Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

By Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schäffer, Jinghui Zhang, Zheng Zhang, Webb Miller, David J. Lipman. 1997

Subfields: Computational Biology & Bioinformatics

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Citations in the corpus

Protein Sequence Analysis Using the MPI Bioinformatics Toolkit (2020, methodology): “Over the years, many high-quality sequence search methods [e.g., BLAST (Altschul et al., 1997; Ladunga, 2017), HMMER (Potter et al., 2018; Prakash, Jeffryes, Bateman, & Finn, 2017), HHblits (Remmert, Biegert, Hauser, & Soding, 2011), HHpred (Soding, 2005; Steinegger et al., 2019)]; protein sequence…”
SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “Sequence similarity among these proteins is <25% according to BLASTClust [37].”
The SWISS-MODEL Repository—new features and functionality (2016, methodology): “The SMTL library of amino acid sequences is searchable by BLAST (20) and HHBlits (16).”
ECOD: An Evolutionary Classification of Protein Domains (2014, methodology): “E) PSI-BLAST alignment of the Duf371 repeats detected with Mefer0473 sequence supports the duplication event, with sequence similarities indicated between N-terminal and C-terminal halves.”
Toward the estimation of the absolute quality of individual protein structure models (2010, background): “Most current scoring functions are primarily designed to rank alternative models of the same sequence supporting model selection, whereas the prediction of the absolute quality of an individual protein model has received little attention in the field.”
Hidden Markov model speed heuristic and iterative HMM search procedure (2010, methodology): “In comparison, the heuristic pairwise sequence comparison method WU-BLAST is greater than 200-fold faster [2,3].”
Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “Consequently, methods as fast as PSI-BLAST [33] have to be replaced by faster solutions such as HHblits [34].”
Evaluating Protein Transfer Learning with TAPE (2019, methodology): “The key technique for modeling sequence relationships in computational biology is alignment [13, 16, 21, 22].”
DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, methodology): “Profilesequence alignment method [13] and profile-profile alignment methods based hidden Markov model (HMM) [9] or Markov random fields (MRFs) [14] are more sensitive in recognize proteins that have the same fold, but little sequence similarity, than sequence-sequence alignment methods.”
Better prediction of functional effects for sequence variants (2015, positive): “Starting with these, we built an undirected graph, where vertices are proteins and edges link vertices to the corresponding BLAST hits.”