A Hybrid Approach using Progressive and Genetic Algorithms for

Improvements in Multiple Sequence Alignments

Geraldo Francisco Doneg

a Zafalon

1,3 a

, Vitoria Zanon Gomes

1 b

, Anderson Rici Amorim

2,1 c

and Carlos Roberto Val

encio

1 d

Department of Computer Science and Statistics, Universidade Estadual Paulista (UNESP),

Rua Crist

ao Colombo, 2265, Jardim Nazareth, S

ao Jos

e do Rio Preto, SP, 15054-000, Brazil

Department of Computer and Digital Systems Engineering, Universidade de S

ao Paulo (USP), Escola Polit

ecnica,

Av. Prof. Luciano Gualberto, Travessa 3, 158, Butant

a, S

ao Paulo, SP, 05508-010, Brazil

Department ICET, Universidade Paulista, Avenida Presidente Juscelino Kubitschek de Oliveira,

s/n, Jardim Tarraf II, S

ao Jos

e do Rio Preto, SP, 15091-450, Brazil

Keywords:

Genetic Algorithm, Multiple Sequence Alignment, Hybrid Multiple Sequence Alignment, Bioinformatics.

Abstract:

The multiple sequence alignment is one of the main tasks in bioinformatics. It is used in different important bi-

ological analysis, such as function and structure prediction of unknown proteins. There are several approaches

to perform multiple sequence alignment and the use of heuristics and meta-heuristics stands out because of

the search ability of these methods, which generally leads to good results in a reasonable amount of time.

The progressive alignment and genetic algorithm are among the most used heuristics and meta-heuristics to

perform multiple sequence alignment. However, both methods have disadvantages, such as error propagation

in the case of progressive alignment and local optima results in the case of genetics algorithm. Thus, this work

proposes a new hybrid reﬁnement phase using a progressive approach to locally realign the multiple sequence

alignment produced by genetic algorithm based tools. Our results show that our method is able to improve

the quality of the alignments of all families from BAliBase. Considering Q and TC quality measures from

BaliBase, we have obtained the improvements of 55% for Q and 167% for TC. Then, with these results we

can provide more biologically signiﬁcant results.

1 INTRODUCTION

Nowadays, we can notice a constant growth in the

biological data available, thus, computational meth-

ods are essential to assist biological analysis (Baxeva-

nis et al., 2020). This fact originated bioinformatics,

which is a research ﬁeld that provides computational

tools and methods to many biological problems, such

as SNP analysis (Elek et al., 2020), pattern recog-

nition (Kasabov, 2019; Martino et al., 2018; Agger

et al., 2017), phylogenetic analysis (Lemieux et al.,

2020; Zhang et al., 2018; Nascimento et al., 2017)

and sequence alignment (Gao and Skolnick, 2020;

Smirnov and Warnow, 2020; Suplatov et al., 2018).

The sequence alignment is a well-known method

in bioinformatics, because it is used in many bio-

https://orcid.org/0000-0003-2384-011X

https://orcid.org/0000-0003-4176-566X

https://orcid.org/0000-0001-7862-7530

https://orcid.org/0000-0002-9325-3159

logical analysis, such as structure prediction of pro-

teins (Sievers and Higgins, 2020; Bawono et al., 2017;

Le et al., 2017), evolutionary studies (Zhang et al.,

2020a; Li et al., 2020; Edgar and Batzoglou, 2006),

phylogenetic analysis (Asnicar et al., 2020), among

others. We can cite the importance of sequence align-

ment methods on the efforts against the COVID-19

pandemic (Angeletti et al., 2020; Zhang et al., 2020b;

Tilocca et al., 2020; Ibrahim et al., 2020). Basically,

the sequence alignment consists in rearranging the nu-

cleotide or amino acid bases of the sequences, using

the systematic insertion of gaps, in order to optimize

metrics related to the biological signiﬁcance of the

alignment (Amorim et al., 2018).

The best sequence alignment can be obtained

with dynamic programming approaches, such as

Needleman-Wunsch (Needleman and Wunsch, 1970),

to perform global sequence alignment, or Smith-

Waterman (Smith et al., 1981), to perform local align-

ment. However, due to the fact that these algorithms

were ideally developed to perform pairwise sequence

384

Zafalon, G., Gomes, V., Amorim, A. and Valêncio, C.

A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments.

DOI: 10.5220/0010495303840391

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 384-391

ISBN: 978-989-758-509-8

alignments, it is unfeasible, in terms of computational

complexity, to perform the alignment for more than

three sequences (Wang and Jiang, 1994). Thus, it

was necessary to develop new methods to deal with

many biological sequences simultaneously and per-

form Multiple Sequence Alignments (MSA), which

is high desired nowadays (Bawono et al., 2017). The

MSA algorithms are stochastic approaches that gener-

ally can produce results with relevant biological sig-

niﬁcance in a feasible amount of time (Nute et al.,

2019).

There are different approaches to perform Mul-

tiple Sequence Alignment, based on many heuris-

tics and meta-heuristics, such as Progressive Align-

ment (Armstrong et al., 2020; Sievers and Higgins,

2018), Fast Fourier Transform (Katoh et al., 2002;

Rozewicki et al., 2019; Nakamura et al., 2018), Tabu

Search (Riaz et al., 2004), Simulated Annealing (Cor-

rea et al., 2012), Particle Swarm Optimization (Tran

and Wallinga, 2017; Zhang et al., 2014; Rasmussen

and Krink, 2003) Genetic Algorithms (GA) (Chen-

touﬁ et al., 2016; Kaya et al., 2016; Kumar, 2015),

among others. Some of the most used tools to per-

form MSA, such as Clustal family (Sievers and Hig-

gins, 2018; Thompson et al., 1994), Kalign (Lass-

mann, 2019) and MUSCLE (Edgar, 2004), are based

on Progressive Alignment. However, this heuristic

has known disadvantages, such as error propagation

(Gondro and Kinghorn, 2007) and order dependency

in the input sequences (Boyce et al., 2015), which

may lead to noisy results that could be improved.

On the other hand, GA based approaches do not face

these disadvantages, once the iterative nature of the

method may deal with the error propagation problem

(Gondro and Kinghorn, 2007). This fact lead GA to

be one of the most-used meta-heuristics to perform

MSA (Chowdhury and Garai, 2017). Nonetheless,

GA based tools have other disadvantages, such as lo-

cal optima solutions, which means that the produced

alignment is not the global best alignment and still

could be improved (Lee et al., 2008).

As we can notice, the advantages and disadvan-

tages of Progressive Alignment and Genetic Algo-

rithm methods are complementary. Thus, hybrid ap-

proaches can be used to smooth the disadvantages

of the methods, which may lead to better Multiple

Sequence Alignments in terms of biological signiﬁ-

cance. Thus, this work aims to develop a hybrid re-

ﬁnement phase, based on Progressive Alignment, to

improve the quality of the alignments of Genetic Al-

gorithm based tools. With this, we are able to smooth

the local optima problem and to obtain results with

greater biological signiﬁcance.

This work is organized as follows: in section 2 we

better explain the Multiple Sequence Alignment, in

section 3 we show the related works, in section 4 we

show our methodology, in section 5 we show the tests

and obtained results and, ﬁnally, in section 6 we show

our conclusions.

2 MULTIPLE SEQUENCE

ALIGNMENT

As we have aforementioned, the Multiple Sequence

Alignment is the alignment of three or more biolog-

ical sequences. The MSA problem can be deﬁned

as a set of input sequences to be aligned. Theses se-

quences may be deﬁned over an alphabet {A,T,C, G}

when dealing with nucleotide sequences and

{A,C, D, E, F, G,H, I, K, L, M, N, P,Q, R, S, T,V,W,Y }

when dealing with amino acids sequences. Thus, the

MSA is given by a new set where all input sequences

must have the same length and the alphabet is

deﬁned over the old one plus the gap symbol (-).

The gap represents insertions and deletions on the

sequences and it is used to equal the length of the

input sequences.

This process is executed through the manipula-

tion of bases positions, using the gaps to make the

sequences length the same, in some systematic way.

The main goal is to optimize quality metrics, known

as objective functions. Examples of objective func-

tions optimization in MSA are maximize the num-

ber of correspondent bases, i.e. when bases at the

same column of the alignment are the same, minimize

the number of gaps in the sequences, among others.

When we consider its implementation, the MSA is

a matrix structure where the rows represent the se-

quences and the columns represent the aligned bases.

3 RELATED WORKS

The Genetic Algorithm is a meta-heuristic based on

the natural selection, where individuals of a popula-

tion are exposed to selection, mutation and recom-

bination processes, in order that only the best indi-

viduals are selected to the next generations. Thus,

as the process continues, the algorithm converge to

the optimal solution. When applied to solve the MSA

problem, each individual represent a possible Multi-

ple Sequence Alignment (Amorim et al., 2018). The

ﬁrst tool that applied GA to MSA was SAGA, which

has implemented a complex group of 22 operators

of mutation and recombination (Notredame and Hig-

gins, 1996). However, other works showed that this

A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments

385

complexity does not improve the quality of the align-

ment when compared with simpler GA (Thomsen and

Boomsma, 2004). Thus the MSA-GA tool was devel-

oped by Gondro and Kinghorn (2007) with a reduced

group of operators. The authors show that MSA-

GA is able to obtain better results when compared

to widely used tools, such as Clustal W (Thompson

et al., 1994).

We can notice many recent published works that

use GA to perform MSA. Fan et al. (2012) and Kaya

et al. (2014) proposed different GA approaches to

solve the MSA problem, focusing on improvements

in the genetic operators and also the objective func-

tions that evaluate the quality of the produced solu-

tion. Both works were able to produce good results,

but authors show that the results could be still reﬁned

to reach greater biological signiﬁcance.

Rani and Ramyachitra (2017) proposed a GA to

perform MSA, focused on the parameters of the re-

combination operators. The quality of the solutions is

computed through a multi-objective approach, which

simultaneously optimizes different characteristics of

the alignment. One of the main contributions of this

work is that the authors showed that the horizontal

crossover operator is better to improve the MSA qual-

ity when compared to other crossover operators. The

proposed method was able to obtain good results, but

in some cases other consistency-based tools, such as

T-Coffee (Notredame et al., 2000), were able to pro-

duce better results. Thus, authors claim that a hybrid

approach may produce better results.

With this, Chatterjee et al. (2019) proposed a hy-

brid GA and Chemical Reaction Optimization (CRO)

to perform MSA. The GA executes the routine and

produce the Multiple Sequence Alignments, then, the

CRO is called as a reﬁnement phase after each GA it-

eration. The authors show that the method obtained

good results, but it still face problems to correctly

align less similar sequences sets.

To smooth this problem, Rubio-Largo et al. (2016)

proposed a hybrid scheme, based on Progressive

Alignment, to locally reﬁne the MSA produced by

an Artiﬁcial Bee Colony Optimization (ABCO) ap-

proach, using the Kalign tool (Lassmann, 2019). The

authors show that the method was able to obtain better

results and claim that the reﬁnement phase was essen-

tial to the quality improvement.

Thus, once GA and ABCO are population-based

approaches with the same nature, the improvements

reached by the ABCO through this hybrid approach

may also be extended to Genetic Algorithm based

tools.

4 MATERIAL AND METHODS

4.1 The MSA-GA Tool

In this work, we choose the MSA-GA as GA based

tool to perform the Multiple Sequence Alignments.

This choice is due to the fact that MSA-GA has a

simpler GA scheme when compared with other GA

tools and it is able to produce good results, even better

than some Progressive Alignment based tools, such as

Clustal W (Gondro and Kinghorn, 2007).

In the MSA-GA, the population is initial-

ized based on pairwise alignments, computed with

Needleman-Wunsch. After that, the ﬁtness of each

individual is calculated with the Weighted Sum-of-

Pairs (WSP) scoring function. A tournament phase is

executed, selecting the individuals based on their ﬁt-

ness, as a ranking scheme, in order to expose them to

the genetic operators. In the MSA-GA, it was imple-

mented two crossover operators: horizontal crossover

and vertical crossover. The ﬁrst one selects two in-

dividuals to recombine, deﬁnes a horizontal cut point

and selects entire sequences from both individuals to

generate new ones. The vertical crossover selects two

individuals, apply a vertical cut point, separating the

sequences into two parts, and generate new individ-

uals based on these parts. Moreover, the MSA-GA

tool implements three types of mutation operators to

optimize the gap positions: gap opening, gap exten-

sion and gap reduction. The ﬁrst one randomly se-

lects a position and a block of gaps is inserted into the

sequence. The second one randomly selects a block

of gaps and then a gap position is inserted into the

sequence. Finally, the third one randomly selects a

block of gaps and a gap position is deleted from it.

The GA executes this process over the iterations

until a stop criteria is reached, such as maximum

number of generations. After that, the best individ-

ual is given as output as the best MSA found. Thus,

we have developed a reﬁnement stage into the MSA-

GA tool to deal with eventual alignments trapped in a

local optima position, based on a hybrid scheme with

Progressive Alignment.

4.2 Hybrid Reﬁnement Phase

Initially, we must deﬁne when our reﬁnement phase

will be called into the GA routine. Thus, we have set

a user-deﬁned parameter responsible for calling the

reﬁnement routine after n iterations without any ﬁt-

ness improvement in the best individual of the GA.

We ﬁnd this is a good way to tell if a MSA is trapped

in a local optima position.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

386

We can see in the Figure 1 the ﬂowchart of the

proposed method. The basic idea of our reﬁnement

phase is to locally realign a region of the MSA, based

on the Progressive Alignment heuristic.

When the reﬁnement routine is called, initially we

deﬁne the part of the MSA that will be realigned. The

size of this part is randomly deﬁned over an interval

between 5% and 25% of the MSA size. Once the size

was deﬁned, the correspondent part of the alignment

is randomly selected. After that, we take the selected

part of the alignment, delete all gap positions and gen-

erate a new ﬁle with sequences related to the selected

bases, without the gaps. Thus, we give this ﬁle as

an input to the Kalign tool (Lassmann, 2019), which

will realign the selected part. In this work, we choose

the Kalign tool as a hybrid scheme because it is able

to quickly and precisely realign local parts of a MSA

(Rubio-Largo et al., 2016). Once Kalign realigned the

given part, we take this output and reinsert it into the

original MSA. All this realignment process is illus-

trated in Figure 2. After that, we evaluate the ﬁtness

of the new individual and replace the old one in case

of ﬁtness improvement. When the reﬁnement phase

ﬁnishes its execution, the GA routine continues its ex-

ecution normally and the number of iterations without

any improvement is set to zero.

5 RESULTS AND DISCUSSION

5.1 Benchmark and Test Parameters

In this work, we have used the test cases from BAl-

iBase (Thompson et al., 2005), which is a benchmark

widely used to validate MSA methods. The BAliBase

contains different test case with different biological

characteristics, such as sequence similarity and size,

divided into different families. The BAliBase bench-

mark also provides reference alignments, which are

considered the correct alignments. This is important

because we are able to compare the produced align-

ments with the ideal ones.

To measure the quality of the alignments, we have

used the qscore tool

to calculate the Q and TC scores,

which are metrics related to the biological signiﬁ-

cance of the alignment. This tool compares the pro-

duced alignment with the reference alignment and

gives a score between 0 and 1, for both of Q and TC

metrics. The greater the value, more biologically sig-

niﬁcant is the MSA.

The tests were executed in a computer with

Windows 10 Pro 64 bits, Intel Core i3-6100

https://www.drive5.com/qscore/

CPU@3.70GHz processor and 8GB of RAM. The pa-

rameters of the MSA-GA are the default values de-

scribed by Gondro and Kinghorn (2007).

5.2 Results

We have executed test cases from all BAliBase fami-

lies: RV11, RV20, RV30, RV40, RV50. The ﬁrst one

is related to sequence sets with less than 20% of sim-

ilarity; the second one is related to sequence sets with

similarity between 20% and 40%; the third one is re-

lated to sequence sets with at least one divergent se-

quence; the fourth one is related with more than 40%

of similarity but less than 20% with other families; the

ﬁfth one is related to sequence sets with many inser-

tions.

We have compared the results obtained by the

original MSA-GA and the MSA-GA with our hybrid

reﬁnement stage, here named HMSA-GA. Due to the

stochastic nature of GA, we have executed each test

case ten times and the considered value is the average

of all the ten executions.

In Table 1 we can see the Q scores obtained by

the original MSA-GA and our method. We can notice

that HMSA-GA is able to reach better results in all

the ﬁve families when compared to MSA-GA. More-

over, we can see the that average improvement of the

alignment quality for Q score is 55%.

We can see in the Table 2 the TC scores obtained

by the original MSA-GA and our HMSA-GA. Here,

we can notice that our method is able to reﬁne the

alignments and reach also better results in all the ﬁve

families. In this case, we can see an average improve-

ment of 167%.

In order to verify whether the improvements

are statistically signiﬁcant, we have execute the

Wilcoxon signed-rank test (Woolson, 2007). This

non-parametric test is well-suited to Multiple Se-

quence Alignment because the test cases do not fol-

low a normal distribution (Rubio-Largo et al., 2016).

We have used a conﬁdence level of 1% (p

value <

0.01), once this value is considered appropriate to val-

idate MSA (Rubio-Largo et al., 2016).

In our hypothesis statistic test, when we com-

pared the differences between the results of MSA-GA

and our HMSA-GA, we have obtained a p value of

0.0013. Once the obtained p value is less than 1%,

we can say that the obtained improvements are statis-

tically signiﬁcant.

Thus, the better results reached by HMSA-GA

show the ability of our reﬁnement stage to improve

the quality of the MSA computed by Genetic Algo-

rithms.

A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments

387

Figure 1: Reﬁnement phase ﬂowchart.

Table 1: Q scores obtained for all families of BAliBase.

RV11 RV20 RV30 RV40 RV50 Average

MSA-GA 0.2369 0.2507 0.2667 0.3626 0.3261 0.2886

HMSA-GA 0.3408 0.4470 0.4110 0.5611 0.4791 0.4478

Table 2: TC scores obtained for all families of BAliBase.

RV11 RV20 RV30 RV40 RV50 Average

MSA-GA 0.0971 0.0013 0.0024 0.0353 0.0135 0.0299

HMSA-GA 0.1150 0.0351 0.0303 0.1852 0.0341 0.0799

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

388

Figure 2: The local realignment process.

6 CONCLUSIONS

In this work, we presented a new hybrid reﬁnement

phase, based on Progressive Alignment, to improve

the quality of the alignments produced by GA. We

have used the Kalign tool to systematically realign

parts of the alignment that may be responsible for a

local optima trap.

Our results show that our method was able to con-

siderably improve the quality of the alignments for all

the families of BAliBase when compared to the re-

sults obtained by the GA. Thus, we can conclude that

our method is able to reﬁne the results of the MSA

produced by GA based tools and provide more bio-

logically signiﬁcant alignments.

Moreover, the statistic hypothesis test show that

the difference between the obtained results are sta-

tistically signiﬁcant and so are the obtained improve-

ments.

As future work, we propose to use other heuris-

tics, such as consistency based methods, in our hybrid

scheme. This may allow us to smooth other difﬁcul-

ties and to obtain even better alignments.

ACKNOWLEDGEMENTS

The authors would like to thank S

ao Paulo Research

Foundation (FAPESP) for the ﬁnancial support, un-

der grant 2019/00030-3, and Universidade Paulista

(Unip/ICET) to partially support this research under

grant number 7-03/1116/2019.

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