Sequence-based identification of 3D structural modules in RNA with RMDetect

被引:0
|
作者
Cruz, Jose Almeida
Westhof, Eric [1 ]
机构
[1] Univ Strasbourg, Architecture & Reactiv ARN, Inst Biol Mol, Strasbourg, France
关键词
SECONDARY STRUCTURE; COMPARATIVE GENOMICS; RIBOSOMAL-RNA; ISOSTERICITY MATRICES; MOTIFS; BACTERIA; ALIGNMENTS; ALGORITHM; CMFINDER; DATABASE;
D O I
10.1038/NMETH.1603
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Structural RNA modules, sets of ordered non-Watson-Crick base pairs embedded between Watson-Crick pairs, have central roles as architectural organizers and sites of ligand binding in RNA molecules, and are recurrently observed in RNA families throughout the phylogeny. Here we describe a computational tool, RNA three-dimensional (3D) modules detection, or RMDetect, for identifying known 3D structural modules in single and multiple RNA sequences in the absence of any other information. Currently, four modules can be searched for: G-bulge loop, kink-turn, C-loop and tandem-GA loop. In control test sequences we found all of the known modules with a false discovery rate of 0.23. Scanning through 1,444 publicly available alignments, we identified 21 yet unreported modules and 141 known modules. RMDetect can be used to refine RNA 2D structure, assemble RNA 3D models, and search and annotate structured RNAs in genomic data.
引用
收藏
页码:513 / U115
页数:9
相关论文
共 50 条
  • [1] Sequence-based identification of 3D structural modules in RNA with RMDetect
    Cruz J.A.
    Westhof E.
    Nature Methods, 2011, 8 (6) : 513 - 519
  • [2] Automated identification of RNA 3D modules with discriminative power in RNA structural alignments
    Theis, Corinna
    Siederdissen, Christian Hoener Zu
    Hofacker, Ivo L.
    Gorodkin, Jan
    NUCLEIC ACIDS RESEARCH, 2013, 41 (22) : 9999 - 10009
  • [3] Improving sequence-based fold recognition by using 3D model quality assessment
    Pettitt, CS
    McGuffin, LJ
    Jones, DT
    BIOINFORMATICS, 2005, 21 (17) : 3509 - 3515
  • [4] Reliability of Sequence-Based Identification of Microorganism
    Sung, Heungsup
    Kim, Mi-Na
    INFECTION AND CHEMOTHERAPY, 2008, 40 (06): : 355 - 356
  • [5] Sequence-based classification and identification of Fungi
    Hibbett, David
    Abarenkov, Kessy
    Koljalg, Urmas
    Opik, Maarja
    Chai, Benli
    Cole, James
    Wang, Qiong
    Crous, Pedro
    Robert, Vincent
    Helgason, Thorunn
    Herr, Joshua R.
    Kirk, Paul
    Lueschow, Shiloh
    O'Donnell, Kerry
    Nilsson, R. Henrik
    Oono, Ryoko
    Schoch, Conrad
    Smyth, Christopher
    Walker, Donald M.
    Porras-Alfaro, Andrea
    Taylor, John W.
    Geiser, David M.
    MYCOLOGIA, 2016, 108 (06) : 1049 - 1068
  • [6] Sequence-based identification of aerobic actinomycetes
    Patel, JB
    Wallace, RJ
    Brown-Elliott, BA
    Taylor, T
    Imperatrice, C
    Leonard, DGB
    Wilson, RW
    Mann, L
    Jost, KC
    Nachamkin, I
    JOURNAL OF CLINICAL MICROBIOLOGY, 2004, 42 (06) : 2530 - 2540
  • [7] Nucleotide sequence-based multitarget identification
    Vinayagamoorthy, T
    Mulatz, K
    Hodkinson, R
    JOURNAL OF CLINICAL MICROBIOLOGY, 2003, 41 (07) : 3284 - 3292
  • [8] Sequence-Based Machine Learning Reveals 3D Genome Differences between Bonobos and Chimpanzees
    Brand, Colin M.
    Kuang, Shuzhen
    Gilbertson, Erin N.
    McArthur, Evonne
    Pollard, Katherine S.
    Webster, Timothy H.
    Capra, John A.
    GENOME BIOLOGY AND EVOLUTION, 2024, 16 (11):
  • [9] A feature sequence-based 3D convolutional method for wetland classification from multispectral images
    Pan, Hong
    REMOTE SENSING LETTERS, 2020, 11 (09) : 837 - 846
  • [10] 16S ribosomal RNA sequence-based identification of veterinary clinical bacteria
    Cai, H
    Archambault, M
    Prescott, JF
    JOURNAL OF VETERINARY DIAGNOSTIC INVESTIGATION, 2003, 15 (05) : 465 - 469