Estimating the number of change-points in a two-dimensional segmentation model without penalization

In computational biology, numerous recent studies have been dedicated to the analysis of the chromatin structure within the cell by two-dimensional segmentation methods. Motivated by this application, we consider the problem of retrieving the diagonal blocks in a matrix of observations. The theoretical properties of the least-squares estimators of both the boundaries and the number of blocks proposed by L\'evy-Leduc et al. [2014] are investigated. More precisely, the contribution of the paper is to establish the consistency of these estimators. A surprising consequence of our results is that, contrary to the onedimensional case, a penalty is not needed for retrieving the true number of diagonal blocks. Finally, the results are illustrated on synthetic data.Comment: 30 pages, 8 figure

An Infrared Multiplicity Survey of Class I/Flat-Spectrum Systems in the Rho Ophiuchi and Serpens Molecular Clouds

We present new near- and mid-infrared observations of 19 Class I/flat-spectrum young stellar objects in the nearby Rho Oph (d=125pc) and Serpens (d=310pc) dark clouds. These observations are part of a larger systematic infrared multiplicity survey of Class I/flat-spectrum objects in the nearest dark clouds. We find 7/19 (37% +/- 14%) of the sources surveyed to be multiple systems over a separation range of ~150 - 1800 AU. This is consistent with the fraction of multiple systems found among older pre-main-sequence stars in each of the Taurus, Rho Oph, Chamaeleon, Lupus, and Corona Australis star-forming regions over a similar separation range. However, solar-type main-sequence stars in the solar neighborhood have a fraction approximately one-third that of our Class I/flat- spectrum sample (11% +/- 3%). This may be attributed to evolutionary effects or environmental differences. An examination of the spectral energy distributions of the SVS 20 and WL 1 binaries reveals that the individual components of each source exhibit the same SED classifications, similar to what one typically finds for binary T Tauri star (TTS) systems, where the companion of a classical TTS also tends to be of the same SED type.Comment: 30 pages, 10 figures, 4 tables, accepted for publication in the A

RNA isolation for transcriptomics of human and mouse small skin biopsies

<p>Abstract</p> <p>Background</p> <p>Isolation of RNA from skin biopsies presents a challenge, due to the tough nature of skin tissue and a high presence of RNases. As we lacked the dedicated equipment, i.e. homogenizer or bead-beater, needed for the available RNA from skin isolation methods, we adapted and tested our zebrafish single-embryo RNA-isolation protocol for RNA isolation from skin punch biopsies.</p> <p>Findings</p> <p>We tested our new RNA-isolation protocol in two experiments: a large-scale study with 97 human skin samples, and a small study with 16 mouse skin samples. Human skin was sampled with 4.0 mm biopsy punches and for the mouse skin different punch diameter sizes were tested; 1.0, 1.5, 2.0, and 2.5 mm. The average RNA yield in human samples was 1.5 μg with an average RNA quality RIN value of 8.1. For the mouse biopsies, the average RNA yield was 2.4 μg with an average RIN value of 7.5. For 96% of the human biopsies and 100% of the mouse biopsies we obtained enough high-quality RNA. The RNA samples were successfully tested in a transcriptomics analysis using the Affymetrix and Roche NimbleGen platforms.</p> <p>Conclusions</p> <p>Using our new RNA-isolation protocol, we were able to consistently isolate high-quality RNA, which is apt for further transcriptomics analysis. Furthermore, this method is already useable on biopsy material obtained with a punch diameter as small as 1.5 mm.</p

The INCREASE project: Intelligent Collections of food‐legume genetic resources for European agrofood systems

Food legumes are crucial for all agriculture-related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co-benefits for human health. The characterization, maintenance and exploitation of food-legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin (Lupinus albus and L. mutabilis), a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single-seed descent-purified accessions (i.e., inbred lines), will be developed, exploiting germplasm available both from genebanks and on-farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agri-food and non-food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources

Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies

High-throughput post-genomic studies are now routinely and promisingly investigated in biological and biomedical research. The main statistical approach to select genes differentially expressed between two groups is to apply a t-test, which is subject of criticism in the literature. Numerous alternatives have been developed based on different and innovative variance modeling strategies. However, a critical issue is that selecting a different test usually leads to a different gene list. In this context and given the current tendency to apply the t-test, identifying the most efficient approach in practice remains crucial. To provide elements to answer, we conduct a comparison of eight tests representative of variance modeling strategies in gene expression data: Welch's t-test, ANOVA [1], Wilcoxon's test, SAM [2], RVM [3], limma [4], VarMixt [5] and SMVar [6]. Our comparison process relies on four steps (gene list analysis, simulations, spike-in data and re-sampling) to formulate comprehensive and robust conclusions about test performance, in terms of statistical power, false-positive rate, execution time and ease of use. Our results raise concerns about the ability of some methods to control the expected number of false positives at a desirable level. Besides, two tests (limma and VarMixt) show significant improvement compared to the t-test, in particular to deal with small sample sizes. In addition limma presents several practical advantages, so we advocate its application to analyze gene expression data

Microarray Analysis in the Archaeon Halobacterium salinarum Strain R1

Background: Phototrophy of the extremely halophilic archaeon Halobacterium salinarum was explored for decades. The research was mainly focused on the expression of bacteriorhodopsin and its functional properties. In contrast, less is known about genome wide transcriptional changes and their impact on the physiological adaptation to phototrophy. The tool of choice to record transcriptional profiles is the DNA microarray technique. However, the technique is still rarely used for transcriptome analysis in archaea. Methodology/Principal Findings: We developed a whole-genome DNA microarray based on our sequence data of the Hbt. salinarum strain R1 genome. The potential of our tool is exemplified by the comparison of cells growing under aerobic and phototrophic conditions, respectively. We processed the raw fluorescence data by several stringent filtering steps and a subsequent MAANOVA analysis. The study revealed a lot of transcriptional differences between the two cell states. We found that the transcriptional changes were relatively weak, though significant. Finally, the DNA microarray data were independently verified by a real-time PCR analysis. Conclusion/Significance: This is the first DNA microarray analysis of Hbt. salinarum cells that were actually grown under phototrophic conditions. By comparing the transcriptomics data with current knowledge we could show that our DNA microarray tool is well applicable for transcriptome analysis in the extremely halophilic archaeon Hbt. salinarum. The reliability of our tool is based on both the high-quality array of DNA probes and the stringent data handling including MAANOVA analysis. Among the regulated genes more than 50% had unknown functions. This underlines the fact that haloarchaeal phototrophy is still far away from being completely understood. Hence, the data recorded in this study will be subject to future systems biology analysis

Adipose Gene Expression Prior to Weight Loss Can Differentiate and Weakly Predict Dietary Responders

BACKGROUND: The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. METHODOLOGY/PRINCIPAL FINDINGS: The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%+/-2.2%. CONCLUSION: Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition

The p53 Inhibitor MDM2 Facilitates Sonic Hedgehog-Mediated Tumorigenesis and Influences Cerebellar Foliation

Disruption of cerebellar granular neuronal precursor (GNP) maturation can result in defects in motor coordination and learning, or in medulloblastoma, the most common childhood brain tumor. The Sonic Hedgehog (Shh) pathway is important for GNP proliferation; however, the factors regulating the extent and timing of GNP proliferation, as well as GNP differentiation and migration are poorly understood. The p53 tumor suppressor has been shown to negatively regulate the activity of the Shh effector, Gli1, in neural stem cells; however, the contribution of p53 to the regulation of Shh signaling in GNPs during cerebellar development has not been determined. Here, we exploited a hypomorphic allele of Mdm2 (Mdm2puro), which encodes a critical negative regulator of p53, to alter the level of wild-type MDM2 and p53 in vivo. We report that mice with reduced levels of MDM2 and increased levels of p53 have small cerebella with shortened folia, reminiscent of deficient Shh signaling. Indeed, Shh signaling in Mdm2-deficient GNPs is attenuated, concomitant with decreased expression of the Shh transducers, Gli1 and Gli2. We also find that Shh stimulation of GNPs promotes MDM2 accumulation and enhances phosphorylation at serine 166, a modification known to increase MDM2-p53 binding. Significantly, loss of MDM2 in Ptch1+/− mice, a model for Shh-mediated human medulloblastoma, impedes cerebellar tumorigenesis. Together, these results place MDM2 at a major nexus between the p53 and Shh signaling pathways in GNPs, with key roles in cerebellar development, GNP survival, cerebellar foliation, and MB tumorigenesis