Update paper.md

paper/paper.md

@@ -26,27 +26,27 @@ aas-journal: Astrophysical Journal <- The name of the AAS journal.
 ---
 
 # Summary
-The cross-platform application for phylogenetic tree analysis with climate parameters, `aPhyloGeo`, is a robust pipeline designed for comprehensive phylogenetic analyses using genetic and climate data. This Python API, available on [PyPI](https://pypi.org/project/aphylogeo/), offers a suite of analyses tailored to various scenarios, enabling the examination of datasets at three distinct levels: 1) genetic, 2) climatic, and 3) biogeography correlation, all within a unified package. Similarity at these levels, evaluated through metrics such as least squares distance and Robinson and Foulds distance, significantly influences the assumptions guiding the identification of correlations between a species' genetics and its habitat during the reconstruction of the multiple alignment necessary for phylogenetic inference.
+The cross-platform application for phylogenetic tree analysis with climate parameters, *aPhyloGeo*, is a robust pipeline designed for comprehensive phylogenetic analyses using genetic and climate data. This Python API, available on [PyPI](https://pypi.org/project/aphylogeo/), offers a suite of analyses tailored to various scenarios, enabling the examination of datasets at three distinct levels: 1) genetic, 2) climatic, and 3) biogeography correlation, all within a unified package. Similarity at these levels, evaluated through metrics such as least squares distance, Euclidean distance, and Robinson-Foulds distance, significantly influences the assumptions guiding the identification of correlations between a genetic of species and its habitat during the reconstruction of the multiple alignment necessary for phylogenetic inference.
 
-By utilizing the `aPhyloGeo` Python API, users can programmatically implement sophisticated phylogenetic analyses without the need for a graphical interface. This API provides a powerful and flexible toolset for conducting analyses, allowing users to tailor the application to their specific research needs. Through this approach, aPhyloGeo facilitates a nuanced understanding of the interplay between genetic evolution and environmental factors in the context of species adaptation, all within the Python programming environment.
+By utilizing the *aPhyloGeo* Python API, users can programmatically implement sophisticated phylogenetic analyses without the need for a graphical interface. This API provides a powerful and flexible toolset for conducting analyses, allowing users to tailor the application to their specific research needs. Through this approach, *aPhyloGeo* facilitates a nuanced understanding of the interplay between genetic evolution and environmental factors in the context of species adaptation, all within the Python programming environment.
 
-By selecting an appropriate gene list for the available data defined on a set of species to explain the adaptation of the species according to the Darwinian hypothesis, the user can be confident that these assumptions are taken into account in `aPhyloGeo`.
+By selecting an appropriate gene list for the available data defined on a set of species to explain the adaptation of the species according to the Darwinian hypothesis, the user can be confident that these assumptions are taken into account in *aPhyloGeo*.
 
 # Statement of Need
 
-The rapid impacts of climate change and anthropogenic variables on biodiversity and population dynamics underscore the need for more advanced tools capable of resolving the complexities of ecosystems under perturbation. Biologists use phylogeographic approaches to closely examine the interplay between the genetic structures of study populations and their geographic distributions, taking into account both current and historical geoclimatic contexts.
+The rapid impacts of climate change and anthropogenic variables on biodiversity and population dynamics underscore the necessity for more advanced tools capable of resolving the complexities of ecosystems under perturbation. Biologists utilize phylogeographic approaches to closely examine the interplay between the genetic structures of study populations and their geographic distributions, considering both current and historical geoclimatic contexts.
 
-This software package is dedicated to advancing state-of-the-art bioinformatics tools specifically designed for detailed phylogeographic analysis. Given the urgency of the current climate crisis (COP27 - Climate Change and COP15 - Convention on Biological Diversity) and the anticipated future challenges, there is an urgent need to develop tools that not only meet but exceed bioinformatics software development standards. These tools will be designed to allow accurate characterization of genetic diversity and phenotypic traits in strict accordance with environmental conditions. By maintaining the highest standards, this research aims to make a significant contribution to our understanding of the evolving ecological landscape and provide the scientific community with robust tools for comprehensive analysis and interpretation.
+This software package is dedicated to advancing state-of-the-art bioinformatics tools specifically designed for detailed phylogeographic analysis. Given the urgency of the current climate crisis and the anticipated future challenges, there is a pressing need to develop tools that not only meet but also exceed bioinformatics software development standards. These tools will be crafted to enable accurate characterization of genetic diversity and phenotypic traits in strict accordance with environmental conditions. By maintaining the highest standards, this research aims to make a significant contribution to our understanding of the evolving ecological landscape and provide the scientific community with robust tools for comprehensive analysis and interpretation.
 
-# State of the Field
+# State of the Field - Advancements in Genomic Analysis
 
-In 2021, Tahiri lab team [@nadia_tahiri-proc-scipy-2022] proposed a new algorithm to allow finding sub-sequences of genes giving an increased topological similarity between the reference tree (obtained from gene sequences) and the phylogenetic tree (obtained from genome sequences). It can help find which genes or subparts of a gene are sensitive or favourable to a given environment. Finally, [@nadia_tahiri-proc-scipy-2023] explored this algorithm with SARS-CoV-2 data.
+In 2021, the Tahiri lab team introduced an algorithm aimed at identifying sub-sequences within genes, enhancing the topological similarity between reference trees (constructed from gene sequences) and phylogenetic trees (derived from genome sequences) [@nadia_tahiri-proc-scipy-2022]. This algorithm proves instrumental in pinpointing genes or gene segments sensitive or favorable to specific environments.
 
-# Pipeline
+Subsequently, the team extended their research, applying the algorithm to SARS-CoV-2 data in 2023 [@nadia_tahiri-proc-scipy-2023]. These developments contribute significantly to the methodological landscape, shedding light on genetic factors influencing adaptability in diverse environments. The ongoing dedication to refining tools and methodologies by the Tahiri lab ensures continuous progress and elevates the overall quality of genomic analysis within the scientific community.
 
-Exploring the aPhyloGeo workflow (\autoref{fig:figure1}) is essential to harness the full potential of this bioinformatics pipeline. Follow these steps to perform phylogeographic analysis effectively:
+# Pipeline
 
-## Algorithm Workflow
+Exploring the *aPhyloGeo* workflow (\autoref{fig:figure1}) is essential to harness the full potential of this bioinformatics pipeline. Follow these steps to perform phylogeographic analysis effectively:
 
 ![The workflow of the algorithm. The operations within this workflow include several blocks.\label{fig:figure1}](../img/workflow_en.png)
 
@@ -66,63 +66,32 @@ The algorithm supports multiprocessing, allowing simultaneous analysis of multip
 
 ## Dependencies
 
-This work relies on the following software packages:
+This work relies on the following main software packages:
 
-- [Biopython](https://biopython.org/) version 1.79 (BSD 3-Clause License)
-- [Bio](https://pandas.pydata.org/) version 1.5.2 (New BSD License)
-- [numpy](https://numpy.org/) version 1.21.6 (BSD 3-Clause License)
+- [ete3](https://pypi.org/project/ete3/) version 3.1.3 (GNU General Public License (GPL) (GPLv3))
+- [Bio](https://pypi.org/project/bio/) version 1.5.9 (New BSD License)
+- [robinson-foulds](https://pypi.org/project/robinson-foulds/) version 1.2 (GNU General Public License v3 (GPLv3))
+- [dendropy](https://pypi.org/project/DendroPy/) version 4.6.1 (BSD License (BSD))
 
-# Metrics
+# Methods
 
 ## Tree Comparison
 
 The comparison between phylogenetic trees (i.e., trees based on genetic data) and climatic trees involves a phylogeography step using Robinson and Foulds distance (i.e., topology distance) and Least Square distance (i.e., branch length distance).
 
-### Least Squares Distance
-
-$$
-LS(T_1, T_2) = \sum_{i=1}^{n-1} \sum_{j=i}^{n} | \delta(i,j) - \xi(i,j) |
-$$
-
-where $T_1$ is the phylogenetic tree 1, $T_2$ is the phylogenetic tree 2, $i$ and $j$ are two species, $\delta(i,j)$ is the distance between species $i$ and species $j$ in $T_1$, $\xi(i,j)$ is the distance between species $i$ and species $j$ in $T_2$, and $n$ is the total number of species.
-
-### Robinson-Foulds Distance
-
-The _RF_ distance between the phylogenetic tree $T_1$ and reference tree $T_2$ is the number of non-trivial bipartitions of $T_1$ that are not in $T_2$ plus the number of non-trivial bipartitions of $T_2$ that are not in $T_1$. This distance _RF_ between $T_1$ and $T_2$ is computed by the following formula:
-
-$$
-RF(T_1,T_2) = \frac{|(Q \backslash P) \cup (P \backslash Q)|}{2n-6}
-$$
-
-where $Q$ is a set of all possible bipartitions in the phylogenetic tree (denoted $T_1$), $P$ is a set of all possible bipartitions in the reference tree (denoted $T_2$), and $n$ is the number of leaves in $T_1$ (or $T_2$). It is often relevant to normalize this distance by the maximum possible value of _RF_ (equal to $2n-6$ for two binary trees with $n$ common leaves).
-
-
-# Editing Multiple Sequence Alignment Methods
-
-Multiple Sequence Alignment (MSA) is a crucial step in bioinformatics for comparing and analyzing biological sequences. Here's an overview of some commonly used MSA methods, including pairwise alignment and popular tools like MUSCLE, CLUSTALW, and MAFFT:
-
-## Pairwise Alignment
-Pairwise alignment forms the bedrock of MSA, focusing on aligning two sequences to uncover similarities and differences. It provides a foundation for more complex multiple sequence alignment methodologies and aids in identifying conserved regions across evolutionary lineages.
-
-## MUSCLE (Multiple Sequence Comparison by Log-Expectation)
-MUSCLE stands out as a robust tool for multiple sequence alignment, leveraging a progressive approach to align sequences. Its algorithm employs iterative refinement, enhancing accuracy by considering evolutionary relationships between sequences. MUSCLE is recognized for its efficiency in handling large datasets while maintaining high alignment quality.
-
-## CLUSTALW
-CLUSTALW is a widely-used MSA tool known for its versatility and user-friendly interface. It employs a progressive alignment method, iteratively aligning sequences based on pairwise alignments. CLUSTALW accommodates a range of sequence types and is often chosen for its ease of use in diverse bioinformatics applications.
-
-## MAFFT (Multiple Alignment using Fast Fourier Transform)
-MAFFT utilizes a Fast Fourier Transform algorithm to align multiple sequences efficiently. Its progressive alignment strategy ensures accurate alignment, particularly in cases involving distantly related sequences. MAFFT is known for its speed and scalability, making it suitable for handling large datasets and accommodating the diverse demands of sequence analysis.
+## Editing Multiple Sequence Alignment Methods
 
-Understanding these MSA methods and tools equips researchers with the flexibility to choose the most suitable approach based on the characteristics of their biological data and the specific goals of their analyses.
+Multiple Sequence Alignment (MSA) is a crucial step in bioinformatics for comparing and analyzing biological sequences. Here's an overview of some commonly used MSA methods, including pairwise alignment and popular tools like MUSCLE, CLUSTALW, and MAFFT.
 
+## 
 
 # Conclusion
 
-The `aPhyloGeo` pipeline represents an integrative framework, synthesizing an expansive repertoire of advanced analytical methodologies suited to diverse datasets, encompassing both genetic and climatic dimensions. By consolidating these multifaceted analyses within a unified platform, users benefit not only from simplifying the intricacies associated with navigating diverse tools but also from the assurance of heightened reproducibility in research outcomes.
+The *aPhyloGeo* pipeline represents an integrative framework, synthesizing an expansive repertoire of advanced analytical methodologies suited to diverse datasets, encompassing both genetic and climatic dimensions. By consolidating these multifaceted analyses within a unified platform, users benefit not only from simplifying the intricacies associated with navigating diverse tools but also from the assurance of heightened reproducibility in research outcomes.
 
-In the anticipation of forthcoming advancements, `aPhyloGeo`  aims to integrate of cutting-edge functionalities. This includes the incorporation of clustering techniques predicated upon similarity derived from multiple sequence alignments, concomitant with the introduction of a more computationally efficient alignment methodology. To fortify the analytical arsenal, the integration of novel metrics, such as the Quartet metric and bipartition, aims to endow users with enhanced discernment for making nuanced decisions concerning their datasets through a comprehensive and robust assessment of genetic diversity.
+In the anticipation of forthcoming advancements, *aPhyloGeo*  aims to integrate of cutting-edge functionalities. This includes the incorporation of clustering techniques predicated upon similarity derived from multiple sequence alignments, concomitant with the introduction of a more computationally efficient alignment methodology. To fortify the analytical arsenal, the integration of novel metrics, such as the Quartet metric and bipartition, aims to endow users with enhanced discernment for making nuanced decisions concerning their datasets through a comprehensive and robust assessment of genetic diversity.
 
-By adhering resolutely adhering to the pinnacle of standards in software development, this research not only aspires to furnish an immediate solution but also endeavors to position `aPhyloGeo` as a dynamic and evolving platform. Aspiring to be a vanguard in the field of phylogeographic analysis, the pipeline is dedicated to furnishing users with a sophisticated suite of tools that seamlessly adapt to the evolving landscape of genetic research. Through these advancements, the pipeline seeks to make a substantial and enduring contribution to the corpus of scientific knowledge, elevating the benchmarks of reproducibility and usability within the broader scientific community.
+By adhering resolutely adhering to the pinnacle of standards in software development, this research not only aspires to furnish an immediate solution but also endeavors to position *aPhyloGeo* as a dynamic and evolving platform. Aspiring to be a vanguard in the field of phylogeographic analysis, the pipeline is dedicated to furnishing users with a sophisticated suite of tools that seamlessly adapt to the evolving landscape of genetic research. Through these advancements, the pipeline seeks to make a substantial and enduring contribution to the corpus of scientific knowledge, elevating the benchmarks of reproducibility and usability within the broader scientific community.
 
 # Acknowledgements