63 resultados para Dust Devils Tracks
Resumo:
Background Strand specific RNAseq data is now more common in RNAseq projects. Visualizing RNAseq data has become an important matter in Analysis of sequencing data. The most widely used visualization tool is the UCSC genome browser that introduced the custom track concept that enabled researchers to simultaneously visualize gene expression at a particular locus from multiple experiments. Our objective of the software tool is to provide friendly interface for visualization of RNAseq datasets. Results This paper introduces a visualization tool (RNASeqBrowser) that incorporates and extends the functionality of the UCSC genome browser. For example, RNASeqBrowser simultaneously displays read coverage, SNPs, InDels and raw read tracks with other BED and wiggle tracks -- all being dynamically built from the BAM file. Paired reads are also connected in the browser to enable easier identification of novel exon/intron borders and chimaeric transcripts. Strand specific RNAseq data is also supported by RNASeqBrowser that displays reads above (positive strand transcript) or below (negative strand transcripts) a central line. Finally, RNASeqBrowser was designed for ease of use for users with few bioinformatic skills, and incorporates the features of many genome browsers into one platform. Conclusions The features of RNASeqBrowser: (1) RNASeqBrowser integrates UCSC genome browser and NGS visualization tools such as IGV. It extends the functionality of the UCSC genome browser by adding several new types of tracks to show NGS data such as individual raw reads, SNPs and InDels. (2) RNASeqBrowser can dynamically generate RNA secondary structure. It is useful for identifying non-coding RNA such as miRNA. (3) Overlaying NGS wiggle data is helpful in displaying differential expression and is simple to implement in RNASeqBrowser. (4) NGS data accumulates a lot of raw reads. Thus, RNASeqBrowser collapses exact duplicate reads to reduce visualization space. Normal PC’s can show many windows of NGS individual raw reads without much delay. (5) Multiple popup windows of individual raw reads provide users with more viewing space. This avoids existing approaches (such as IGV) which squeeze all raw reads into one window. This will be helpful for visualizing multiple datasets simultaneously. RNASeqBrowser and its manual are freely available at http://www.australianprostatecentre.org/research/software/rnaseqbrowser webcite or http://sourceforge.net/projects/rnaseqbrowser/ webcite
Resumo:
PBDE concentrations are higher in children compared to adults with exposure suggested to include dust ingestion. Besides the home environment, children spend a great deal of time in school classrooms which may be a source of exposure. As part of the “Ultrafine Particles from Traffic Emissions and Children's Health (UPTECH)” project, dust samples (n=28) were obtained in 2011/12 from 10 Brisbane, Australia metropolitan schools and analysed using GC and LC–MS for polybrominated diphenyl ethers (PBDEs) -17, -28, -47, -49, -66, -85, -99, -100, -154, -183, and -209. Σ11PBDEs ranged from 11–2163 ng/g dust; with a mean and median of 600 and 469 ng/g dust, respectively. BDE-209 (range n.d. −2034 ng/g dust; mean (median) 402 (217) ng/g dust) was the dominant congener in most classrooms. Frequencies of detection were 96%, 96%, 39% and 93% for BDE-47, -99, -100 and -209, respectively. No seasonal variations were apparent and from each of the two schools where XRF measurements were carried out, only two classroom items had detectable bromine. PBDE intake for 8–11 year olds can be estimated at 0.094 ng/day BDE-47; 0.187 ng/day BDE-99 and 0.522 ng/day BDE-209 as a result of ingestion of classroom dust, based on mean PBDE concentrations. The 97.5% percentile intake is estimated to be 0.62, 1.03 and 2.14 ng/day for BDEs-47, -99 and -209, respectively. These PBDE concentrations in dust from classrooms, which are higher than in Australian homes, may explain some of the higher body burden of PBDEs in children compared to adults when taking into consideration age-dependant behaviours which increase dust ingestion.
Resumo:
Road deposited dust is a complex mixture of pollutants derived from a wide range of sources. Accurate identification of these sources is seminal for effective source-oriented control measures. A range of techniques such as enrichment factor analysis (EF), principal component analysis (PCA) and hierarchical cluster analysis (HCA) are available for identifying sources of complex mixtures. However, they have multiple deficiencies when applied individually. This study presents an approach for the effective utilisation of EF, PCA and HCA for source identification, so that their specific deficiencies on an individual basis are eliminated. EF analysis confirmed the non-soil origin of metals such as Na, Cu, Cd, Zn, Sn, K, Ca, Sb, Ba, Ti, Ni and Mo providing guidance in the identification of anthropogenic sources. PCA and HCA identified four sources, with soil and asphalt wear in combination being the most prominent sources. Other sources were tyre wear, brake wear and sea salt.