PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data.

Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to and obscure any factors of interest. Existing batch effect correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce new multivariate and non-parametric batch effect correction methods based on Partial Least Squares Discriminant Analysis (PLSDA). PLSDA-batch first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data. The resulting batch-effect-corrected data can then be input in any downstream statistical analysis. Two variants are proposed to handle unbalanced batch x treatment designs and to avoid overfitting when estimating the components via variable selection. We compare our approaches with popular methods managing batch effects, namely, removeBatchEffect, ComBat and Surrogate Variable Analysis, in simulated and three case studies using various visual and numerical assessments. We show that our three methods lead to competitive performance in removing batch variation while preserving treatment variation, especially for unbalanced batch $\times $ treatment designs. Our downstream analyses show selections of biologically relevant taxa. This work demonstrates that batch effect correction methods can improve microbiome research outputs. Reproducible code and vignettes are available on GitHub.

Sincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references.

Characterizing the molecular identity of a cell is an essential step in single-cell RNA sequencing (scRNA-seq) data analysis. Numerous tools exist for predicting cell identity using single-cell reference atlases. However, many challenges remain, including correcting for inherent batch effects between reference and query data andinsufficient phenotype data from the reference. One solution is to project single-cell data onto established bulk reference atlases to leverage their rich phenotype information. Sincast is a computational framework to query scRNA-seq data by projection onto bulk reference atlases. Prior to projection, single-cell data are transformed to be directly comparable to bulk data, either with pseudo-bulk aggregation or graph-based imputation to address sparse single-cell expression profiles. Sincast avoids batch effect correction, and cell identity is predicted along a continuum to highlight new cell states not found in the reference atlas. In several case study scenarios, we show that Sincast projects single cells into the correct biological niches in the expression space of the bulk reference atlas. We demonstrate the effectiveness of our imputation approach that was specifically developed for querying scRNA-seq data based on bulk reference atlases. We show that Sincast is an efficient and powerful tool for single-cell profiling that will facilitate downstream analysis of scRNA-seq data.

Statistical challenges in longitudinal microbiome data analysis.

The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.

A field guide to cultivating computational biology.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Interpretation of network-based integration from multi-omics longitudinal data.

Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.

A multi-modal data harmonisation approach for discovery of COVID-19 drug targets.

Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug-target analysis, resulting in a list of 84 drug-target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.

timeOmics: an R package for longitudinal multi-omics data integration.

MOTIVATION: Multi-omics data integration enables the global analysis of biological systems and discovery of new biological insights. Multi-omics experimental designs have been further extended with a longitudinal dimension to study dynamic relationships between molecules. However, methods that integrate longitudinal multi-omics data are still in their infancy. RESULTS: We introduce the R package timeOmics, a generic analytical framework for the integration of longitudinal multi-omics data. The framework includes pre-processing, modeling and clustering to identify molecular features strongly associated with time. We illustrate this framework in a case study to detect seasonal patterns of mRNA, metabolites, gut taxa and clinical variables in patients with diabetes mellitus from the integrative Human Microbiome Project. AVAILABILITYAND IMPLEMENTATION: timeOmics is available on Bioconductor and github.com/abodein/timeOmics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Large scale experience of two ultrahypofractionated 5 fractions regimes after breast conserving surgery from a single centre.

PURPOSE: Ultra-hypofractionation breast radiotherapy is a safe alternative to moderate hypofractionation. This study reports the results of two ultrahypofractionated regimens used in clinical practice in a high-volume radiotherapy center in terms of efficacy and of tolerance. METHODS: we included all patients treated in an adjuvant setting with five fractions after breast conserving surgery (BCS), for a histologically-confirmed invasive or in situ breast carcinoma. Radiotherapy regimens after BCS were either a 5-week schedule with 5 weekly fractions of 5,7 Gy or a one-week schedule with 5 daily fractions of 5,2 Gy. Adverse events were recorded and local-relapse free survival (LRFS), locoregional-relapse free survival (LRRFS), metastasis-free survival (MFS), for breast-cancer specific survival (BCSS) and overall survival (OS) were evaluated. RESULTS: Between December 2014 and December 2022, 396 patients (400 breasts) were treated with ultrahypofractionated radiotherapy. Five-year LRFS was 98.8% (95% confidence interval: 97.1%-100%), and 5-year OS was 96.0% (95%CI: 92.6-99.5%). Age was statistically associated with OS in univariate analysis (HR: 1.16, 95%CI: 1.04-1.42, p = .01). Four patients (1.0%) experienced acute grade 3 radiation-induced adverse events, and 8 patients (2.3%) acute grade 2 toxicities. Twenty-three patients (5.8%) experienced late toxicity, all of them being graded as grade 1. The use of the 5.7 Gy-weekly-fraction regimen and the delivery of a tumor bed boost were significantly associated with acute radiodermatitis (p < .01; p = .02; respectively) and late fibrosis (p < .01; p = .049; respectively). CONCLUSIONS: ultrahypofractionated radiotherapy was associated with an excellent tumor control rate in our 'real-life' cohort with low-risk breast cancer patients. However, delivery of a tumor bed boost and using weekly 5.7-Gy fractions were associated with an increased risk of acute and late cutaneous toxicities.

How does the early life environment influence the oral microbiome and determine oral health outcomes in childhood?

The first 1000 days of life, from conception to 2 years, are a critical window for the influence of environmental exposures on the assembly of the oral microbiome, which is the precursor to dental caries (decay), one of the most prevalent microbially induced disorders worldwide. While it is known that the human microbiome is susceptible to environmental exposures, there is limited understanding of the impact of prenatal and early childhood exposures on the oral microbiome trajectory and oral health. A barrier has been the lack of technology to directly measure the foetal "exposome", which includes nutritional and toxic exposures crossing the placenta. Another barrier has been the lack of statistical methods to account for the high dimensional data generated by-omic assays. Through identifying which early life exposures influence the oral microbiome and modify oral health, these findings can be translated into interventions to reduce dental decay prevalence.

Epistatic interactions between killer immunoglobulin-like receptors and human leukocyte antigen ligands are associated with ankylosing spondylitis.

The killer immunoglobulin-like receptors (KIRs), found predominantly on the surface of natural killer (NK) cells and some T-cells, are a collection of highly polymorphic activating and inhibitory receptors with variable specificity for class I human leukocyte antigen (HLA) ligands. Fifteen KIR genes are inherited in haplotypes of diverse gene content across the human population, and the repertoire of independently inherited KIR and HLA alleles is known to alter risk for immune-mediated and infectious disease by shifting the threshold of lymphocyte activation. We have conducted the largest disease-association study of KIR-HLA epistasis to date, enabled by the imputation of KIR gene and HLA allele dosages from genotype data for 12,214 healthy controls and 8,107 individuals with the HLA-B*27-associated immune-mediated arthritis, ankylosing spondylitis (AS). We identified epistatic interactions between KIR genes and their ligands (at both HLA subtype and allele resolution) that increase risk of disease, replicating analyses in a semi-independent cohort of 3,497 cases and 14,844 controls. We further confirmed that the strong AS-association with a pathogenic variant in the endoplasmic reticulum aminopeptidase gene ERAP1, known to alter the HLA-B*27 presented peptidome, is not modified by carriage of the canonical HLA-B receptor KIR3DL1/S1. Overall, our data suggests that AS risk is modified by the complement of KIRs and HLA ligands inherited, beyond the influence of HLA-B*27 alone, which collectively alter the proinflammatory capacity of KIR-expressing lymphocytes to contribute to disease immunopathogenesis.

Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments.

Single cell RNA-sequencing (scRNA-seq) technology has undergone rapid development in recent years, leading to an explosion in the number of tailored data analysis methods. However, the current lack of gold-standard benchmark datasets makes it difficult for researchers to systematically compare the performance of the many methods available. Here, we generated a realistic benchmark experiment that included single cells and admixtures of cells or RNA to create 'pseudo cells' from up to five distinct cancer cell lines. In total, 14 datasets were generated using both droplet and plate-based scRNA-seq protocols. We compared 3,913 combinations of data analysis methods for tasks ranging from normalization and imputation to clustering, trajectory analysis and data integration. Evaluation revealed pipelines suited to different types of data for different tasks. Our data and analysis provide a comprehensive framework for benchmarking most common scRNA-seq analysis steps.

New photobiomodulation device for prevention and cure of radiotherapy-induced oral mucositis and dermatitis: results of the prospective Safe PBM study.

PURPOSE: The study aims to assess the feasibility, safety, and tolerability of CareMin650, a new photobiomodulation device, in patients treated by radiotherapy (RT) and to collect preliminary data on efficacy for prevention and treatment of oral mucositis (OM) and radiation dermatitis (RD). METHODS: Safe PBM is a French, multicentric, prospective, non-comparative study which include patients with head and neck cancer (H&NC, cohort A) or breast cancer (BC, cohort B) treated in prophylactic (cohorts A1 and B1) or curative setting (cohort A2 and B2). Prophylactic treatment was administered from D1 to end of RT, at a dose of 3 J/cm2. Curative treatment started when a grade 1 to grade 3 lesion had occurred and was pursued until end of RT. Primary endpoint was incidence of device-related adverse events (AEs). OM and RD lesions were graded according to CTCAE V3. RESULTS: Overall, 72 patients were included (22, 9, 23, and 18 in cohorts A1, A2, B1, and B2, respectively). No device-related AE was reported after 1312 CareMin650 sessions. In cohorts A1 and B1, median time to first OM or RD lesion was 20 days. One BC patient developed G3 RD after completion of RT and discontinuation of CareMin650. Four H&NC patients developed G3 OM. In cohorts A2 and B2, lesions improved or stabilized in 71% of patients. Rates of satisfaction were high among patients and users. CONCLUSION: CareMin650 is feasible, safe, and well tolerated for preventive or curative treatment of OM and RD in cancer patients treated with RT. Preliminary efficacy results are promising.

An integrated metagenomics and metabolomics approach implicates the microbiota-gut-brain axis in the pathogenesis of Huntington's disease.

BACKGROUND: Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder with onset and severity of symptoms influenced by various environmental factors. Recent discoveries have highlighted the importance of the gastrointestinal microbiome in mediating the gut-brain-axis bidirectional communication via circulating factors. Using shotgun sequencing, we investigated the gut microbiome composition in the R6/1 transgenic mouse model of HD from 4 to 12 weeks of age (early adolescent through to adult stages). Targeted metabolomics was also performed on the blood plasma of these mice (n = 9 per group) at 12 weeks of age to investigate potential effects of gut dysbiosis on the plasma metabolome profile. RESULTS: Modelled time profiles of each species, KEGG Orthologs and bacterial genes, revealed heightened volatility in the R6/1 mice, indicating potential early effects of the HD mutation in the gut. In addition to gut dysbiosis in R6/1 mice at 12 weeks of age, gut microbiome function was perturbed. In particular, the butanoate metabolism pathway was elevated, suggesting increased production of the protective SCFA, butyrate, in the gut. No significant alterations were found in the plasma butyrate and propionate levels in the R6/1 mice at 12 weeks of age. The statistical integration of the metagenomics and metabolomics unraveled several Bacteroides species that were negatively correlated with ATP and pipecolic acid in the plasma. CONCLUSIONS: The present study revealed the instability of the HD gut microbiome during the pre-motor symptomatic stage of the disease which may have dire consequences on the host's health. Perturbation of the HD gut microbiome function prior to significant cognitive and motor dysfunction suggest the potential role of the gut in modulating the pathogenesis of HD, potentially via specific altered plasma metabolites which mediate gut-brain signaling.

Combination of Olaparib with radiotherapy for triple-negative breast cancers: One-year toxicity report of the RADIOPARP Phase I trial.

Triple-negative breast cancer (TNBC) cells are sensitive to PARP1 inhibitors in vitro. The combination of Olaparib and radiotherapy for TNBC is currently evaluated in the Phase I RADIOPARP trial. RADIOPARP is a monocentric prospective open-label Phase I dose-escalation trial evaluating the combination of breast radiotherapy and Olaparib in TNBC patients with inflammatory, locoregionally advanced or metastatic disease, or with residual disease after neoadjuvant chemotherapy. Olaparib was orally given at increasing dose levels (50, 100, 150 or 200 mg twice a day [BID]); radiotherapy consisted of 50 Gy to the breast or chest wall with or without lymph node irradiation. Twenty-four TNBC patients were enrolled between September 2017 and November 2019. Olaparib was escalated to 200 mg BID without dose-limiting toxicities. At 1-year follow-up, no treatment-related grade ≥3 toxicity was observed. One patient (4.2%) had persistent grade 2 adverse events (breast pain, fibrosis and deformity). There was no cardiac, pulmonary or digestive toxicity related to treatment. The 1-year follow-up report of the RADIOPARP Phase I trial, evaluating Olaparib associated with breast radiotherapy in TNBC patients, consequently demonstrated an excellent toxicity profile of this combination with few low-grade adverse events.

Streptococcus species enriched in the oral cavity of patients with RA are a source of peptidoglycan-polysaccharide polymers that can induce arthritis in mice.

OBJECTIVES: Analysis of oral dysbiosis in individuals sharing genetic and environmental risk factors with rheumatoid arthritis (RA) patients may illuminate how microbiota contribute to disease susceptibility. We studied the oral microbiota in a prospective cohort of patients with RA, first-degree relatives (FDR) and healthy controls (HC), then genomically and functionally characterised streptococcal species from each group to understand their potential contribution to RA development. METHODS: After DNA extraction from tongue swabs, targeted 16S rRNA gene sequencing and statistical analysis, we defined a microbial dysbiosis score based on an operational taxonomic unit signature of disease. After selective culture from swabs, we identified streptococci by sequencing. We examined the ability of streptococcal cell walls (SCW) from isolates to induce cytokines from splenocytes and arthritis in ZAP-70-mutant SKG mice. RESULTS: RA and FDR were more likely to have periodontitis symptoms. An oral microbial dysbiosis score discriminated RA and HC subjects and predicted similarity of FDR to RA. Streptococcaceae were major contributors to the score. We identified 10 out of 15 streptococcal isolates as S. parasalivarius sp. nov., a distinct sister species to S. salivarius. Tumour necrosis factor and interleukin 6 production in vitro differed in response to individual S. parasalivarius isolates, suggesting strain specific effects on innate immunity. Cytokine secretion was associated with the presence of proteins potentially involved in S. parasalivarius SCW synthesis. Systemic administration of SCW from RA and HC-associated S. parasalivarius strains induced similar chronic arthritis. CONCLUSIONS: Dysbiosis-associated periodontal inflammation and barrier dysfunction may permit arthritogenic insoluble pro-inflammatory pathogen-associated molecules, like SCW, to reach synovial tissue.

A simple, scalable approach to building a cross-platform transcriptome atlas.

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows.

Integrative Analyses to Investigate the Link between Microbial Activity and Metabolite Degradation during Anaerobic Digestion.

Anaerobic digestion (AD) is a promising biological process that converts waste into sustainable energy. To fully exploit AD's capability, we need to deepen our knowledge of the microbiota involved in this complex bioprocess. High-throughput methodologies open new perspectives to investigate the AD process at the molecular level, supported by recent data integration methodologies to extract relevant information. In this study, we investigated the link between microbial activity and substrate degradation in a lab-scale anaerobic codigestion experiment, where digesters were fed with nine different mixtures of three cosubstrates (fish waste, sewage sludge, and grass). Samples were profiled using 16S rRNA sequencing and untargeted metabolomics. In this article, we propose a suite of multivariate tools to statistically integrate these data and identify coordinated patterns between groups of microbial and metabolic profiles specific of each cosubstrate. Five main groups of features were successfully evidenced, including cadaverine degradation found to be associated with the activity of microorganisms from the order Clostridiales and the genus Methanosarcina. This study highlights the potential of data integration toward a comprehensive understanding of AD microbiota.

Microbiome profiling reveals gut dysbiosis in a transgenic mouse model of Huntington's disease.

Huntington's disease (HD) is a progressive neurodegenerative disorder caused by a trinucleotide repeat expansion in the huntingtin (HTT) gene, which is expressed ubiquitously throughout the brain and peripheral tissues. Whilst the focus of much research has been on the cognitive, psychiatric and motor symptoms of HD, the extent of peripheral pathology and its potential impact on central symptoms has been less intensely explored. Disruption of the gastrointestinal microbiome (gut dysbiosis) has been recently reported in a number of neurological and psychiatric disorders, and therefore we hypothesized that it might also occur in HD. We have used 16S rRNA amplicon sequencing to characterize the gut microbiome in the R6/1 transgenic mouse model of HD, relative to littermate wild-type controls. We report that there is a significant difference in microbiota composition in HD mice at 12 weeks of age. Specifically, we observed an increase in Bacteriodetes and a proportional decrease in Firmicutes in the HD gut microbiome. In addition, we observed an increase in microbial diversity in male HD mice, compared to wild-type controls, but no differences in diversity were observed in female HD mice. The gut dysbiosis observed coincided with impairment in body weight gain despite higher food intake as well as motor deficits at 12 weeks of age. Gut dysbiosis was also associated with a change in the gut microenvironment, as we observed higher fecal water content in HD mice at 12 weeks of age. This study provides the first evidence of gut dysbiosis in HD.

DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays.

MOTIVATION: In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups. RESULTS: Using simulations and benchmark multi-omics studies, we show that DIABLO identifies features with superior biological relevance compared with existing unsupervised integrative methods, while achieving predictive performance comparable to state-of-the-art supervised approaches. DIABLO is versatile, allowing for modular-based analyses and cross-over study designs. In two case studies, DIABLO identified both known and novel multi-omics biomarkers consisting of mRNAs, miRNAs, CpGs, proteins and metabolites. AVAILABILITY AND IMPLEMENTATION: DIABLO is implemented in the mixOmics R Bioconductor package with functions for parameters' choice and visualization to assist in the interpretation of the integrative analyses, along with tutorials on http://mixomics.org and in our Bioconductor vignette. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide characterization of the routes to pluripotency.

Somatic cell reprogramming to a pluripotent state continues to challenge many of our assumptions about cellular specification, and despite major efforts, we lack a complete molecular characterization of the reprograming process. To address this gap in knowledge, we generated extensive transcriptomic, epigenomic and proteomic data sets describing the reprogramming routes leading from mouse embryonic fibroblasts to induced pluripotency. Through integrative analysis, we reveal that cells transition through distinct gene expression and epigenetic signatures and bifurcate towards reprogramming transgene-dependent and -independent stable pluripotent states. Early transcriptional events, driven by high levels of reprogramming transcription factor expression, are associated with widespread loss of histone H3 lysine 27 (H3K27me3) trimethylation, representing a general opening of the chromatin state. Maintenance of high transgene levels leads to re-acquisition of H3K27me3 and a stable pluripotent state that is alternative to the embryonic stem cell (ESC)-like fate. Lowering transgene levels at an intermediate phase, however, guides the process to the acquisition of ESC-like chromatin and DNA methylation signature. Our data provide a comprehensive molecular description of the reprogramming routes and is accessible through the Project Grandiose portal at http://www.stemformatics.org.