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Conventional diagnostics could benefit greatly from the high-throughput genomics technologies that have arisen over the past 10 years in basic research. Whether it is for DNA analysis (CGH arrays, SNP analysis) or RNA analysis (gene-expression arrays), information at the molecular level would undoubtedly improve the accuracy of diagnoses and prognoses. In the past 3 years, several efforts have been made to standardize assays, experimental procedures, and data processing, and governmental institutions such as the FDA, microarray manufacturers, and the scientific community have worked diligently to bring genomics into daily medicine. This talk will review the recent developments in molecular classification, prognostication, and treatments.

Omics techniques have changed the way we depict the molecular features of a cell. The integrative and quantitative analysis of omics data raises unprecedented expectations for understanding biological systems on a global scale. However, its inherently noisy nature, together with limited knowledge of potential sources of variation impacting health and disease, require the use of proper mathematical and computational methods for its analysis and integration. Bayesian inference of probabilistic models allows propagation of the uncertainty from the experimental data to our beliefs of the model parameters, allowing us to appropriately answer complex biological questions. In this chapter, we build probabilistic models of gene expression from RNA-seq data and make inference about their parameters using Bayesian methods. We present models of increasing complexity, from the quantification of a single gene expression to differential gene expression for a whole transcriptome, comparing them to the available tools for analysis of gene expression data. We provide Stan scripts that introduce the reader into the implementation of Bayesian statistics for omics data. The rationale that we apply for transcriptomics data may be easily extended to model the particularities of other omics data and to integrate the different regulatory layers. ; FS-C received support from the Spanish Ministerio de Economía y Competitividad [grant no. RTI2018-102084-B-I00]; EL-P received support from the Spanish Ministerio de Economía y Competitividad (RTI2018-096961-B-I00), from the European Union (CardioNeT-ITN-289600 and CardioNext-ITN-608027) and the Spanish Carlos III Institute of Health (RD12/0042/066); M.A.d.P received support from the Spanish Ministerio de Economía y Competitividad (SAF2017-83130-R) and from the European Union Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement nº 641639 BIOPOL-ITN-641639; V.J-J. received an ESR contract from BIOPOL-ITN-641639). M.A.d.P is member of the Tec4Bio consortium (ref. S2018/NMT4443). The CNIC is supported by MCIU and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence [MCIU award SEV-2015-0505]. ; Sí

Drug repositioning using multiple gene expression profiles
Chuo University's Professor Y-h. Taguchi places focus on drug repositioning using multiple gene expression profiles. In my previous manuscripts (1-3), I introduced our studies of in silico drug repositioning using gene expression profiles. Nevertheless, in these studies, we could use the single gene expression profile to perform in silico drug repositioning. In this manuscript, the last one in this series, I introduce our recent study (4) in which we could use multiple gene expression profiles. Please see my previous article for more details about the in silico drug repositioning. (5)In the studies (1-3) introduced previously, I used tensor decomposition (TD) based unsupervised feature extraction (FE) (6,7), which was a mathematical/computational method for data processing and was recently implemented in Bioconductor packages. (8)

The current study was carried out to explore gene expression of the LTB4R gene with the development of chronic myeloid leukemia (CML) in Iraqi patients. The differences in the expression of this gene between patients and healthy controls were studied. The correlation of gender and age with CML patients compared with controls was included as well as the correlation of gene expression folding 2-ΔΔCt of LTB4R with clinical parameters (WBC, RBC, haemoglobin, platelets, and BCR-ABL gene). Results revealed significant increases in the mean of gene expression level (ΔCt) of patient groups compared to the corresponding ΔCt means in the healthy control group, the gene expression folding (2-∆∆Ct) of the LTB4R gene reflects significant differences in the expression showed the highest level in CML patients which reached to (9.12 ), no significant differences were exhibited according to age and gender between CML patients and control. The study revealed a non-significant positive correlation between LTB4R gene expression level with both the BCR-ABL gene and WBC. Our results concluded that the LTB4R gene expression level could act as a marker for the prognosis of CML.

Abstract
Over 60% of trained social workers provide mental health services in their practices, and in all these settings clients are likely to have experienced trauma influencing their current circumstances, including childhood maltreatment and neglect. The 1998 Adverse Childhood Experiences Study has heightened interest in the long-term effects of trauma, especially early in life. Research has shown that our experiences influence our genes' activity through biochemical changes in what is known as epigenetic marks. Yet social work practice has participated minimally in targeting the influences of genes on behavior for research or intervention, partially because of a lack of scientific knowledge. This systematic review examines published research that investigated the influences of early trauma experiences on changes in gene expression related to emotionality and stress response. Authors conducted a systematic literature review according to the steps outlined by Shuster in Google Scholar, PubMed, and PsycINFO. Collected literature was reduced to 76 peer-reviewed articles after applying exclusion criteria. A strong pattern of relationships emerged from the review. Trauma and early life stress were associated with epigenetic marks in offspring on genes linked to stress reactivity (22 studies) and emotionality (23 studies).

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License. ; [Motivation]: Systems biology demands the use of several point of views to get a more comprehensive understanding of biological problems. This usually leads to take into account different data regarding the problem at hand, but it also has to do with using different perspectives of the same data. This multifaceted aspect of systems biology often requires the use of several tools, and it is often hard to get a seamless integration of all of them, which would help the analyst to have an interactive discourse with the data. [Results]: Focusing on expression profiling, BicOverlapper 2.0 visualizes the most relevant aspects of the analysis, including expression data, profiling analysis results and functional annotation. It also integrates several state-of-the-art numerical methods, such as differential expression analysis, gene set enrichment or biclustering. © 2014 The Author. Published by Oxford University Press. All rights reserved. ; This work was supported by the Spanish Government, under the Ministerio de Economía y Competitividad-MINECO (projects BFU2011-28804 and Consolider-Ingenio CSD007-00015) and by the Ministerio de Ciencia e Innovación - MICINN (project FI2010-16234). ; Peer Reviewed

Hibernation is a complex physiological response that some mammalian species employ to evade energetic demands. Previous work in mammalian hibernators suggests that hibernation is activated not by a set of genes unique to hibernators, but by differential expression of genes that are present in all mammals. This question of universal genetic mechanisms requires further investigation and can only be tested through additional investigations of phylogenetically dispersed species. To explore this question, we use RNA-Seq to investigate gene expression dynamics as they relate to the varying physiological states experienced throughout the year in a group of primate hibernators—Madagascar's dwarf lemurs (genus Cheirogaleus). In a novel experimental approach, we use longitudinal sampling of biological tissues as a method for capturing gene expression profiles from the same individuals throughout their annual hibernation cycle. We identify 90 candidate genes that have variable expression patterns when comparing two active states (Active 1 and Active 2) with a torpor state. These include genes that are involved in metabolic pathways, feeding behavior, and circadian rhythms, as might be expected to correlate with seasonal physiological state changes. The identified genes appear to be critical for maintaining the health of an animal that undergoes prolonged periods of metabolic depression concurrent with the hibernation phenotype. By focusing on these differentially expressed genes in dwarf lemurs, we compare gene expression patterns in previously studied mammalian hibernators. Additionally, by employing evolutionary rate analysis, we find that hibernation-related genes do not evolve under positive selection in hibernating species relative to nonhibernators. ; This work was supported by grants from the Society of Integrative and Comparative Biology and Sigma Xi to S.L.F., a National Science Foundation Doctoral Research Improvement Grant (NSF-BCS 1455809 to A.D.Y. and S.L.F.), a grant from the Duke University Institute for Genome Science and Policy to A.D.Y., and funding from the Ministerio de Economía y Competitividad of the Spanish Government (BIO2009-08160 to J.L.V.C. and BFU2012-36820 to J.L.V.C. and M.M.A.); and Fundació ICREA to M.M.A. ; Peer reviewed