Statistical Methods for Identifying Conserved Residues in Multiple Sequence Alignment
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| 001 | 378925962 | ||
| 003 | CHVBK | ||
| 005 | 20180305123617.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 161128e20041030xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.2202/1544-6115.1074 |2 doi |
| 035 | |a (NATIONALLICENCE)gruyter-10.2202/1544-6115.1074 | ||
| 245 | 0 | 0 | |a Statistical Methods for Identifying Conserved Residues in Multiple Sequence Alignment |h [Elektronische Daten] |c [Virpi Ahola, Tero Aittokallio, Esa Uusipaikka, Mauno Vihinen] |
| 520 | 3 | |a The assessment of residue conservation in a multiple sequence alignment is a central issue in bioinformatics. Conserved residues and regions are used to determine structural and functional motifs or evolutionary relationships between the sequences of a multiple sequence alignment. For this reason, residue conservation is a valuable measure for database and motif search or for estimating the quality of alignments. In this paper, we present statistical methods for identifying conserved residues in multiple sequence alignments. While most earlier studies examine the positional conservation of the alignment, we focus on the detection of individual conserved residues at a position. The major advantages of multiple comparison methods originate from their ability to select conserved residues simultaneously and to consider the variability of the residue estimates. Large-scale simulations were used for the comparative analysis of the methods. Practical performance was studied by comparing the structurally and functionally important residues of Src homology 2 (SH2) domains to the assignments of the conservation indices. The applicability of the indices was also compared in three additional protein families comprising different degrees of entropy and variability in alignment positions. The results indicate that statistical multiple comparison methods are sensitive and reliable in identifying conserved residues. | |
| 540 | |a ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston | ||
| 690 | 7 | |a Computational Biology/Bioinformatics |2 nationallicence | |
| 690 | 7 | |a Protein sequence analysis |2 nationallicence | |
| 690 | 7 | |a residue conservation |2 nationallicence | |
| 690 | 7 | |a conservation index |2 nationallicence | |
| 690 | 7 | |a multiple comparison |2 nationallicence | |
| 690 | 7 | |a SH2 domain |2 nationallicence | |
| 700 | 1 | |a Ahola |D Virpi |u University of Turku |4 aut | |
| 700 | 1 | |a Aittokallio |D Tero |u University of Turku |4 aut | |
| 700 | 1 | |a Uusipaikka |D Esa |u University of Turku |4 aut | |
| 700 | 1 | |a Vihinen |D Mauno |u University of Tampere |4 aut | |
| 773 | 0 | |t Statistical Applications in Genetics and Molecular Biology |d De Gruyter |g 3/1(2004-10-30), 1-28 |q 3:1<1 |1 2004 |2 3 |o sagmb | |
| 856 | 4 | 0 | |u https://doi.org/10.2202/1544-6115.1074 |q text/html |z Onlinezugriff via DOI |
| 908 | |D 1 |a research article |2 jats | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.2202/1544-6115.1074 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ahola |D Virpi |u University of Turku |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Aittokallio |D Tero |u University of Turku |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Uusipaikka |D Esa |u University of Turku |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Vihinen |D Mauno |u University of Tampere |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Statistical Applications in Genetics and Molecular Biology |d De Gruyter |g 3/1(2004-10-30), 1-28 |q 3:1<1 |1 2004 |2 3 |o sagmb | ||
| 900 | 7 | |b CC0 |u http://creativecommons.org/publicdomain/zero/1.0 |2 nationallicence | |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-gruyter | ||