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      Computational analysis of sense-antisense chimeric transcripts reveals their potential regulatory features and the landscape of expression in human cells

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          Abstract

          Many human genes are transcribed from both strands and produce sense-antisense gene pairs. Sense-antisense (SAS) chimeric transcripts are produced upon the coalescing of exons/introns from both sense and antisense transcripts of the same gene. SAS chimera was first reported in prostate cancer cells. Subsequently, numerous SAS chimeras have been reported in the ChiTaRS-2.1 database. However, the landscape of their expression in human cells and functional aspects are still unknown. We found that longer palindromic sequences are a unique feature of SAS chimeras. Structural analysis indicates that a long hairpin-like structure formed by many consecutive Watson-Crick base pairs appears because of these long palindromic sequences, which possibly play a similar role as double-stranded RNA (dsRNA), interfering with gene expression. RNA–RNA interaction analysis suggested that SAS chimeras could significantly interact with their parental mRNAs, indicating their potential regulatory features. Here, 267 SAS chimeras were mapped in RNA-seq data from 16 healthy human tissues, revealing their expression in normal cells. Evolutionary analysis suggested the positive selection favoring sense-antisense fusions that significantly impacted the evolution of their function and structure. Overall, our study provides detailed insight into the expression landscape of SAS chimeras in human cells and identifies potential regulatory features.

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          Most cited references110

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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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              Minimap2: pairwise alignment for nucleotide sequences

              Heng Li (2018)
              Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.
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                Author and article information

                Contributors
                Journal
                NAR Genom Bioinform
                NAR Genom Bioinform
                nargab
                NAR Genomics and Bioinformatics
                Oxford University Press
                2631-9268
                September 2021
                25 August 2021
                25 August 2021
                : 3
                : 3
                : lqab074
                Affiliations
                Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University , Safed 1311502, Israel
                Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University , Safed 1311502, Israel
                Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University , Safed 1311502, Israel
                Department of Bioengineering and Bioinformatics, Lomonosov Moscow State University , Moscow 119234, Russian Federation
                Institute of Bioengineering, Research Centre of Biotechnology, Russian Academy of Sciences , Moscow 117312, Russian Federation
                Institute of Bioengineering, Research Centre of Biotechnology, Russian Academy of Sciences , Moscow 117312, Russian Federation
                Department of Biomedical Physics, Moscow Institute of Technology , Dolgoprudny 141701, Russian Federation
                Barcelona Supercomputing Center (BSC) , C/ Jordi Girona 29, 08034, Barcelona, Spain
                ICREA, Pg. Lluís Companys 23 , 08010 Barcelona, Spain
                Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University , Safed 1311502, Israel
                Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University , Safed 1311502, Israel
                Author notes
                To whom correspondence should be addressed. Tel: +972 72 264 4901; Email: milana.morgenstern@ 123456biu.ac.il

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

                Author information
                https://orcid.org/0000-0001-9807-7994
                https://orcid.org/0000-0003-2617-6998
                https://orcid.org/0000-0001-8669-0150
                https://orcid.org/0000-0002-7587-1666
                https://orcid.org/0000-0002-4126-9781
                https://orcid.org/0000-0002-0329-4599
                Article
                lqab074
                10.1093/nargab/lqab074
                8386243
                34458728
                191a1c1e-f27e-4080-8b46-eb7b881fa723
                © The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 09 December 2020
                : 02 July 2021
                : 20 August 2021
                Page count
                Pages: 15
                Funding
                Funded by: Council for Higher Education, DOI 10.13039/501100005385;
                Funded by: Israel Innovation Authority;
                Award ID: 66824
                Funded by: RSF, DOI 10.13039/100000935;
                Award ID: 18–14-00240
                Categories
                AcademicSubjects/SCI00030
                AcademicSubjects/SCI00980
                AcademicSubjects/SCI01060
                AcademicSubjects/SCI01140
                AcademicSubjects/SCI01180
                Standard Article

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