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      • Record: found
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      BRAD V3.0: an upgraded Brassicaceae database

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          Abstract

          The Brassicaceae Database (BRAD version 3.0, BRAD V3.0; http://brassicadb.cn) has evolved from the former Brassica Database (BRAD V2.0), and represents an important community portal hosting genome information for multiple Brassica and related Brassicaceae plant species. Since the last update in 2015, the complex genomes of numerous Brassicaceae species have been decoded, accompanied by many omics datasets. To provide an up-to-date service, we report here a major upgrade of the portal. The Model-View-ViewModel (MVVM) framework of BRAD has been re-engineered to enable easy and sustainable maintenance of the database. The collection of genomes has been increased to 26 species, along with optimization of the user interface. Features of the previous version have been retained, with additional new tools for exploring syntenic genes, gene expression and variation data. In the ‘Syntenic Gene @ Subgenome’ module, we added features to view the sequence alignment and phylogenetic relationships of syntenic genes. New modules include ‘MicroSynteny’ for viewing synteny of selected fragment pairs, and ‘Polymorph’ for retrieval of variation data. The updated BRAD provides a substantial expansion of genomic data and a comprehensive improvement of the service available to the Brassicaceae research community.

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          featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

          Y. Liao, G K Smyth, W Shi (2014)
          Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            R. C. Edgar (2004)
            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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              HISAT: a fast spliced aligner with low memory requirements.

              Daehwan Kim, Ben Langmead, Steven L Salzberg (2018)
              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 5815 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Haixu Chen
                Tianpeng Wang
                Xiaoning He
                Xu Cai
                Runmao Lin
                Jianli Liang
                Jian Wu
                Graham King
                Xiaowu Wang:
                ORCID: https://orcid.org/0000-0001-6753-227X
                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: 07 January 2022
                Publication date (Electronic): 10 November 2021
                Publication date PMC-release: 10 November 2021
                Volume: 50
                Issue: D1
                Pages: D1432-D1441
                Affiliations
                Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences , No.12, Haidian District, Beijing 100081, China
                Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences , No.12, Haidian District, Beijing 100081, China
                Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences , No.12, Haidian District, Beijing 100081, China
                Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences , No.12, Haidian District, Beijing 100081, China
                Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences , No.12, Haidian District, Beijing 100081, China
                Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences , No.12, Haidian District, Beijing 100081, China
                Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences , No.12, Haidian District, Beijing 100081, China
                Southern Cross Plant Science, Southern Cross University , Lismore, New South Wales, Australia
                Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences , No.12, Haidian District, Beijing 100081, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 10 82105971; Fax: +86 61 274123; Email: wangxiaowu@ 123456caas.cn
                Author information
                https://orcid.org/0000-0001-6753-227X
                Article
                Publisher ID: gkab1057
                DOI: 10.1093/nar/gkab1057
                PMC ID: 8728314
                PubMed ID: 34755871
                SO-VID: ea54c137-7c97-4c79-99b1-3aaede8778e1
                Copyright © © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                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
                Date accepted : 19 October 2021
                Date revision received : 14 October 2021
                Date received : 13 September 2021
                Page count
                Pages: 10
                Funding
                Funded by: National Programon Key Research Project;
                Award ID: 2021YFF1000047
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 31630068
                Funded by: National Program on Key Research Project;
                Award ID: 2016YFD0100307
                Funded by: Agricultural Science and Technology Innovation Program, DOI 10.13039/501100012421;
                Funded by: Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, DOI 10.13039/501100014962;
                Categories
                Subject: AcademicSubjects/SCI00010
                Subject: Database Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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