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      Locus-specific expression analysis of transposable elements

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          Abstract

          Transposable elements (TEs) have been associated with many, frequently detrimental, biological roles. Consequently, the regulations of TEs, e.g. via DNA-methylation and histone modifications, are considered critical for maintaining genomic integrity and other functions. Still, the high-throughput study of TEs is usually limited to the family or consensus-sequence level because of alignment problems prompted by high-sequence similarities and short read lengths. To entirely comprehend the effects and reasons of TE expression, however, it is necessary to assess the TE expression at the level of individual instances. Our simulation study demonstrates that sequence similarities and short read lengths do not rule out the accurate assessment of (differential) expression of TEs at the instance-level. With only slight modifications to existing methods, TE expression analysis works surprisingly well for conventional paired-end sequencing data. We find that SalmonTE and Telescope can accurately tally a considerable amount of TE instances, allowing for differential expression recovery in model and non-model organisms.

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          Most cited references34

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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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            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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              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Brief Bioinform
                Brief Bioinform
                bib
                Briefings in Bioinformatics
                Oxford University Press
                1467-5463
                1477-4054
                January 2022
                19 October 2021
                19 October 2021
                : 23
                : 1
                : bbab417
                Affiliations
                Computational Biology Group, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) Beutenbergstrasse 11, 07745 Jena, Germany
                CF Life Science Computing, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) Beutenbergstrasse 11, 07745 Jena, Germany
                CF Life Science Computing, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) Beutenbergstrasse 11, 07745 Jena, Germany
                Computational Biology Group, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) Beutenbergstrasse 11, 07745 Jena, Germany
                Author notes
                Corresponding authors: Steve Hoffmann. Tel.: +493,641 656,810; Fax: 493,641 656,255; E-mail: steve.hoffmann@ 123456leibniz-fli.de ; Robert Schwarz. Tel.: +493,641 656,057; E-mail: robert.schwarz@ 123456leibniz-fli.de

                Jeanne Wilbrandt and Steve Hoffmann authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-0654-0943
                https://orcid.org/0000-0003-2825-7943
                https://orcid.org/0000-0002-0363-3837
                https://orcid.org/0000-0002-5239-7201
                Article
                bbab417
                10.1093/bib/bbab417
                8769692
                34664075
                a2560771-b44f-46f5-a590-a8a90aa8fc30
                © The Author(s) 2021. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 2 July 2021
                : 24 August 2021
                : 10 September 2021
                Page count
                Pages: 10
                Funding
                Funded by: Klaus Tschira Stiftung, DOI 10.13039/501100007316;
                Award ID: 03.136.2018
                Funded by: German Federal Ministry of Education and Research;
                Award ID: 031L0106D
                Categories
                Problem Solving Protocol
                AcademicSubjects/SCI01060

                Bioinformatics & Computational biology
                rna sequencing,transposable elements,tool comparison,simulation,differential expression analysis

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