# Bioinspired Algorithms for Multiple Sequence Alignment:
## 4 Methods
### Synthesis Results
#### 5.2.3 RQ3: Performance Evaluation Measures for MSA
The alignment accuracy, compared to the genuine alignment (known from the simulation), is often evaluated using two metrics: the sum-of-pairs (SP) and the true column (TC) scores [111]. The SP score refers to the fraction of aligned residue pairsthat are consistent between the reconstructed and actual alignments, averaged across all pairwise comparisons between individual sequences. The TC score measures the proportion of completely aligned columns replicated in the reconstructed alignment. Consistency-Based Method  
An alternative technique for estimating quality is based on the similarities between alignments produced using various Multiple Sequence Alignment strategies rather than a reference alignment. It is predicated on the notion that two residues are probably accurately aligned if many software programs reliably align them. However, a significant flaw in this approach is that if two residues are regularly aligned incorrectly by separate software programs, the scoring system will accept this error as accurate. This approach employs specific scoring techniques, including the multiple overlap score [113] and the head-or-tail (HoT) score [114, 115], in contrast to the estimating methods that rely on reference alignment. Two popular scoring approaches that quantify the degree of similarity between two alignments by counting common pairs and columns between them are the total column or the column (TC) score and the sum-of-pairs (SP) score [116]. The Wilcoxon signed-rank test [117] and the Friedman rank test [76, 118] may be used for alignment accuracy discrimination by providing a \(p\)-value, which indicates the likelihood that the performance difference between different methods is due to chance. Based on the idea that biological sequences lack direction, this consistency test assumes that alignments should remain unchanged regardless of whether the input sequences are provided in the original order or inverted. The overlap measurements mentioned above can be used to quantify the degree of agreement between the alignments produced from the original and reversed sequences. Consistency among aligners and score are two intriguing consistency approaches that may be used easily because they do not assume a reference alignment or sequence of evolution model. Moreover, a set of precise aligners must have great consistency, which makes it attractive. Although most aligners share numerous characteristics and are therefore not \"independent\", the consistency requirement nevertheless appeals to the intuitive notion of \"independent validation\". The consistency method has a significant flaw in not ensuring accuracy; approaches can be consistently incorrect. More quietly, the selection of aligners within the set impacts consistency. While this can be somewhat alleviated by presenting as many diverse alignments as possible, it is still possible for an accurate alignment to be graded negatively if inaccurate but similar alignments outnumber it. Table 7 shows the performance evaluation measures used for the MSA problem. From Table 7, it can be observed that several performance quality measures are used to validate the effectiveness of the Multiple Sequence Alignment solutions. Based on the published papers from the literature selected for our SLR study, the most popularly used performance quality evaluation measures include structural-based techniques, simulated- based approaches and consistency-based approaches. From Figure 9, it can be observed that structural-based techniques based on the idea that amino acid residues that correspond to the same position in the three-dimensional structure should be aligned are the most popularly used quality measures for MSA with 73%. Next is the consistency predicated on the notion that two residues are probably accurately aligned if many software programs reliably align them with 17%. And finally, the simulation-based techniques use a probabilistic model to simulate sequence evolution and produce evolved sequences and reference alignments with 10%.