Proposing candidate genes under telomeric control based on cross-species position data.

In this paper, we present a comprehensive computational framework aimed at suggesting genes whose transcriptional regulation is likely to be influenced by their chromosomal position. This framework provides a user-friendly web interface, enabling researchers to explore the positional properties of all human genes and their orthologs across species, with a focus on their relation to the telomeres. Our approach involves multiple scoring methods, each adjustable by users, representing different features of the genes' positional variation across species. The resulting rankings can be combined to identify candidate genes that may be subject to position effects. Furthermore, the ranking can be tailored to a specific set of reference genes. We evaluate the method within the context of TPE-OLD, a mechanism where telomeres can exert a direct influence on gene expression across considerable genomic distances, and empower researchers to delve deeper into genes of interest, analyzing their position across species and estimating their susceptibility to position effects like TPE-OLD. We also provide simple enrichment analyses of user-provided gene lists in relation to top-ranking candidate genes.

Gene co-expression analyses of health(span) across multiple species.

Health(span)-related gene clusters/modules were recently identified based on knowledge about the cross-species genetic basis of health, to interpret transcriptomic datasets describing health-related interventions. However, the cross-species comparison of health-related observations reveals a lot of heterogeneity, not least due to widely varying health(span) definitions and study designs, posing a challenge for the exploration of conserved healthspan modules and, specifically, their transfer across species. To improve the identification and exploration of conserved/transferable healthspan modules, here we apply an established workflow based on gene co-expression network analyses employing GEO/ArrayExpress data for human and animal models, and perform a comprehensive meta-study of the resulting modules related to health(span), yielding a small set of literature backed health(span) candidate genes. For each experiment, WGCNA (weighted gene correlation network analysis) was used to infer modules of genes which correlate in their expression with a 'health phenotype score' and to determine the most-connected (hub) genes (and their interactions) for each such module. After mapping these hub genes to their human orthologs, 12 health(span) genes were identified in at least two species (ACTN3, ANK1, MRPL18, MYL1, PAXIP1, PPP1CA, SCN3B, SDCBP, SKIV2L, TUBG1, TYROBP, WIPF1), for which enrichment analysis by g:profiler found an association with actin filament-based movement and associated organelles, as well as muscular structures. We conclude that a meta-study of hub genes from co-expression network analyses for the complex phenotype health(span), across multiple species, can yield molecular-mechanistic insights and can direct experimentalists to further investigate the contribution of individual genes and their interactions to health(span).

A conserved long-distance telomeric silencing mechanism suppresses mTOR signaling in aging human fibroblasts.

Telomeres are repetitive nucleotide sequences at the ends of each chromosome. It has been hypothesized that telomere attrition evolved as a tumor suppressor mechanism in large long-lived species. Long telomeres can silence genes millions of bases away through a looping mechanism called telomere position effect over long distances (TPE-OLD). The function of this silencing mechanism is unknown. We determined a set of 2322 genes with high positional conservation across replicatively aging species that includes known and candidate TPE-OLD genes that may mitigate potentially harmful effects of replicative aging. Notably, we identified PPP2R2C as a tumor suppressor gene, whose up-regulation by TPE-OLD in aged human fibroblasts leads to dephosphorylation of p70S6 kinase and mammalian target of rapamycin suppression. A mechanistic link between telomeres and a tumor suppressor mechanism supports the hypothesis that replicative aging fulfills a tumor suppressor function and motivates previously unknown antitumor and antiaging strategies.

Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives.

Accurate transfer learning of clinical outcomes from one cellular context to another, between cell types, developmental stages, omics modalities or species, is considered tremendously useful. When transferring a prediction task from a source domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring states or processes common to both the source and the target that can be learned by the predictor reflected by shared denominators. These may form a compendium of knowledge that is learned in the source to enable predictions in the target, usually with few, if any, labeled target training samples to learn from. Transductive transfer learning refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive transfer learning considers cases where the target predictor is performing a different yet related task as compared with the source predictor. Often, there is also a need to first map the variables in the input/feature spaces and/or the variables in the output/outcome spaces. We here discuss and juxtapose various recently published transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in vivo) outcomes based on preclinical (mostly animal-based) molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.

Perspectives on automated composition of workflows in the life sciences.

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the "big picture" of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

Genes implicated in Caenorhabditis elegans and human health regulate stress resistance and physical abilities in aged Caenorhabditis elegans.

Recently, nine Caenorhabditis elegans genes, grouped into two pathways/clusters, were found to be implicated in healthspan in C. elegans and their homologues in humans, based on literature curation, WormBase data mining and bioinformatics analyses. Here, we further validated these genes experimentally in C. elegans. We downregulated the nine genes via RNA interference (RNAi), and their effects on physical function (locomotion in a swim assay) and on physiological function (survival after heat stress) were analysed in aged nematodes. Swim performance was negatively affected by the downregulation of acox-1.1, pept-1, pak-2, gsk-3 and C25G6.3 in worms with advanced age (twelfth day of adulthood) and heat stress resistance was decreased by RNAi targeting of acox-1.1, daf-22, cat-4, pig-1, pak-2, gsk-3 and C25G6.3 in moderately (seventh day of adulthood) or advanced aged nematodes. Only one gene, sad-1, could not be linked to a health-related function in C. elegans with the bioassays we selected. Thus, most of the healthspan genes could be re-confirmed by health measurements in old worms.

Health and longevity studies in C. elegans: the "healthy worm database" reveals strengths, weaknesses and gaps of test compound-based studies.

Several biogerontology databases exist that focus on genetic or gene expression data linked to health as well as survival, subsequent to compound treatments or genetic manipulations in animal models. However, none of these has yet collected experimental results of compound-related health changes. Since quality of life is often regarded as more valuable than length of life, we aim to fill this gap with the "Healthy Worm Database" ( http://healthy-worm-database.eu ). Literature describing health-related compound studies in the aging model Caenorhabditis elegans was screened, and data for 440 compounds collected. The database considers 189 publications describing 89 different phenotypes measured in 2995 different conditions. Besides enabling a targeted search for promising compounds for further investigations, this database also offers insights into the research field of studies on healthy aging based on a frequently used model organism. Some weaknesses of C. elegans-based aging studies, like underrepresented phenotypes, especially concerning cognitive functions, as well as the convenience-based use of young worms as the starting point for compound treatment or phenotype measurement are discussed. In conclusion, the database provides an anchor for the search for compounds affecting health, with a link to public databases, and it further highlights some potential shortcomings in current aging research.

Scoring functions for drug-effect similarity.

MOTIVATION: The difficulty to find new drugs and bring them to the market has led to an increased interest to find new applications for known compounds. Biological samples from many disease contexts have been extensively profiled by transcriptomics, and, intuitively, this motivates to search for compounds with a reversing effect on the expression of characteristic disease genes. However, disease effects may be cell line-specific and also depend on other factors, such as genetics and environment. Transcription profile changes between healthy and diseased cells relate in complex ways to profile changes gathered from cell lines upon stimulation with a drug. Despite these differences, we expect that there will be some similarity in the gene regulatory networks at play in both situations. The challenge is to match transcriptomes for both diseases and drugs alike, even though the exact molecular pathology/pharmacogenomics may not be known. RESULTS: We substitute the challenge to match a drug effect to a disease effect with the challenge to match a drug effect to the effect of the same drug at another concentration or in another cell line. This is welldefined, reproducible in vitro and in silico and extendable with external data. Based on the Connectivity Map (CMap) dataset, we combined 26 different similarity scores with six different heuristics to reduce the number of genes in the model. Such gene filters may also utilize external knowledge e.g. from biological networks. We found that no similarity score always outperforms all others for all drugs, but the Pearson correlation finds the same drug with the highest reliability. Results are improved by filtering for highly expressed genes and to a lesser degree for genes with large fold changes. Also a network-based reduction of contributing transcripts was beneficial, here implemented by the FocusHeuristics. We found no drop in prediction accuracy when reducing the whole transcriptome to the set of 1000 landmark genes of the CMap's successor project Library of Integrated Network-based Cellular Signatures. All source code to re-analyze and extend the CMap data, the source code of heuristics, filters and their evaluation are available to propel the development of new methods for drug repurposing. AVAILABILITY: https://bitbucket.org/ibima/moldrugeffectsdb. CONTACT: steffen.moeller@uni-rostock.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.

Natural products improve healthspan in aged mice and rats: A systematic review and meta-analysis.

Over the last decades a decrease in mortality has paved the way for late onset pathologies such as cardiovascular, metabolic or neurodegenerative diseases. This evidence has led many researchers to shift their focus from researching ways to extend lifespan to finding ways to increase the number of years spent in good health; "healthspan" is indeed the emerging concept of such quest for ageing without chronic or disabling diseases and dysfunctions. Regular consumption of natural products might improve healthspan, although the mechanisms of action are still poorly understood. Since preclinical studies aimed to assess the efficacy and safety of these compounds are growing, we performed a systematic review and meta-analysis on the effects of natural products on healthspan in mouse and rat models of physiological ageing. Results indicate that natural compounds show robust effects improving stress resistance and cognitive abilities. These promising data call for further studies investigating the underlying mechanisms in more depth.

Towards biomarkers for outcomes after pancreatic ductal adenocarcinoma and ischaemic stroke, with focus on (co)-morbidity and ageing/cellular senescence (SASKit): protocol for a prospective cohort study.

INTRODUCTION: Ageing-related processes such as cellular senescence are believed to underlie the accumulation of diseases in time, causing (co)morbidity, including cancer, thromboembolism and stroke. Interfering with these processes may delay, stop or reverse morbidity. The aim of this study is to investigate the link between (co)morbidity and ageing by exploring biomarkers and molecular mechanisms of disease-triggered deterioration in patients with pancreatic ductal adenocarcinoma (PDAC) and (thromboembolic) ischaemic stroke (IS). METHODS AND ANALYSIS: We will recruit 50 patients with PDAC, 50 patients with (thromboembolic) IS and 50 controls at Rostock University Medical Center, Germany. We will gather routine blood data, clinical performance measurements and patient-reported outcomes at up to seven points in time, alongside in-depth transcriptomics and proteomics at two of the early time points. Aiming for clinically relevant biomarkers, the primary outcome is a composite of probable sarcopenia, clinical performance (described by ECOG Performance Status for patients with PDAC and the Modified Rankin Scale for patients with stroke) and quality of life. Further outcomes cover other aspects of morbidity such as cognitive decline and of comorbidity such as vascular or cancerous events. The data analysis is comprehensive in that it includes biostatistics and machine learning, both following standard role models and additional explorative approaches. Prognostic and predictive biomarkers for interventions addressing senescence may become available if the biomarkers that we find are specifically related to ageing/cellular senescence. Similarly, diagnostic biomarkers will be explored. Our findings will require validation in independent studies, and our dataset shall be useful to validate the findings of other studies. In some of the explorative analyses, we shall include insights from systems biology modelling as well as insights from preclinical animal models. We anticipate that our detailed study protocol and data analysis plan may also guide other biomarker exploration trials. ETHICS AND DISSEMINATION: The study was approved by the local ethics committee (Ethikkommission an der Medizinischen Fakultät der Universität Rostock, A2019-0174), registered at the German Clinical Trials Register (DRKS00021184), and results will be published following standard guidelines.

Suppression of the TGF-ß pathway by a macrolide antibiotic decreases fibrotic responses by ocular fibroblasts in vitro.

To elucidate and to inhibit post-surgical fibrotic processes after trabeculectomy in glaucoma therapy, we measured gene expression in a fibrotic cell culture model, based on transforming growth factor TGF-ß induction in primary human tenon fibroblasts (hTFs), and used Connectivity Map (CMap) data for drug repositioning. We found that specific molecular mechanisms behind fibrosis are the upregulation of actins, the downregulation of CD34, and the upregulation of inflammatory cytokines such as IL6, IL11 and BMP6. The macrolide antibiotic Josamycin (JM) reverses these molecular mechanisms according to data from the CMap, and we thus tested JM as an inhibitor of fibrosis. JM was first tested for its toxic effects on hTFs, where it showed no influence on cell viability, but inhibited hTF proliferation in a concentration-dependent manner. We then demonstrated that JM suppresses the synthesis of extracellular matrix (ECM) components. In hTFs stimulated with TGF-ß1, JM specifically inhibited α-smooth muslce actin expression, suggesting that it inhibits the transformation of fibroblasts into fibrotic myofibroblasts. In addition, a decrease of components of the ECM such as fibronectin, which is involved in in vivo scarring, was observed. We conclude that JM may be a promising candidate for the treatment of fibrosis after glaucoma filtration surgery or drainage device implantation in vivo.

Single-cell analyses of aging, inflammation and senescence.

Single-cell gene expression (transcriptomics) data are becoming robust and abundant, and are increasingly used to track organisms along their life-course. This allows investigation into how aging affects cellular transcriptomes, and how changes in transcriptomes may underlie aging, including chronic inflammation (inflammaging), immunosenescence and cellular senescence. We compiled and tabulated aging-related single-cell datasets published to date, collected and discussed relevant findings, and inspected some of these datasets ourselves. We specifically note insights that cannot (or not easily) be based on bulk data. For example, in some datasets, the fraction of cells expressing p16 (CDKN2A), one of the most prominent markers of cellular senescence, was reported to increase, in addition to its upregulated mean expression over all cells. Moreover, we found evidence for inflammatory processes in most datasets, some of these driven by specific cells of the immune system. Further, single-cell data are specifically useful to investigate whether transcriptional heterogeneity (also called noise or variability) increases with age, and many (but not all) studies in our review report an increase in such heterogeneity. Finally, we demonstrate some stability of marker gene expression patterns across closely similar studies and suggest that single-cell experiments may hold the key to provide detailed insights whenever interventions (countering aging, inflammation, senescence, disease, etc.) are affecting cells depending on cell type.

Healthspan pathway maps in C. elegans and humans highlight transcription, proliferation/biosynthesis and lipids.

The molecular basis of aging and of aging-associated diseases is being unraveled at an increasing pace. An extended healthspan, and not merely an extension of lifespan, has become the aim of medical practice. Here, we define health based on the absence of diseases and dysfunctions. Based on an extensive review of the literature, in particular for humans and C. elegans, we compile a list of features of health and of the genes associated with them. These genes may or may not be associated with survival/lifespan. In turn, survival/lifespan genes that are not known to be directly associated with health are not considered. Clusters of these genes based on molecular interaction data give rise to maps of healthspan pathways for humans and for C. elegans. Overlaying healthspan-related gene expression data onto the healthspan pathway maps, we observe the downregulation of (pro-inflammatory) Notch signaling in humans and of proliferation in C. elegans. We identify transcription, proliferation/biosynthesis and lipids as a common theme on the annotation level, and proliferation-related kinases on the gene/protein level. Our literature-based data corpus, including visualization, should be seen as a pilot investigation of the molecular underpinnings of health in two different species. Web address: http://pathways.h2020awe.eu.

Genetic association analysis of CLEC5A and CLEC7A gene single-nucleotide polymorphisms and Crohn's disease.

BACKGROUND: Crohn's disease (CD) is characterized by a multifactorial etiology and a significant impact of genetic traits. While NOD2 mutations represent well established risk factors of CD, the role of other genes is incompletely understood. AIM: To challenge the hypothesis that single nucleotide polymorphisms (SNPs) in the genes CLEC5A and CLEC7A, two members of the C-type lectin domain family of pattern recognition receptors, may be associated with CD. METHODS: SNPs in CLEC5A, CLEC7A and the known CD risk gene NOD2 were studied using real time PCR-based SNP assays. Therefore, DNA samples from 175 patients and 157 healthy donors were employed. Genotyping data were correlated with clinical characteristics of the patients and the results of gene expression data analyses. RESULTS: In accordance with previous studies, rs2066844 and rs2066847 in NOD2 were found to be significantly associated with CD (allelic P values = 0.0368 and 0.0474, respectively). Intriguingly, for genotype AA of rs1285933 in CLEC5A, a potential association with CD (recessive P = 0.0523; odds ratio = 1.90) was observed. There were no associations between CD and SNPs rs2078178 and rs16910631 in CLEC7A. Variants of rs1285933 had no impact on CLEC5A gene expression. In contrast, genotype-dependent differences of CXCL5 expression in peripheral blood mononuclear cells were observed. There is no statistical interaction between the tested SNPs of NOD2 and CLEC5A, suggesting of a novel pathway contributing to the disease. CONCLUSION: Our data encourage enlarged follow-up studies to further address an association of SNP rs1285933 in CLEC5A with CD. The C-type lectin domain family member also deserves attention regarding a potential role in the pathophysiology of CD.

Gene-diet interactions associated with complex trait variation in an advanced intercross outbred mouse line.

Phenotypic variation of quantitative traits is orchestrated by a complex interplay between the environment (e.g. diet) and genetics. However, the impact of gene-environment interactions on phenotypic traits mostly remains elusive. To address this, we feed 1154 mice of an autoimmunity-prone intercross line (AIL) three different diets. We find that diet substantially contributes to the variability of complex traits and unmasks additional genetic susceptibility quantitative trait loci (QTL). By performing whole-genome sequencing of the AIL founder strains, we resolve these QTLs to few or single candidate genes. To address whether diet can also modulate genetic predisposition towards a given trait, we set NZM2410/J mice on similar dietary regimens as AIL mice. Our data suggest that diet modifies genetic susceptibility to lupus and shifts intestinal bacterial and fungal community composition, which precedes clinical disease manifestation. Collectively, our study underlines the importance of including environmental factors in genetic association studies.

Health and Aging: Unifying Concepts, Scores, Biomarkers and Pathways.

Despite increasing research efforts, there is a lack of consensus on defining aging or health. To understand the underlying processes, and to foster the development of targeted interventions towards increasing one's health, there is an urgent need to find a broadly acceptable and useful definition of health, based on a list of (molecular) features; to operationalize features of health so that it can be measured; to identify predictive biomarkers and (molecular) pathways of health; and to suggest interventions, such as nutrition and exercise, targeted at putative causal pathways and processes. Based on a survey of the literature, we propose to define health as a state of an individual characterized by the core features of physiological, cognitive, physical and reproductive function, and a lack of disease. We further define aging as the aggregate of all processes in an individual that reduce its wellbeing, that is, its health or survival or both. We define biomarkers of health by their attribute of predicting future health better than chronological age. We define healthspan pathways as molecular features of health that relate to each other by belonging to the same molecular pathway. Our conceptual framework may integrate diverse operationalizations of health and guide precision prevention efforts.

Scalable Workflows and Reproducible Data Analysis for Genomics.

Biological, clinical, and pharmacological research now often involves analyses of genomes, transcriptomes, proteomes, and interactomes, within and between individuals and across species. Due to large volumes, the analysis and integration of data generated by such high-throughput technologies have become computationally intensive, and analysis can no longer happen on a typical desktop computer.In this chapter we show how to describe and execute the same analysis using a number of workflow systems and how these follow different approaches to tackle execution and reproducibility issues. We show how any researcher can create a reusable and reproducible bioinformatics pipeline that can be deployed and run anywhere. We show how to create a scalable, reusable, and shareable workflow using four different workflow engines: the Common Workflow Language (CWL), Guix Workflow Language (GWL), Snakemake, and Nextflow. Each of which can be run in parallel.We show how to bundle a number of tools used in evolutionary biology by using Debian, GNU Guix, and Bioconda software distributions, along with the use of container systems, such as Docker, GNU Guix, and Singularity. Together these distributions represent the overall majority of software packages relevant for biology, including PAML, Muscle, MAFFT, MrBayes, and BLAST. By bundling software in lightweight containers, they can be deployed on a desktop, in the cloud, and, increasingly, on compute clusters.By bundling software through these public software distributions, and by creating reproducible and shareable pipelines using these workflow engines, not only do bioinformaticians have to spend less time reinventing the wheel but also do we get closer to the ideal of making science reproducible. The examples in this chapter allow a quick comparison of different solutions.

CD154 Costimulation Shifts the Local T-Cell Receptor Repertoire Not Only During Thymic Selection but Also During Peripheral T-Dependent Humoral Immune Responses.

CD154 is a transmembrane cytokine expressed transiently on activated CD4 T cells upon T-cell receptor (TCR) stimulation that interacts with CD40 on antigen-presenting cells. The signaling via CD154:CD40 is essential for B-cell maturation and germinal center formation and also for the final differentiation of CD4 T cells during T-dependent humoral immune responses. Recent data demonstrate that CD154 is critically involved in the selection of T-cell clones during the negative selection process in the thymus. Whether CD154 signaling influences the TCR repertoire during peripheral T-dependent humoral immune responses has not yet been elucidated. To find out, we used CD154-deficient mice and assessed the global TCRß repertoire in T-cell zones (TCZ) of spleens by high-throughput sequencing after induction of a Th2 response to the multiepitopic antigen sheep red blood cells. Qualitative and quantitative comparison of the splenic TCZ-specific TCRß repertoires revealed that CD154 deficiency shifts the distribution of Vß-Jß genes after antigen exposure. This data led to the conclusion that costimulation via CD154:CD40 during the interaction of T cells with CD40-matured B cells contributes to the recruitment of T-cell clones into the immune response and thereby shapes the peripheral TCR repertoire.

A workflow for the integrative transcriptomic description of molecular pathology and the suggestion of normalizing compounds, exemplified by Parkinson's disease.

The volume of molecular observations on human diseases in public databases is continuously increasing at accelerating rates. A bottleneck is their computational integration into a coherent description, from which researchers may derive new well-founded hypotheses. Also, the need to integrate data from different technologies (genetics, coding and regulatory RNA, proteomics) emerged in order to identify biomarkers for early diagnosis and prognosis of complex diseases and therefore facilitating the development of novel treatment approaches. We propose here a workflow for the integrative transcriptomic description of the molecular pathology in Parkinsons's Disease (PD), including suggestions of compounds normalizing disease-induced transcriptional changes as a paradigmatic example. We integrated gene expression profiles, miRNA signatures, and publicly available regulatory databases to specify a partial model of the molecular pathophysiology of PD. Six genetic driver elements (2 genes and 4 miRNAs) and several functional network modules that are associated with PD were identified. Functional modules were assessed for their statistical significance, cellular functional homogeneity, literature evidence, and normalizing small molecules. In summary, our workflow for the joint regulatory analysis of coding and non-coding RNA, has the potential to yield clinically as well as biologically relevant information, as demonstrated here on PD data.

ClonoCalc and ClonoPlot: immune repertoire analysis from raw files to publication figures with graphical user interface.

BACKGROUND: Next generation sequencing (NGS) technologies enable studies and analyses of the diversity of both T and B cell receptors (TCR and BCR) in human and animal systems to elucidate immune functions in health and disease. Over the last few years, several algorithms and tools have been developed to support respective analyses of raw sequencing data of the immune repertoire. These tools focus on distinct aspects of the data processing and require a strong bioinformatics background. To facilitate the analysis of T and B cell repertoires by less experienced users, software is needed that combines the most common tools for repertoire analysis. RESULTS: We introduce a graphical user interface (GUI) providing a complete analysis pipeline for processing raw NGS data for human and animal TCR and BCR clonotype determination and advanced differential repertoire studies. It provides two applications. ClonoCalc prepares the raw data for downstream analyses. It combines a demultiplexer for barcode splitting and employs MiXCR for paired-end read merging and the extraction of human and animal TCR/BCR sequences. ClonoPlot wraps the R package tcR and further contributes self-developed plots for the descriptive comparative investigation of immune repertoires. CONCLUSION: This workflow reduces the amount of programming required to perform the respective analyses and supports both communication and training between scientists and technicians, and across scientific disciplines. The Open Source development in Java and R is modular and invites advanced users to extend its functionality. Software and documentation are freely available at https://bitbucket.org/ClonoSuite/clonocalc-plot .