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      Adaptor Template Oligo-Mediated Sequencing (ATOM-Seq) is a new ultra-sensitive UMI-based NGS library preparation technology for use with cfDNA and cfRNA

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          Abstract

          Liquid biopsy testing utilising Next Generation Sequencing (NGS) is rapidly moving towards clinical adoption for personalised oncology. However, before NGS can fulfil its potential any novel testing approach must identify ways of reducing errors, allowing separation of true low-frequency mutations from procedural artefacts, and be designed to improve upon current technologies. Popular NGS technologies typically utilise two DNA capture approaches; PCR and ligation, which have known limitations and seem to have reached a development plateau with only small, stepwise improvements being made. To maximise the ultimate utility of liquid biopsy testing we have developed a highly versatile approach to NGS: Adaptor Template Oligo Mediated Sequencing (ATOM-Seq). ATOM-Seq's strengths and versatility avoid the major limitations of both PCR- and ligation-based approaches. This technology is ligation free, simple, efficient, flexible, and streamlined, and it offers novel advantages that make it perfectly suited for use on highly challenging clinical material. Using reference and clinical materials, we demonstrate detection of known SNVs down to allele frequencies of 0.1% using as little as 20–25 ng of cfDNA, as well as the ability to detect fusions from RNA. We illustrate ATOM-Seq’s suitability for clinical testing by showing high concordance rates between paired cfDNA and FFPE clinical samples.

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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 and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              fastp: an ultra-fast all-in-one FASTQ preprocessor

              Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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                Author and article information

                Contributors
                Guoliang.Fu@genefirst.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 February 2021
                4 February 2021
                2021
                : 11
                : 3138
                Affiliations
                [1 ]GRID grid.417687.b, ISNI 0000 0001 0742 9289, GeneFirst Ltd, Building E5, Culham Science Centre, ; Abingdon, OX14 3DB UK
                [2 ]Pathology and Data Analytics, Leeds Institute of Medical Research, University of Leeds, St James University Hospital, Leeds, LS9 7TF UK
                [3 ]Guangzhou Biotron Technology Co., Ltd, Room 204, Zone C, Science and Technology Innovation Base, No. 80, Lanyue Road, Science City, Guangzhou, China
                [4 ]GRID grid.52522.32, ISNI 0000 0004 0627 3560, Department of Oncology, , St. Olav’s Hospital, Trondheim University Hospital, ; Trondheim, Norway
                [5 ]GRID grid.5947.f, ISNI 0000 0001 1516 2393, Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, , Norwegian University of Science and Technology (NTNU), ; Trondheim, Norway
                [6 ]GRID grid.52522.32, ISNI 0000 0004 0627 3560, Department of Pathology, , St. Olav’s Hospital, Trondheim University Hospital, ; Trondheim, Norway
                Article
                82737
                10.1038/s41598-021-82737-9
                7862664
                33542447
                367e3c98-f506-4670-9dc0-816677c83e45
                © 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 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
                : 28 June 2020
                : 22 January 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100006041, Innovate UK;
                Award ID: Open Programmes Round 3 - 1050
                Funded by: Shanghai International Science
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                biological techniques,cancer,genetics
                Uncategorized
                biological techniques, cancer, genetics

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