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      TSNAD v2.0: A one-stop software solution for tumor-specific neoantigen detection

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          Abstract

          TSNAD is a one-stop software solution for predicting neoantigens from the whole genome/exome sequencing data of tumor-normal pairs. Here we present TSNAD v2.0 which provides several new features such as the function of RNA-Seq analysis including gene expression and gene fusion analysis, the support of different versions of the reference genome. Most importantly, we replace the NetMHCpan with DeepHLApan we developed previously, which considers both the binding between peptide and major histocompatibility complex (MHC) and the immunogenicity of the presented peptide-MHC complex (pMHC). TSNAD v2.0 achieves good performamce on a standard dataset. For better usage, we provide the Docker version and the web service of TSNAD v2.0. The source code of TSNAD v2.0 is freely available at https://github.com/jiujiezz/tsnad. And the web service of TSNAD v2.0 is available at http://biopharm.zju.edu.cn/tsnad/.

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          Most cited references26

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)
          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            STAR: ultrafast universal RNA-seq aligner.

            Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)
            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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              HISAT: a fast spliced aligner with low memory requirements.

              Daehwan Kim, Ben Langmead, Steven L Salzberg (2019)
              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
              Article 0 comments Cited 6454 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Zhan Zhou
                Jian Wu
                Shuqing Chen
                Journal
                Journal ID (nlm-ta): Comput Struct Biotechnol J
                Journal ID (iso-abbrev): Comput Struct Biotechnol J
                Title: Computational and Structural Biotechnology Journal
                Publisher: Research Network of Computational and Structural Biotechnology
                ISSN (Electronic): 2001-0370
                Publication date PMC-release: 12 August 2021
                Publication date Collection: 2021
                Publication date (Electronic): 12 August 2021
                Volume: 19
                Pages: 4510-4516
                Affiliations
                [a ]Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
                [b ]Collaborative Innovation Center of Artificial Intelligence by MOE and Zhejiang Provincial Government, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
                [c ]Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA
                [d ]Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou 310018, China
                [e ]Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Alibaba DAMO Academy, Hangzhou 311121, China
                [f ]Polytechnic Institute, Zhejiang University, Hangzhou 310015, China
                [g ]Real Doctor AI Research Centre, School of Medicine, Zhejiang University, Hangzhou 310058, China
                Author notes
                [* ]Corresponding authors at: College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China (Z. Zhou, S. Chen). College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China (J. Wu). zhanzhou@ 123456zju.edu.cn wujian2000@ 123456zju.edu.cn chenshuqing@ 123456zju.edu.cn
                [1]

                Authors who contributed equally to this work.

                Article
                Publisher Item ID: S2001-0370(21)00347-0
                DOI: 10.1016/j.csbj.2021.08.016
                PMC ID: 8385119
                PubMed ID: 34471496
                SO-VID: a32018e4-2b54-4b16-a345-cea67ad0ce5e
                Copyright © © 2021 The Author(s)
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 1 April 2021
                Date revision received : 10 August 2021
                Date accepted : 10 August 2021
                Categories
                Subject: Research Article

                Keywords: neoantigens,somatic mutations,major histocompatibility complex,tumor immunotherapy,one-stop software
                Data availability:
                Keywords: neoantigens, somatic mutations, major histocompatibility complex, tumor immunotherapy, one-stop software

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