# Bioinspired Algorithms for Multiple Sequence Alignment:
## 4 Methods
### Synthesis Results
#### 5.2.2 RQ2: Benchmark Methods Used in MSA  
Benchmarks for Multiple Sequence Alignment are used to evaluate the effectiveness of various MSA techniques and algorithms. There must be some benchmark sequences and \"gold standard\" reference alignments of those sequences to compare various alignment methods efficiently. It is necessary to determine a quantifiable accuracy concerning gold standard reference alignments to evaluate the performance of various automated alignment systems. Several benchmarks are already available, with the primary objective being to evaluate the quality of the various Multiple Sequence Alignment tools. MSA solutions have been assessed using a variety of benchmarks based on the papers chosen for this SLR. Thus, the effectiveness of various MSA techniques and algorithms is evaluated using benchmarks. These benchmarks usually comprise a selection of well-chosen reference alignments for a wide range of protein or nucleotide sequences. Many benchmarks are available for free, all of which aim to evaluate the various Multiple Sequence Alignment tools. Several benchmarks have been utilized to assess MSA solutions based on the papers that were chosen for this SLR. This includes HOMSTRAD [64], BAliBASE 2.0 [59], OXBench [105], PREFAB [90], SABmark [94], and BAliBASE version 3 [71]. Table 6 shows the summary of the benchmarks identified in the selected studies and their explanation given as follows:  
* HOMSTRAD [64]: This database contains protein domains grouped according to structural and sequence similarities. Several authors have used HOMSTRAD as a benchmark database, even though it was not intended to be one. The database offers combined information on protein structure and sequence that has been taken from the PDB [95] as well as other databases, such as Pfam [96] and SCOP [97]; the most recent version of the database includes 9602 single-member families and 1032 domain families, with each family containing 2 to 41 sequences. * BAliBASE [106; 107]: This is the first extensive benchmark created especially for the alignment of multiple sequences. To guarantee the proper alignment of conserved residues, the alignment test cases are based on 3D structural superpositions that are manually improved. A wide range of issues encountered by multiple alignment methods is represented by the 217 alignments in the current version of BAliBASE, ranging from 4 to 142 sequences. These alignments are arranged into six reference sets, which include sequences with large NC-terminal extensions or internal insertions, transmembrane regions, repeated or inverted domains, and eukaryotic linear motifs. * OXBench [105]: This offers several automatically constructed protein alignments based on aligning techniques for sequence and structure. Three data sets comprise the benchmark; between two and twenty-two sequences per alignment, the master collection now consists of 673 alignments of protein domains with known three  
Figure 7: Distribution of the bioinspired algorithms used in MSA. -dimensional structures. Sequences with an unknown structure are added to the master set to create the extended dataset. Lastly, the full-length sequences for the domains in the master data set are included in the full-length data collection. * PREFAB [90]: This dataset has 1932 multiple alignments and was built with a fully automated protocol. Two approaches of 3D structure superposition were used to identify and align pairs of sequences with known 3D structures. Next, for every pair of structures, multiple alignments were created using 50 homologous sequences found through sequence database searches; since the building is automatic, many tests can be incorporated. This benchmark's drawback is that it only infers multiple alignment accuracy from aligning the first two sequences with known 3D structures.