Collection Of Many Samples Research Articles

Evaluation of critical data processing steps for reliable prediction of gene co-expression from large collections of RNA-seq data.

MotivationGene co-expression analysis is an attractive tool for leveraging enormous amounts of public RNA-seq datasets for the prediction of gene functions and regulatory mechanisms. However, the optimal data processing steps for the accurate prediction of gene co-expression from such large datasets remain unclear. Especially the importance of batch effect correction is understudied.ResultsWe processed RNA-seq data of 68 human and 76 mouse cell types and tissues using 50 different workflows into 7,200 genome-wide gene co-expression networks. We then conducted a systematic analysis of the factors that result in high-quality co-expression predictions, focusing on normalization, batch effect correction, and measure of correlation. We confirmed the key importance of high sample counts for high-quality predictions. However, choosing a suitable normalization approach and applying batch effect correction can further improve the quality of co-expression estimates, equivalent to a >80% and >40% increase in samples. In larger datasets, batch effect removal was equivalent to a more than doubling of the sample size. Finally, Pearson correlation appears more suitable than Spearman correlation, except for smaller datasets.ConclusionA key point for accurate prediction of gene co-expression is the collection of many samples. However, paying attention to data normalization, batch effects, and the measure of correlation can significantly improve the quality of co-expression estimates.

K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy

We propose the combination of k-means clustering with Gaussian Process (GP) regression in the analysis and exploration of 4D angle-resolved photoemission spectroscopy (ARPES) data. Using cluster labels as the driving metric on which the GP is trained, this method allows us to reconstruct the experimental phase diagram from as low as 12% of the original dataset size. In addition to the phase diagram, the GP is able to reconstruct spectra in energy-momentum space from this minimal set of data points. These findings suggest that this methodology can be used to improve the efficiency of ARPES data collection strategies for unknown samples. The practical feasibility of implementing this technology at a synchrotron beamline and the overall efficiency implications of this method are discussed with a view on enabling the collection of more samples or rapid identification of regions of interest.

Remote Sensing Applied to the Evaluation of Spatial and Temporal Variation of Water Quality in a Coastal Environment, Southeast Brazil

The monitoring of water quality in large coastal regions demands great analytical efforts through the collection of many samples, over long periods. Remote sensing is a reliable tool that can provide valuable information on the spatial and temporal variations of environmental parameters, particularly turbidity and chlorophyll a. The aim of the present research was to evaluate the spatial and temporal distribution of water quality from 2005 to 2017 along the north coast of Sao Paulo and its responses to the implementation of industrial developments and to variations in rainfall. Fifty-two MODIS images were used, showing concentrations of chlorophyll a and turbidity, in the dry season and wet season, from 2005 to 2017. The results showed that dilution processes (due to rainfall) control chlorophyll a concentrations. However, a notable increase in concentrations could be identified after the installation of some of the developments in the region, particularly roads and ports. Turbidity was also shown to be affected by dilution processes, and during the wet season this parameter presented lower values. No effect in the results of turbidity could be identified from the installation of roads or ports, showing that vegetation cover exerts an important control on the erosional processes.

Sequencing DNA, RNA and related molecules as a tool to advance space exploration

As humans seek to return to the Moon, and eventually to Mars and beyond, new challenges must be overcome to keep astronauts safe and healthy. This includes protecting crew members from harmful organisms in their environment, treating infections that may arise, monitoring nutrition and understanding how the human body adapts to spaceflight during missions that could last multiple years. Since the International Space Station (ISS) was first occupied in 2000, crew health has been monitored with thorough check-ups before and after flight, and the collection of many samples during flight that are brought back to Earth for analysis. During longer missions to more distant solar system locales, where returning samples to Earth is no longer practical, being able to analyse samples aboard the spacecraft could be very important.

Two-dimensional chronostratigraphic modelling of OSL ages from recent beach-ridge deposits, SE Australia

Abstract Optically-stimulated luminesecne (OSL) dating, in concert with two-dimensional ground-penetrating radar (GPR) profiling, has contributed to significant advances in our understanding of beach-ridge systems and other sedimentary landforms in various settings. For recent beach-ridges, the good OSL properties of coastal quartz permit a high sample throughput thanks to shorter measurement times and simpler sample preparation prompting the collection of more samples at higher sampling resolution. However, sampling at high resolution increases the chance of age inversions because random errors between samples may be larger than the difference in sample ages. Age inversions can be avoided, however, if the stratigraphic constraints are included in the age estimation process. Here, we create a custom Bayesian chronological model for a recent (

Genetic diversity of the floury race of maize Avati Morotî from the Guaraní tribe in Paraguay

Avati Morotî is a race of floury maize widely used by the Guarani people in South America, whose variability and potential value for breeding has been neglected so far. The objective of this research was to explore the genetic variability within the main Paraguayan race Avati Morotî. We studied the genetic variability available in the 20 accessions of Paraguayan Avati Morotî included in the South American core collection made by CIMMYT. Thirty individuals per accession were genotyped with 30 SSR (simple sequence repeat); we determined genetic diversity and made a cluster analysis in order to define genetic relationships among accessions. Mean of polymorphic loci (0.96), alleles per locus (3.57), alleles per polymorphic locus (3.65), expected (0.48) and observed (0.43) heterozygosity, and coefficient of consanguinity (0.12) revealed that Avati Morotî contains a genetic diversity comparable to the most variable maize races of maize. The cluster analysis classified the 20 populations in eight groups, five of them with a single accession, and a large group representing a central pool of germplasm. These results indicate that there is a large variability available in this race, and encourage the collection of more samples of Avati Morotî, particularly in marginal areas that were scarcely sampled.

Estimating the hourly electricity profile of Japanese households – Coupling of engineering and statistical methods

Understanding the hourly electricity profile and the electricity consumption by each appliance is essential for encouraging energy-saving measures in the household sector. There are two methods for identifying energy consumption for households in existing studies: the engineering and the statistical methods. Both methods have strengths and limitations. In this study, we developed a hybrid method based on the statistical method by combining following three steps using knowledge of the engineering method; externalizing the electricity consumption for the refrigerator, adding the number of at-home-and-awake members as explanatory variables, and restricting appliance usage hours. The proposed hybrid method could adequately reproduce the total hourly electricity consumption and seasonal variation compared to the engineering method, and could decompose major appliances, some of which that were not disaggregated by the statistical method. For the quantitative analysis of the model improvement, we calculated Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) for each method with direct metering data. For most of appliances, RMSE and MAE of hybrid model were improved from 11% to 71% compared to the existing methods. The collection of more samples to increase the accuracy of the estimation and application to areas of low statistical data availability are future steps.

PhyloSift: phylogenetic analysis of genomes and metagenomes

Like all organisms on the planet, environmental microbes are subject to the forces of molecular evolution. Metagenomic sequencing provides a means to access the DNA sequence of uncultured microbes. By combining DNA sequencing of microbial communities with evolutionary modeling and phylogenetic analysis we might obtain new insights into microbiology and also provide a basis for practical tools such as forensic pathogen detection.In this work we present an approach to leverage phylogenetic analysis of metagenomic sequence data to conduct several types of analysis. First, we present a method to conduct phylogeny-driven Bayesian hypothesis tests for the presence of an organism in a sample. Second, we present a means to compare community structure across a collection of many samples and develop direct associations between the abundance of certain organisms and sample metadata. Third, we apply new tools to analyze the phylogenetic diversity of microbial communities and again demonstrate how this can be associated to sample metadata.These analyses are implemented in an open source software pipeline called PhyloSift. As a pipeline, PhyloSift incorporates several other programs including LAST, HMMER, and pplacer to automate phylogenetic analysis of protein coding and RNA sequences in metagenomic datasets generated by modern sequencing platforms (e.g., Illumina, 454).

Abstract A27: Contribution of DNA methylation in development of lung cancer in former smokers

Abstract A27 Background Lung cancer is the most common cause of cancer death worldwide with more than 1.2 million people dying of the disease each year. Half of newly diagnosed lung cancer patients are former smokers. Understanding why former smokers develop lung cancer is clearly important to the development of early detection, prevention and treatment strategies for these people. The effects of cigarette smoke on the epigenome are widespread as both global DNA methylation and local DNA methylation have been identified, associated with genomic instability and tumour suppressor gene (TSG) silencing, respectively. In a cancer-specific context, upregulation of oncogenes and silencing of tumour suppressor genes both occur as a result of tobacco smoke exposure. Therefore, molecular studies examining tumors at genomic and epigenomic levels will likely identify causal genetic events involved in cancer development in former smokers. Objective The objective of this study is to determine the contribution of DNA methylation as a mechanism to lung cancer development in former smokers. Hypothesis As smoking induces methylation changes in bronchioepithelial cells, we hypothesize that this constitutes the first hit to TSG inactivation. In order to inactivate both alleles, these methylation changes would be maintained in tumors where a second hit would be found at the same gene loci. Materials and Methods Epithelial cells from former smokers (those with >10 years of smoking cessation) were collected from peripheral airways during routine bronchoscopy. Half of the cells were fixed in Cytolyt and the other half in RNAlater for DNA and RNA extraction, respectively. Copy number profiling was performed by array comparative genomic hybridization (aCGH) using whole-genome tiling path SMRT v2 BAC array. Methylation analysis was performed by coupling affinity based enrichment of methylated sequences with hybridization to the same aCGH platform described above. Expression status of genes was determined by gene expression microarray analysis using Agilent 44K expression arrays. RESULTS: Preliminary MeDIP aCGH of bronchial brush cells from eight former smokers who had previous surgical removal of Stage I NSCLC, and eight former smokers without NSCLC, of similar age (68±7 versus 62±6), revealed distinct differences in frequency of DNA methylation between cancer and non-cancer groups. For example, at 11p13 the cancer group shows hypermethylation at the WT1 locus, a known TSG. Analysis in 62 NSCLC tumors for gene dosage, showed a high frequency of loss at the WT1 locus. To examine downstream effects of these events in tumours, expression of WT1 was assessed and found to be significantly underexpressed in the majority of NSCLC tumours compared to a normal lung reference. Collection of more samples and further integrative analysis is currently underway. Conclusion Differences in methylation between these two groups may explain why some former smokers develop cancer while others remain cancer free despite similar lifestyle changes. As methylation is a reversible DNA modification, this knowledge would prompt the development and application of DNA demethylation chemopreventative agents and unique therapeutic strategies. Citation Information: Cancer Prev Res 2008;1(7 Suppl):A27.

Assessment of the value of reducing uncertainty by sampling in a groundwater remediation system

The success of cleaning up a contaminated site depends on the degree of knowledge of the site's characteristics. When there is much uncertainty associated with the knowledge, the uncertainty regarding whether the remediation will work increases consequently. It is therefore essential to know how much reduction of uncertainty is needed for the purpose of designing a successful and reliable remediation system. The understanding of the site characteristics is basically increased by site investigation, and thus the uncertainty is decreased by sampling information. This study develops a method to evaluate the value of reducing uncertainty by sampling the hydrogelogical parameters in a groundwater remediation system. Hydraulic conductivity being taken as an example of the site characteristics, random field generation and conditional simulation are coupled to obtain a range of hydraulic conductivity fields based on the sampling outputs. A multiple-realization management model that incorporates a chance constraint of health risk is used to find the lowest remediation cost under specific remediation criteria of risk through genetic algorithm. The remediation cost, which is expected to decrease with collection of more samples, serves as the measure of the value of uncertainty reduction by sampling. A case study shows that the variation of the hydraulic conductivity fields among the potential sites as well as the remediation cost is reduced as a result of increase of samples. It also shows that the risk after remediation decreases with the collection of more samples, which implies that the reduction of risk can also be used to assess the value of sampling.

Multiplex Solution Hybridization‐Ribonuclease Protection Assay for Quantitation of Different Ribonucleic Acid Transcripts from Snap‐Frozen Neuroendocrine Tissues of Individual Animals

We have compiled a protocol for simultaneous quantitation of a variety of gene transcripts in multiple individual brain and pituitary gland dissections for studying pretranslational regulation of neuroendocrine systems in vivo, using experimental designs compatible with meaningful statistical power. To facilitate collection of many samples at a time, the tissue was snap-frozen in chilled liquid Freon and stored at -80 °C until further processing. In this way, as many as five different brain and pituitary gland dissections per rat could be collected from eight rats in about an hour. The snap-frozen tissue was suitable for isolation of separate cytoplasmic and nuclear RNA fractions using homogenization in the presence of detergents. To facilitate homogenization of many samples at a time, we devised a method in which the tissue was repeatedly expelled through a 22 gauge hypodermic needle attached to a 1-ml plastic syringe used as a disposable, ready-to-use homogenizer. In order to promote dissolution of lipid membrane structures which are prevalent in the brain, the lysis buffer has been optimized to include the detergent sodium deoxycholate in addition to Nonidet P-40. Specific RNA transcripts were analyzed using a quantitative solution hybridization-ribonuclease protection assay coupled with polyacrylamide gel electrophoresis. The value of this highly sensitive assay has been expanded by including several molecular probes against a variety of neuroendocrine mRNA sequences simultaneously (e.g. progonadotropin-releasing hormone, proopiomelanocortin, tyrosine hydroxylase, dopamine D2 receptor, prolactin), thus increasing the amount of information obtained from each sample in one assay. Furthermore, each sample was routinely co-assayed for cyclophilin mRNA, an abundant, generally non-regulated mRNA whose levels reflected the individual variability in sample processing, thus serving as an internal reference. Once stored in hybridization solution, as many as 100 samples could be analyzed simultaneously for several different RNA transcripts in one assay. This protocol provides a powerful tool for studying regulation of neuroendocrine systems at the molecular level in vivo, using sample sizes suitable for applying statistical analysis of meaningful statistical power.