ABSTRACT
Basic local alignment search tool (BLAST) is one of the popular sequence similarity analysis tools. However, some students and researchers just blindly use the default parameters. Moreover, some students are confused about how to choose the right program. In a word, it is prone to be misused and researchers often draw conclusions incorrectly. In view of this, we traced back the internet hot topic in early 2020 - "MORDERATELY STRONG CONFIRMATION OF A LABORATORY ORIGIN OF COVID-19", and took it as teaching materials to guide the student to use BLAST currently through reanalyzing and reproducing the source of errors. Then we arranged an interesting experiment about fabricating dinosaur genes through modifying a chicken gene. In the experimental design to make the students grasp the BLAST tools better, one group fabricated the dinosaur gene and the other group decrypted the added bases. This instructional design could be conducive to cultivate students ' ability about distinguishing different viewpoints correctly, and we hope it can be enlightening and helpful to the teaching of BLAST tools.
ABSTRACT
Adaboost algorithm with improved K-nearest neighbor classifiers is proposed to predict protein subcellular locations. Improved K-nearest neighbor classifier uses three sequence feature vectors including amino acid composition, dipeptide and pseudo amino acid composition of protein sequence. K-nearest neighbor uses Blast in classification stage. The overall success rates by the jackknife test on two data sets of CH317 and Gram1253 are 92.4% and 93.1%. Adaboost algorithm with the novel K-nearest neighbor improved by Blast is an effective method for predicting subcellular locations of proteins.