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      Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse

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          Abstract

          Prostate cancer is heterogeneous and patients would benefit from methods that stratify those who are likely to respond to systemic therapy. Here, we employ single-cell assays for transposase-accessible chromatin (ATAC) and RNA sequencing in models of early treatment response and resistance to enzalutamide. In doing so, we identify pre-existing and treatment-persistent cell subpopulations that possess regenerative potential when subjected to treatment. We find distinct chromatin landscapes associated with enzalutamide treatment and resistance that are linked to alternative transcriptional programs. Transcriptional profiles characteristic of persistent cells are able to stratify the treatment response of patients. Ultimately, we show that defining changes in chromatin and gene expression in single-cell populations from pre-clinical models can reveal as yet unrecognized molecular predictors of treatment response. This suggests that the application of single-cell methods with high analytical resolution in pre-clinical models may powerfully inform clinical decision-making.

          Abstract

          Identifying the molecular mechanisms of response to systemic therapy in prostate cancer remains crucial. Here, the authors apply single cell-ATAC and RNAseq to models of early treatment response and resistance to enzalutamide and identify chromatin and gene expression patterns that can predict treatment response.

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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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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                matti.nykter@tuni.fi
                alfonsourbanucci@gmail.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                6 September 2021
                6 September 2021
                2021
                : 12
                : 5307
                Affiliations
                [1 ]GRID grid.502801.e, ISNI 0000 0001 2314 6254, Prostate Cancer Research Center, Faculty of Medicine and Health Technology, , Tampere University and Tays Cancer Center, ; Tampere, Finland
                [2 ]GRID grid.55325.34, ISNI 0000 0004 0389 8485, Department of Tumor Biology, Institute for Cancer Research, , Oslo University Hospital, ; Oslo, Norway
                [3 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Department of Bioinformatics and Computational Biology, , The University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                [4 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, ; Leuven, Belgium
                [5 ]GRID grid.5361.1, ISNI 0000 0000 8853 2677, Department of Urology, Division of Experimental Urology, , Medical University of Innsbruck, ; Innsbruck, Austria
                [6 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Nuffield Department of Surgical Sciences, , University of Oxford, ; Oxford, UK
                [7 ]GRID grid.430814.a, Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, ; Amsterdam, The Netherlands
                [8 ]GRID grid.83440.3b, ISNI 0000000121901201, University College London Cancer Institute, ; London, UK
                [9 ]GRID grid.412330.7, ISNI 0000 0004 0628 2985, Department of Pathology, Fimlab Laboratories, , Tampere University Hospital, ; Tampere, Finland
                [10 ]GRID grid.410569.f, ISNI 0000 0004 0626 3338, Department of Urology, UZ Leuven, ; Leuven, Belgium
                [11 ]GRID grid.9668.1, ISNI 0000 0001 0726 2490, Institute of Biomedicine, , University of Eastern Finland, ; Kuopio, Finland
                [12 ]GRID grid.502801.e, ISNI 0000 0001 2314 6254, Faculty of Medicine and Health Technology, , Tampere University and Tays Cancer Center, ; Tampere, Finland
                [13 ]GRID grid.412330.7, ISNI 0000 0004 0628 2985, Fimlab Laboratories, Ltd, , Tampere University Hospital, ; Tampere, Finland
                [14 ]GRID grid.415719.f, ISNI 0000 0004 0488 9484, Department of Urology, Churchill Hospital Cancer Centre, ; Oxford, UK
                [15 ]GRID grid.4777.3, ISNI 0000 0004 0374 7521, Patrick G Johnston Centre for Cancer Research, , Queen’s University of Belfast, ; Belfast, UK
                [16 ]GRID grid.7914.b, ISNI 0000 0004 1936 7443, Centre for Cancer Biomarkers (CCBIO), , University of Bergen, ; Bergen, Norway
                Author information
                http://orcid.org/0000-0001-7559-9853
                http://orcid.org/0000-0003-3718-3464
                http://orcid.org/0000-0002-7558-5635
                http://orcid.org/0000-0002-7051-9321
                http://orcid.org/0000-0003-2029-6497
                http://orcid.org/0000-0002-1320-0621
                http://orcid.org/0000-0001-7419-0773
                http://orcid.org/0000-0003-4050-4935
                http://orcid.org/0000-0002-5962-9503
                http://orcid.org/0000-0002-8676-7709
                http://orcid.org/0000-0001-6549-7810
                http://orcid.org/0000-0002-2968-7155
                http://orcid.org/0000-0002-4811-7983
                http://orcid.org/0000-0003-0617-9438
                http://orcid.org/0000-0001-6956-2843
                http://orcid.org/0000-0003-2931-3652
                Article
                25624
                10.1038/s41467-021-25624-1
                8421417
                34489465
                cbab64bc-e869-41c6-993d-aac95c022f9e
                © The Author(s) 2021

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 April 2021
                : 23 August 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100008730, Kreftforeningen (Norwegian Cancer Society);
                Award ID: 198016-2018
                Award ID: 198016-2018
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100002341, Academy of Finland (Suomen Akatemia);
                Award ID: 312043
                Award Recipient :
                Categories
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                © The Author(s) 2021

                Uncategorized
                prostate cancer,data integration
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                prostate cancer, data integration

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