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Background: In all organisms, the ribosome performs the unique and essential function of translating mRNA into protein. Since deletion of any ribosomal protein (RP) is embryonic lethal in complex eukaryotes, the ribosome is believed to be structurally uniform throughout the organism, with all RPs essential for cell viability. Results: We tested the structural homogeneity of RP use in ribosomes for normal and cancer tissues and cell lines using RP mRNA, ribosome profiling, and protein data. Analysis of RP mRNA data from 11,688 normal samples for 53 human tissues from 714 subjects and 10,363 tumor samples for 33 human cancers, normalized by total RP mRNA level per sample, showed that both normal (non-diseased) and tumor samples cluster by tissue type in humans. Matrix factorization showed that at least 3 RP mRNA signatures are necessary to describe normal blood and brain tissues, and a minimum of 16 RP mRNA signatures are necessary to describe data from 53 different normal tissues. A pan-cancer analysis of copy number variation (CNV) in 10,845 tumor samples for 33 human cancers showed that loss of one or both copies of RP genes was prevalent in every cancer type. Furthermore, there was no association between the number of double-deleted RP genes in tumors and patient survival, showing that RP loss does not reduce tumor fitness. CRISPR-Cas9 deletion of RP genes shows that many RPs are not essential in many cell lines. In several cancers, multiple RP-subtypes exist, with significant survival and genomic differences among them. These RP-subtypes often map to known molecular subtypes, and various RP genes are often deleted in one or both subtypes. This suggests that when genomic landscapes are modified in tumors, genes coding RPs are often lost, but these losses do not affect tumor viability. Analysis of mRNA data and ribosome profiling data of RP genes for cells and tissues from human, mouse and rat showed that these are highly correlated, showing that transcripts encoding ribosomal proteins are being translated into ribosomal proteins at rates proportional to their mRNA levels. Consistently, both mRNA data and ribosome profiling data of RP genes, normalized by total level per sample, showed tissue specific and development-stage specific clusters. Finally, analysis of RP protein levels in human adult and fetal tissues, standardized per sample, showed both development-stage and tissue specificity, showing that there is both tissue and development-stage specific heterogeneity of RP protein usage. Conclusions: These results suggest that there are multiple ribosome types in complex eukaryotes, with different RP composition which are regulated in a tissue and development-stage specific manner by some novel, yet unknown mechanism. Citation Format: Anshuman Panda, Anupama Yadav, Huwate Yeerna, Amartya Singh, Michael Biehl, Markus Lux, Alexander Schulz, Tyler Klecha, Sebastian Doniach, Hossein Khiabanian, Shridar Ganesan, Pablo Tamayo, Gyan Bhanot. The composition of the human ribosome varies significantly in different normal and malignant tissues [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5865.

We give results from a detailed analysis of human Ribosomal Protein (RP) levels in normal and cancer samples and cell lines from large mRNA, copy number variation and ribosome profiling datasets. After normalizing total RP mRNA levels per sample, we find highly consistent tissue specific RP mRNA signatures in normal and tumor samples. Multiple RP mRNA-subtypes exist in several cancers, with significant survival and genomic differences. Some RP mRNA variations among subtypes correlate with copy number loss of RP genes. In kidney cancer, RP subtypes map to molecular subtypes related to cell-of-origin. Pan-cancer analysis of TCGA data showed widespread single/double copy loss of RP genes, without significantly affecting survival. In several cancer cell lines, CRISPR-Cas9 knockout of RP genes did not affect cell viability. Matched RP ribosome profiling and mRNA data in humans and rodents stratified by tissue and development stage and were strongly correlated, showing that RP translation rates were proportional to mRNA levels. In a small dataset of human adult and fetal tissues, RP protein levels showed development stage and tissue specific heterogeneity of RP levels. Our results suggest that heterogeneous RP levels play a significant functional role in cellular physiology, in both normal and disease states. © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.

Motivation Identification of cell populations in flow cytometry is a critical part of the analysis and lays the groundwork for many applications and research discovery. The current paradigm of manual analysis is time consuming and subjective. A common goal of users is to replace manual analysis with automated methods that replicate their results. Supervised tools provide the best performance in such a use case, however they require fine parameterization to obtain the best results. Hence, there is a strong need for methods that are fast to setup, accurate and interpretable. Results flowLearn is a semi-supervised approach for the quality-checked identification of cell populations. Using a very small number of manually gated samples, through density alignments it is able to predict gates on other samples with high accuracy and speed. On two state-of-the-art datasets, our tool achieves median(F1)-measures exceeding 0.99 for 31%, and 0.90 for 80% of all analyzed populations. Furthermore, users can directly interpret and adjust automated gates on new sample files to iteratively improve the initial training. Availability and implementation FlowLearn is available as an R package on https://github.com/mlux86/flowLearn. Evaluation data is publicly available online. Details can be found in the Supplementary Material. Supplementary information Supplementary data are available at Bioinformatics online.

By developing a high-density murine immunophenotyping platform compatible with high-throughput genetic screening, we have established profound contributions of genetics and structure to immune variation. Specifically, high-throughput phenotyping of 530 knockout mouse lines identified 140 monogenic “hits” (>25%), most of which had never hitherto been implicated in immunology. Furthermore, they were conspicuously enriched in genes for which humans show poor tolerance to loss-of-function. The immunophenotyping platform also exposed dense correlation networks linking immune parameters with one another and with specific physiologic traits. By limiting the freedom of individual immune parameters, such linkages impose genetically regulated “immunological structures”, whose integrity was found to be associated with immunocompetence. Hence, our findings provide an expanded genetic resource and structural perspective for understanding and monitoring immune variation in health and disease.

Binning constitutes a crucial step of de novo metagenomics data analysis, and several promising attempts to partially automate this process have been proposed; quite a few recent approaches rely on machine learning techniques, in particular clustering. However, so far, there does not exist a fully automated process, nor a thorough evaluation of its accuracy and robustness with respect to parameterisation. This contribution addresses the following issues: (i) an integration of modern dimensionality reduction and clustering techniques suitable for high dimensional data, and an automated selection of the number of clusters, (ii) a formal quantitative evaluation of the pipeline in benchmarks, (iii) and an evaluation of an optimum parameter choice, resulting in a complete automation of the process.

Novel methods for the sequencing of single-cell DNA offer tremendous opportunities. However, many techniques are still in their infancy and a major obstacle is given by sample contamination with foreign DNA. In this contribution, we present a pipeline that allows for fast, automated detection of contaminated samples by the use of modern machine learning methods. First, a vectorial representation of the genomic data is obtained using oligonucleotide signatures. Using non-linear subspace projections, data is transformed to be suitable for automatic clustering. This allows for the detection of one vs. more genomes (clusters) in a sample. As clustering is an ill-posed problem, the pipeline relies on a thorough choice of all involved methods and parameters. We give an overview of the problem and evaluate techniques suitable for this task.

In the mid-1980s, Mikhail Gorbachev’s policies of glasnost (‘openness’) and perestroika (political and economic ‘restructuring’) marked the beginning of a transformation process in Central and Eastern Europe. The collapse of communism in this region from 1989 onwards resulted in a new openness for Western foreign languages and new methods of teaching them. As a consequence, an increasing number of teachers from Europe and the United States were sent to Eastern Europe and later also to Asia (mainly to China), starting in the early 1990s. Germany and Austria took the lead in this development for two reasons. One is their geographical proximity, and also the historical ties that had been severed more than 40 years before. The other is the German language, which had played an important role in the USSR. German was the most important Western foreign language. It was, after all, spoken in the German Democratic Republic (GDR [East Germany], 1949–90), one of the ‘socialist brother countries’. Therefore, these German-speaking countries promoted their language so that German, rather than English, would attain the status of a lingua franca. As we now know, those attempts were futile; English has become the global language in this region, too, and has even turned into an emblem of a global identity (Jackson, 2010: 9).

In the last few years, Germany has become a much more attractive destination for foreign students. In absolute numbers, Germany is third after the USA and the UK in the list of preferred countries in which to study abroad (Coughlan, 2011). “Global Gauge”, a survey conducted by the British Council, names Germany as the most supportive country for overseas students. Also, German students count among the most mobile students worldwide (Coughlan, 2011). However, internationalisation is a task involving all participants: “With diverse classrooms also comes diversity in learning behaviour of students, which sometimes poses serious challenges for students, faculty, and administration in higher education. It is therefore important for all stakeholders to gain a better understanding of cross-cultural differences.” (Apfelthaler, 2006, p.3) The formidable challenges become obvious if you look at the example of the largest foreign student group in Germany, the Chinese. This chapter describes the situation of Chinese students before moving on to the underlying causes. Much of it will sound familiar to readers from other countries experiencing internationalisation, but some elements also differ from those in the UK. An examination of the steps that have been taken so far by German universities makes it plain that Germany is a few years behind the UK or the USA, both in identifying the problems and in initiating measures.

Background A major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data. Results We present acdc, a tool specifically developed to aid the quality control process of genomic sequence data. By combining supervised and unsupervised methods, it reliably detects both known and de novo contaminants. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables the removal of any contaminant, yielding a clean sample. Furthermore, given the data complexity and the ill-posedness of clustering, acdc employs bootstrapping techniques to provide statistically profound confidence values. Tested on a large number of samples from diverse sequencing projects, our software is able to quickly and accurately identify contamination. Results are displayed in an interactive user interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing project analysis workflows. Conclusions Acdc can reliably detect contamination in single-cell genome data. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de novo contaminants. Our contribution has the potential to drastically reduce the amount of resources put into these processes, particularly in the context of limited availability of reference species. As single-cell genome data continues to grow rapidly, acdc adds to the toolkit of crucial quality assurance tools. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1397-7) contains supplementary material, which is available to authorized users.