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Statistical Approaches to Omics Data: Searching for Biological Truth
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Statistical Approaches to Omics Data: Searching for Biological Truth

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (20 June 2025) | Viewed by 2829

Special Issue Editor

Dr. Alfonso Olaya Abril

E-Mail Website
Guest Editor
Departamento de Bioquímica y Biología Molecular, Edificio Severo Ochoa, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, Spain
Interests: bioremediation; biodegradation; proteomics; omics; cyanide; plastics; denitrification

Special Issue Information

Dear Colleagues,

Omics approaches are very powerful tools for generating new knowledge about molecular processes, especially those from shotgun analyses. However, the information obtained must be reliable. To achieve this, it is essential to implement correct analyzes and statistical treatments for each stage of an omics workflow, without forgetting that the results must represent, in the best possible way, a biological reality. Thus, in addition to justifying one type of analysis or another (transcriptome based on reference vs. de novo, choice between quantification and sequence normalization methods, protein quantification methods, etc.), or performing a hypothesis contrast test or an alternative (t-test, ANOVA, Limma, DEqMS, negative binomial models, etc.), it is essential to know if the omics results fit those obtained through other methodologies within the context of functional validation. Additionally, differences between statistical and biological significance should be considered. For this Special Issue, we welcome works that compare different types of analysis and statistical treatments to elucidate which ones best fit with the “biological reality”, demonstrated through the implementation of other methodological approaches.

Dr. Alfonso Olaya Abril
Guest Editor

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Keywords

  • omics
  • statistics
  • hypothesis contrast
  • statistical confidence

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Published Papers (3 papers)

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Research

25 pages, 7550 KB  
Article
CG-Based Stratification of 8-mers Highlights Functional Roles and Phylogenetic Divergence Markers
by Guojun Liu, Hu Meng, Zhenhua Yang, Guoqing Liu, Yongqiang Xing and Ningkun Xiao
Int. J. Mol. Sci. 2025, 26(19), 9477; https://doi.org/10.3390/ijms26199477 - 27 Sep 2025
Viewed by 432
Abstract
K-mer analysis is a powerful tool for understanding genome structure and evolution. A “k-mer” refers to a short DNA sequence made up of k nucleotides (where k is a specific integer), while an “m-mer” is a similar concept but with a shorter sequence [...] Read more.
K-mer analysis is a powerful tool for understanding genome structure and evolution. A “k-mer” refers to a short DNA sequence made up of k nucleotides (where k is a specific integer), while an “m-mer” is a similar concept but with a shorter sequence length. The functional mechanisms of CG-containing k-mers, as well as their potential role in evolutionary processes, remain unclear. To explore this issue, we analyzed 8-mers in several species with varying genomic complexities and evolutionary divergences: Homo sapiens, Saccharomyces cerevisiae, Bombyx mori, Ciona intestinalis, Danio rerio, and Caenorhabditis elegans, which were grouped by CG dinucleotide content (0CG, 1CG, and 2CG). We examined the relative frequencies of shorter m-mers (with m = 3 and 4) within each CG-defined group, using information-theoretic, distance-based, and angular metrics. Our results show that 0CG motifs follow random patterns, while 1CG and 2CG motifs display significant deviations, likely due to functional constraints such as nucleosome-binding and CpG island association. The observed unimodal distribution of 8-mers arises from the convergence of the three CG-defined groups. Among them, the 2CG group shows the highest divergence in m-mer composition, followed by 1CG, reflecting varying degrees of selective pressure. Furthermore, species-specific differences in CG-classified 8-mer patterns could provide valuable insights into phylogenetic relationships. Through extensive comparison, we explore how CG content and sequence composition influence genomic organization and contribute to evolutionary divergence across different taxa. These findings deepen our understanding of short motif functions, genome organization, and sequence evolution. Full article
(This article belongs to the Special Issue Statistical Approaches to Omics Data: Searching for Biological Truth)
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21 pages, 8351 KB  
Article
Reproducibility Crossroads: Impact of Statistical Choices on Proteomics Functional Enrichment
by Karolina A. Biełło, José V. Die, Francisco Amil, Carlos Fuentes-Almagro, Javier Pérez-Rodríguez and Alfonso Olaya-Abril
Int. J. Mol. Sci. 2025, 26(18), 9232; https://doi.org/10.3390/ijms26189232 - 21 Sep 2025
Viewed by 605
Abstract
Quantitative proteomics relies on robust statistical methods for differential expression, critically impacting downstream functional enrichment. This meta-analysis systematically investigated how statistical hypothesis testing approaches and criteria for defining biological relevance influence functional enrichment concordance. We reanalyzed five independent label-free quantitative proteomics datasets using [...] Read more.
Quantitative proteomics relies on robust statistical methods for differential expression, critically impacting downstream functional enrichment. This meta-analysis systematically investigated how statistical hypothesis testing approaches and criteria for defining biological relevance influence functional enrichment concordance. We reanalyzed five independent label-free quantitative proteomics datasets using diverse frequentist (t-test, Limma, DEqMS, MSstats) and Bayesian (rstanarm) approaches. Concordance of Gene Ontology (GO) and KEGG pathways was assessed using Jaccard indices and correlation metrics, grouping comparisons by statistical test and biological relevance consistency. The results demonstrated highly significant differences in similarity distributions among the comparison groups. Comparisons varying only hypothesis testing methods (with constant relevance criteria, FC or Bayesian) showed the highest consistency. Conversely, comparisons with differing biological relevance criteria (or varied methodological choices) yielded significantly lower consistency, highlighting this definition’s critical impact on GO term overlaps. KEGG pathways displayed more uniform, method-insensitive concordance. Sensitivity analysis confirmed the findings’ robustness, underscoring that methodological choices profoundly influence functional enrichment outcomes. This work emphasizes the critical need for transparency and careful consideration of analytical decisions in proteomics research to ensure reproducible and biologically sound interpretations. Full article
(This article belongs to the Special Issue Statistical Approaches to Omics Data: Searching for Biological Truth)
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22 pages, 16880 KB  
Article
Research on the Molecular Mechanisms and Key Gene Discovery in Quercus variabilis Root Pruning Based on Transcriptomics and Hormone Profiling
by Hao Dou, Jiajia Sun, Xi Feng, Huyang Lyu, Zhen Qin, Ruoyi Ni, Yilin Wang, Huijuan Sun, Xin Zhou, Wu Tang, Jin’e Quan and Xitian Yang
Int. J. Mol. Sci. 2024, 25(21), 11541; https://doi.org/10.3390/ijms252111541 - 27 Oct 2024
Viewed by 1308
Abstract
Quercus variabilis (Q. variabilis), a significant broadleaf species used in afforestation across high, sandy, and mountainous regions, presents unique challenges for transplantation. This species is characterized by its slow above-ground growth and rapid taproot development, which suppresses the proliferation of lateral [...] Read more.
Quercus variabilis (Q. variabilis), a significant broadleaf species used in afforestation across high, sandy, and mountainous regions, presents unique challenges for transplantation. This species is characterized by its slow above-ground growth and rapid taproot development, which suppresses the proliferation of lateral and fibrous roots, negatively impacting post-transplant survival. Research indicates that targeted root pruning—specifically, the removal of one-third of the roots—promotes the development of lateral roots in these seedlings. This study involved pruning the root systems of Q. variabilis and assessing the subsequent root development in comparison to an unpruned control group. Our analysis, which included transcriptome sequencing and plant hormone metabolism assays conducted at 2, 12, and 25 days post-pruning, yielded 126.02 Gb of clean data and identified 7662 differentially expressed genes (DEGs). These genes were primarily enriched in the plant hormone signal transduction pathway. Further investigation of this pathway, along with hormone content measurements, elucidated the mechanisms that contribute to enhanced root growth following pruning. Additionally, through a weighted correlation network analysis (WGCNA), we identified 20 key genes that are instrumental in promoting root development in Q. variabilis saplings. This research advances the theoretical framework for forestry seedling production and afforestation, laying the groundwork for scientifically informed vegetation restoration techniques. Full article
(This article belongs to the Special Issue Statistical Approaches to Omics Data: Searching for Biological Truth)
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