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      Cistrome and transcriptome analysis identifies unique androgen receptor (AR) and AR-V7 splice variant chromatin binding and transcriptional activities

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          Abstract

          The constitutively active androgen receptor (AR) splice variant, AR-V7, plays an important role in resistance to androgen deprivation therapy in castration resistant prostate cancer (CRPC). Studies seeking to determine whether AR-V7 is a partial mimic of the AR, or also has unique activities, and whether the AR-V7 cistrome contains unique binding sites have yielded conflicting results. One limitation in many studies has been the low level of AR variant compared to AR. Here, LNCaP and VCaP cell lines in which AR-V7 expression can be induced to match the level of AR, were used to compare the activities of AR and AR-V7. The two AR isoforms shared many targets, but overall had distinct transcriptomes. Optimal induction of novel targets sometimes required more receptor isoform than classical targets such as PSA. The isoforms displayed remarkably different cistromes with numerous differential binding sites. Some of the unique AR-V7 sites were located proximal to the transcription start sites (TSS). A de novo binding motif similar to a half ARE was identified in many AR-V7 preferential sites and, in contrast to conventional half ARE sites that bind AR-V7, FOXA1 was not enriched at these sites. This supports the concept that the AR isoforms have unique actions with the potential to serve as biomarkers or novel therapeutic targets.

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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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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                basil@connect.hku.hk
                coarfa@bcm.edu
                nweigel@bcm.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 March 2022
                30 March 2022
                2022
                : 12
                : 5351
                Affiliations
                [1 ]GRID grid.39382.33, ISNI 0000 0001 2160 926X, Department of Molecular & Cellular Biology, , Baylor College of Medicine, ; Houston, TX 77030 USA
                [2 ]GRID grid.39382.33, ISNI 0000 0001 2160 926X, Dan L Duncan Comprehensive Cancer Center, , Baylor College of Medicine, ; Houston, TX 77030 USA
                [3 ]GRID grid.39382.33, ISNI 0000 0001 2160 926X, Center for Precision Environmental Health, , Baylor College of Medicine, ; Houston, TX 77030 USA
                [4 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Present Address: Department of Critical Care Medicine, Anesthesiology, , MD Anderson Cancer Center, ; Y6.6028, 1515 Holcombe Blvd, Houston, TX 77030 USA
                [5 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Present Address: Department of Cellular & Molecular Pharmacology School of Medicine, , University of California, ; San Francisco, CA USA
                [6 ]GRID grid.265436.0, ISNI 0000 0001 0421 5525, Present Address: Center for Prostate Disease Research, Henry M. Jackson Foundation for the Advancement of Military Medicine, , USU Walter Reed Surgery, ; 6720A Rockledge Drive, Bethesda, MD 20817 USA
                Article
                9371
                10.1038/s41598-022-09371-x
                8969163
                35354884
                ac88c6ca-c767-463e-86bc-d2bb29909aab
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 December 2021
                : 14 March 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000005, U.S. Department of Defense;
                Award ID: DAMD W81XWH- 17-1-0236
                Funded by: FundRef http://dx.doi.org/10.13039/100004917, Cancer Prevention and Research Institute of Texas;
                Award ID: RP150648
                Award ID: RP170005
                Award ID: RP200504
                Award ID: RP210227
                Award ID: RP100320
                Award ID: RP100320
                Award ID: RP150648
                Award ID: RP150648
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P30 CA125123
                Funded by: FundRef http://dx.doi.org/10.13039/100000892, Prostate Cancer Foundation;
                Award ID: Challenge award
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: P30CA125123
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;
                Award ID: 1P30ES030285
                Award Recipient :
                Categories
                Article
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                © The Author(s) 2022

                Uncategorized
                transcription,transcriptomics
                Uncategorized
                transcription, transcriptomics

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