Nextcast: A software suite to analyse and model toxicogenomics data.

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

Multiparametric Profiling of Engineered Nanomaterials: Unmasking the Surface Coating Effect.

Despite considerable efforts, the properties that drive the cytotoxicity of engineered nanomaterials (ENMs) remain poorly understood. Here, the authors inverstigate a panel of 31 ENMs with different core chemistries and a variety of surface modifications using conventional in vitro assays coupled with omics-based approaches. Cytotoxicity screening and multiplex-based cytokine profiling reveals a good concordance between primary human monocyte-derived macrophages and the human monocyte-like cell line THP-1. Proteomics analysis following a low-dose exposure of cells suggests a nonspecific stress response to ENMs, while microarray-based profiling reveals significant changes in gene expression as a function of both surface modification and core chemistry. Pathway analysis highlights that the ENMs with cationic surfaces that are shown to elicit cytotoxicity downregulated DNA replication and cell cycle responses, while inflammatory responses are upregulated. These findings are validated using cell-based assays. Notably, certain small, PEGylated ENMs are found to be noncytotoxic yet they induce transcriptional responses reminiscent of viruses. In sum, using a multiparametric approach, it is shown that surface chemistry is a key determinant of cellular responses to ENMs. The data also reveal that cytotoxicity, determined by conventional in vitro assays, does not necessarily correlate with transcriptional effects of ENMs.

BMDx: a graphical Shiny application to perform Benchmark Dose analysis for transcriptomics data.

MOTIVATION: The analysis of dose-dependent effects on the gene expression is gaining attention in the field of toxicogenomics. Currently available computational methods are usually limited to specific omics platforms or biological annotations and are able to analyse only one experiment at a time. RESULTS: We developed the software BMDx with a graphical user interface for the Benchmark Dose (BMD) analysis of transcriptomics data. We implemented an approach based on the fitting of multiple models and the selection of the optimal model based on the Akaike Information Criterion. The BMDx tool takes as an input a gene expression matrix and a phenotype table, computes the BMD, its related values, and IC50/EC50 estimations. It reports interactive tables and plots that the user can investigate for further details of the fitting, dose effects and functional enrichment. BMDx allows a fast and convenient comparison of the BMD values of a transcriptomics experiment at different time points and an effortless way to interpret the results. Furthermore, BMDx allows to analyse and to compare multiple experiments at once. AVAILABILITY AND IMPLEMENTATION: BMDx is implemented as an R/Shiny software and is available at https://github.com/Greco-Lab/BMDx/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments.

BACKGROUND: Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Several tools are already available to the community to represent and compare functional profiles of omics experiments. However, when the number of experiments and/or enriched functional terms is high, it becomes difficult to interpret the results even when graphically represented. Therefore, there is currently a need for interactive and user-friendly tools to graphically navigate and further summarize annotations in order to facilitate results interpretation also when the dimensionality is high. RESULTS: We developed an approach that exploits the intrinsic hierarchical structure of several functional annotations to summarize the results obtained through enrichment analyses to higher levels of interpretation and to map gene related information at each summarized level. We built a user-friendly graphical interface that allows to visualize the functional annotations of one or multiple experiments at once. The tool is implemented as a R-Shiny application called FunMappOne and is available at https://github.com/grecolab/FunMappOne . CONCLUSION: FunMappOne is a R-shiny graphical tool that takes in input multiple lists of human or mouse genes, optionally along with their related modification magnitudes, computes the enriched annotations from Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, or Reactome databases, and reports interactive maps of functional terms and pathways organized in rational groups. FunMappOne allows a fast and convenient comparison of multiple experiments and an easy way to interpret results.

eUTOPIA: solUTion for Omics data PreprocessIng and Analysis.

BACKGROUND: Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R language that have become standard for high-throughput analytics. However, application of these tools is technically challenging for generic users and require specific computational skills. There is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results. RESULTS: We propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application. CONCLUSIONS: eUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods.

INfORM: Inference of NetwOrk Response Modules.

Summary: Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps. Availability and implementation: INfORM is freely available for academic use at https://github.com/Greco-Lab/INfORM. Supplementary information: Supplementary data are available at Bioinformatics online.

Allelic Expression Imbalance in the Human Retinal Transcriptome and Potential Impact on Inherited Retinal Diseases.

Inherited retinal diseases (IRDs) are often associated with variable clinical expressivity (VE) and incomplete penetrance (IP). Underlying mechanisms may include environmental, epigenetic, and genetic factors. Cis-acting expression quantitative trait loci (cis-eQTLs) can be implicated in the regulation of genes by favoring or hampering the expression of one allele over the other. Thus, the presence of such loci elicits allelic expression imbalance (AEI) that can be traced by massive parallel sequencing techniques. In this study, we performed an AEI analysis on RNA-sequencing (RNA-seq) data, from 52 healthy retina donors, that identified 194 imbalanced single nucleotide polymorphisms(SNPs) in 67 IRD genes. Focusing on SNPs displaying AEI at a frequency higher than 10%, we found evidence of AEI in several IRD genes regularly associated with IP and VE (BEST1, RP1, PROM1, and PRPH2). Based on these SNPs commonly undergoing AEI, we performed pyrosequencing in an independent sample set of 17 healthy retina donors in order to confirm our findings. Indeed, we were able to validate CDHR1, BEST1, and PROM1 to be subjected to cis-acting regulation. With this work, we aim to shed light on differentially expressed alleles in the human retina transcriptome that, in the context of autosomal dominant IRD cases, could help to explain IP or VE.

The genetic basis of undiagnosed muscular dystrophies and myopathies: Results from 504 patients.

OBJECTIVE: To apply next-generation sequencing (NGS) for the investigation of the genetic basis of undiagnosed muscular dystrophies and myopathies in a very large cohort of patients. METHODS: We applied an NGS-based platform named MotorPlex to our diagnostic workflow to test muscle disease genes with a high sensitivity and specificity for small DNA variants. We analyzed 504 undiagnosed patients mostly referred as being affected by limb-girdle muscular dystrophy or congenital myopathy. RESULTS: MotorPlex provided a complete molecular diagnosis in 218 cases (43.3%). A further 160 patients (31.7%) showed as yet unproven candidate variants. Pathogenic variants were found in 47 of 93 genes, and in more than 30% of cases, the phenotype was nonconventional, broadening the spectrum of disease presentation in at least 10 genes. CONCLUSIONS: Our large DNA study of patients with undiagnosed myopathy is an example of the ongoing revolution in molecular diagnostics, highlighting the advantages in using NGS as a first-tier approach for heterogeneous genetic conditions.

Identification of optimum sequencing depth especially for de novo genome assembly of small genomes using next generation sequencing data.

Next Generation Sequencing (NGS) is a disruptive technology that has found widespread acceptance in the life sciences research community. The high throughput and low cost of sequencing has encouraged researchers to undertake ambitious genomic projects, especially in de novo genome sequencing. Currently, NGS systems generate sequence data as short reads and de novo genome assembly using these short reads is computationally very intensive. Due to lower cost of sequencing and higher throughput, NGS systems now provide the ability to sequence genomes at high depth. However, currently no report is available highlighting the impact of high sequence depth on genome assembly using real data sets and multiple assembly algorithms. Recently, some studies have evaluated the impact of sequence coverage, error rate and average read length on genome assembly using multiple assembly algorithms, however, these evaluations were performed using simulated datasets. One limitation of using simulated datasets is that variables such as error rates, read length and coverage which are known to impact genome assembly are carefully controlled. Hence, this study was undertaken to identify the minimum depth of sequencing required for de novo assembly for different sized genomes using graph based assembly algorithms and real datasets. Illumina reads for E.coli (4.6 MB) S.kudriavzevii (11.18 MB) and C.elegans (100 MB) were assembled using SOAPdenovo, Velvet, ABySS, Meraculous and IDBA-UD. Our analysis shows that 50X is the optimum read depth for assembling these genomes using all assemblers except Meraculous which requires 100X read depth. Moreover, our analysis shows that de novo assembly from 50X read data requires only 6-40 GB RAM depending on the genome size and assembly algorithm used. We believe that this information can be extremely valuable for researchers in designing experiments and multiplexing which will enable optimum utilization of sequencing as well as analysis resources.