Joint modeling of ChIP-seq data via a Markov random field model

Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first-order Markov dependence and, for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modeling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared with existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies

PASSion: a pattern growth algorithm-based pipeline for splice junction detection in paired-end RNA-Seq data

Motivation: RNA-seq is a powerful technology for the study of transcriptome profiles that uses deep-sequencing technologies. Moreover, it may be used for cellular phenotyping and help establishing the etiology of diseases characterized by abnormal splicing patterns. In RNA-Seq, the exact nature of splicing events is buried in the reads that span exon–exon boundaries. The accurate and efficient mapping of these reads to the reference genome is a major challenge

Study protocol for the multicentre cohorts of Zika virus infection in pregnant women, infants, and acute clinical cases in Latin America and the Caribbean: the ZIKAlliance consortium.

BACKGROUND: The European Commission (EC) Horizon 2020 (H2020)-funded ZIKAlliance Consortium designed a multicentre study including pregnant women (PW), children (CH) and natural history (NH) cohorts. Clinical sites were selected over a wide geographic range within Latin America and the Caribbean, taking into account the dynamic course of the ZIKV epidemic. METHODS: Recruitment to the PW cohort will take place in antenatal care clinics. PW will be enrolled regardless of symptoms and followed over the course of pregnancy, approximately every 4 weeks. PW will be revisited at delivery (or after miscarriage/abortion) to assess birth outcomes, including microcephaly and other congenital abnormalities according to the evolving definition of congenital Zika syndrome (CZS). After birth, children will be followed for 2 years in the CH cohort. Follow-up visits are scheduled at ages 1-3, 4-6, 12, and 24 months to assess neurocognitive and developmental milestones. In addition, a NH cohort for the characterization of symptomatic rash/fever illness was designed, including follow-up to capture persisting health problems. Blood, urine, and other biological materials will be collected, and tested for ZIKV and other relevant arboviral diseases (dengue, chikungunya, yellow fever) using RT-PCR or serological methods. A virtual, decentralized biobank will be created. Reciprocal clinical monitoring has been established between partner sites. Substudies of ZIKV seroprevalence, transmission clustering, disabilities and health economics, viral kinetics, the potential role of antibody enhancement, and co-infections will be linked to the cohort studies. DISCUSSION: Results of these large cohort studies will provide better risk estimates for birth defects and other developmental abnormalities associated with ZIKV infection including possible co-factors for the variability of risk estimates between other countries and regions. Additional outcomes include incidence and transmission estimates of ZIKV during and after pregnancy, characterization of short and long-term clinical course following infection and viral kinetics of ZIKV. STUDY REGISTRATIONS: clinicaltrials.gov NCT03188731 (PW cohort), June 15, 2017; clinicaltrials.gov NCT03393286 (CH cohort), January 8, 2018; clinicaltrials.gov NCT03204409 (NH cohort), July 2, 2017

Measuring, in solution, multiple-fluorophore labeling by combining Fluorescence Correlation Spectroscopy and photobleaching

Determining the number of fluorescent entities that are coupled to a given molecule (DNA, protein, etc.) is a key point of numerous biological studies, especially those based on a single molecule approach. Reliable methods are important, in this context, not only to characterize the labeling process, but also to quantify interactions, for instance within molecular complexes. We combined Fluorescence Correlation Spectroscopy (FCS) and photobleaching experiments to measure the effective number of molecules and the molecular brightness as a function of the total fluorescence count rate on solutions of cDNA (containing a few percent of C bases labeled with Alexa Fluor 647). Here, photobleaching is used as a control parameter to vary the experimental outputs (brightness and number of molecules). Assuming a Poissonian distribution of the number of fluorescent labels per cDNA, the FCS-photobleaching data could be easily fit to yield the mean number of fluorescent labels per cDNA strand (@ 2). This number could not be determined solely on the basis of the cDNA brightness, because of both the statistical distribution of the number of fluorescent labels and their unknown brightness when incorporated in cDNA. The statistical distribution of the number of fluorophores labeling cDNA was confirmed by analyzing the photon count distribution (with the cumulant method), which showed clearly that the brightness of cDNA strands varies from one molecule to the other.Comment: 38 pages (avec les figures

Special considerations for studies of extracellular vesicles from parasitic helminths: a community-led roadmap to increase rigour and reproducibility

Over the last decade, research interest in defining how extracellular vesicles (EVs) shape cross-species communication has grown rapidly. Parasitic helminths, worm species found in the phyla Nematoda and Platyhelminthes, are well-recognised manipulators of host immune function and physiology. Emerging evidence supports a role for helminth-derived EVs in these processes and highlights EVs as an important participant in cross-phylum communication. While the mammalian EV field is guided by a community-agreed framework for studying EVs derived from model organisms or cell systems [e.g., Minimal Information for Studies of Extracellular Vesicles (MISEV)], the helminth community requires a supplementary set of principles due to the additional challenges that accompany working with such divergent organisms. These challenges include, but are not limited to, generating sufficient quantities of EVs for descriptive or functional studies, defining pan-helminth EV markers, genetically modifying these organisms, and identifying rigorous methodologies for in vitro and in vivo studies. Here, we outline best practices for those investigating the biology of helminth-derived EVs to complement the MISEV guidelines. We summarise community-agreed standards for studying EVs derived from this broad set of non-model organisms, raise awareness of issues associated with helminth EVs and provide future perspectives for how progress in the field will be achieved

Anomalous Isotope Effect in Rattling-Induced Superconductor

In order to clarify that the Cooper pair in $\beta$-pyrochlore oxides is mediated by anharmonic oscillation of guest atom, i.e., rattling, we propose an experiment to detect anomalous isotope effect. In the formula of $T_{\rm c} \propto M^{-\eta}$, where $T_{\rm c}$ is superconducting transition temperature and $M$ denotes mass of the oscillator, it is found that the exponent $\eta$ is increased with the increase of anharmonicity of a potential for the guest atom. We predict that $\eta$ becomes larger than 1/2 in rattling-induced superconductor, in sharp contrast to $\eta=1/2$ for weak-coupling superconductivity due to harmonic phonons and $\eta<1/2$ for strong-coupling superconductivity with the inclusion of the effect of Coulomb interaction.Comment: 7 pages, 5 figures. Submitted to J. Phys. Soc. Jp

Protocol Dependence of Sequencing-Based Gene Expression Measurements

RNA Seq provides unparalleled levels of information about the transcriptome including precise expression levels over a wide dynamic range. It is essential to understand how technical variation impacts the quality and interpretability of results, how potential errors could be introduced by the protocol, how the source of RNA affects transcript detection, and how all of these variations can impact the conclusions drawn. Multiple human RNA samples were used to assess RNA fragmentation, RNA fractionation, cDNA synthesis, and single versus multiple tag counting. Though protocols employing polyA RNA selection generate the highest number of non-ribosomal reads and the most precise measurements for coding transcripts, such protocols were found to detect only a fraction of the non-ribosomal RNA in human cells. PolyA RNA excludes thousands of annotated and even more unannotated transcripts, resulting in an incomplete view of the transcriptome. Ribosomal-depleted RNA provides a more cost-effective method for generating complete transcriptome coverage. Expression measurements using single tag counting provided advantages for assessing gene expression and for detecting short RNAs relative to multi-read protocols. Detection of short RNAs was also hampered by RNA fragmentation. Thus, this work will help researchers choose from among a range of options when analyzing gene expression, each with its own advantages and disadvantages

Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2x)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted