## 3 Results and Discussion
### CleanSeq Is Practical when Applied to either Simulated or Real Experimental Datasets
coli_ still could reach -133\(\times\) (Supplemental Data S3), ensuring the correctness of the following variant analysis. After variant calling by GATK, we obtained all mutation information from P2 (free of contamination), P16 cell passage, and its derived five _E. coli_ single colonies (P16S1-5). As shown in Figure 5C, P16 and P16S1-5 contained the same seven mutation sites, six of which were also detected from P2 passages. It should be highlighted here that MutScan can only call out five variants, partially because MutScan has a limitation in detecting long insertions and deletions (INDELs) [17]. Another possibility is the overcleaning of the reads, which were initially from _E. coli_. Those variants were then further confirmed by re-alignment (Clustal Omega) of the corresponding reads extracted using the generated six _k_-mers (One of the generated _k_-mers contained two close mutation sites, Supplemental Data S4) [17; 28]. No misalignment was found for all seven variants, suggesting the high correctness of the GATK-integrated CleanSeq pipeline. The consistency of called-out genome mutations among the contaminated sample and its derived single colonies indicated that the decontamination and verification processes are sufficient for real WGS analysis like this study. A rational  
Figure 3: Comparison of the mutation calling performance between CleanSeq and MutScan. The simulated datasets of five kinds of bacterial species (_P. aeruginosa_ (CP007224.1), _E. coli_ (NZ_LR881938.1), _Citrobacter freundii_ (CP016762.1), _B. fragilis_ (CR626927.1) and _Streptococcus pyogenes_ (AE014074.1)) with different sequencing coverage ((**A**), 30\(\times\); **(B**), 100\(\times\)) and mutation rates (1 \(\times\) 10\({}^{-4}\), 1 \(\times\) 10\({}^{-5}\), and 1 \(\times\) 10\({}^{-6}\)) were used to test the efficiency of mutation calling and verification. decision could be made according to those results on whether further experiments or reruns should be performed or not. Figure 4: Performance of CleanSeq on the simulated dataset mimicking bacterial contamination. The contamination test set was a reads mixture of _E. coli_ and _P. aeruginosa_ in a ratio of 2:1, with _E. coli_ as the target bacteria and _P. aeruginosa_ as the contaminating bacteria. The analytical flow and species reports were shown in (**A**). Detailed statistical results of the cleanup and mutation calling were listed in (**B**). Among the called genomic mutations, it was desirable to obtain two shared genomic mutations, _diJA_ (Phe54 Leu, missense variant) and _wecA_ (Gln104*, stop gained) in P2 and P16 passage lines, and one specific mutation _vigA_ (Thr94_Asp103 delinsAsn, disruptive inframe deletion) in P16 passage lines. _diJA_ is a cell inner membrane protein involved in regulating the synthesis of the colonic acid capsule, which might contribute to cell viability in harsh conditions [44; 45]. _wecA_ is also a cell inner member protein reported to initiate the biosynthesis of entrepreneurial common antigen and O-antigen lipopolysaccharideaccharide (LPS) [46]. _vigA_ is a DUF484 domain-containing protein with unknown functions. All three mutated genes found in passage lines of this study were nonessential, and both of _diJA_ and _wecA_ were potentially related to the cell membrane or cell wall functions and were probably targeted by cell wall-targeting reagents like Mecillinam and lysozyme used in this study [47], although no direct evidence was provided. Further study is crucial to investigate and uncover the possible involvement and mechanism of sorted cells with the above gene mutations.