Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSIBLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

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  1. Gullotto, Danilo: Fine tuned exploration of evolutionary relationships within the protein universe (2021)
  2. Ahmad, Jamal; Hayat, Maqsood: MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components (2019)
  3. Randić, Milan: Mathematical chemistry illustrations: a personal view of less known results (2019)
  4. Rout, Subhashree; Mahapatra, Rajani Kanta: \textitInsilico analysis of \textitplasmodiumfalciparum CDPK5 protein through molecular modeling, docking and dynamics (2019)
  5. Tao, Jin; Liu, Xiaoqing; Yang, Siqian; Bao, Chaohui; He, Pingan; Dai, Qi: An efficient genomic signature ranking method for genomic island prediction from a single genome (2019)
  6. Wang, Lei; Yan, Xin; Liu, Meng-Lin; Song, Ke-Jian; Sun, Xiao-Fei; Pan, Wen-Wen: Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method (2019)
  7. Abboud, Amir; Bringmann, Karl: Tighter connections between formula-SAT and shaving logs (2018)
  8. Abboud, Amir; Rubinstein, Aviad: Fast and deterministic constant factor approximation algorithms for LCS imply new circuit lower bounds (2018)
  9. Charikar, Moses; Geri, Ofir; Kim, Michael P.; Kuszmaul, William: On estimating edit distance: alignment, dimension reduction, and embeddings (2018)
  10. Kinjo, Akira R.: Cooperative “folding transition” in the sequence space facilitates function-driven evolution of protein families (2018)
  11. Liang, Yunyun; Zhang, Shengli: Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence (2018)
  12. Tian, Kun; Zhao, Xin; Yau, Stephen S.-T.: Convex hull analysis of evolutionary and phylogenetic relationships between biological groups (2018)
  13. Zhang, Shengli; Liang, Yunyun: Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC (2018)
  14. Abboud, Amir; Backurs, Arturs: Towards hardness of approximation for polynomial time problems (2017)
  15. Dehzangi, Abdollah; López, Yosvany; Lal, Sunil Pranit; Taherzadeh, Ghazaleh; Michaelson, Jacob; Sattar, Abdul; Tsunoda, Tatsuhiko; Sharma, Alok: PSSM-Suc: accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction (2017)
  16. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  17. Pai, Priyadarshini P.; Dash, Tirtharaj; Mondal, Sukanta: Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach (2017)
  18. Shatabda, Swakkhar; Saha, Sanjay; Sharma, Alok; Dehzangi, Abdollah: iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features (2017)
  19. Jiao, Ya-Sen; Du, Pu-Feng: Prediction of Golgi-resident protein types using general form of Chou’s pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection (2016)
  20. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)

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