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      Rapid and accurate alignment of nucleotide conversion sequencing reads with HISAT-3N

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          Abstract

          Sequencing technologies using nucleotide conversion techniques such as cytosine to thymine in bisulfite-seq and thymine to cytosine in SLAM seq are powerful tools to explore the chemical intricacies of cellular processes. To date, no one has developed a unified methodology for aligning converted sequences and consolidating alignment of these technologies in one package. In this paper, we describe hierarchical indexing for spliced alignment of transcripts–3 nucleotides (HISAT-3N), which can rapidly and accurately align sequences consisting of any nucleotide conversion by leveraging the powerful hierarchical index and repeat index algorithms originally developed for the HISAT software. Tests on real and simulated data sets show that HISAT-3N is faster than other modern systems, with greater alignment accuracy, higher scalability, and smaller memory requirements. HISAT-3N therefore becomes an ideal aligner when used with converted sequence technologies.

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          Most cited references26

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                July 2021
                : 31
                : 7
                : 1290-1295
                Affiliations
                Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
                Author notes
                Author information
                http://orcid.org/0000-0002-6125-6662
                http://orcid.org/0000-0003-3329-2567
                http://orcid.org/0000-0003-1182-629X
                Article
                9509184
                10.1101/gr.275193.120
                8256862
                34103331
                5f7a4afa-0601-4806-afd8-c8388f9d2c27
                © 2021 Zhang et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see https://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 29 December 2020
                : 3 June 2021
                Page count
                Pages: 6
                Funding
                Funded by: National Institute of General Medical Sciences (NIH) , open-funder-registry 10.13039/100000057;
                Award ID: R01-GM135341
                Funded by: Cancer Prevention Research Institute of Texas (CPRIT) , open-funder-registry 10.13039/100004917;
                Award ID: RR170068
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                Method

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