Analytical Comparison Among Oceanographic Instruments Operations

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Internet addiction and its psychosocial risks (depression, anxiety, stress and loneliness) among Iranian adolescents and young adults: a structural equation model in a cross-sectional study

Internet addiction has become an increasingly researched area in many Westernized countries. However, there has been little research in developing countries such as Iran, and when research has been conducted, it has typically utilized small samples. This study investigated the relationship of Internet addiction with stress, depression, anxiety, and loneliness in 1,052 Iranian adolescents and young adults. The participants were randomly selected to complete a battery of psychometrically validated instruments including the Internet Addiction Test, Depression Anxiety Stress Scale, and the Loneliness Scale. Structural equation modeling and Pearson correlation coefficients were used to determine the relationship between Internet addiction and psychological impairments (depression, anxiety, stress and loneliness). Pearson correlation, path analysis, multivariate analysis of variance (MANOVA), and t-tests were used to analyze the data. Results showed that Internet addiction is a predictor of stress, depression, anxiety, and loneliness. Findings further indicated that addictive Internet use is gender sensitive and that the risk of Internet addiction is higher in males than in females. The results showed that male Internet addicts differed significantly from females in terms of depression, anxiety, stress, and loneliness. The implications of these results are discussed

Robust detection of translocations in lymphoma FFPE samples using targeted locus capture-based sequencing

Preservation of cancer biopsies by FFPE introduces DNA fragmentation, hindering analysis of rearrangements. Here the authors introduce FFPE Targeted Locus Capture for identification of translocations in preserved samples.In routine diagnostic pathology, cancer biopsies are preserved by formalin-fixed, paraffin-embedding (FFPE) procedures for examination of (intra-) cellular morphology. Such procedures inadvertently induce DNA fragmentation, which compromises sequencing-based analyses of chromosomal rearrangements. Yet, rearrangements drive many types of hematolymphoid malignancies and solid tumors, and their manifestation is instructive for diagnosis, prognosis, and treatment. Here, we present FFPE-targeted locus capture (FFPE-TLC) for targeted sequencing of proximity-ligation products formed in FFPE tissue blocks, and PLIER, a computational framework that allows automated identification and characterization of rearrangements involving selected, clinically relevant, loci. FFPE-TLC, blindly applied to 149 lymphoma and control FFPE samples, identifies the known and previously uncharacterized rearrangement partners. It outperforms fluorescence in situ hybridization (FISH) in sensitivity and specificity, and shows clear advantages over standard capture-NGS methods, finding rearrangements involving repetitive sequences which they typically miss. FFPE-TLC is therefore a powerful clinical diagnostics tool for accurate targeted rearrangement detection in FFPE specimens.Immunobiology of allogeneic stem cell transplantation and immunotherapy of hematological disease

MC-4C pipeline: test data

This is a reduced MC-4C dataset provided to be used as a test case for running MC-4C pipeline. The MC-4C pipeline can be freely downloaded from: https://github.com/aallahyar/mc4c_pyChanges compared to original upload: - Changed name of fastq file from "raw_BMaj-test.fastq" to "fq_BMaj-test.fastq.gz" - Fields are now separated by comma instead of semi-colo

Molecular interactomes: Network-guided cancer prognosis prediction & multi-way chromatin interaction analysis

In the last two decades, our understanding of the molecular mechanisms within the cell has witnessed a great leap forward. For the most part this is due to the fast innovation of the genomic measurements technologies and wide spread usage of computational methods which enables knowledge extraction from the massive datasets produced by these measurements. A notable example of a field that has substantially benefitted from this progress is cancer patient outcome prediction, in which the aim is to predict patient prognosis from common clinical variables such as tumor size, age or histological parameters. With the application of machine learning methods to gene expression profiles of the tumor a major improvement of the prediction accuracy could be realized. These models are later succeeded by Network based Outcome Predictors (NOP) that consider the cellular wiring diagram of cell in the model to identify stable and relevant markers that can accurately estimate outcome of patients. Problematically, after a decade of research in this area, NOPs did not find extensive application compared to the classical models due to contradicting reports regarding their performance, stability and relevance of markers in the literature. In this thesis, we introduce a new NOP - called FERAL - that alleviates several fundamental issues in state-of-the-art NOPs which prevented these models to reach the optimal prediction performance, stability and marker relevance. We furthermore demonstrate that generic biological networks do not contain sufficiently informative interactions to truly aid NOP. We therefore infer a phenotype-specific network called SyNet which connects pairs of genes that together achieve patient outcome prediction performance beyond what is attainable by individually genes. We show that a NOP that use identical gene expression datasets, yields superior performance merely by considering groups of genes suggested by SyNet. We, moreover, show that model performance is severely reduced if nodes in SyNet are shuffled, which confirms that also the links in SyNet are relevant to outcome prediction. An important limitation of current biological networks is that they are restricted to pairwise interactions. We show that higher order interactions between functional elements in the cell are relevant in outcome prediction. We later introduce a novel genomics method called Multi-Contact 4C (MC-4C) to measure and investigate multi-way interactions between functional elements. In contrast to existing methods, MC-4C exploits long-read 3rd generation sequencing technologies and detects higher order interactions that occur in a region of interest at the level of a single allele. We further devise a well-founded statistical model that is required for significance estimation of observed interactions. UsingMC-4C, we experimentally confirm a 26 years old hypothesis regarding the looping and co-localization of enhancers in the O -globin region in the mouse genome. Additionally, we provide the first experimental explanation for the “vermicelli” phenomenon that was observed through microscopic inspection of cells depleted of WAPL (the element responsible for unwinding of loops in mammalian cells). Therefore, targeted multi-way conformation analysis methods like MC-4C promise to uncover how the multitude of regulatory sequences and genes coordinate their activity in the spatial context of the genome.Pattern Recognition and Bioinformatic

MC4C: Locus-Specific Enhancer Hubs And Architectural Loop Collisions Uncovered From Single Allele DNA Topologies

Chromatin folding is increasingly recognized as a regulator of genomic processes such as gene activity. Chromosome conformation capture (3C) methods have been developed to unravel genome topology through the analysis of pair-wise chromatin contacts and have identified many genes and regulatory sequences that, in populations of cells, are engaged in multiple DNA interactions. However, pair-wise methods cannot discern whether contacts occur simultaneously or in competition on the individual chromosome. We present a novel 3C method, Multi-Contact 4C (MC-4C), that applies Nanopore sequencing to study multi-way DNA conformations of tens of thousands individual alleles for distinction between cooperative, random and competing interactions. MC-4C can uncover previously missed structures in sub-populations of cells. It reveals unanticipated cooperative clustering between regulatory chromatin loops, anchored by enhancers and gene promoters, and CTCF and cohesin-bound architectural loops. For example, we show that the constituents of the active -globin super-enhancer cooperatively form an enhancer hub that can host two genes at a time. We also find cooperative interactions between further dispersed regulatory sequences of the active proto-cadherin locus. When applied to CTCF-bound domain boundaries, we find evidence that chromatin loops can collide, a process that is negatively regulated by the cohesin release factor WAPL. Loop collision is further pronounced in WAPL knockout cells, suggestive of a “cohesin traffic jam”. In summary, single molecule multi-contact analysis methods can reveal how the myriad of regulatory sequences spatially coordinate their actions on individual chromosomes. Insight into these single allele higher-order topological features will facilitate interpreting the consequences of natural and induced genetic variation and help uncovering the mechanisms shaping our genome

MC-4C: processing pipeline

This is a reduced MC-4C dataset provided to be used as a test case for running MC-4C pipeline. The MC-4C pipeline can be freely downloaded from: https://github.com/aallahyar/mc4c_py Changes compared to original upload: - Changed name of fastq file from "raw_BMaj-test.fastq" to "fq_BMaj-test.fastq.gz

MC-4C: Enhancer hubs and loop collisions identified from single-allele topologies

Chromatin folding is increasingly recognized as a regulator of genomic processes such as gene activity. Chromosome conformation capture (3C) methods have been developed to unravel genome topology through the analysis of pair-wise chromatin contacts and have identified many genes and regulatory sequences that, in populations of cells, are engaged in multiple DNA interactions. However, pair-wise methods cannot discern whether contacts occur simultaneously or in competition on the individual chromosome. We present a novel 3C method, Multi-Contact 4C (MC-4C), that applies Nanopore sequencing to study multi-way DNA conformations of tens of thousands individual alleles for distinction between cooperative, random and competing interactions. MC-4C can uncover previously missed structures in sub-populations of cells. It reveals unanticipated cooperative clustering between regulatory chromatin loops, anchored by enhancers and gene promoters, and CTCF and cohesin-bound architectural loops. For example, we show that the constituents of the active -globin super-enhancer cooperatively form an enhancer hub that can host two genes at a time. We also find cooperative interactions between further dispersed regulatory sequences of the active proto-cadherin locus. When applied to CTCF-bound domain boundaries, we find evidence that chromatin loops can collide, a process that is negatively regulated by the cohesin release factor WAPL. Loop collision is further pronounced in WAPL knockout cells, suggestive of a “cohesin traffic jam”. In summary, single molecule multi-contact analysis methods can reveal how the myriad of regulatory sequences spatially coordinate their actions on individual chromosomes. Insight into these single allele higher-order topological features will facilitate interpreting the consequences of natural and induced genetic variation and help uncovering the mechanisms shaping our genome

MC4C: Locus-Specific Enhancer Hubs And Architectural Loop Collisions Uncovered From Single Allele DNA Topologies

Chromatin folding is increasingly recognized as a regulator of genomic processes such as gene activity. Chromosome conformation capture (3C) methods have been developed to unravel genome topology through the analysis of pair-wise chromatin contacts and have identified many genes and regulatory sequences that, in populations of cells, are engaged in multiple DNA interactions. However, pair-wise methods cannot discern whether contacts occur simultaneously or in competition on the individual chromosome. We present a novel 3C method, Multi-Contact 4C (MC-4C), that applies Nanopore sequencing to study multi-way DNA conformations of tens of thousands individual alleles for distinction between cooperative, random and competing interactions. MC-4C can uncover previously missed structures in sub-populations of cells. It reveals unanticipated cooperative clustering between regulatory chromatin loops, anchored by enhancers and gene promoters, and CTCF and cohesin-bound architectural loops. For example, we show that the constituents of the active -globin super-enhancer cooperatively form an enhancer hub that can host two genes at a time. We also find cooperative interactions between further dispersed regulatory sequences of the active proto-cadherin locus. When applied to CTCF-bound domain boundaries, we find evidence that chromatin loops can collide, a process that is negatively regulated by the cohesin release factor WAPL. Loop collision is further pronounced in WAPL knockout cells, suggestive of a “cohesin traffic jam”. In summary, single molecule multi-contact analysis methods can reveal how the myriad of regulatory sequences spatially coordinate their actions on individual chromosomes. Insight into these single allele higher-order topological features will facilitate interpreting the consequences of natural and induced genetic variation and help uncovering the mechanisms shaping our genome