miND pipeline AWS EC2 installation and setup v2

miND is a NGS data analysis pipeline for smallRNA sequencing data. In this protocol, the pipeline is setup and run on an AWS EC2 instance with example data from a public repository. Please see the publication paper on F1000 for more details on the pipeline and how to use it.

miND pipeline AWS EC2 installation and setup v1

miND is a NGS data analysis pipeline for smallRNA sequencing data. In this protocol, the pipeline is setup and run on an AWS EC2 instance with example data from a public repository. Please see the publication paper on F1000 for more details on the pipeline and how to use it.

HPV DeepSeq: An Ultra-Fast Method of NGS Data Analysis and Visualization Using Automated Workflows and a Customized Papillomavirus Database in CLC Genomics Workbench

Next-generation sequencing (NGS) has actualized the human papillomavirus (HPV) virome profiling for in-depth investigation of viral evolution and pathogenesis. However, viral computational analysis remains a bottleneck due to semantic discrepancies between computational tools and curated reference genomes. To address this, we developed and tested automated workflows for HPV taxonomic profiling and visualization using a customized papillomavirus database in the CLC Microbial Genomics Module. HPV genomes from Papilloma Virus Episteme were customized and incorporated into CLC “ready-to-use” workflows for stepwise data processing to include: (1) Taxonomic Analysis, (2) Estimate Alpha/Beta Diversities, and (3) Map Reads to Reference. Low-grade (n = 95) and high-grade (n = 60) Pap smears were tested with ensuing collective runtimes: Taxonomic Analysis (36 min); Alpha/Beta Diversities (5 s); Map Reads (45 min). Tabular output conversion to visualizations entailed 1–2 keystrokes. Biodiversity analysis between low- (LSIL) and high-grade squamous intraepithelial lesions (HSIL) revealed loss of species richness and gain of dominance by HPV-16 in HSIL. Integrating clinically relevant, taxonomized HPV reference genomes within automated workflows proved to be an ultra-fast method of virome profiling. The entire process named “HPV DeepSeq” provides a simple, accurate and practical means of NGS data analysis for a broad range of applications in viral research.

HPV DeepSeq: Ultra-Fast Method of NGS Data Analysis and Visualization Using Automated Workflows and a Customized Papillomavirus Database in CLC Genomics Workbench

Next-generation sequencing (NGS) has actualized human papillomavirus (HPV) virome profiling for in-depth investigation of viral evolution and pathogenesis. However, viral computational analysis remains a bottleneck due to semantic discrepancies between computational tools and curated reference genomes. To address this, we developed and tested automated workflows for HPV taxonomic profiling and visualization using a customized Papillomavirus database in CLC Microbial Genomics Module. HPV genomes from Papilloma Virus Episteme were customized and incorporated into CLC “ready-to-use” workflows for stepwise data processing to include: 1) Taxonomic Analysis, 2) Estimate Alpha/Beta Diversities, and 3) Map Reads to Reference. Low-grade (n = 95) and high-grade (n = 60) Pap smears were tested with ensuing collective runtimes: Taxonomic Analysis (36 min); Alpha/Beta Diversities (5 sec); Map Reads (45 min). Tabular output conversion to visualizations entailed 1-2 keystrokes. Biodiversity analysis between low- (LSIL) and high-grade squamous intraepithelial lesions (HSIL) revealed loss of species richness and gain of dominance by HPV-16 in HSIL. Integrating clinically relevant, taxonomized HPV reference genomes within automated workflows proved to be an ultra-fast method of virome profiling. The entire process named “HPV DeepSeq” provides a simple, accurate and practical means of NGS data analysis for a broad range of applications in viral research.

P–582 High level of concordance between invasive and non-invasive preimplantation genetic testing for aneuploidies (niPGT-A) at day5 and day6–7

Study question To explore ploidy concordance between invasive and non-invasive PGTA (niPGT-A) at different embryo culture time. Summary answer High level (>84%) of concordance rate for ploidy and sex, sensitivity (>88%), and specificity (76%) were obtained for both day6/7 samples and day5 samples. What is known already The analysis of embryo cell free DNA (cfDNA) that are released into culture media during in vitro embryo development has the potential to evaluate embryo ploidy status. However, obtaining sufficient quality and quantity of cfDNA is essential to achieve interpretable results for niPGT-A. More culture time is expected to be directly proportional to the release of more cfDNA. But embryo culture time is limited due to in-vitro embryo survival potential. Therefore, it is important to estimate the duration of the culture that will provide the maximum cfDNA that can be obtained without adversely affecting the development of the embryo. Study design, size, duration A total of 105 spent culture media (SCM) from day5-day7 blastocyst stage embryos have been included in this cohort study. The cfDNA of SCM samples were amplified and analyzed for niPGT-A by NGS analysis. The SCM samples were divided into 2 subgroups according the embryo culture hours (Day5 and Day6/7 group). The DNA concentration, informativity and euploidy results have then been compared with their corresponding embryos after trophectoderm biopsy (TE) and PGT-A analysis by NGS Participants/materials, setting, methods Embryos cultured until Day3 washed and cultured again in 20µl fresh culture media until embryo biopsy on Day5, 6, or 7. After biopsy SCM samples were immediately collected in PCR tubes and conserved at –20 °C until whole genome amplification by MALBAC® (Yicon Genomics). The TE and SCM samples were analyzed by next-generation sequencing (NGS) using Illumina MiSeq® System. NGS data analysis has been done by Bluefuse Multi Software 4.5 (Illumina) for SCM and TE samples Main results and the role of chance Only the SCM samples which have an embryo with a conclusive result were included in this cohort (n = 105). Overall 97.1% (102/105) of SCM samples gave a successful DNA amplification with a concentration ranging 32.4–128.5ng/µl. Non-informative (NI) results including a chaotic profile (>5 chromosome aneuploidies) were observed in 17 samples, so 83.3%(85/102) of SCM samples were informative for NGS data analysis. Ploidy concordance rate with the corresponding TE biopsies (euploid vs euploid, aneuploid vs aneuploid) was 84.7% (72/85). Sensitivity and specificity were 92,8% and 76,7%, respectively with no significant difference for all parameters for day 6/7 samples compared with day 5 samples. The false-negative rate was 3.5% (3/85), and false-positive rate was 11.7% (10/85). Limitations, reasons for caution The sample size is relatively small. Larger prospective studies are needed. As this is a single-center study, the impact of the variations in embryo culture conditions can be underestimated. Maternal DNA contamination risk cannot be revealed in SCM, therefore the use of molecular markers would increase the reliability. Wider implications of the findings: Non-invasive analysis of embryo cfDNA analyzed in spent culture media demonstrates high concordance with TE biopsy results in both early and late culture time. A non-invasive approach for aneuploidy screening offers important advantages such as avoiding invasive embryo biopsy and decreased cost, potentially increasing accessibility for a wider patient population. Trial registration number Not applicable

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

Patient Derived Xenografts for Genome-Driven Therapy of Osteosarcoma

Osteosarcoma (OS) is a rare malignant primary tumor of mesenchymal origin affecting bone. It is characterized by a complex genotype, mainly due to the high frequency of chromothripsis, which leads to multiple somatic copy number alterations and structural rearrangements. Any effort to design genome-driven therapies must therefore consider such high inter- and intra-tumor heterogeneity. Therefore, many laboratories and international networks are developing and sharing OS patient-derived xenografts (OS PDX) to broaden the availability of models that reproduce OS complex clinical heterogeneity. OS PDXs, and new cell lines derived from PDXs, faithfully preserve tumor heterogeneity, genetic, and epigenetic features and are thus valuable tools for predicting drug responses. Here, we review recent achievements concerning OS PDXs, summarizing the methods used to obtain ectopic and orthotopic xenografts and to fully characterize these models. The availability of OS PDXs across the many international PDX platforms and their possible use in PDX clinical trials are also described. We recommend the coupling of next-generation sequencing (NGS) data analysis with functional studies in OS PDXs, as well as the setup of OS PDX clinical trials and co-clinical trials, to enhance the predictive power of experimental evidence and to accelerate the clinical translation of effective genome-guided therapies for this aggressive disease.

Characterization of New ATM Deletion Associated with Hereditary Breast Cancer

Next-generation sequencing (NGS)-based cancer risk screening with multigene panels has become the most successful method for programming cancer prevention strategies. ATM germ-line heterozygosity has been described to increase tumor susceptibility. In particular, families carrying heterozygous germ-line variants of ATM gene have a 5- to 9-fold risk of developing breast cancer. Recent studies identified ATM as the second most mutated gene after CHEK2 in BRCA-negative patients. Nowadays, more than 170 missense variants and several truncating mutations have been identified in ATM gene. Here, we present the molecular characterization of a new ATM deletion, identified thanks to the CNV algorithm implemented in the NGS analysis pipeline. An automated workflow implementing the SOPHiA Genetics’ Hereditary Cancer Solution (HCS) protocol was used to generate NGS libraries that were sequenced on Illumina MiSeq Platform. NGS data analysis allowed us to identify a new inactivating deletion of exons 19–27 of ATM gene. The deletion was characterized both at the DNA and RNA level.

PM4NGS, a project management framework for next-generation sequencing data analysis

Background FAIR (Findability, Accessibility, Interoperability, and Reusability) next-generation sequencing (NGS) data analysis relies on complex computational biology workflows and pipelines to guarantee reproducibility, portability, and scalability. Moreover, workflow languages, managers, and container technologies have helped address the problem of data analysis pipeline execution across multiple platforms in scalable ways. Findings Here, we present a project management framework for NGS data analysis called PM4NGS. This framework is composed of an automatic creation of a standard organizational structure of directories and files, bioinformatics tool management using Docker or Bioconda, and data analysis pipelines in CWL format. Pre-configured Jupyter notebooks with minimum Python code are included in PM4NGS to produce a project report and publication-ready figures. We present 3 pipelines for demonstration purposes including the analysis of RNA-Seq, ChIP-Seq, and ChIP-exo datasets. Conclusions PM4NGS is an open source framework that creates a standard organizational structure for NGS data analysis projects. PM4NGS is easy to install, configure, and use by non-bioinformaticians on personal computers and laptops. It permits execution of the NGS data analysis on Windows 10 with the Windows Subsystem for Linux feature activated. The framework aims to reduce the gap between researcher in experimental laboratories producing NGS data and workflows for data analysis. PM4NGS documentation can be accessed at https://pm4ngs.readthedocs.io/.

DIANA-mAP: Analyzing miRNA from Raw NGS Data to Quantification

microRNAs (miRNAs) are small non-coding RNAs (~22 nts) that are considered central post-transcriptional regulators of gene expression and key components in many pathological conditions. Next-Generation Sequencing (NGS) technologies have led to inexpensive, massive data production, revolutionizing every research aspect in the fields of biology and medicine. Particularly, small RNA-Seq (sRNA-Seq) enables small non-coding RNA quantification on a high-throughput scale, providing a closer look into the expression profiles of these crucial regulators within the cell. Here, we present DIANA-microRNA-Analysis-Pipeline (DIANA-mAP), a fully automated computational pipeline that allows the user to perform miRNA NGS data analysis from raw sRNA-Seq libraries to quantification and Differential Expression Analysis in an easy, scalable, efficient, and intuitive way. Emphasis has been given to data pre-processing, an early, critical step in the analysis for the robustness of the final results and conclusions. Through modularity, parallelizability and customization, DIANA-mAP produces high quality expression results, reports and graphs for downstream data mining and statistical analysis. In an extended evaluation, the tool outperforms similar tools providing pre-processing without any adapter knowledge. Closing, DIANA-mAP is a freely available tool. It is available dockerized with no dependency installations or standalone, accompanied by an installation manual through Github.