A Novel Comparative Sequence Analysis Method for ncRNA Secondary Structure Prediction without Multiple Sequence Alignment

2008 ◽  
Author(s):  
Quan Zou ◽  
Mao-Zu Guo ◽  
Yang Liu ◽  
Zhi-An Xing
Keyword(s):  
Sequence Analysis ◽  
Secondary Structure ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Comparative Sequence Analysis ◽  
Analysis Method ◽  
Multiple Sequence ◽  
Comparative Sequence

scholarly journals Predicting Consensus Structures for RNA Alignments via Pseudo-Energy Minimization

2009 ◽  
Vol 3 ◽  
pp. BBI.S2578 ◽  
Author(s):  
Junilda Spirollari ◽  
Jason T.L. Wang ◽  
Kaizhong Zhang ◽  
Vivian Bellofatto ◽  
Yongkyu Park ◽  
...  
Keyword(s):  
Free Energy ◽  
Secondary Structure ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Energy Minimization ◽  
Secondary Structure Prediction ◽  
Sequence Alignments ◽  
Rna Sequences ◽  
Multiple Sequence ◽  
Consensus Secondary Structure

Thermodynamic processes with free energy parameters are often used in algorithms that solve the free energy minimization problem to predict secondary structures of single RNA sequences. While results from these algorithms are promising, an observation is that single sequence-based methods have moderate accuracy and more information is needed to improve on RNA secondary structure prediction, such as covariance scores obtained from multiple sequence alignments. We present in this paper a new approach to predicting the consensus secondary structure of a set of aligned RNA sequences via pseudo-energy minimization. Our tool, called RSpredict, takes into account sequence covariation and employs effective heuristics for accuracy improvement. RSpredict accepts, as input data, a multiple sequence alignment in FASTA or ClustalW format and outputs the consensus secondary structure of the input sequences in both the Vienna style Dot Bracket format and the Connectivity Table format. Our method was compared with some widely used tools including KNetFold, Pfold and RNAalifold. A comprehensive test on different datasets including Rfam sequence alignments and a multiple sequence alignment obtained from our study on the Drosophila X chromosome reveals that RSpredict is competitive with the existing tools on the tested datasets. RSpredict is freely available online as a web server and also as a jar file for download at http://datalab.njit.edu/biology/RSpredict .

scholarly journals Multiple Sequence Alignment Reveals Diversity among Eight African Bush Mango (Irvingia gabonensis Aubry-Lecomte ex O’Rorke) Cultivars

2021 ◽  
pp. 91-96
Author(s):  
U. G. Adebo ◽  
J. O. Matthew
Keyword(s):  
Sequence Analysis ◽  
Phylogenetic Tree ◽  
Genetic Resources ◽  
Data Base ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence ◽  
Bush Mango ◽  
Irvingia Gabonensis ◽  
Cluster 2

Multiple sequence analysis is one of the most widely used model in estimating similarity among genotypes. In a bid to access useful information for the utilization of bush mango genetic resources, nucleotide sequences of eight bush mango (Irvingia gabonensis) cultivars were sourced for and retrieved form NCBI data base, and evaluated for diversity and similarity using computational biology approach. The highest alignment score (26.18), depicting the highest similarity, was between two pairs of sequence combinations; BM07:BM58 and BM12:BM69 respectively, while the least score (19.43) was between BM01: BM13. The phylogenetic tree broadly divided the cultivars into four distinct groups; BM07, BM58 (cluster one), BM01 (cluster 2), BM15, BM13 and BM35 (cluster 3), and BM12, BM69 (cluster 4), while the sequences obtained from the analysis revealed only few fully conserved regions, with the single nucleotides A, and T, which were consistent throughout the evolution. Results obtained from this study indicate that the bush mango cultivars are divergent and can be useful genetic resources for bush mango improvement through breeding.

QuanTest2: benchmarking multiple sequence alignments using secondary structure prediction

2019 ◽  
Author(s):  
Fabian Sievers ◽  
Desmond G Higgins
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Reference Sequence ◽  
Supplementary Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Reference Sequences ◽  
Selection Of

Abstract Motivation Secondary structure prediction accuracy (SSPA) in the QuanTest benchmark can be used to measure accuracy of a multiple sequence alignment. SSPA correlates well with the sum-of-pairs score, if the results are averaged over many alignments but not on an alignment-by-alignment basis. This is due to a sub-optimal selection of reference and non-reference sequences in QuanTest. Results We develop an improved strategy for selecting reference and non-reference sequences for a new benchmark, QuanTest2. In QuanTest2, SSPA and SP correlate better on an alignment-by-alignment basis than in QuanTest. Guide-trees for QuanTest2 are more balanced with respect to reference sequences than in QuanTest. QuanTest2 scores correlate well with other well-established benchmarks. Availability and implementation QuanTest2 is available at http://bioinf.ucd.ie/quantest2.tar, comprises of reference and non-reference sequence sets and a scoring script. Supplementary information Supplementary data are available at Bioinformatics online

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