21 Motivation 22 Subcellular location plays an essential role in protein synthesis, transport, and secretion, thus it is an 23 important step in understanding the mechanisms of trait-related proteins. Generally, homology methods provide 24 reliable homology-based results with small E-values. We must resort to pattern recognition algorithms (SVM, 25 Fisher discriminant, KNN, random forest, etc.) for proteins that do not share significant homologous domains 26 with known proteins. However, satisfying results are seldom obtained. 27 Results 28 Here, a novel hybrid method "Basic Local Alignment Search Tool+Smith-Waterman+Needleman-Wunsch" 29 or BLAST+SWNW, has been obtained by integrating a loosened E-value Basic Local Alignment Search Tool 30 (BLAST) with the Smith-Waterman (SW) and Needleman-Wunsch (NW) algorithms, and this method has been 31 introduced to predict protein subcellular localization in eukaryotes. When tested on Dataset I and Dataset II, 32 BLAST+SWNW showed an average accuracy of 97.18% and 99.60%, respectively, surpassing the performance 33 of other algorithms in predicting eukaryotic protein subcellular localization. 34 Availability and Implementation 35 BLAST+SWNW is an open source collaborative initiative available in the GitHub repository 36 (https://github.com/ZHANGDAHAN/BLAST-SWNW-for-SLP or http://202.206.64.158:80/link/72016CAC 37 26E4298B3B7E0EAF42288935 ) 38 Contact 39