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We present, on behalf of EuroGentest and the European Society of Human Genetics, guidelines for the evaluation and validation of next-generation sequencing (NGS) applications for the diagnosis of genetic disorders. The work was performed by a group of laboratory geneticists and bioinformaticians, and discussed with clinical geneticists, industry and patients’ representatives, and other stakeholders in the field of human genetics. The statements that were written during the elaboration of the guidelines are presented here. The background document and full guidelines are available as supplementary material. They include many examples to assist the laboratories in the implementation of NGS and accreditation of this service. The work and ideas presented by others in guidelines that have emerged elsewhere in the course of the past few years were also considered and are acknowledged in the full text. Interestingly, a few new insights that have not been cited before have emerged during the preparation of the guidelines. The most important new feature is the presentation of a ‘rating system’ for NGS-based diagnostic tests. The guidelines and statements have been applauded by the genetic diagnostic community, and thus seem to be valuable for the harmonization and quality assurance of NGS diagnostics in Europe.

Grouping the microbiota of individual subjects into compositional categories, or enterotypes, based on the dominance of certain genera may have oversimplified a complex situation. Grouping the microbiota of individual subjects into compositional categories, or enterotypes, based on the dominance of certain genera may have oversimplified a complex situation.

Background Cone and cone-rod dystrophies are clinically and genetically heterogeneous inherited retinal disorders with predominant cone impairment. They should be distinguished from the more common group of rod-cone dystrophies (retinitis pigmentosa) due to their more severe visual prognosis with early central vision loss. The purpose of our study was to document mutation spectrum of a large French cohort of cone and cone-rod dystrophies. Methods We applied Next-Generation Sequencing targeting a panel of 123 genes implicated in retinal diseases to 96 patients. A systematic filtering approach was used to identify likely disease causing variants, subsequently confirmed by Sanger sequencing and co-segregation analysis when possible. Results Overall, the likely causative mutations were detected in 62.1 % of cases, revealing 33 known and 35 novel mutations. This rate was higher for autosomal dominant (100 %) than autosomal recessive cases (53.8 %). Mutations in ABCA4 and GUCY2D were responsible for 19.2 % and 29.4 % of resolved cases with recessive and dominant inheritance, respectively. Furthermore, unexpected genotype-phenotype correlations were identified, confirming the complexity of inherited retinal disorders with phenotypic overlap between cone-rod dystrophies and other retinal diseases. Conclusions In summary, this time-efficient approach allowed mutation detection in the most important cohort of cone-rod dystrophies investigated so far covering the largest number of genes. Association of known gene defects with novel phenotypes and mode of inheritance were established.

Next-generation sequencing (NGS) has contributed to the identification of many monogenic epilepsy syndromes and is favouring earlier and more accurate diagnosis in a subset of paediatric patients with epilepsy. The cumulative information emerging from NGS studies is rapidly changing our comprehension of the relations between early-onset severe epilepsy and the associated neurological impairment, progressively delineating specific entities previously gathered under the umbrella definition of epileptic encephalopathies, thereby influencing treatment choices and limiting the most aggressive drug regimens only to those conditions that are likely to actually benefit from them. Although ion channel genes represent the gene family most frequently causally related to epilepsy, other genes have gradually been associated with complex developmental epilepsy conditions, revealing the pathogenic role of mutations affecting diverse molecular pathways that regulate membrane excitability, synaptic plasticity, presynaptic neurotransmitter release, postsynaptic receptors, transporters, cell metabolism, and many formative steps in early brain development. Some of these discoveries are being followed by proof-of-concept laboratory studies that might open new pathways towards personalized treatment choices. No specific treatment is available for most of the monogenic disorders that can now be diagnosed early using NGS, and the main benefits of knowing the specific cause include etiological diagnosis, better prognostication and genetic counselling; however, for a limited number of disorders, timely treatment based on their known molecular pathology is already possible and sometimes decisive. Discovery of a causative gene defect associated with a non-progressive course may reduce the need for further diagnostic investigations in the search for a progressive disorder at the biochemical and imaging level. NGS has also improved the turnaround time for molecular diagnosis and allowed more timely and straightforward treatment choices for specific conditions as well as avoiding needless investigations and inappropriate or unnecessary treatment choices.

Since the 2013 Supreme Court ruling on BRCA1 / BRCA2 patenting, hereditary cancer gene panels now include BRCA1 and BRCA2 , making these panels an option for first-tier testing. However, questions remain about the clinical utility and implications of these panels for medical management with inclusion of genes of unknown to moderate penetrance. To better understand how use of these panels affected our practice, we reviewed patients who underwent testing in our clinic from July 1, 2013 through May 23, 2014. Indications for testing included personal and/or family history of breast and/or ovarian cancer. A total of 136 patients underwent panel testing via a single commercial laboratory; 12 (8.8 %) patients were positive for a pathogenic or likely pathogenic mutation (four BRCA2 mutations, two TP53 mutations, one CDH1 mutation, two ATM mutations, and one patient each with a CHEK2 , NBN , or PALB2 mutation). Of these positive patients, 100 % met the National Comprehensive Cancer Network (NCCN) guidelines for Hereditary Breast and Ovarian Cancer genetic testing (2.2014). Mutations in seven of twelve (58 %) patients led to changes in medical management; three of seven (43 %) had a non- BRCA1 or BRCA2 gene mutation. Our findings suggest that there is clinical utility of panels that include genes of unknown to moderate penetrance.

This Perspective examines single-cell RNA-seq data challenges and the need for normalization methods designed specifically for single-cell data in order to remove technical biases. Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users.

Background Some applications, especially those clinical applications requiring high accuracy of sequencing data, usually have to face the troubles caused by unavoidable sequencing errors. Several tools have been proposed to profile the sequencing quality, but few of them can quantify or correct the sequencing errors. This unmet requirement motivated us to develop AfterQC, a tool with functions to profile sequencing errors and correct most of them, plus highly automated quality control and data filtering features. Different from most tools, AfterQC analyses the overlapping of paired sequences for pair-end sequencing data. Based on overlapping analysis, AfterQC can detect and cut adapters, and furthermore it gives a novel function to correct wrong bases in the overlapping regions. Another new feature is to detect and visualise sequencing bubbles, which can be commonly found on the flowcell lanes and may raise sequencing errors. Besides normal per cycle quality and base content plotting, AfterQC also provides features like polyX (a long sub-sequence of a same base X) filtering, automatic trimming and K-MER based strand bias profiling. Results For each single or pair of FastQ files, AfterQC filters out bad reads, detects and eliminates sequencer’s bubble effects, trims reads at front and tail, detects the sequencing errors and corrects part of them, and finally outputs clean data and generates HTML reports with interactive figures. AfterQC can run in batch mode with multiprocess support, it can run with a single FastQ file, a single pair of FastQ files (for pair-end sequencing), or a folder for all included FastQ files to be processed automatically. Based on overlapping analysis, AfterQC can estimate the sequencing error rate and profile the error transform distribution. The results of our error profiling tests show that the error distribution is highly platform dependent. Conclusion Much more than just another new quality control (QC) tool, AfterQC is able to perform quality control, data filtering, error profiling and base correction automatically. Experimental results show that AfterQC can help to eliminate the sequencing errors for pair-end sequencing data to provide much cleaner outputs, and consequently help to reduce the false-positive variants, especially for the low-frequency somatic mutations. While providing rich configurable options, AfterQC can detect and set all the options automatically and require no argument in most cases.

Current GMO detection systems have limited abilities to detect unauthorized genetically modified organisms (GMOs). Here, we propose a new workflow, based on next-generation sequencing (NGS) technology, to overcome this problem. In providing information about DNA sequences, this high-throughput workflow can distinguish authorized and unauthorized GMOs by strengthening the tools commonly used by enforcement laboratories with the help of NGS technology. In addition, thanks to its massive sequencing capacity, this workflow could be used to monitor GMOs present in the food and feed chain. In view of its potential implementation by enforcement laboratories, we discuss this innovative approach, its current limitations, and its sustainability of use over time. Most European Union (EU)-unauthorized GMOs can be detected by enforcement laboratories via the current GMO detection system. However, based on the current approach, distinguishing EU-unauthorized GMOs from EU-authorized GMOs is almost impossible. One possible way to overcome this issue is a new workflow using NGS technology. This new workflow may improve the ability of the current system to detect EU-unauthorized GMOs. The high-throughput property of NGS technology, enabling the generation of massive amounts of sequence data from several samples in parallel, may also enable the monitoring of GMOs that are present in the food and feed chain.

Genome‐wide mutations induced by ethyl methanesulfonate (EMS) and gamma irradiation in the tomato Micro‐Tom genome were identified by a whole‐genome shotgun sequencing analysis to estimate the spectrum and distribution of whole‐genome DNA mutations and the frequency of deleterious mutations. A total of ~370 Gb of paired‐end reads for four EMS‐induced mutants and three gamma‐ray‐irradiated lines as well as a wild‐type line were obtained by next‐generation sequencing technology. Using bioinformatics analyses, we identified 5920 induced single nucleotide variations and insertion/deletion (indel) mutations. The predominant mutations in the EMS mutants were C/G to T/A transitions, while in the gamma‐ray mutants, C/G to T/A transitions, A/T to T/A transversions, A/T to G/C transitions and deletion mutations were equally common. Biases in the base composition flanking mutations differed between the mutagenesis types. Regarding the effects of the mutations on gene function, >90% of the mutations were located in intergenic regions, and only 0.2% were deleterious. In addition, we detected 1 140 687 spontaneous single nucleotide polymorphisms and indel polymorphisms in wild‐type Micro‐Tom lines. We also found copy number variation, deletions and insertions of chromosomal segments in both the mutant and wild‐type lines. The results provide helpful information not only for mutation research, but also for mutant screening methodology with reverse‐genetic approaches.

Next‐Generation Sequencing (NGS) is being increasingly adopted in clinical settings as a tool to increase diagnostic yield in genetically determined pathologies. However, for patients in critical conditions the time to results of data analysis is crucial for a rapid diagnosis and response. Sentieon DNASeq and Clara Parabricks Germline are two widely used pipelines for ultra‐rapid NGS analysis, but their high computational demands often exceed the resources available in many healthcare facilities. Cloud platforms, like Google Cloud Platform (GCP), offer scalable solutions to address these limitations. Yet, setting up these pipelines in a cloud environment can be complex. This work provides a benchmark of the two solutions, and offers a comprehensive tutorial aimed at easing their implementation on GCP by healthcare bioinformaticians. Additionally, it presents valuable cost guidance to healthcare managers who consider implementing cloud‐based NGS processing. Using five publicly available exome (WES) and five genome (WGS) samples, we benchmarked both pipelines on GCP in terms of runtime, cost, and resource utilization. Our results show that Sentieon and Parabricks perform comparably. Both pipelines are viable options for rapid, cloud‐based NGS analysis, enabling healthcare providers to access advanced genomic tools without the need for extensive local infrastructure.