QuickProbs 2: Towards rapid construction of high-quality alignments of large protein families

被引:7
|
作者
Gudys, Adam [1 ]
Deorowicz, Sebastian [1 ]
机构
[1] Silesian Tech Univ, Inst Informat, Akad 16, PL-44100 Gliwice, Poland
来源
SCIENTIFIC REPORTS | 2017年 / 7卷
关键词
MULTIPLE SEQUENCE ALIGNMENT; GUIDE TREES; ACCURACY; IMPROVEMENT; ALGORITHMS; DATABASE; MODELS; MAFFT;
D O I
10.1038/srep41553
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The ever-increasing size of sequence databases caused by the development of high throughput sequencing, poses to multiple alignment algorithms one of the greatest challenges yet. As we show, well-established techniques employed for increasing alignment quality, i.e., refinement and consistency, are ineffective when large protein families are investigated. We present QuickProbs 2, an algorithm for multiple sequence alignment. Based on probabilistic models, equipped with novel column-oriented refinement and selective consistency, it offers outstanding accuracy. When analysing hundreds of sequences, Quick-Probs 2 is noticeably better than ClustalO and MAFFT, the previous leaders for processing numerous protein families. In the case of smaller sets, for which consistency-based methods are the best performing, QuickProbs 2 is also superior to the competitors. Due to low computational requirements of selective consistency and utilization of massively parallel architectures, presented algorithm has similar execution times to ClustalO, and is orders of magnitude faster than full consistency approaches, like MSAProbs or PicXAA. All these make QuickProbs 2 an excellent tool for aligning families ranging from few, to hundreds of proteins.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] Generating high-quality protein binders: a large screening effort pays off
    Nollau, Peter
    NATURE METHODS, 2011, 8 (07) : 545 - 546
  • [22] Generating high-quality protein binders: a large screening effort pays off
    Peter Nollau
    Nature Methods, 2011, 8 : 545 - 546
  • [23] High-quality bilingual subtitle document alignments with application to spontaneous speech translation
    Tsiartas, Andreas
    Ghosh, Prasanta
    Georgiou, Panayiotis
    Narayanan, Shrikanth
    COMPUTER SPEECH AND LANGUAGE, 2013, 27 (02): : 572 - 591
  • [24] CIMON: Towards High-quality Hash Codes
    Luo, Xiao
    Wu, Daqing
    Ma, Zeyu
    Chen, Chong
    Deng, Minghua
    Ma, Jinwen
    Jin, Zhongming
    Huang, Jianqiang
    Hua, Xian-Sheng
    PROCEEDINGS OF THE THIRTIETH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2021, 2021, : 902 - 908
  • [25] UNJUST GRUDGE TOWARDS HIGH-QUALITY SLAG
    BERGH, S
    JKA-JERNKONTORETS ANNALER, 1985, 169 (06): : 18 - 20
  • [26] TOWARDS HIGH-QUALITY INTRINSIC IMAGES IN THE WILD
    Fu, Gang
    Zhang, Qing
    Xiao, Chunxia
    2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), 2019, : 175 - 180
  • [27] Towards High-Quality Visualization of Superfluid Vortices
    Guo, Yulong
    Liu, Xiaopei
    Xiong, Chi
    Xu, Xuemiao
    Fu, Chi-Wing
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2018, 24 (08) : 2440 - 2455
  • [28] Mirage2's high-quality spliced protein-to-genome mappings produce accurate multiple-sequence alignments of isoforms
    Nord, Alexander
    Wheeler, Travis
    PLOS ONE, 2023, 18 (05):
  • [29] A database of high-quality protein residues for reference data, library construction and motif analysis
    Williams, Christopher J.
    Richardson, David C.
    Richardson, Jane S.
    ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES, 2019, 75 : A439 - A439
  • [30] PartlmageNet: A Large, High-Quality Dataset of Parts
    He, Ju
    Yang, Shuo
    Yang, Shaokang
    Kortylewski, Adam
    Yuan, Xiaoding
    Chen, Jie-Neng
    Liu, Shuai
    Yang, Cheng
    Yu, Qihang
    Yuille, Alan
    COMPUTER VISION, ECCV 2022, PT VIII, 2022, 13668 : 128 - 145