scholarly journals Cascade PSI-BLAST web server: a remote homology search tool for relating protein domains

2006 ◽  
Vol 34 (Web Server) ◽  
pp. W143-W146 ◽  
Author(s):  
R. Bhadra ◽  
S. Sandhya ◽  
K. R. Abhinandan ◽  
S. Chakrabarti ◽  
R. Sowdhamini ◽  
...  
Genomics ◽  
2019 ◽  
Vol 111 (6) ◽  
pp. 1514-1516 ◽  
Author(s):  
Fernanda Cornejo-Granados ◽  
Juan Manuel Hurtado-Ramírez ◽  
Rogelio Hernández-Pando ◽  
Adrián Ochoa-Leyva
Keyword(s):  

2007 ◽  
Vol 8 (S2) ◽  
Author(s):  
Lydia Ng ◽  
Chris Lau ◽  
Rob Young ◽  
Sayan Pathak ◽  
Leonard Kuan ◽  
...  

2010 ◽  
Vol 17 (6) ◽  
pp. 819-823 ◽  
Author(s):  
Mats Aspnäs ◽  
Kimmo Mattila ◽  
Kristoffer Osowski ◽  
Jan Westerholm

Author(s):  
Xiaopeng Jin ◽  
Qing Liao ◽  
Hang Wei ◽  
Jun Zhang ◽  
Bin Liu

Abstract Motivation As one of the most important and widely used mainstream iterative search tool for protein sequence search, an accurate Position-Specific Scoring Matrix (PSSM) is the key of PSI-BLAST. However, PSSMs containing non-homologous information obviously reduce the performance of PSI-BLAST for protein remote homology. Results To further study this problem, we summarize three types of Incorrectly Selected Homology (ISH) errors in PSSMs. A new search tool Supervised-Manner-based Iterative BLAST (SMI-BLAST) is proposed based on PSI-BLAST for solving these errors. SMI-BLAST obviously outperforms PSI-BLAST on the Structural Classification of Proteins-extended (SCOPe) dataset. Compared with PSI-BLAST on the ISH error subsets of SCOPe dataset, SMI-BLAST detects 1.6–2.87 folds more remote homologous sequences, and outperforms PSI-BLAST by 35.66% in terms of ROC1 scores. Furthermore, this framework is applied to JackHMMER, DELTA-BLAST and PSI-BLASTexB, and their performance is further improved. Availability and implementation User-friendly webservers for SMI-BLAST, JackHMMER, DELTA-BLAST and PSI-BLASTexB are established at http://bliulab.net/SMI-BLAST/, by which the users can easily get the results without the need to go through the mathematical details. Supplementary information Supplementary data are available at Bioinformatics online.


2016 ◽  
Vol 32 (18) ◽  
pp. 2744-2752 ◽  
Author(s):  
Mindaugas Margelevičius

Proteomes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Yoji Igarashi ◽  
Daisuke Mori ◽  
Susumu Mitsuyama ◽  
Kazutoshi Yoshitake ◽  
Hiroaki Ono ◽  
...  

Metagenomic data have mainly been addressed by showing the composition of organisms based on a small part of a well-examined genomic sequence, such as ribosomal RNA genes and mitochondrial DNAs. On the contrary, whole metagenomic data obtained by the shotgun sequence method have not often been fully analyzed through a homology search because the genomic data in databases for living organisms on earth are insufficient. In order to complement the results obtained through homology-search-based methods with shotgun metagenomes data, we focused on the composition of protein domains deduced from the sequences of genomes and metagenomes, and we utilized them in characterizing genomes and metagenomes, respectively. First, we compared the relationships based on similarities in the protein domain composition with the relationships based on sequence similarities. We searched for protein domains of 325 bacterial species produced using the Pfam database. Next, the correlation coefficients of protein domain compositions between every pair of bacteria were examined. Every pairwise genetic distance was also calculated from 16S rRNA or DNA gyrase subunit B. We compared the results of these methods and found a moderate correlation between them. Essentially, the same results were obtained when we used partial random 100 bp DNA sequences of the bacterial genomes, which simulated raw sequence data obtained from short-read next-generation sequences. Then, we applied the method for analyzing the actual environmental data obtained by shotgun sequencing. We found that the transition of the microbial phase occurred because the seasonal change in water temperature was shown by the method. These results showed the usability of the method in characterizing metagenomic data based on protein domain compositions.


PLoS ONE ◽  
2012 ◽  
Vol 7 (5) ◽  
pp. e36060 ◽  
Author(s):  
Shuji Suzuki ◽  
Takashi Ishida ◽  
Ken Kurokawa ◽  
Yutaka Akiyama
Keyword(s):  

2008 ◽  
Vol 25 (1) ◽  
pp. 121-122 ◽  
Author(s):  
I. Melvin ◽  
J. Weston ◽  
C. Leslie ◽  
W. S. Noble

2017 ◽  
Vol 18 (S12) ◽  
Author(s):  
Prapaporn Techa-Angkoon ◽  
Yanni Sun ◽  
Jikai Lei

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