Phylogenetic analysis using Machine learning

Introduction:

The interpretation of the phylogenetic tree is an essential yet challenging aspect of evolutionary

studies. To conduct an evolutionary study of the organisms is the core of biological research. The

resulting phylogeny is then subjected to a plethora of analyses essential for further genomic

research (Azouri 2021). The phylogenetic analysis involves several methods that can be used to

interpret data. Recently, researchers have begun studying the use of machine learning in inferring

phylogenetic trees.

Phylogenetic Analysis:

The study of the evolutionary history of a species or a group of organisms is known as

phylogenetic analysis. Here, the evolutionary relationship between different species or organisms

having a common ancestor is represented with the help of branching diagrams. This diagram is

called the phylogenetic tree, which can be either rooted or unrooted. Phylogenetic analysis can

also be used to study the relationship between characteristics of an organism, including genes

and proteins.

The applications of phylogenetic analysis are numerous. These include – reconstruction of the

ancestral gene for the derivation of extant genes, study of human disease and epidemiology,

interpretation of the evolution of ecological and behavioural traits, estimation of historical

biogeographic relationships, and many more.

Interesting Blog: Performance Evaluation Metrics for Machine-Learning Based

Dissertation

Currently available methods for inference:

Previously, morphological features were used in the assessment of similarities among species

and in phylogenetic analysis. It has drastically changed over time. Nowadays, this analysis uses

information extracted from DNA, RNA or protein. The generation of a phylogenetic tree

involves the alignment of sequences. The most widely-used tool for this is the alignment-based

methodology. In this method, the two sequences are stacked in a way to highlight their common

symbols and substrings. This comparison of sequences helps to identify patterns of shared

ancestry between species. (Munjal 2019). However, exploiting these large-scale molecular data

poses significant challenges. One of the most difficult tasks is to develop effective techniques for

the extraction of missing data.

The Maximum likelihood or Markov Chain Monte Carlo (MCMC) methods and probabilistic

models of sequence evolution are highly reliable statistical methods used for the reconstruction

of gene and species trees. Even so, many of these approaches are not scalable enough to study

phylogenomic datasets of hundreds or thousands of genes and taxa. Thus, the development of a

quick and efficient method is the need of the hour ( Bhattacharjee 2020).

Fig. 1: Major components of phylogenetic analysis

Application of machine learning:

PHYLOGENETIC ANALYSIS

Sequence AlignmentIdentificat

-ion of Similarity

Analysis of Data

Machine learning has found various applications in the field of technology-driven research. One

such usage of machine learning is in the inference of the phylogenetic tree. In a recent study,

researchers utilized the machine learning method to predict the best model for the most common

prediction task: phylogenetic tree reconstruction for a given collection of sequences (Abadi

2020).

A research study gave a detailed analysis of plant diversity trends to date, demonstrating that

using machine learning to forecast future diversity could be tremendously beneficial. They

applied machine learning approaches to phylogenetic diversity in vascular plants (Park 2020).

Bhattacharjee et al., for the very first time, demonstrated the potential and feasibility of using

deep learning techniques to compute distance matrices. The study evaluated both matrix

factorization (ME) and autoencoder (AE) and aimed to develop improvised models for better

results. They showed that both these methods are reliable and can be applied for handling large-

scale datasets. They also highlighted the ability of these techniques over the heuristic-based

techniques to automatically learn complicated inter-variable associations. Their research can also

be used as a model for applying machine learning methods to the phylogenetic analysis

(Bhattacharjee 2020).

In another research, a machine learning framework was developed to rank the neighbouring

trees in accordance with their prosperity to increase the likelihood. They applied multiple

features and utilized machine learning to improve an optimal tool. The study suggested specific

ways to practice machine learning algorithms in phylogenetic analysis. Furthermore, they

presented a methodology that can significantly speed up tree-search algorithms without

sacrificing accuracy(Azouri 2021).

A recent review focused on the application of machine learning-based techniques in the data

analysis of the human microbiome. It provided an insight into the plethora of advantages that

machine learning has to offer over classical methods. The most common techniques covered in

this review involved Support Vector Machines, Random Forest, k-NN and Logistic Regression.

This review suggested how machine learning can contribute to the development of new models

that can be useful in predicting classifications in the field of microbiology, inferring host

phenotypes to predict diseases and characterization of state-specific microbial signatures using

microbial communities(Macros 2021).

Future scope:

All the recently conducted research emphasizes the potential of artificial intelligence and

machine learning in the inference of phylogenetic trees. These studies highlight the ability of

machine learning in elevating the scale of analyzed datasets and the degree of sophistication in

evolutionary models(Azouri 2021). Machine learning can thus be of high interest in the near

future and contribute to efficient phylogenetic analysis in biological research.

METHODS

DOMAIN PURPOSE References

Machine learning &

Phylogenetic analysis

TSS (The Substitution

Score)

ISS (Internal substitution

score)

To predict the

pathogenecity of human

mtDNA variants

(Akpinar 2020).

Machine learning &

Phylogenetic analysis

ModelTeller

(computational

methodology)

To examine the accuracy of

phylogenetic analyses,

using machine learning

(Abadi 2020).

Machine learning &

Phylogenetic analysis

Random Forest (RF)

based learning and

NeoPLE (prediction

approach)

To highlight the use of

candidate trees and

successfully establish a

model that can describe the

relationship between

likelihood and extracted

features through the

exploitation of deep

neighbor information of

each individual tree

(Ling 2020)

References:

1. Abadi, S.,Avram, O., Rosset, S., Pupko, T., & Mayrose, I. (2020). ModelTeller: model

selection for optimal phylogenetic reconstruction using machine learning. Molecular

Biology and Evolution, 37(11), 3338-3352.

2. Akpınar, B. A., Carlson, P. O., Paavilainen, V. O., & Dunn, C. D. (2020). A novel

phylogenetic analysis and machine learning predict pathogenicity of human mtDNA

variants. bioRxiv.

3. Azouri, D., Abadi, S., Manour, Y. Et al. (2021). Harnessing machine learning to guide

phylogenetic-tree search algorithms. Nat Commun 12, 1983.

4. Bhattacharjee, A., & Bayzid, M. S. (2020). Machine learning based imputation

techniques for estimating phylogenetic trees from incomplete distance matrices. BMC

genomics, 21(1),497.

5. C. Ling, W. Cheng, H. Zhang, H. Zhu and H. Zhang, “Deep Neighbor Information

Learning From Evolution Trees for Phylogenetic Likelihood Estimates,” in IEEE Access,

vol. 8, pp. 220692-220702, 2020. Doi: 10.1109/ACCESS.2020.3043150

6. Hillis, David M (1997/03/01). Phylogenetic analysis. Current Biology, 7, R129-R131.

Doi: 10.1016/S0960-9822(97)70070-8.

7. Macros-Zambrano, L.J., Karaduzovic-Hadziabdic, K., Loncar Turukalo, T., Przymus, P.,

Trajkovik, V., Aasmets, O., Berland, M., Gruca, A., Hasic, J., Hron, K. And

Klammsteinner, T. (2021). Application of machine learning in human microbiome

studies: a review on feature selection, biomarker identification, disease prediction and

treatment. Frontiers in microbiology, 12,p.313.

8. Munjal G., Hanmandlu M., Srivastava S. (2019) Phylogenetics algorithms and

applications. In: Hu YC., Tiwari S., Mishra K., Trivedi M. (eds) Ambient

communications and computer systems. Advances in intelligent system and computing,

vol 904. Springer, Singapore.

9. Park, D. S., Willis, C. G., Xi, Z., Kartesz, J. T., Davis, C. C., & Worthington, S. (2020).

Machine learning predicts large scale declines in native phylogenetic diversity. New

Phytologist, 227(5), 1544-1566.