Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing

Mechanisms generating cancer genome complexity from a single cell division error

The chromosome breakage-fusion-bridge (BFB) cycle is a mutational process that produces gene amplification and genome instability. Signatures of BFB cycles can be observed in cancer genomes alongside chromothripsis, another catastrophic mutational phenomenon. We explain this association by elucidating a mutational cascade that is triggered by a single cell division error-chromosome bridge formation-that rapidly increases genomic complexity. We show that actomyosin forces are required for initial bridge breakage. Chromothripsis accumulates, beginning with aberrant interphase replication of bridge DNA. A subsequent burst of DNA replication in the next mitosis generates extensive DNA damage. During this second cell division, broken bridge chromosomes frequently missegregate and form micronuclei, promoting additional chromothripsis. We propose that iterations of this mutational cascade generate the continuing evolution and subclonal heterogeneity characteristic of many human cancers.

Identification of genomic insertion and flanking sequences of the transgenic drought-tolerant maize line "SbSNAC1-382" using the single-molecule real-time (SMRT) sequencing method

Safety assessment of genetically modified (GM) crops is crucial at the product-development phase before GM crops are placed on the market. Determining characteristics of sequences flanking exogenous insertion sequences is essential for the safety assessment and marketing of transgenic crops. In this study, we used genome walking and whole-genome sequencing (WGS) to identify the flanking sequence characteristics of the SbSNAC1 transgenic drought-tolerant maize line "SbSNAC1-382", but both of the two methods failed. Then, we constructed a genomic fosmid library of the transgenic maize line, which contained 4.18×105 clones with an average insertion fragment of 35 kb, covering 5.85 times the maize genome. Subsequently, three positive clones were screened by pairs of specific primers, and one of the three positive clones was sequenced by using single-molecule real-time (SMRT) sequencing technology. More than 1.95 Gb sequence data (~105× coverage) for the sequenced clone were generated. The junction reads mapped to the boundaries of T-DNA, and the flanking sequences in the transgenic line were identified by comparing all sequencing reads with the maize reference genome and the sequence of the transgenic vector. Furthermore, the putative insertion loci and flanking sequences were confirmed by PCR amplification and Sanger sequencing. The results indicated that two copies of the exogenous T-DNA fragments were inserted at the same genomic site, and the exogenous T-DNA fragments were integrated at the position of Chromosome 5 from 177155650 to 177155696 in the transgenic line 382. In this study, we demonstrated the successful application of the SMRT technology for the characterization of genomic insertion and flanking sequences.

Mosaicism in genodermatoses

Genodermatoses are inherited disorders presenting with cutaneous manifestations with or without the involvement of other systems. The majority of these disorders, particularly in cases that present with a cutaneous patterning, may be explained in the context of genetic mosaicism. Despite the barriers to the genetic analysis of mosaic disorders, next-generation sequencing has led to a substantial progress in understanding their pathogenesis, which has significant implications for the clinical management and genetic counseling. Advances in paired and deep sequencing technologies in particular have made the study of mosaic disorders more feasible. In this review, we provide an overview of genetic mosaicism as well as mosaic cutaneous disorders and the techniques required to study them.

Cellular and genomic approaches for exploring structural chromosomal rearrangements

Human chromosomes are arranged in a linear and conserved sequence order that undergoes further spatial folding within the three-dimensional space of the nucleus. Although structural variations in this organization are an important source of natural genetic diversity, cytogenetic aberrations can also underlie a number of human diseases and disorders. Approaches for studying chromosome structure began half a century ago with karyotyping of Giemsa-banded chromosomes and has now evolved to encompass high-resolution fluorescence microscopy, reporter-based assays, and next-generation DNA sequencing technologies. Here, we provide a general overview of experimental methods at different resolution and sensitivity scales and discuss how they can be complemented to provide synergistic insight into the study of human chromosome structural rearrangements. These approaches range from kilobase-level resolution DNA fluorescence in situ hybridization (FISH)-based imaging approaches of individual cells to genome-wide sequencing strategies that can capture nucleotide-level information from diverse sample types. Technological advances coupled to the combinatorial use of multiple methods have resulted in the discovery of new rearrangement classes along with mechanistic insights into the processes that drive structural alterations in the human genome.

Patterns of somatic structural variation in human cancer genomes

A key mutational process in cancer is structural variation, in which rearrangements delete, amplify or reorder genomic segments that range in size from kilobases to whole chromosomes1-7. Here we develop methods to group, classify and describe somatic structural variants, using data from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumour types8. Sixteen signatures of structural variation emerged. Deletions have a multimodal size distribution, assort unevenly across tumour types and patients, are enriched in late-replicating regions and correlate with inversions. Tandem duplications also have a multimodal size distribution, but are enriched in early-replicating regions-as are unbalanced translocations. Replication-based mechanisms of rearrangement generate varied chromosomal structures with low-level copy-number gains and frequent inverted rearrangements. One prominent structure consists of 2-7 templates copied from distinct regions of the genome strung together within one locus. Such cycles of templated insertions correlate with tandem duplications, and-in liver cancer-frequently activate the telomerase gene TERT. A wide variety of rearrangement processes are active in cancer, which generate complex configurations of the genome upon which selection can act.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Tobacco smoking and somatic mutations in human bronchial epithelium

Tobacco smoking causes lung cancer1-3, a process that is driven by more than 60 carcinogens in cigarette smoke that directly damage and mutate DNA4,5. The profound effects of tobacco on the genome of lung cancer cells are well-documented6-10, but equivalent data for normal bronchial cells are lacking. Here we sequenced whole genomes of 632 colonies derived from single bronchial epithelial cells across 16 subjects. Tobacco smoking was the major influence on mutational burden, typically adding from 1,000 to 10,000 mutations per cell; massively increasing the variance both within and between subjects; and generating several distinct mutational signatures of substitutions and of insertions and deletions. A population of cells in individuals with a history of smoking had mutational burdens that were equivalent to those expected for people who had never smoked: these cells had less damage from tobacco-specific mutational processes, were fourfold more frequent in ex-smokers than current smokers and had considerably longer telomeres than their more-mutated counterparts. Driver mutations increased in frequency with age, affecting 4-14% of cells in middle-aged subjects who had never smoked. In current smokers, at least 25% of cells carried driver mutations and 0-6% of cells had two or even three drivers. Thus, tobacco smoking increases mutational burden, cell-to-cell heterogeneity and driver mutations, but quitting promotes replenishment of the bronchial epithelium from mitotically quiescent cells that have avoided tobacco mutagenesis.

ChiTaRS 5.0: the comprehensive database of chimeric transcripts matched with druggable fusions and 3D chromatin maps

Chimeric RNA transcripts are formed when exons from two genes fuse together, often due to chromosomal translocations, transcriptional errors or trans-splicing effect. While these chimeric RNAs produce functional proteins only in certain cases, they play a significant role in disease phenotyping and progression. ChiTaRS 5.0 (http://chitars.md.biu.ac.il/) is the latest and most comprehensive chimeric transcript repository, with 111 582 annotated entries from eight species, including 23 167 known human cancer breakpoints. The database includes unique information correlating chimeric breakpoints with 3D chromatin contact maps, generated from public datasets of chromosome conformation capture techniques (Hi-C). In this update, we have added curated information on druggable fusion targets matched with chimeric breakpoints, which are applicable to precision medicine in cancers. The introduction of a new section that lists chimeric RNAs in various cell-lines is another salient feature. Finally, using text-mining techniques, novel chimeras in Alzheimer's disease, schizophrenia, dyslexia and other diseases were collected in ChiTaRS. Thus, this improved version is an extensive catalogue of chimeras from multiple species. It extends our understanding of the evolution of chimeric transcripts in eukaryotes and contributes to the analysis of 3D genome conformational changes and the functional role of chimeras in the etiopathogenesis of cancers and other complex diseases.

Identification of Chimeric RNAs Using RNA-Seq Reads and Protein-Protein Interactions of Translated Chimeras

Chimeric RNA moieties typically consist of exons from two genes expressed from different genomic locations and produced by chromosomal translocations, trans-splicing or transcription errors. Recent advances in next-generation sequencing procedures have opened new horizons for identification of novel chimeric transcripts in various diseases in a personalized manner. Here we describe the detailed computational procedures to identify chimeric transcripts using RNA-seq reads. Moreover, we elaborate on the domain-domain co-occurrence method to detect alterations in chimeric protein-protein interaction (ChiPPI) networks produced by chimeric RNA that are translated to chimeric proteins.

Integrated Genomic, Epigenomic, and Expression Analyses of Ovarian Cancer Cell Lines

To improve our understanding of ovarian cancer, we performed genome-wide analyses of 45 ovarian cancer cell lines. Given the challenges of genomic analyses of tumors without matched normal samples, we developed approaches for detection of somatic sequence and structural changes and integrated these with epigenetic and expression alterations. Alterations not previously implicated in ovarian cancer included amplification or overexpression of ASXL1 and H3F3B, deletion or underexpression of CDC73 and TGF-beta receptor pathway members, and rearrangements of YAP1-MAML2 and IKZF2-ERBB4. Dose-response analyses to targeted therapies revealed unique molecular dependencies, including increased sensitivity of tumors with PIK3CA and PPP2R1A alterations to PI3K inhibitor GNE-493, MYC amplifications to PARP inhibitor BMN673, and SMAD3/4 alterations to MEK inhibitor MEK162. Genome-wide rearrangements provided an improved measure of sensitivity to PARP inhibition. This study provides a comprehensive and broadly accessible resource of molecular information for the development of therapeutic avenues in ovarian cancer.

The overall survival of patients with late-stage ovarian cancer is dismal. To identify therapeutic opportunities, Papp et al. integrate genomic, epigenomic, and expression analyses to provide a resource of molecular abnormalities in ovarian cancer cell lines and use these to identify tumors sensitive to PARP, MEK, and PI3K inhibitors.

Noncoding RNAs and Liquid Biopsy in Lung Cancer: A Literature Review

Lung cancer represents a genetically heterogeneous disease with low survival rates. Recent data have evidenced key roles of noncoding RNAs in lung cancer initiation and progression. These functional RNA molecules that can act as both oncogenes and tumor suppressors may become future biomarkers and more efficient therapeutic targets. In the precision medicine era, circulating nucleic acids have the potential to reshape the management and prognosis of cancer patients. Detecting genomic alterations and level variations of circulating nucleic acids in liquid biopsy samples represents a noninvasive method for portraying tumor burden. Research is currently trying to validate the potential role of liquid biopsy in lung cancer screening, prognosis, monitoring of disease progression, and treatment response. However, this method requires complex detection assays, and implementation of plasma genotyping in clinical practice continues to be hindered by discrepancies that arise when compared to tissue genotyping. Understanding the genomic landscape of lung cancer is essential in order to provide useful and innovative research in the age of patient-tailored therapy. In this landscape, the noncoding RNAs play a crucial role due to their target genes that dramatically influence the tumor microenvironment and the response to therapy. This article addresses present and future possible roles of liquid biopsy in lung cancer. It also discusses how the complex role of noncoding RNAs in lung tumorigenesis could influence the management of this pathology.

LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data

Linked-read sequencing provides long-range information on short-read sequencing data by barcoding reads originating from the same DNA molecule, and can improve detection and breakpoint identification for structural variants (SVs). Here we present LinkedSV for SV detection on linked-read sequencing data. LinkedSV considers barcode overlapping and enriched fragment endpoints as signals to detect large SVs, while it leverages read depth, paired-end signals and local assembly to detect small SVs. Benchmarking studies demonstrate that LinkedSV outperforms existing tools, especially on exome data and on somatic SVs with low variant allele frequencies. We demonstrate clinical cases where LinkedSV identifies disease-causal SVs from linked-read exome sequencing data missed by conventional exome sequencing, and show examples where LinkedSV identifies SVs missed by high-coverage long-read sequencing. In summary, LinkedSV can detect SVs missed by conventional short-read and long-read sequencing approaches, and may resolve negative cases from clinical genome/exome sequencing studies.

Assessing the Concordance of Genomic Alterations between Circulating-Free DNA and Tumour Tissue in Cancer Patients

Somatic alterations to the genomes of solid tumours, which in some cases represent actionable drivers, provide diagnostic and prognostic insight into these complex diseases. Spatial and longitudinal tracking of somatic genomic alterations (SGAs) in patient tumours has emerged as a new avenue of investigation, not only as a disease monitoring strategy, but also to improve our understanding of heterogeneity and clonal evolution from diagnosis through disease progression. Furthermore, analysis of circulating-free DNA (cfDNA) in the so-called "liquid biopsy" has emerged as a non-invasive method to identify genomic information to inform targeted therapy and may also capture the heterogeneity of the primary and metastatic tumours. Considering the potential of cfDNA analysis as a translational laboratory tool in clinical practice, establishing the extent to which cfDNA represents the SGAs of tumours, particularly actionable driver alterations, becomes a matter of importance, warranting standardisation of methods and practices. Here, we assess the utilisation of cfDNA for molecular profiling of SGAs in tumour tissue across a broad range of solid tumours. Moreover, we examine the underlying factors contributing to discordance of detected SGAs between cfDNA and tumour tissue.

Somatic mutations and clonal dynamics in healthy and cirrhotic human liver

The most common causes of chronic liver disease are excess alcohol intake, viral hepatitis and non-alcoholic fatty liver disease, with the clinical spectrum ranging in severity from hepatic inflammation to cirrhosis, liver failure or hepatocellular carcinoma (HCC). The genome of HCC exhibits diverse mutational signatures, resulting in recurrent mutations across more than 30 cancer genes1-7. Stem cells from normal livers have a low mutational burden and limited diversity of signatures8, which suggests that the complexity of HCC arises during the progression to chronic liver disease and subsequent malignant transformation. Here, by sequencing whole genomes of 482 microdissections of 100-500 hepatocytes from 5 normal and 9 cirrhotic livers, we show that cirrhotic liver has a higher mutational burden than normal liver. Although rare in normal hepatocytes, structural variants, including chromothripsis, were prominent in cirrhosis. Driver mutations, such as point mutations and structural variants, affected 1-5% of clones. Clonal expansions of millimetres in diameter occurred in cirrhosis, with clones sequestered by the bands of fibrosis that surround regenerative nodules. Some mutational signatures were universal and equally active in both non-malignant hepatocytes and HCCs; some were substantially more active in HCCs than chronic liver disease; and others-arising from exogenous exposures-were present in a subset of patients. The activity of exogenous signatures between adjacent cirrhotic nodules varied by up to tenfold within each patient, as a result of clone-specific and microenvironmental forces. Synchronous HCCs exhibited the same mutational signatures as background cirrhotic liver, but with higher burden. Somatic mutations chronicle the exposures, toxicity, regeneration and clonal structure of liver tissue as it progresses from health to disease.

Revisiting NTRKs as an emerging oncogene in hematological malignancies

NTRK fusions are dominant oncogenic drivers found in rare solid tumors. These fusions have also been identified in more common cancers, such as lung and colorectal carcinomas, albeit at low frequencies. Patients harboring these fusions demonstrate significant clinical response to inhibitors such as entrectinib and larotrectinib. Although current trials have focused entirely on solid tumors, there is evidence supporting the use of these drugs for patients with leukemia. To assess the broader applicability for Trk inhibitors in hematological malignancies, this review describes the current state of knowledge about alterations in the NTRK family in these disorders. We present these findings in relation to the discovery and therapeutic targeting of BCR–ABL1 in chronic myeloid leukemia. The advent of deep sequencing technologies has shown that NTRK fusions and somatic mutations are present in a variety of hematologic malignancies. Efficacy of Trk inhibitors has been demonstrated in NTRK-fusion positive human leukemia cell lines and patient-derived xenograft studies, highlighting the potential clinical utility of these inhibitors for a subset of leukemia patients.

Interphase Cytogenetic Analysis of Micronucleated and Multinucleated Cells Supports the Premature Chromosome Condensation Hypothesis as the Mechanistic Origin of Chromothripsis

The discovery of chromothripsis in cancer genomes challenges the long-standing concept of carcinogenesis as the result of progressive genetic events. Despite recent advances in describing chromothripsis, its mechanistic origin remains elusive. The prevailing conception is that it arises from a massive accumulation of fragmented DNA inside micronuclei (MN), whose defective nuclear envelope ruptures or leads to aberrant DNA replication, before main nuclei enter mitosis. An alternative hypothesis is that the premature chromosome condensation (PCC) dynamics in asynchronous micronucleated cells underlie chromosome shattering in a single catastrophic event, a hallmark of chromothripsis. Specifically, when main nuclei enter mitosis, premature chromatin condensation provokes the shattering of chromosomes entrapped inside MN, if they are still undergoing DNA replication. To test this hypothesis, the agent RO-3306, a selective ATP-competitive inhibitor of CDK1 that promotes cell cycle arrest at the G2/M boundary, was used in this study to control the degree of cell cycle asynchrony between main nuclei and MN. By delaying the entrance of main nuclei into mitosis, additional time was allowed for the completion of DNA replication and duplication of chromosomes inside MN. We performed interphase cytogenetic analysis using asynchronous micronucleated cells generated by exposure of human lymphocytes to γ-rays, and heterophasic multinucleated Chinese hamster ovary (CHO) cells generated by cell fusion procedures. Our results demonstrate that the PCC dynamics during asynchronous mitosis in micronucleated or multinucleated cells are an important determinant of chromosome shattering and may underlie the mechanistic origin of chromothripsis.

Characterization of Structural Variations in the Context of 3D Chromatin Structure

Chromosomes located in the nucleus form discrete units of genetic material composed of DNA and protein complexes. The genetic information is encoded in linear DNA sequences, but its interpretation requires an understanding of threedimensional (3D) structure of the chromosome, in which distant DNA sequences can be juxtaposed by highly condensed chromatin packing in the space of nucleus to precisely control gene expression. Recent technological innovations in exploring higher-order chromatin structure have uncovered organizational principles of the 3D genome and its various biological implications. Very recently, it has been reported that large-scale genomic variations may disrupt higher-order chromatin organization and as a consequence, greatly contribute to disease-specific gene regulation for a range of human diseases. Here, we review recent developments in studying the effect of structural variation in gene regulation, and the detection and the interpretation of structural variations in the context of 3D chromatin structure.

High-Resolution Copy Number Patterns From Clinically Relevant FFPE Material

Systematic tumour profiling is essential for biomarker research and clinically for assessing response to therapy. Solving the challenge of delivering informative copy number (CN) profiles from formalin-fixed paraffin embedded (FFPE) material, the only likely readily available biospecimen for most cancers, involves successful processing of small quantities of degraded DNA. To investigate the potential for analysis of such lesions, whole-genome CNVseq was applied to 300 FFPE primary tumour samples, obtained from a large-scale epidemiological study of melanoma. The quality and the discriminatory power of CNVseq was assessed. Libraries were successfully generated for 93% of blocks, with input DNA quantity being the only predictor of success (success rate dropped to 65% if <20 ng available); 3% of libraries were dropped because of low sequence alignment rates. Technical replicates showed high reproducibility. Comparison with targeted CN assessment showed consistency with the Next Generation Sequencing (NGS) analysis. We were able to detect and distinguish CN changes with a resolution of ≤10 kb. To demonstrate performance, we report the spectrum of genomic CN alterations (CNAs) detected at 9p21, the major site of CN change in melanoma. This successful analysis of CN in FFPE material using NGS provides proof of principle for intensive examination of population-based samples.

Chromosome segregation errors generate a diverse spectrum of simple and complex genomic rearrangements

Cancer genomes are frequently characterized by numerical and structural chromosomal abnormalities. Here we integrated a centromere-specific inactivation approach with selection for a conditionally essential gene, a strategy termed CEN-SELECT, to systematically interrogate the structural landscape of mis-segregated chromosomes. We show that single-chromosome mis-segregation into a micronucleus can directly trigger a broad spectrum of genomic rearrangement types. Cytogenetic profiling revealed that mis-segregated chromosomes exhibit 120-fold-higher susceptibility to developing seven major categories of structural aberrations, including translocations, insertions, deletions, and complex reassembly through chromothripsis coupled to classical non-homologous end joining. Whole-genome sequencing of clonally propagated rearrangements identified random patterns of clustered breakpoints with copy-number alterations resulting in interspersed gene deletions and extrachromosomal DNA amplification events. We conclude that individual chromosome segregation errors during mitotic cell division are sufficient to drive extensive structural variations that recapitulate genomic features commonly associated with human disease.

Characterization and evolutionary dynamics of complex regions in eukaryotic genomes

Complex regions in eukaryotic genomes are typically characterized by duplications of chromosomal stretches that often include one or more genes repeated in a tandem array or in relatively close proximity. Nevertheless, the repetitive nature of these regions, together with the often high sequence identity among repeats, have made complex regions particularly recalcitrant to proper molecular characterization, often being misassembled or completely absent in genome assemblies. This limitation has prevented accurate functional and evolutionary analyses of these regions. This is becoming increasingly relevant as evidence continues to support a central role for complex genomic regions in explaining human disease, developmental innovations, and ecological adaptations across phyla. With the advent of long-read sequencing technologies and suitable assemblers, the development of algorithms that can accommodate sample heterozygosity, and the adoption of a pangenomic-like view of these regions, accurate reconstructions of complex regions are now within reach. These reconstructions will finally allow for accurate functional and evolutionary studies of complex genomic regions, underlying the generation of genotype-phenotype maps of unprecedented resolution.

Integrative detection and analysis of structural variation in cancer genomes

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.

A Projection Neural Network for Identifying Copy Number Variants

The identification of copy number variations (CNVs) helps the diagnosis of many diseases. One major hurdle in the path of CNVs discovery is that the boundaries of normal and aberrant regions cannot be distinguished from the raw data, since various types of noise contaminate them. To tackle this challenge, the total variation regularization is mostly used in the optimization problems to approximate the noise-free data from corrupted observations. The minimization using such regularization is challenging to deal with since it is non-differentiable. In this paper, we propose a projection neural network to solve the non-smooth problem. The proposed neural network has a simple one-layer structure and is theoretically assured to have the global exponential convergence to the solution of the total variation-regularized problem. The experiments on several real and simulated datasets illustrate the reasonable performance of the proposed neural network and show that its performance is comparable with those of more sophisticated algorithms.

Somatic Mutagenesis in Mammals and Its Implications for Human Disease and Aging

DNA mutations as a consequence of errors during DNA damage repair, replication, or mitosis are the substrate for evolution. In multicellular organisms, mutations can occur in the germline and also in somatic tissues, where they are associated with cancer and other chronic diseases and possibly with aging. Recent advances in high-throughput sequencing have made it relatively easy to study germline de novo mutations, but in somatic cells, the vast majority of mutations are low-abundant and can be detected only in clonal lineages, such as tumors, or single cells. Here we review recent results on somatic mutations in normal human and animal tissues with a focus on their possible functional consequences.

MALBAC-based chromosomal imbalance analysis: a novel technique enabling effective non-invasive diagnosis and monitoring of bladder cancer

Background

The gold standard for bladder cancer detection is cystoscopy, which is an invasive procedure that causes discomfort in patients. The currently available non-invasive approaches either show limited sensitivity in low-grade tumours or possess unsatisfying specificity. The aim of the present study is to develop a new non-invasive strategy based on chromosomal imbalance levels to detect bladder cancer effectively.

Methods

We enrolled 74 patients diagnosed with bladder cancer (BC), 51 healthy participants and 27 patients who were diagnosed with non-malignant urinary disease (UD). The Chromosomal Imbalance Analysis (CIA) was conducted in the tumours and urine of participants via the multiple annealing and looping-based amplification cycles-next-generation sequencing (MALBAC-NGS) strategy. The threshold of the CIA was determined with the receiver operating characteristic (ROC) curve. The comparison of the CIA with voided urine cytology was also performed in a subgroup of 55 BC patients. The consistency and discrepancy of the different assays were studied with the Kappa analysis and the McNemar test, respectively. The performance of the urinary CIA was also validated in an additional group of 120 BC patients, 15 UD and 45 healthy participants.

Results

Good concordance (87.0%) in the assessments of patient tumour tissues and urine was observed. The urine-based evaluation also demonstrated a good performance (accuracy = 89.0%, sensitivity = 83.1%, specificity = 94.5%, NPV = 85.4% and PPV = 93.7%; AUC = 0.917, 95%CI =0.868–0.966, P < 0.001) in the training group, particularly in the patients with CIA-positive tumours (accuracy = 92.7%, sensitivity = 89.8%). The sensitivity and specificity in the validation group were 89.2 and 90.0%, respectively. Even in Ta/T1 and low-grade tumour patients, the sensitivity was 85–90%. The CIA also exhibited a significantly improved sensitivity compared to voided urine cytology.

Conclusions

This is the first study employing the concept of whole genome imbalance combined with the MALBAC technique to detect bladder cancer in urine. MALBAC-CIA yielded significant diagnostic power, even in early-stage/low-grade tumour patients, and it may be used as a non-invasive approach for diagnosis and recurrence surveillance in bladder cancer prior to the use of cystoscopy, which would largely reduce the burden on patients.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4571-7) contains supplementary material, which is available to authorized users.

Picky comprehensively detects high-resolution structural variants in nanopore long reads

Acquired genomic structural variants (SVs) are major hallmarks of cancer genomes, but they are challenging to reconstruct from short-read sequencing data. Here we exploited the long reads of the nanopore platform using our customized pipeline, Picky ( https://github.com/TheJacksonLaboratory/Picky ), to reveal SVs of diverse architecture in a breast cancer model. We identified the full spectrum of SVs with superior specificity and sensitivity relative to short-read analyses, and uncovered repetitive DNA as the major source of variation. Examination of genome-wide breakpoints at nucleotide resolution uncovered micro-insertions as the common structural features associated with SVs. Breakpoint density across the genome is associated with the propensity for interchromosomal connectivity and was found to be enriched in promoters and transcribed regions of the genome. Furthermore, we observed an over-representation of reciprocal translocations from chromosomal double-crossovers through phased SVs. We demonstrate that Picky analysis is an effective tool for comprehensive detection of SVs in cancer genomes from long-read data.

Timing the Landmark Events in the Evolution of Clear Cell Renal Cell Cancer: TRACERx Renal

Clear cell renal cell carcinoma (ccRCC) is characterized by near-universal loss of the short arm of chromosome 3, deleting several tumor suppressor genes. We analyzed whole genomes from 95 biopsies across 33 patients with clear cell renal cell carcinoma. We find hotspots of point mutations in the 5' UTR of TERT, targeting a MYC-MAX-MAD1 repressor associated with telomere lengthening. The most common structural abnormality generates simultaneous 3p loss and 5q gain (36% patients), typically through chromothripsis. This event occurs in childhood or adolescence, generally as the initiating event that precedes emergence of the tumor's most recent common ancestor by years to decades. Similar genomic changes drive inherited ccRCC. Modeling differences in age incidence between inherited and sporadic cancers suggests that the number of cells with 3p loss capable of initiating sporadic tumors is no more than a few hundred. Early development of ccRCC follows well-defined evolutionary trajectories, offering opportunity for early intervention.

Xome-Blender: A novel cancer genome simulator

The adoption of next generation sequencing based methods in cancer research allowed for the investigation of the complex genetic structure of tumor samples. In the last few years, considerable importance was given to the research of somatic variants and several computational approaches were developed for this purpose. Despite continuous improvements to these programs, the validation of their results it’s a hard challenge due to multiple sources of error. To overcome this drawback different simulation approaches are used to generate synthetic samples but they are often based on the addition of artificial mutations that mimic the complexity of genomic variations. For these reasons, we developed a novel software, Xome-Blender, that generates synthetic cancer genomes with user defined features such as the number of subclones, the number of somatic variants and the presence of copy number alterations (CNAs), without the addition of any synthetic element. The singularity of our method is the “morphological approach” used to generate mutation events. To demonstrate the power of our tool we used it to address the hard challenge of evaluating the performance of nine state-of-the-art somatic variant calling methods for small and large variants (VarScan2, MuTect, Shimmer, BCFtools, Strelka, EXCAVATOR2, Control-FREEC and CopywriteR). Through these analyses we observed that by using Xome-Blender data it is possible to appraise small differences between their performance and we have designated VarScan2 and EXCAVATOR2 as best tool for this kind of applications. Xome-Blender is unix-based, licensed under the GPLv3 and freely available at https://github.com/rsemeraro/XomeBlender.

Genomic hotspots but few recurrent fusion genes in breast cancer

The advent of next generation sequencing technologies has boosted the interest in exploring the role of fusion genes in the development and progression of solid tumors. In breast cancer, most of the detected gene fusions seem to be "passenger" events while the presence of recurrent and driver fusions is still under study. We performed RNA sequencing in 55 well-characterized breast cancer samples and 10 adjacent normal breast tissues, complemented by an analysis of SNP array data. We explored the presence of fusion genes and defined their association with breast cancer subtypes, clinical-pathologic characteristics and copy number aberrations. Overall, 370 fusions were detected across the majority of the samples. HER2+ samples had significantly more fusions than triple negative and luminal subtypes. The number of fusions was correlated with histological grade, Ki67 and tumor size. Clusters of fusion genes were observed across the genome and a significant correlation of fusions with copy number aberrations and more specifically amplifications was also revealed. Despite the large number of fusion events, only a few were recurrent, while recurrent individual genes forming fusions with different partners were also detected including the estrogen receptor 1 gene in the previously detected ESR1-CCDC170 fusion. Overall we detected novel gene fusion events while we confirmed previously reported fusions. Genomic hotspots of fusion genes, differences between subtypes and small number of recurrent fusions are the most relevant characteristics of these events in breast cancer. Further investigation is necessary to comprehend the biological significance of these fusions.

Whole genome sequencing of matched tumor, adjacent non-tumor tissues and corresponding normal blood samples of hepatocellular carcinoma patients revealed dynamic changes of the mutations profiles during hepatocarcinogenesis

Hepatocellular carcinoma (HCC) has become the third most deadly disease worldwide and HBV is the major factor in Asia and Africa. We conducted 9 WGS (whole genome sequencing) analyses for matched samples of tumor, adjacent non-tumor tissues and normal blood samples of HCC patients from three HBV positive patients. We then validated the mutations identified in a larger cohort of 177 HCC patients. We found that the number of the unique somatic mutations (average of 59,136) in tumor samples is significantly less than that in adjacent non-tumor tissues (average 83, 633). We discovered that the TP53 R249S mutation occurred in 7.7% of the HCC patients, and it was significantly associated with poor diagnosis. In addition, we found that the L104P mutation in the VCX gene (Variable charge, X-linked) was absent in white blood cell samples, but present at 11.1% frequency in the adjacent tissues and increased to 14.6% in HCC tissues, suggesting that this mutation might be a tumor driver gene driving HCC carcinogenesis. Finally, we identified a TK1-RNU7 fusion, which would result in a deletion of 103 amino acids from its C-terminal. The frequencies of this fusion event decreased from the adjacent tissues (29.2%) to the tumors (16.7%), suggesting that a truncated thymidine Kinase1 (TK1) caused by the fusion event might be deleterious and be selected against during tumor progression. The three-way comparisons allow the identification of potential driver mutations of carcinogenesis. Furthermore, our dataset provides the research community a valuable dataset for identifying dynamic changes of mutation profiles and driver mutations for HCC.

Efficient genome-wide genotyping strategies and data integration in crop plants

Next-generation sequencing (NGS) has revolutionized plant and animal research by providing powerful genotyping methods. This review describes and discusses the advantages, challenges and, most importantly, solutions to facilitate data processing, the handling of missing data, and cross-platform data integration. Next-generation sequencing technologies provide powerful and flexible genotyping methods to plant breeders and researchers. These methods offer a wide range of applications from genome-wide analysis to routine screening with a high level of accuracy and reproducibility. Furthermore, they provide a straightforward workflow to identify, validate, and screen genetic variants in a short time with a low cost. NGS-based genotyping methods include whole-genome re-sequencing, SNP arrays, and reduced representation sequencing, which are widely applied in crops. The main challenges facing breeders and geneticists today is how to choose an appropriate genotyping method and how to integrate genotyping data sets obtained from various sources. Here, we review and discuss the advantages and challenges of several NGS methods for genome-wide genetic marker development and genotyping in crop plants. We also discuss how imputation methods can be used to both fill in missing data in genotypic data sets and to integrate data sets obtained using different genotyping tools. It is our hope that this synthetic view of genotyping methods will help geneticists and breeders to integrate these NGS-based methods in crop plant breeding and research.

Read Depth Analysis to Identify CNV in Bacteria Using CNOGpro

Whole-genome sequencing with short-read technologies is well suited for calling single nucleotide polymorphisms, but has major problems with the detection of structural variants larger than the read length. One such type of variation is copy number variation (CNV), which entails deletion or duplication of genomic regions, and the expansion or contraction of repeated elements. Duplicated and deleted regions will typically be collapsed during de novo assembly of sequence data, or ignored when mapping reads toward a reference. However, signatures of the copy number variation can be detected in the resultant read depth at each position in the genome. We here provide instructions on how to analyze this read depth signal with the R package CNOGpro, allowing for estimation of copy numbers with uncertainty for each feature in a genome.

Using SAAS-CNV to Detect and Characterize Somatic Copy Number Alterations in Cancer Genomes from Next Generation Sequencing and SNP Array Data

Somatic copy number alterations (SCNAs) are profound in cancer genomes at different stages: oncogenesis, progression, and metastasis. Accurate detection and characterization of SCNA landscape at genome-wide scale are of great importance. Next-generation sequencing and SNP array are current technology of choice for SCNA analysis. They are able to quantify SCNA with high resolution and meanwhile raise great challenges in data analysis. To this end, we have developed an R package saasCNV for SCNA analysis using (1) whole-genome sequencing (WGS), (2) whole-exome sequencing (WES) or (3) whole-genome SNP array data. In this chapter, we provide the features of the package and step-by-step instructions in detail.

Copy Number Variation

Differences between genomes can be due to single nucleotide variants (SNPs), translocations, inversions and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 250 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease or phenotypic traits.While the link between SNPs and disease susceptibility has been well studied, to date there are still very few published CNV genome-wide association studies; probably owing to the fact that CNV analysis remains a slightly more complex task than SNP analysis (both in term of bioinformatics workflow and uncertainty in the CNV calling leading to high false positive rates and unknown false negative rates). This chapter aims at explaining computational methods for the analysis of CNVs, ranging from study design, data processing and quality control, up to genome-wide association study with clinical traits.

The DNA resection protein CtIP promotes mammary tumorigenesis

Many DNA repair factors act to suppress tumor formation by preserving genomic stability. Similarly, the CtIP protein, which interacts with the BRCA1 tumor suppressor, is also thought to have tumor suppression activity. Through its role in DNA end resection, CtIP facilitates DNA double-strand break (DSB) repair by homologous recombination (DSBR-HR) and microhomology-mediated end joining (MMEJ). In addition, however, CtIP has also been implicated in the formation of aberrant chromosomal rearrangements in an MMEJ-dependent manner, an activity that could potentially promote tumor development by increasing genome instability. To clarify whether CtIP acts in vivo to suppress or promote tumorigenesis, we have examined its oncogenic potential in mouse models of human breast cancer. Surprisingly, mice heterozygous for a null Ctip allele did not display an increased susceptibility to tumor formation. Moreover, mammary-specific biallelic CtIP ablation did not elicit breast tumors in a manner reminiscent of BRCA1 loss. Instead, CtIP inactivation dramatically reduced the kinetics of mammary tumorigenesis in mice bearing mammary-specific lesions of the p53 gene. Thus, unlike other repair factors, CtIP is not a tumor suppressor, but has oncogenic properties that can promote tumorigenesis, consistent with its ability to facilitate MMEJ-dependent chromosomal instability. Consequently, inhibition of CtIP-mediated MMEJ may prove effective against tumor types, such as human breast cancer, that display MMEJ-dependent chromosomal rearrangements.

aCNViewer: Comprehensive genome-wide visualization of absolute copy number and copy neutral variations

Motivation

Copy number variations (CNV) include net gains or losses of part or whole chromosomal regions. They differ from copy neutral loss of heterozygosity (cn-LOH) events which do not induce any net change in the copy number and are often associated with uniparental disomy. These phenomena have long been reported to be associated with diseases and particularly in cancer. Losses/gains of genomic regions are often correlated with lower/higher gene expression. On the other hand, loss of heterozygosity (LOH) and cn-LOH are common events in cancer and may be associated with the loss of a functional tumor suppressor gene. Therefore, identifying recurrent CNV and cn-LOH events can be important as they may highlight common biological components and give insights into the development or mechanisms of a disease. However, no currently available tools allow a comprehensive whole-genome visualization of recurrent CNVs and cn-LOH in groups of samples providing absolute quantification of the aberrations leading to the loss of potentially important information.

Results

To overcome these limitations, we developed aCNViewer (Absolute CNV Viewer), a visualization tool for absolute CNVs and cn-LOH across a group of samples. aCNViewer proposes three graphical representations: dendrograms, bi-dimensional heatmaps showing chromosomal regions sharing similar abnormality patterns, and quantitative stacked histograms facilitating the identification of recurrent absolute CNVs and cn-LOH. We illustrated aCNViewer using publically available hepatocellular carcinomas (HCCs) Affymetrix SNP Array data (Fig 1A). Regions 1q and 8q present a similar percentage of total gains but significantly different copy number gain categories (p-value of 0.0103 with a Fisher exact test), validated by another cohort of HCCs (p-value of 5.6e-7) (Fig 2B).

Availability and implementation

aCNViewer is implemented in python and R and is available with a GNU GPLv3 license on GitHub https://github.com/FJD-CEPH/aCNViewer and Docker https://hub.docker.com/r/fjdceph/acnviewer/.

Contact

aCNViewer@cephb.fr

Copy number variation and disease resistance in plants

Plant genome diversity varies from single nucleotide polymorphisms to large-scale deletions, insertions, duplications, or re-arrangements. These re-arrangements of sequences resulting from duplication, gains or losses of DNA segments are termed copy number variations (CNVs). During the last decade, numerous studies have emphasized the importance of CNVs as a factor affecting human phenotype; in particular, CNVs have been associated with risks for several severe diseases. In plants, the exploration of the extent and role of CNVs in resistance against pathogens and pests is just beginning. Since CNVs are likely to be associated with disease resistance in plants, an understanding of the distribution of CNVs could assist in the identification of novel plant disease-resistance genes. In this paper, we review existing information about CNVs; their importance, role and function, as well as their association with disease resistance in plants.

Molecular Approaches for High Throughput Detection and Quantification of Genetically Modified Crops: A Review

As long as the genetically modified crops are gaining attention globally, their proper approval and commercialization need accurate and reliable diagnostic methods for the transgenic content. These diagnostic techniques are mainly divided into two major groups, i.e., identification of transgenic (1) DNA and (2) proteins from GMOs and their products. Conventional methods such as PCR (polymerase chain reaction) and enzyme-linked immunosorbent assay (ELISA) were routinely employed for DNA and protein based quantification respectively. Although, these Techniques (PCR and ELISA) are considered as significantly convenient and productive, but there is need for more advance technologies that allow for high throughput detection and the quantification of GM event as the production of more complex GMO is increasing day by day. Therefore, recent approaches like microarray, capillary gel electrophoresis, digital PCR and next generation sequencing are more promising due to their accuracy and precise detection of transgenic contents. The present article is a brief comparative study of all such detection techniques on the basis of their advent, feasibility, accuracy, and cost effectiveness. However, these emerging technologies have a lot to do with detection of a specific event, contamination of different events and determination of fusion as well as stacked gene protein are the critical issues to be addressed in future.

Recurrent Fusion Genes in Leukemia: An Attractive Target for Diagnosis and Treatment

INTRODUCTION

Since the first fusion gene was discovered decades ago, a considerable number of fusion genes have been detected in leukemia. The majority of them are generated through chromosomal rearrangement or abnormal transcription. With the development of techniques, high-throughput sequencing method makes it possible to detect fusion genes systematically in multiple human cancers. Owing to their biological significance and tumor-specific expression, some of the fusion genes are attractive diagnostic tools and therapeutic targets. Tyrosine kinase inhibitors (TKI) targeting BCR-ABL1 fusions have been widely used to treat CML. The combination of ATRA and ATO targeting PML-RARA fusions has proven to be effective in acute promyelocytic leukemia (APL). Moreover, therapy with high dose cytarabine (HDAC) has significantly improved the prognosis of core binding factor (CBF) acute myeloid leukemia (AML) patients. Therefore, studies on fusion genes may benefit patients with leukemia by providing more diagnostic markers and therapies in the future.

CONCLUSION

The presented review focuses on the history of fusion genes, mechanisms of formation, and treatments against specific fusion genes in leukemia.

Detection of breed-specific copy number variations in domestic chicken genome

Copy number variations (CNVs) are important large-scale variants. They are widespread in the genome and may contribute to phenotypic variation. Detection and characterization of CNVs can provide new insights into the genetic basis of important traits. Here, we perform whole-genome short read sequence analysis to identify CNVs in two indigenous and commercial chicken breeds to evaluate the impact of the identified CNVs on breed-specific traits. After filtration, a total of 12 955 CNVs spanning (on average) about 9.42% of the chicken genome were found that made up 5467 CNV regions (CNVRs). Chicken quantitative trait loci (QTL) datasets and Ensembl gene annotations were used as resources for the estimation of potential phenotypic effects of our CNVRs on breed-specific traits. In total, 34% of our detected CNVRs were also detected in earlier CNV studies. These CNVRs partly overlap several previously reported QTL and gene ontology terms associated with some important traits, including shank length QTL in Creeper-specific CNVRs and body weight and egg production characteristics, as well as muscle and body organ growth, in the Arian commercial breed. Our findings provide new insights into the genomic structure of the chicken genome for an improved understanding of the potential roles of CNVRs in differentiating between breeds or lines.

A short review of variants calling for single-cell-sequencing data with applications

The field of single-cell sequencing is fleetly expanding, and many techniques have been developed in the past decade. With this technology, biologists can study not only the heterogeneity between two adjacent cells in the same tissue or organ, but also the evolutionary relationships and degenerative processes in a single cell. Calling variants is the main purpose in analyzing single cell sequencing (SCS) data. Currently, some popular methods used for bulk-cell-sequencing data analysis are tailored directly to be applied in dealing with SCS data. However, SCS requires an extra step of genome amplification to accumulate enough quantity for satisfying sequencing needs. The amplification yields large biases and thus raises challenge for using the bulk-cell-sequencing methods. In order to provide guidance for the development of specialized analyzed methods as well as using currently developed tools for SNS, this paper aims to bridge the gap. In this paper, we firstly introduced two popular genome amplification methods and compared their capabilities. Then we introduced a few popular models for calling single-nucleotide polymorphisms and copy-number variations. Finally, break-through applications of SNS were summarized to demonstrate its potential in researching cell evolution.

New insights into non-conventional epitopes as T cell targets: The missing link for breaking immune tolerance in autoimmune disease?

The mechanism by which immune tolerance is breached in autoimmune disease is poorly understood. One possibility is that post-translational modification of self-antigens leads to peripheral recognition of neo-epitopes against which central and peripheral tolerance is inadequate. Accumulating evidence points to multiple mechanisms through which non-germline encoded sequences can give rise to these non-conventional epitopes which in turn engage the immune system as T cell targets. In particular, where these modifications alter the rules of epitope engagement with MHC molecules, such non-conventional epitopes offer a persuasive explanation for associations between specific HLA alleles and autoimmune diseases. In this review article, we discuss current understanding of mechanisms through which non-conventional epitopes may be generated, focusing on several recently described pathways that can transpose germline-encoded sequences. We contextualise these discoveries around type 1 diabetes, the prototypic organ-specific autoimmune disease in which specific HLA-DQ molecules confer high risk. Non-conventional epitopes have the potential to act as tolerance breakers or disease drivers in type 1 diabetes, prompting a timely re-evaluation of models of a etiopathogenesis. Future studies are required to elucidate the disease-relevance of a range of potential non-germline epitopes and their relationship to the natural peptide repertoire.

Development of zebrafish medulloblastoma-like PNET model by TALEN-mediated somatic gene inactivation

Genetically engineered animal tumor models have traditionally been generated by the gain of single or multiple oncogenes or the loss of tumor suppressor genes; however, the development of live animal models has been difficult given that cancer phenotypes are generally induced by somatic mutation rather than by germline genetic inactivation. In this study, we developed somatically mutated tumor models using TALEN-mediated somatic gene inactivation of cdkn2a/b or rb1 tumor suppressor genes in zebrafish. One-cell stage injection of cdkn2a/b-TALEN mRNA resulted in malignant peripheral nerve sheath tumors with high frequency (about 39%) and early onset (about 35 weeks of age) in F0 tp53^e7/e7 mutant zebrafish. Injection of rb1-TALEN mRNA also led to the formation of brain tumors at high frequency (58%, 31 weeks of age) in F0 tp53^e7/e7 mutant zebrafish. Analysis of each tumor induced by somatic inactivation showed that the targeted genes had bi-allelic mutations. Tumors induced by rb1 somatic inactivation were characterized as medulloblastoma-like primitive neuroectodermal tumors based on incidence location, histopathological features, and immunohistochemical tests. In addition, 3' mRNA Quanti-Seq analysis showed differential activation of genes involved in cell cycle, DNA replication, and protein synthesis; especially, genes involved in neuronal development were up-regulated.

CLOVE: classification of genomic fusions into structural variation events

BACKGROUND

A precise understanding of structural variants (SVs) in DNA is important in the study of cancer and population diversity. Many methods have been designed to identify SVs from DNA sequencing data. However, the problem remains challenging because existing approaches suffer from low sensitivity, precision, and positional accuracy. Furthermore, many existing tools only identify breakpoints, and so not collect related breakpoints and classify them as a particular type of SV. Due to the rapidly increasing usage of high throughput sequencing technologies in this area, there is an urgent need for algorithms that can accurately classify complex genomic rearrangements (involving more than one breakpoint or fusion).

RESULTS

We present CLOVE, an algorithm for integrating the results of multiple breakpoint or SV callers and classifying the results as a particular SV. CLOVE is based on a graph data structure that is created from the breakpoint information. The algorithm looks for patterns in the graph that are characteristic of more complex rearrangement types. CLOVE is able to integrate the results of multiple callers, producing a consensus call.

CONCLUSIONS

We demonstrate using simulated and real data that re-classified SV calls produced by CLOVE improve on the raw call set of existing SV algorithms, particularly in terms of accuracy. CLOVE is freely available from http://www.github.com/PapenfussLab .