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      Physiological and transcriptomic effects of hexafluoropropylene oxide dimer acid in Caenorhabditis elegans during development

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      Ecotoxicology and Environmental Safety
      Elsevier BV

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          Abstract

          <p class="first" id="d1197675e134">Per- and polyfluoroalkyl substances (PFAS) are chemicals resistant to degradation. While such a feature is desirable in consumer and industrial products, some PFAS, including perfluorooctanoic acid (PFOA), are toxic and bioaccumulate. Hexafluoropropylene oxide dimer acid (HFPO-DA), an emerging PFAS developed to replace PFOA, has not been extensively studied. To evaluate the potential toxicity of HFPO-DA with a cost- and time-efficient approach, we exposed C. elegans larvae for 48 h to 4 × 10-9-4 g/L HFPO-DA in liquid media and measured developmental, behavioral, locomotor, and transcriptional effects at various exposure levels. Worms exposed to 1.5-4 g/L HFPO-DA were developmentally delayed, and progeny production was significantly delayed (p &lt; 0.05) in worms exposed to 2-4 g/L HFPO-DA. Statistically significant differential gene expression was identified in all fourteen HFPO-DA exposure groups ranging from 1.25 × 10-5 to 4 g/L, except for 6.25 × 10-5 g/L. Among 10298 analyzed genes, 2624 differentially expressed genes (DEGs) were identified in the developmentally delayed 4 g/L group only, and 78 genes were differentially expressed in at least one of the thirteen groups testing 1.25 × 10-5-2 g/L HFPO-DA exposures. Genes encoding for detoxification enzymes including cytochrome P450 and UDP glucuronosyltransferases were upregulated in 0.25-4 g/L acute exposure groups. DEGs were also identified in lower exposure level groups, though they did not share biological functions except for six ribosomal protein-coding genes. While our transcriptional data is inconclusive to infer mechanisms of toxicity, the significant gene expression differences at 1.25 × 10-5 g/L, the lowest concentration tested for transcriptional changes, calls for further targeted analyses of low-dose HFPO-DA exposure effects. </p>

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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                Author and article information

                Journal
                Ecotoxicology and Environmental Safety
                Ecotoxicology and Environmental Safety
                Elsevier BV
                01476513
                October 2022
                October 2022
                : 244
                : 114047
                Article
                10.1016/j.ecoenv.2022.114047
                36075119
                c4bb5c47-096a-41fe-8307-3f6b7c3d39aa
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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