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In Silico Analyses: Translating and Making Sense of Omics Data 2.0

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 (31 January 2022) | Viewed by 20367

Special Issue Editors

Dr. Ronald Moura

E-Mail Website
Guest Editor
Department of Advanced Diagnostics, Institute for Maternal and Child Health-IRCCS "Burlo Garofolo", 34137 Trieste, Italy
Interests: human genetics; bioinformatics
Prof. Dr. Sergio Crovella

E-Mail Website
Guest Editor
1. Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, 34137 Trieste, Italy
2. Department of Biological and Environmental Sciences, College of Arts and Sciences, Qatar University, Doha, Qatar
Interests: immunology; clinical pathology; molecular genetics; molecular biology; OMICs; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent of the new millennium, biology has entered the “omics” world. Enormous progress in technology has allowed one to perform high-throughput analyses of genomes, transcriptomes, proteomes, variomes, metabolomes, epigenomes, and microbiomes. Consequently, the “omics” era generated large amount of data posing the increasingly hard challenge of finding “the needle in the haystack”. Extracting crucial information for advancing knowledge is becoming progressively complex and has become a bottleneck. Expertise in a wide range of fields including biology, computer science, mathematics, statistics, and physics is needed to overcome this challenge. The return of these collaborative efforts will find a wide range of applications from systems biology to drug discovery, complex bioprocesses, and human healthcare in the context of a personalized medicine available to all patients.

This Special Issue aims to gain deeper insight into complex systems, regulatory networks, deciphering, decoding, and interpreting information from publicly available databases. Original articles focusing on neural networks, computer simulations, prediction tools, novel databases, omics global analyses, and variant interpretations are welcome. All topics should cover applications from biochemistry, molecular and cell biology, the life sciences, molecular biophysics studies. Review articles will also be accepted.

Dr. Ronald Moura
Prof. Dr. Sergio Crovella
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • In silico biology
  • Prediction tools
  • Prediction algorithms
  • Database collection
  • Variant interpretation
  • Global profiling
  • Network interactions

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Published Papers (5 papers)

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Research

16 pages, 775 KiB  
Article
Inferring Time-Lagged Causality Using the Derivative of Single-Cell Expression
by Huanhuan Wei, Hui Lu and Hongyu Zhao
Int. J. Mol. Sci. 2022, 23(6), 3348; https://doi.org/10.3390/ijms23063348 - 20 Mar 2022
Cited by 1 | Viewed by 2340
Abstract
Many computational methods have been developed to infer causality among genes using cross-sectional gene expression data, such as single-cell RNA sequencing (scRNA-seq) data. However, due to the limitations of scRNA-seq technologies, time-lagged causal relationships may be missed by existing methods. In this work, [...] Read more.
Many computational methods have been developed to infer causality among genes using cross-sectional gene expression data, such as single-cell RNA sequencing (scRNA-seq) data. However, due to the limitations of scRNA-seq technologies, time-lagged causal relationships may be missed by existing methods. In this work, we propose a method, called causal inference with time-lagged information (CITL), to infer time-lagged causal relationships from scRNA-seq data by assessing the conditional independence between the changing and current expression levels of genes. CITL estimates the changing expression levels of genes by “RNA velocity”. We demonstrate the accuracy and stability of CITL for inferring time-lagged causality on simulation data against other leading approaches. We have applied CITL to real scRNA data and inferred 878 pairs of time-lagged causal relationships. Furthermore, we showed that the number of regulatory relationships identified by CITL was significantly more than that expected by chance. We provide an R package and a command-line tool of CITL for different usage scenarios. Full article
(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data 2.0)
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10 pages, 1423 KiB  
Article
Variant Enrichment Analysis to Explore Pathways Functionality in Complex Autoinflammatory Skin Disorders through Whole Exome Sequencing Analysis
by Lucas André Cavalcanti Brandão, Ronald Rodrigues de Moura, Angelo Valerio Marzano, Chiara Moltrasio, Paola Maura Tricarico and Sergio Crovella
Int. J. Mol. Sci. 2022, 23(4), 2278; https://doi.org/10.3390/ijms23042278 - 18 Feb 2022
Cited by 13 | Viewed by 2451
Abstract
The challenge of unravelling the molecular basis of multifactorial disorders nowadays cannot rely just on association studies searching for potential causative variants shared by groups of patients and not present in healthy individuals; indeed, association studies have as a main limitation the lack [...] Read more.
The challenge of unravelling the molecular basis of multifactorial disorders nowadays cannot rely just on association studies searching for potential causative variants shared by groups of patients and not present in healthy individuals; indeed, association studies have as a main limitation the lack of information on the interactions between the disease-causing variants. Thus, new genomic analysis tools focusing on disrupted pathways rather than associated gene variants are required to better understand the complexity of a disease. Therefore, we developed the Variant Enrichment Analysis (VEA) workflow, a tool applicable for whole exome sequencing data, able to find differences between the numbers of genetic variants in a given pathway in comparison with a reference dataset. In this study, we applied VEA to discover novel pathways altered in patients with complex autoinflammatory skin disorders, namely PASH (n = 9), 3 of whom are overlapping with SAPHO) and PAPASH (n = 3). With this approach we have been able to identify pathways related to neutrophil and endothelial cells homeostasis/activations, as disrupted in our patients. We hypothesized that unregulated neutrophil transendothelial migration could elicit increased neutrophil infiltration and tissue damage. Based on our findings, VEA, in our experimental dataset, allowed us to predict novel pathways impaired in subjects with autoinflammatory skin disorders. Full article
(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data 2.0)
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23 pages, 34931 KiB  
Article
In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia
by Hande Yılmaz, Halil Ibrahim Toy, Stephan Marquardt, Gökhan Karakülah, Can Küçük, Panagiota I. Kontou, Stella Logotheti and Athanasia Pavlopoulou
Int. J. Mol. Sci. 2021, 22(17), 9601; https://doi.org/10.3390/ijms22179601 - 5 Sep 2021
Cited by 9 | Viewed by 4429
Abstract
Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression [...] Read more.
Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML. Full article
(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data 2.0)
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18 pages, 4449 KiB  
Article
TF–RBP–AS Triplet Analysis Reveals the Mechanisms of Aberrant Alternative Splicing Events in Kidney Cancer: Implications for Their Possible Clinical Use as Prognostic and Therapeutic Biomarkers
by Meng He and Fuyan Hu
Int. J. Mol. Sci. 2021, 22(16), 8789; https://doi.org/10.3390/ijms22168789 - 16 Aug 2021
Cited by 7 | Viewed by 2686
Abstract
Aberrant alternative splicing (AS) is increasingly linked to cancer; however, how AS contributes to cancer development still remains largely unknown. AS events (ASEs) are largely regulated by RNA-binding proteins (RBPs) whose ability can be modulated by a variety of genetic and epigenetic mechanisms. [...] Read more.
Aberrant alternative splicing (AS) is increasingly linked to cancer; however, how AS contributes to cancer development still remains largely unknown. AS events (ASEs) are largely regulated by RNA-binding proteins (RBPs) whose ability can be modulated by a variety of genetic and epigenetic mechanisms. In this study, we used a computational framework to investigate the roles of transcription factors (TFs) on regulating RBP-AS interactions. A total of 6519 TF–RBP–AS triplets were identified, including 290 TFs, 175 RBPs, and 16 ASEs from TCGA–KIRC RNA sequencing data. TF function categories were defined according to correlation changes between RBP expression and their targeted ASEs. The results suggested that most TFs affected multiple targets, and six different classes of TF-mediated transcriptional dysregulations were identified. Then, regulatory networks were constructed for TF–RBP–AS triplets. Further pathway-enrichment analysis showed that these TFs and RBPs involved in triplets were enriched in a variety of pathways that were associated with cancer development and progression. Survival analysis showed that some triplets were highly associated with survival rates. These findings demonstrated that the integration of TFs into alternative splicing regulatory networks can help us in understanding the roles of alternative splicing in cancer. Full article
(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data 2.0)
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19 pages, 3910 KiB  
Article
Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer
by Vartika Bisht, Katrina Nash, Yuanwei Xu, Prasoon Agarwal, Sofie Bosch, Georgios V. Gkoutos and Animesh Acharjee
Int. J. Mol. Sci. 2021, 22(11), 5763; https://doi.org/10.3390/ijms22115763 - 28 May 2021
Cited by 18 | Viewed by 7182
Abstract
Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk [...] Read more.
Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets. Full article
(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data 2.0)
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