformatics research support.

Life scientists are increasingly turning to high-throughput sequencing technologies in their research programs, owing to the enormous potential of these methods. In a parallel manner, the number of core facilities that provide bioinformatics support are also increasing. Notably, the generation of complex large datasets has necessitated the development of bioinformatics support core facilities that aid laboratory scientists with cost-effective and efficient data management, analysis, and interpretation. In this article, we address the challenges-related to communication, good laboratory practice, and data handling-that may be encountered in core support facilities when providing bioinformatics support, drawing on our own experiences working as support bioinformaticians on multidisciplinary research projects. Most importantly, the article proposes a list of guidelines that outline how these challenges can be preemptively avoided and effectively managed to increase the value of outputs to the end user, covering the entire research project lifecycle, including experimental design, data analysis, and management (i.e., sharing and storage). In addition, we highlight the importance of clear and transparent communication, comprehensive preparation, appropriate handling of samples and data using monitoring systems, and the employment of appropriate tools and standard operating procedures to provide effective bioinformatics support.

Potent Antitumor Activity of Liposomal Irinotecan in an Organoid- and CRISPR-Cas9-Based Murine Model of Gallbladder Cancer.

Gallbladder cancer is associated with a dismal prognosis, and accurate in vivo models will be elemental to improve our understanding of this deadly disease and develop better treatment options. We have generated a transplantation-based murine model for gallbladder cancer that histologically mimics the human disease, including the development of distant metastasis. Murine gallbladder-derived organoids are genetically modified by either retroviral transduction or transfection with CRISPR/Cas9 encoding plasmids, thereby allowing the rapid generation of complex cancer genotypes. We characterize the model in the presence of two of the most frequent oncogenic drivers-Kras and ERBB2-and provide evidence that the tumor histology is highly dependent on the driver oncogene. Further, we demonstrate the utility of the model for the preclinical assessment of novel therapeutic approaches by showing that liposomal Irinotecan (Nal-IRI) is retained in tumor cells and significantly prolongs the survival of gallbladder cancer-bearing mice compared to conventional irinotecan.

MHCquant: Automated and Reproducible Data Analysis for Immunopeptidomics.

Personalized multipeptide vaccines are currently being discussed intensively for tumor immunotherapy. In order to identify epitopes-short, immunogenic peptides-suitable for eliciting a tumor-specific immune response, human leukocyte antigen-presented peptides are isolated by immunoaffinity purification from cancer tissue samples and analyzed by liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS). Here, we present MHCquant, a fully automated, portable computational pipeline able to process LC-MS/MS data automatically and generate annotated, false discovery rate-controlled lists of (neo-)epitopes with associated relative quantification information. We could show that MHCquant achieves higher sensitivity than established methods. While obtaining the highest number of unique peptides, the rate of predicted MHC binders remains still comparable to other tools. Reprocessing of the data from a previously published study resulted in the identification of several neoepitopes not detected by previously applied methods. MHCquant integrates tailor-made pipeline components with existing open-source software into a coherent processing workflow. Container-based virtualization permits execution of this workflow without complex software installation, execution on cluster/cloud infrastructures, and full reproducibility of the results. Integration with the data analysis workbench KNIME enables easy mining of large-scale immunopeptidomics data sets. MHCquant is available as open-source software along with accompanying documentation on our website at https://www.openms.de/mhcquant/ .

Challenges of big data integration in the life sciences.

Big data has been reported to be revolutionizing many areas of life, including science. It summarizes data that is unprecedentedly large, rapidly generated, heterogeneous, and hard to accurately interpret. This availability has also brought new challenges: How to properly annotate data to make it searchable? What are the legal and ethical hurdles when sharing data? How to store data securely, preventing loss and corruption? The life sciences are not the only disciplines that must align themselves with big data requirements to keep up with the latest developments. The large hadron collider, for instance, generates research data at a pace beyond any current biomedical research center. There are three recent major coinciding events that explain the emergence of big data in the context of research: the technological revolution for data generation, the development of tools for data analysis, and a conceptual change towards open science and data. The true potential of big data lies in pattern discovery in large datasets, as well as the formulation of new models and hypotheses. Confirmation of the existence of the Higgs boson, for instance, is one of the most recent triumphs of big data analysis in physics. Digital representations of biological systems have become more comprehensive. This, in combination with advances in machine learning, creates exciting new research possibilities. In this paper, we review the state of big data in bioanalytical research and provide an overview of the guidelines for its proper usage.

BOSC 2019, the 20th annual Bioinformatics Open Source Conference.

The Bioinformatics Open Source Conference is a volunteer-organized meeting that covers open source software development and open science in bioinformatics. Launched in 2000, BOSC has been held every year since. BOSC 2019, the 20th annual BOSC, took place as one of the Communities of Special Interest (COSIs) at the Intelligent Systems for Molecular Biology meeting (ISMB/ECCB 2019). The two-day meeting included a total of 46 talks and 55 posters, as well as eight Birds of a Feather interest groups. The keynote speaker was University of Cape Town professor Dr. Nicola Mulder, who spoke on "Building infrastructure for responsible open science in Africa". Immediately after BOSC 2019, about 50 people participated in the two-day CollaborationFest (CoFest for short), an open and free community-driven event at which participants work together to contribute to bioinformatics software, documentation, training materials, and use cases.

Inferring genetic origins and phenotypic traits of George Bähr, the architect of the Dresden Frauenkirche.

For historic individuals, the outward appearance and other phenotypic characteristics remain often non-resolved. Unfortunately, images or detailed written sources are only scarcely available in many cases. Attempts to study historic individuals with genetic data so far focused on hypervariable regions of mitochondrial DNA and to some extent on complete mitochondrial genomes. To elucidate the potential of in-solution based genome-wide SNP capture methods - as now widely applied in population genetics - we extracted DNA from the 17th century remains of George Bähr, the architect of the Dresdner Frauenkirche. We were able to identify the remains to be of male origin, showing sufficient DNA damage, deriving from a single person and being thus likely authentic. Furthermore, we were able to show that George Bähr had light skin pigmentation and most likely brown eyes. His genomic DNA furthermore points to a Central European origin. We see this analysis as an example to demonstrate the prospects that new in-solution SNP capture methods can provide for historic cases of forensic interest, using methods well established in ancient DNA (aDNA) research and population genetics.

Reconstructing Prehistoric African Population Structure.

We assembled genome-wide data from 16 prehistoric Africans. We show that the anciently divergent lineage that comprises the primary ancestry of the southern African San had a wider distribution in the past, contributing approximately two-thirds of the ancestry of Malawi hunter-gatherers ∼8,100-2,500 years ago and approximately one-third of the ancestry of Tanzanian hunter-gatherers ∼1,400 years ago. We document how the spread of farmers from western Africa involved complete replacement of local hunter-gatherers in some regions, and we track the spread of herders by showing that the population of a ∼3,100-year-old pastoralist from Tanzania contributed ancestry to people from northeastern to southern Africa, including a ∼1,200-year-old southern African pastoralist. The deepest diversifications of African lineages were complex, involving either repeated gene flow among geographically disparate groups or a lineage more deeply diverging than that of the San contributing more to some western African populations than to others. We finally leverage ancient genomes to document episodes of natural selection in southern African populations. PAPERCLIP.

Genetic origins of the Minoans and Mycenaeans.

The origins of the Bronze Age Minoan and Mycenaean cultures have puzzled archaeologists for more than a century. We have assembled genome-wide data from 19 ancient individuals, including Minoans from Crete, Mycenaeans from mainland Greece, and their eastern neighbours from southwestern Anatolia. Here we show that Minoans and Mycenaeans were genetically similar, having at least three-quarters of their ancestry from the first Neolithic farmers of western Anatolia and the Aegean, and most of the remainder from ancient populations related to those of the Caucasus and Iran. However, the Mycenaeans differed from Minoans in deriving additional ancestry from an ultimate source related to the hunter-gatherers of eastern Europe and Siberia, introduced via a proximal source related to the inhabitants of either the Eurasian steppe or Armenia. Modern Greeks resemble the Mycenaeans, but with some additional dilution of the Early Neolithic ancestry. Our results support the idea of continuity but not isolation in the history of populations of the Aegean, before and after the time of its earliest civilizations.

Ancient Egyptian mummy genomes suggest an increase of Sub-Saharan African ancestry in post-Roman periods.

Egypt, located on the isthmus of Africa, is an ideal region to study historical population dynamics due to its geographic location and documented interactions with ancient civilizations in Africa, Asia and Europe. Particularly, in the first millennium BCE Egypt endured foreign domination leading to growing numbers of foreigners living within its borders possibly contributing genetically to the local population. Here we present 90 mitochondrial genomes as well as genome-wide data sets from three individuals obtained from Egyptian mummies. The samples recovered from Middle Egypt span around 1,300 years of ancient Egyptian history from the New Kingdom to the Roman Period. Our analyses reveal that ancient Egyptians shared more ancestry with Near Easterners than present-day Egyptians, who received additional sub-Saharan admixture in more recent times. This analysis establishes ancient Egyptian mummies as a genetic source to study ancient human history and offers the perspective of deciphering Egypt's past at a genome-wide level.

Origin of modern syphilis and emergence of a pandemic Treponema pallidum cluster.

The abrupt onslaught of the syphilis pandemic that started in the late fifteenth century established this devastating infectious disease as one of the most feared in human history1. Surprisingly, despite the availability of effective antibiotic treatment since the mid-twentieth century, this bacterial infection, which is caused by Treponema pallidum subsp. pallidum (TPA), has been re-emerging globally in the last few decades with an estimated 10.6 million cases in 2008 (ref. 2). Although resistance to penicillin has not yet been identified, an increasing number of strains fail to respond to the second-line antibiotic azithromycin3. Little is known about the genetic patterns in current infections or the evolutionary origins of the disease due to the low quantities of treponemal DNA in clinical samples and difficulties in cultivating the pathogen4. Here, we used DNA capture and whole-genome sequencing to successfully interrogate genome-wide variation from syphilis patient specimens, combined with laboratory samples of TPA and two other subspecies. Phylogenetic comparisons based on the sequenced genomes indicate that the TPA strains examined share a common ancestor after the fifteenth century, within the early modern era. Moreover, most contemporary strains are azithromycin-resistant and are members of a globally dominant cluster, named here as SS14-Ω. The cluster diversified from a common ancestor in the mid-twentieth century subsequent to the discovery of antibiotics. Its recent phylogenetic divergence and global presence point to the emergence of a pandemic strain cluster.