Summary Inferring a Gene Regulatory Network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances in high-throughput gene profiling technology, such as single-cell RNA-seq. To equip researchers with a toolset to infer GRNs from large expression datasets, we propose GRNBoost2 and the Arboreto framework. GRNBoost2 is an efficient algorithm for regulatory network inference using gradient boosting, based on the GENIE3 architecture. Arboreto is a computational framework that scales up GRN inference algorithms complying with this architecture. Arboreto includes both GRNBoost2 and an improved implementation of GENIE3, as a user-friendly open source Python package. Availability and implementation Arboreto is available under the 3-Clause BSD license at http://arboreto.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.

The application of high-throughput sequencing methods has raised doubt in the concept of the uniform healthy vaginal microbiota consisting predominantly of lactobacilli by revealing the existence of more variable bacterial community composition. As this needs to be analyzed more extensively and there is little straightforward data regarding the vaginal mycobiome of asymptomatic women we aimed to define bacterial and fungal communities in vaginal samples from 494 asymptomatic, reproductive-age Estonian women. The composition of the vaginal microbiota was determined by amplifying bacterial 16S rRNA and fungal internal transcribed spacer-1 (ITS-1) regions and subsequently sequencing them using 454 Life Sciences pyrosequencing. We delineated five major bacterial community groups with distinctive diversity and species composition. Lactobacilli were among the most abundant bacteria in all groups, but also members of genus Gardnerella had high relative abundance in some of the groups. Microbial diversity increased with higher vaginal pH values, and was also higher when a malodorous discharge was present, indicating that some of the women who consider themselves healthy may potentially have asymptomatic bacterial vaginosis (BV). Our study is the first of its kind to analyze the mycobiome that colonizes the healthy vaginal environment using barcoded pyrosequencing technology. We observed 196 fungal operational taxonomic units (OTUs), including 16 OTUs of Candida spp., which is more diverse than previously recognized. However, assessing true fungal diversity was complicated because of the problems regarding the possible air-borne contamination and bioinformatics used for identification of fungal taxons as significant proportion of fungal sequences were assigned to unspecified OTUs.

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.

We propose Macau, a powerful and flexible Bayesian factorization method for heterogeneous data. Our model can factorize any set of entities and relations that can be represented by a relational model, including tensors and also multiple relations for each entity. Macau can also incorporate side information, specifically entity and relation features, which are crucial for predicting sparsely observed relations. Macau scales to millions of entity instances, hundred millions of observations, and sparse entity features with millions of dimensions. To achieve the scale up, we specially designed sampling procedure for entity and relation features that relies primarily on noise injection in linear regressions. We show performance and advanced features of Macau in a set of experiments, including challenging drugprotein activity prediction task. * Adam Arany and Jaak Simm contributed both equally as first authors. arXiv:1509.04610v2 [stat.ML]

Very few studies have analyzed how the composition of mother’s microbiota affects the development of infant’s gut and oral microbiota during the first months of life. Here, microbiota present in the mothers’ gut, vagina, breast milk, oral cavity, and mammary areola were compared with the gut and oral microbiota of their infants over the first six months following birth. Samples were collected from the aforementioned body sites from seven mothers and nine infants at three different time points over a 6-month period. Each sample was analyzed with 16S rRNA gene sequencing. The gut microbiota of the infants harbored distinct microbial communities that had low similarity with the various maternal microbiota communities. In contrast, the oral microbiota of the infants exhibited high similarity with the microbiota of the mothers’ breast milk, mammary areola and mouth. These results demonstrate that constant contact between microbial communities increases their similarity. A majority of the operational taxonomic units in infant gut and oral microbiota were also shared with the mothers’ gut and oral communities, respectively. The disparity between the similarity and the proportion of the OTUs shared between infants’ and mothers’ gut microbiota might be related to lower diversity and therefore competition in infants’ gut microbiota.

The granulosa cells in the mammalian ovarian follicle respond to gonadotropin signaling and are involved in the processes of folliculogenesis and oocyte maturation. Studies on gene expression and regulation in human granulosa cells are of interest due to their potential for estimating the oocyte viability and in vitro fertilization success. However, the posttranscriptional gene expression studies on micro-RNA (miRNA) level in the human ovary have been scarce. The current study determined the miRNA profile by deep sequencing of the 2 intrafollicular somatic cell types: mural and cumulus granulosa cells (MGCs and CGCs, respectively) isolated from women undergoing controlled ovarian stimulation and in vitro fertilization. Altogether, 936 annotated and 9 novel miRNAs were identified. Ninety of the annotated miRNAs were differentially expressed between MGCs and CGCs. Bioinformatic prediction revealed that TGFβ, ErbB signaling, and heparan sulfate biosynthesis were targeted by miRNAs in both granulosa cell populations, whereas extracellular matrix remodeling, Wnt, and neurotrophin signaling pathways were enriched among miRNA targets in MGCs. Two of the nine novel miRNAs found were of intronic origin: one from the aromatase and the other from the FSH receptor gene. The latter miRNA was predicted to target the activin signaling pathway. In addition to revealing the genome-wide miRNA signature in human granulosa cells, our results suggest that posttranscriptional regulation of gene expression by miRNAs could play an important role in the modification of gonadotropin signaling. miRNA expression studies could therefore lead to new prognostic markers in assisted reproductive technologies.

Multi-task learning is an important area of machine learning that tries to learn multiple tasks simultaneously to improve the accuracy of each individual task. We propose a new tree-based ensemble multi-task learning method for classification and regression (MT-ExtraTrees), based on Extremely Randomized Trees. MT-ExtraTrees is able to share data between tasks minimizing negative transfer while keeping the ability to learn non-linear solutions and to scale well to large datasets.

Objective: an increasing number of studies that are using high-throughput molecular methods are rapidly extending our knowledge of gut microbial colonization in preterm infants whose immaturity and requirement for extensive treatment may result in altered colonization process. We aimed to describe the profile of gut microbiota in 50 extremely low birth weight (<1200 g) critically ill infants at three different time points during the first two months of life by using 16S rrNa gene specific sequencing.Patients and Methods: Stool samples were collected at the age of one week, one month and two months. Bacterial community profiling was done using universal amplification of 16S rrNa gene and 454 pyrosequencing.results: The diversity of gut microbiota in preterm neonates in the first week of life was low but increased significantly over two months. The gut microbiota was dominated by facultative anaerobic bacteria (Staphylococcus spp. and Enterobacteriaceae) and lacked colonization with bacteria known to provide resistance against pathogens (Bacteroides, Bifidobacterium, and Lactobacillus) throughout the study. Colonization of Escherichia coli and uncultured Veillionella was positively correlated with maturity. Infants born to mothers with chorioamnionitis had significantly higher bacterial diversity than those without.Conclusions: High prevalence and abundance of potentially pathogenic Enterobacteriaceae and Staphylococcaceae with low prevalence and abundance of colonization resistance providing taxa bifidobacteria, Bacteroides and lactobacilli may lead to high infection risk via microbial translocation from the gut. additionally, our data suggest that maternal chorioamnionitis may have an effect on the diversity of infants' gut microbiota; however, the mechanisms involved remain to be elucidated.