Recent advances in sequencing, mass spectrometry and cytometry technologies have enabled researchers to collect large-scale omics data from the same set of biological samples. The joint analysis of multiple omics offers the opportunity to uncover coordinated cellular processes acting across different omic layers. In this work, we present a thorough comparison of a selection of recent integrative clustering approaches, including Bayesian (BCC and MDI) and matrix factorization approaches (iCluster, moCluster, JIVE and iNMF). Based on simulations, the methods were evaluated on their sensitivity and their ability to recover both the correct number of clusters and the simulated clustering at the common and data-specific levels. Standard non-integrative approaches were also included to quantify the added value of integrative methods. For most matrix factorization methods and one Bayesian approach (BCC), the shared and specific structures were successfully recovered with high and moderate accuracy, respectively. An opposite behavior was observed on non-integrative approaches, i.e. high performances on specific structures only. Finally, we applied the methods on the Cancer Genome Atlas breast cancer data set to check whether results based on experimental data were consistent with those obtained in the simulations.

Over recent years, there has been increasing interest in the use of the anelloviruses, the major component of the human virome, for the prediction of post-transplant complications such as severe infections. Due to an important diversity, the comprehensive characterization of this viral family over time has been poorly studied. To overcome this challenge, we used a metagenomic next-generation sequencing (mNGS) approach with the aim of determining the individual anellovirus profile of autologous stem cell transplant (ASCT) patients. We conducted a prospective pilot study on a homogeneous patient cohort regarding the chemotherapy regimens that included 10 ASCT recipients. A validated viral mNGS workflow was used on 108 plasma samples collected at 11 time points from diagnosis to 90 days post-transplantation. A complex interindividual variability in terms of abundance and composition was noticed. In particular, a strong sex effect was found and confirmed using quantitative PCR targeting torque teno virus, the most abundant anellovirus. Interestingly, an important turnover in the anellovirus composition was observed during the course of the disease revealing a strong intra-individual variability. Although more studies are needed to better understand anellovirus dynamics, these findings are of prime importance for their future use as biomarkers of immune competence.

BackgroundHuman endogenous retroviruses (HERVs) have received much attention for their implications in the etiology of many human diseases and their profound effect on evolution. Notably, recent studies have highlighted associations between HERVs expression and cancers (Yu et al., Int J Mol Med 32, 2013), autoimmunity (Balada et al., Int Rev Immunol 29:351–370, 2010) and neurological (Christensen, J Neuroimmune Pharmacol 5:326–335, 2010) conditions. Their repetitive nature makes their study particularly challenging, where expression studies have largely focused on individual loci (De Parseval et al., J Virol 77:10414–10422, 2003) or general trends within families (Forsman et al., J Virol Methods 129:16–30, 2005; Seifarth et al., J Virol 79:341–352, 2005; Pichon et al., Nucleic Acids Res 34:e46, 2006).MethodsTo refine our understanding of HERVs activity, we introduce here a new microarray, HERV-V3. This work was made possible by the careful detection and annotation of genomic HERV/MaLR sequences as well as the development of a new hybridization model, allowing the optimization of probe performances and the control of cross-reactions.ResultsHERV-V3 offers an almost complete coverage of HERVs and their ancestors (mammalian apparent LTR-retrotransposons, MaLRs) at the locus level along with four other repertoires (active LINE-1 elements, lncRNA, a selection of 1559 human genes and common infectious viruses). We demonstrate that HERV-V3 analytical performances are comparable with commercial Affymetrix arrays, and that for a selection of tissue/pathological specific loci, the patterns of expression measured on HERV-V3 is consistent with those reported in the literature.ConclusionsGiven its large HERVs/MaLRs coverage and additional repertoires, HERV-V3 opens the door to multiple applications such as enhancers and alternative promoters identification, biomarkers identification as well as the characterization of genes and HERVs/MaLRs modulation caused by viral infection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3669-7) contains supplementary material, which is available to authorized users.

Data preprocessing in microarray technology is a crucial initial step before data analysis is performed. Many preprocessing methods have been proposed but none has proved to be ideal to date. Frequently, datasets are limited by laboratory constraints so that the need is for guidelines on quality and robustness, to inform further experimentation while data are yet restricted. In this paper, we compared the performance of four popular methods, namely MAS5, Li & Wong pmonly (LWPM), Li & Wong subtractMM (LWMM), and Robust Multichip Average (RMA). The comparison is based on the analysis carried out on sets of laboratory-generated data from the Bioinformatics Lab, National Institute of Cellular Biotechnology (NICB), Dublin City University, Ireland. These experiments were designed to examine the effect of Bromodeoxyuridine (5-bromo-2-deoxyuridine, BrdU) treatment in deep lamellar keratoplasty (DLKP) cells. The methodology employed is to assess dispersion across the replicates and analyze the false discovery rate. From the dispersion analysis, we found that variability is reduced more effectively by LWPM and RMA methods. From the false positive analysis, and for both parametric and nonparametric approaches, LWMM is found to perform best. Based on a complementary q-value analysis, LWMM approach again is the strongest candidate. The indications are that, while LWMM is marginally less effective than LWPM and RMA in terms of variance reduction, it has considerably improved discrimination overall.