# Bioinspired Algorithms for Multiple Sequence Alignment:
## 4 Methods
### Synthesis Results
#### 5.2.3 RQ3: Performance Evaluation Measures for MSA  
Due to the utilization of diverse algorithms by different Multiple Sequence Alignment tools, the quality of alignments may vary significantly. In response, various quality estimation techniques have been devised to evaluate and provide guidelines for the design and enhancement of sequence alignment software [55]. This section endeavors to address RQ3 by examining classical quality estimation methods [54] and providing a summary of Table 3 along with their descriptions [55]. Drawing from the literature, prevalent quality estimation techniques encompass structured-based, simulated, and consistency-based approaches. Subsequent subsections will delve into the specifics of these techniques:  
Structural-Based Method  
In particular, benchmarks were created using information on protein structure. The sort of benchmarks that are most commonly used are structural benchmarks [2]. These most frequently use the superposition of known protein structures as a separate alignment method, to which sequence analysis alignments can be compared using the previously mentioned sum-of-pairs and true column metrics. Structural standards are naturally quite relevant when looking for structural concordance across amino acid sequence alignments. One of the most often utilized structured benchmarks for protein alignment software is BAliBASE [56]. These benchmarks provide preset test sets and reference alignments that have been manually and automatically revised based on the three-dimensional structure of proteins. The concept behind these benchmarks is that amino acid residues corresponding to the same position in the three-dimensional structure should be aligned [55]. Many sequence alignment software developers optimize their programs on a limited number of data sets, which can lead to the \"high in score but low in ability\" phenomenon. This is true even though BAliBASE and other similar benchmarks are widely used and provide fixed test sets and reference alignments if these benchmarks are not updated regularly. Other specifically designed structural benchmarks are HOMSTRAD, PREFA, and SABmark, which are not generated by hand annotation of protein alignments like BAliBASE. Reference sets are also available for RNA structures [41; 106]. Another technique uses produced data sets to score Multiple Sequence Alignment software. One of the main goals of MSA is to find residues that have developed from a common ancestor. One benchmarking method is to create artificial sequence families by simulating evolution along a known tree. This simulation-based method tracks the \"real\" homology relationships between specific residue sites while describing nucleotide substitution, deletion, and insertion rates using a probabilistic model of sequence evolution. These methods generate evolved sequences and reference alignments based on the evolution of the sequence using a probabilistic model to simulate sequence evolution [55]. Since the evolution model totally governs the generated mutations, the accuracy of these benchmarks is limited by how well the accepted model represents the natural evolution. Since the evolution model totally governs the generated mutations, the accuracy of these benchmarks is limited by how well the accepted model represents the natural evolution. The likelihood that, in some cases, the probabilistic model may even skew the estimation if it mimics the sequence relationship model that the tested program uses further complicates the probabilistic model's creation and selection. These kinds of benchmarks offer pre-established reference alignments that may be used to assess the performance of recently created software. As a result, a scoring system is required to determine how well the tested program aligns with the reference. Numerous software packages, such as Rose [107], EvolveAGene3 [108], INDELIBE [109], Phylosim [110], Revolver [111], and ALF [112], are capable of conducting simulated sequence evolution.