Address some of the reviewers' comments

bibliography.bib

@@ -283,6 +283,21 @@ @article{clausen2024
   year          = {2024},
   doi           = {10.48550/arXiv.2402.05231}
 }
+@article{cock2009,
+    author = {Cock, Peter J. A. and Antao, Tiago and Chang, Jeffrey T. and Chapman, Brad A. and Cox, Cymon J. and Dalke, Andrew and Friedberg, Iddo and Hamelryck, Thomas and Kauff, Frank and Wilczynski, Bartek and de Hoon, Michiel J. L.},
+    title = {Biopython: freely available Python tools for computational molecular biology and bioinformatics},
+    journal = {Bioinformatics},
+    volume = {25},
+    number = {11},
+    pages = {1422-1423},
+    year = {2009},
+    month = {03},
+    abstract = {Summary: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.Availability: Biopython is freely available, with documentation and source code at www.biopython.org under the Biopython license.Contact: All queries should be directed to the Biopython mailing lists, see www.biopython.org/wiki/\_Mailing\_listspeter.cock@scri.ac.uk.},
+    issn = {1367-4803},
+    doi = {10.1093/bioinformatics/btp163},
+    url = {https://doi.org/10.1093/bioinformatics/btp163},
+    eprint = {https://academic.oup.com/bioinformatics/article-pdf/25/11/1422/48989335/bioinformatics_25_11_1422.pdf},
+}
 @article{delia2023,
   author        = {Domenica D'Elia and Jaak Truu and Leo Lahti and Magali Berland and Georgios Papoutsoglou and Michelangelo Ceci and Aldert Zomer and Marta B. Lopes and Eliana Ibrahimi and Aleksandra Gruca and Alina Nechyporenko and Marcus Frohme and Thomas Klammsteiner andEnrique Carrillo-de Santa Pau and Laura Judith Marcos-Zambrano and Karel Hron and Gianvito Pio and Andrea Simeon and Ramona Suharoschi and Isabel Moreno-Indias and Andriy Temko and Miroslava Nedyalkova and Elena-Simona Apostol and Ciprian-Octavian Truic\u{a} and Rajesh Shigdel and Jasminka Hasi\'{c}-Telalovi\'{c} and Erik Bongcam-Rudloff and Piotr Przymus and Naida Babi\'{c} Jordamovi\'{c} and Laurent Falquet and Sonia Tarazona and Alexia Sampri and Gaetano Isola and David P\'{e}rez-Serrano and Vladimir Trajkovik and Lubos Klucar and Tatjana Loncar-Turukalo and Aki Havulinna and Christian Jansen and Randi Bertelsen and Marcus Claesson},
   title         = {{Advancing microbiome research with machine learning: key findings from the ML4Microbiome {COST} action}},
@@ -1068,6 +1083,16 @@ @article{ramos2017
   abstract      = {Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The {MultiAssayExperiment} software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze {MultiAssayExperiment} objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The {MultiAssayExperiment} Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39-42. \textcopyright{}2017 {AACR}.},
   keywords      = {Software, Humans, Genomics, Computational Biology, Datasets as Topic, Genome, Human, Neoplasms}
 }
+@software{rideout2023,
+  author       = {Jai Ram Rideout and Greg Caporaso and Evan Bolyen and Daniel McDonald and Yoshiki Vázquez Baeza and Jorge Cañardo Alastuey and Anders Pitman and Jamie Morton and Jose Navas and Kestrel Gorlick and Justine Debelius and Zech Xu and llcooljohn and adamrp and Joshua Shorenstein and Laurent Luce and Will Van Treuren and  charudatta-navare and Antonio Gonzalez and Colin J. Brislawn and  Weronika Patena and Karen Schwarzberg and teravest and Jens Reeder and shiffer1 and Igor Sfiligoi and nbresnick and Qiyun Zhu and Dr. K. D. Murray and Karan Sharma},
+  title        = {biocore/scikit-bio: scikit-bio 0.5.9: Maintenance release},
+  month        = aug,
+  year         = 2023,
+  publisher    = {Zenodo},
+  version      = {0.5.9},
+  doi          = {10.5281/zenodo.8209901},
+  url          = {https://doi.org/10.5281/zenodo.8209901},
+}
 @article{righelli2022,
   author        = {Righelli, Dario and Weber, Lukas M and Crowell, Helena L and Pardo, Brenda and Collado-Torres, Leonardo and Ghazanfar, Shila and Lun, Aaron T L and Hicks, Stephanie C and Risso, Davide},
   title         = {{SpatialExperiment}: infrastructure for spatially-resolved~transcriptomics data in {R} using {Bioconductor}},