  Evolutionary modeling applications are the best way to provide full
information to support in-depth understanding of evaluation of organisms. These
applications mainly depend on identifying the evolutionary history of existing
organisms and understanding the relations between them, which is possible
through the deep analysis of their biological sequences. Multiple Sequence
Alignment (MSA) is considered an important tool in such applications, where it
gives an accurate representation of the relations between different biological
sequences. In literature, many efforts have been put into presenting a new MSA
algorithm or even improving existing ones. However, little efforts on
optimizing parallel MSA algorithms have been done. Nowadays, large datasets
become a reality, and big data become a primary challenge in various fields,
which should be also a new milestone for new bioinformatics algorithms. This
survey presents four of the state-of-the-art parallel MSA algorithms, TCoffee,
MAFFT, MSAProbs, and M2Align. We provide a detailed discussion of each
algorithm including its strengths, weaknesses, and implementation details and
the effectiveness of its parallel implementation compared to the other
algorithms, taking into account the MSA accuracy on two different datasets,
BAliBASE and OXBench.
