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In Silico Analyses: Translating and Making Sense of Omics Data

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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 (28 February 2020) | Viewed by 45618

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Prof. Dr. Alessandro Stella

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Department of Biomedical Sciences and Human Oncology, Università degli Studi di Bari Aldo Moro, Bari, Italy
Interests: hereditary diseases; variant interpretation; splicing regulation; cancer genetics
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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 IJMS Special Issue on “In Silico Analysis/Model” will address theoretical model and computational analyses to gain deeper insight into complex systems, regulatory networks, deciphering, decoding, and interpreting information from publicly available databases. Original articles focusing on neural networks, decision trees, random forest and deep learning methods, 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.

Prof. Dr. Alessandro Stella
Guest Editor

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Keywords

In silico biology
Mathematical models
Computer simulation
Prediction tools
Prediction algorithms
Database collection
Variant interpretation
Global profiling
Network interactions.

Published Papers (9 papers)

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Research

27 pages, 12159 KiB

Open AccessArticle

Strain-Level Metagenomic Data Analysis of Enriched In Vitro and In Silico Spiked Food Samples: Paving the Way towards a Culture-Free Foodborne Outbreak Investigation Using STEC as a Case Study

by Assia Saltykova, Florence E. Buytaers, Sarah Denayer, Bavo Verhaegen, Denis Piérard, Nancy H. C. Roosens, Kathleen Marchal and Sigrid C. J. De Keersmaecker

Int. J. Mol. Sci. 2020, 21(16), 5688; https://doi.org/10.3390/ijms21165688 - 08 Aug 2020

Cited by 13 | Viewed by 3372

Abstract

Culture-independent diagnostics, such as metagenomic shotgun sequencing of food samples, could not only reduce the turnaround time of samples in an outbreak investigation, but also allow the detection of multi-species and multi-strain outbreaks. For successful foodborne outbreak investigation using a metagenomic approach, it is, however, necessary to bioinformatically separate the genomes of individual strains, including strains belonging to the same species, present in a microbial community, which has up until now not been demonstrated for this application. The current work shows the feasibility of strain-level metagenomics of enriched food matrix samples making use of data analysis tools that classify reads against a sequence database. It includes a brief comparison of two database-based read classification tools, Sigma and Sparse, using a mock community obtained by in vitro spiking minced meat with a Shiga toxin-producing Escherichia coli (STEC) isolate originating from a described outbreak. The more optimal tool Sigma was further evaluated using in silico simulated metagenomic data to explore the possibilities and limitations of this data analysis approach. The performed analysis allowed us to link the pathogenic strains from food samples to human isolates previously collected during the same outbreak, demonstrating that the metagenomic approach could be applied for the rapid source tracking of foodborne outbreaks. To our knowledge, this is the first study demonstrating a data analysis approach for detailed characterization and phylogenetic placement of multiple bacterial strains of one species from shotgun metagenomic WGS data of an enriched food sample. Full article

(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)

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11 pages, 1568 KiB

Open AccessArticle

Integrative Bioinformatic Analyses of Global Transcriptome Data Decipher Novel Molecular Insights into Cardiac Anti-Fibrotic Therapies

by Maximilian Fuchs, Fabian Philipp Kreutzer, Lorenz A. Kapsner, Saskia Mitzka, Annette Just, Filippo Perbellini, Cesare M. Terracciano, Ke Xiao, Robert Geffers, Christian Bogdan, Hans-Ulrich Prokosch, Jan Fiedler, Thomas Thum and Meik Kunz

Int. J. Mol. Sci. 2020, 21(13), 4727; https://doi.org/10.3390/ijms21134727 - 02 Jul 2020

Cited by 15 | Viewed by 5140

Abstract

Integrative bioinformatics is an emerging field in the big data era, offering a steadily increasing number of algorithms and analysis tools. However, for researchers in experimental life sciences it is often difficult to follow and properly apply the bioinformatical methods in order to unravel the complexity and systemic effects of omics data. Here, we present an integrative bioinformatics pipeline to decipher crucial biological insights from global transcriptome profiling data to validate innovative therapeutics. It is available as a web application for an interactive and simplified analysis without the need for programming skills or deep bioinformatics background. The approach was applied to an ex vivo cardiac model treated with natural anti-fibrotic compounds and we obtained new mechanistic insights into their anti-fibrotic action and molecular interplay with miRNAs in cardiac fibrosis. Several gene pathways associated with proliferation, extracellular matrix processes and wound healing were altered, and we could identify micro (mi) RNA-21-5p and miRNA-223-3p as key molecular components related to the anti-fibrotic treatment. Importantly, our pipeline is not restricted to a specific cell type or disease and can be broadly applied to better understand the unprecedented level of complexity in big data research. Full article

(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)

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18 pages, 2420 KiB

Open AccessArticle

In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification

by Matteo Accetturo, Nicola Bartolomeo and Alessandro Stella

Int. J. Mol. Sci. 2020, 21(3), 721; https://doi.org/10.3390/ijms21030721 - 22 Jan 2020

Cited by 8 | Viewed by 3182

Abstract

Background: With the advent of next-generation sequencing in genetic testing, predicting the pathogenicity of missense variants represents a major challenge potentially leading to misdiagnoses in the clinical setting. In neurofibromatosis type 1 (NF1), where clinical criteria for diagnosis may not be fully present until late infancy, correct assessment of variant pathogenicity is fundamental for appropriate patients’ management. Methods: Here, we analyzed three different computational methods, VEST3, REVEL and ClinPred, and after extracting predictions scores for 1585 NF1 missense variants listed in ClinVar, evaluated their performances and the score distribution throughout the neurofibromin protein. Results: For all the three methods, no significant differences were present between the scores of “likely benign”, “benign”, and “likely pathogenic”, “pathogenic” variants that were consequently collapsed into a single category. The cutoff values for pathogenicity were significantly different for the three methods and among benign and pathogenic variants for all methods. After training five different models with a subset of benign and pathogenic variants, we could reclassify variants in three sharply separated categories. Conclusions: The recently developed metapredictors, which integrate information from multiple components, after gene-specific fine-tuning, could represent useful tools for variant interpretation, particularly in genetic diseases where a clinical diagnosis can be difficult. Full article

(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)

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22 pages, 395 KiB

Open AccessArticle

Targeting the 16S rRNA Gene for Bacterial Identification in Complex Mixed Samples: Comparative Evaluation of Second (Illumina) and Third (Oxford Nanopore Technologies) Generation Sequencing Technologies

by Raf Winand, Bert Bogaerts, Stefan Hoffman, Loïc Lefevre, Maud Delvoye, Julien Van Braekel, Qiang Fu, Nancy HC Roosens, Sigrid CJ De Keersmaecker and Kevin Vanneste

Int. J. Mol. Sci. 2020, 21(1), 298; https://doi.org/10.3390/ijms21010298 - 31 Dec 2019

Cited by 109 | Viewed by 13316

Abstract

Rapid, accurate bacterial identification in biological samples is an important task for microbiology laboratories, for which 16S rRNA gene Sanger sequencing of cultured isolates is frequently used. In contrast, next-generation sequencing does not require intermediate culturing steps and can be directly applied on communities, but its performance has not been extensively evaluated. We present a comparative evaluation of second (Illumina) and third (Oxford Nanopore Technologies (ONT)) generation sequencing technologies for 16S targeted genomics using a well-characterized reference sample. Different 16S gene regions were amplified and sequenced using the Illumina MiSeq, and analyzed with Mothur. Correct classification was variable, depending on the region amplified. Using a majority vote over all regions, most false positives could be eliminated at the genus level but not the species level. Alternatively, the entire 16S gene was amplified and sequenced using the ONT MinION, and analyzed with Mothur, EPI2ME, and GraphMap. Although >99% of reads were correctly classified at the genus level, up to ≈40% were misclassified at the species level. Both technologies, therefore, allow reliable identification of bacterial genera, but can potentially misguide identification of bacterial species, and constitute viable alternatives to Sanger sequencing for rapid analysis of mixed samples without requiring any culturing steps. Full article

(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)

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21 pages, 8933 KiB

Open AccessArticle

Genome-Wide Identification and Analysis of the NPR1-Like Gene Family in Bread Wheat and Its Relatives

by Xian Liu, Zhiguo Liu, Xinhui Niu, Qian Xu and Long Yang

Int. J. Mol. Sci. 2019, 20(23), 5974; https://doi.org/10.3390/ijms20235974 - 27 Nov 2019

Cited by 16 | Viewed by 5002

Abstract

NONEXPRESSOR OF PATHOGENESIS-RELATED GENES 1 (NPR1), and its paralogues NPR3 and NPR4, are bona fide salicylic acid (SA) receptors and play critical regulatory roles in plant immunity. However, comprehensive identification and analysis of the NPR1-like gene family had not been conducted so far in bread wheat and its relatives. Here, a total of 17 NPR genes in Triticum aestivum, five NPR genes in Triticum urartu, 12 NPR genes in Triticum dicoccoides, and six NPR genes in Aegilops tauschii were identified using bioinformatics approaches. Protein properties of these putative NPR1-like genes were also described. Phylogenetic analysis showed that the 40 NPR1-like proteins, together with 40 NPR1-related proteins from other plant species, were clustered into three major clades. The TaNPR1-like genes belonging to the same Arabidopsis subfamilies shared similar exon-intron patterns and protein domain compositions, as well as conserved motifs and amino acid residues. The cis-regulatory elements related to SA were identified in the promoter regions of TaNPR1-like genes. The TaNPR1-like genes were intensively mapped on the chromosomes of homoeologous groups 3, 4, and 5, except TaNPR2-D. Chromosomal distribution and collinearity analysis of NPR1-like genes among bread wheat and its relatives revealed that the evolution of this gene family was more conservative following formation of hexaploid wheat. Transcriptome data analysis indicated that TaNPR1-like genes exhibited tissue/organ-specific expression patterns and some members were induced under biotic stress. These findings lay the foundation for further functional characterization of NPR1-like proteins in bread wheat and its relatives. Full article

(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)

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16 pages, 7738 KiB

Open AccessArticle

In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis

by Francesca Gallivanone, Claudia Cava, Fabio Corsi, Gloria Bertoli and Isabella Castiglioni

Int. J. Mol. Sci. 2019, 20(23), 5825; https://doi.org/10.3390/ijms20235825 - 20 Nov 2019

Cited by 15 | Viewed by 2724

Abstract

Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling. Background: Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype. Methods: We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype. Results: We found six miRNAs correlated with imaging features in Luminal A (miR-1537, -205, -335, -337, -452, and -99a), seven miRNAs (miR-142, -155, -190, -190b, -1910, -3617, and -429) in HER2+, and two miRNAs (miR-135b and -365-2) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone. Conclusion: Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis. Full article

(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)

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16 pages, 2721 KiB

Open AccessArticle

Defining Signatures of Arm-Wise Copy Number Change and Their Associated Drivers in Kidney Cancers

by Graeme Benstead-Hume, Sarah K. Wooller, Jessica A Downs and Frances M. G. Pearl

Int. J. Mol. Sci. 2019, 20(22), 5762; https://doi.org/10.3390/ijms20225762 - 16 Nov 2019

Cited by 8 | Viewed by 2686

Abstract

Using pan-cancer data from The Cancer Genome Atlas (TCGA), we investigated how patterns in copy number alterations in cancer cells vary both by tissue type and as a function of genetic alteration. We find that patterns in both chromosomal ploidy and individual arm copy number are dependent on tumour type. We highlight for example, the significant losses in chromosome arm 3p and the gain of ploidy in 5q in kidney clear cell renal cell carcinoma tissue samples. We find that specific gene mutations are associated with genome-wide copy number changes. Using signatures derived from non-negative factorisation, we also find gene mutations that are associated with particular patterns of ploidy change. Finally, utilising a set of machine learning classifiers, we successfully predicted the presence of mutated genes in a sample using arm-wise copy number patterns as features. This demonstrates that mutations in specific genes are correlated and may lead to specific patterns of ploidy loss and gain across chromosome arms. Using these same classifiers, we highlight which arms are most predictive of commonly mutated genes in kidney renal clear cell carcinoma (KIRC). Full article

(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)

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19 pages, 4124 KiB

Open AccessArticle

Human Cysteine Cathepsins Degrade Immunoglobulin G In Vitro in a Predictable Manner

by Rune Alexander Høglund, Silje Bøen Torsetnes, Andreas Lossius, Bjarne Bogen, E. Jane Homan, Robert Bremel and Trygve Holmøy

Int. J. Mol. Sci. 2019, 20(19), 4843; https://doi.org/10.3390/ijms20194843 - 29 Sep 2019

Cited by 8 | Viewed by 4615

Abstract

Cysteine cathepsins are critical components of the adaptive immune system involved in the generation of epitopes for presentation on human leukocyte antigen (HLA) molecules and have been implicated in degradation of autoantigens. Immunoglobulin variable regions with somatic mutations and random complementarity region 3 amino acid composition are inherently immunogenic. T cell reactivity towards immunoglobulin variable regions has been investigated in relation to specific diseases, as well as reactivity to therapeutic monoclonal antibodies. Yet, how the immunoglobulins, or the B cell receptors, are processed in endolysosomal compartments of professional antigen presenting cells has not been described in detail. Here we present in silico and in vitro experimental evidence suggesting that cysteine cathepsins S, L and B may have important roles in generating peptides fitting HLA class II molecules, capable of being presented to T cells, from monoclonal antibodies as well as from central nervous system proteins including a well described autoantigen. By combining neural net models with in vitro proteomics experiments, we further suggest how such degradation can be predicted, how it fits with available cellular models, and that it is immunoglobulin heavy chain variable family dependent. These findings are relevant for biotherapeutic drug design as well as to understand disease development. We also suggest how these tools can be improved, including improved machine learning methodology. Full article

(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)

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19 pages, 8089 KiB

Open AccessArticle

Consistent Biomarkers and Related Pathogenesis Underlying Asthma Revealed by Systems Biology Approach

by Xiner Nie, Jinyi Wei, Youjin Hao, Jingxin Tao, Yinghong Li, Mingwei Liu, Boying Xu and Bo Li

Int. J. Mol. Sci. 2019, 20(16), 4037; https://doi.org/10.3390/ijms20164037 - 19 Aug 2019

Cited by 23 | Viewed by 4874

Abstract

Asthma is a common chronic airway disease worldwide. Due to its clinical and genetic heterogeneity, the cellular and molecular processes in asthma are highly complex and relatively unknown. To discover novel biomarkers and the molecular mechanisms underlying asthma, several studies have been conducted by focusing on gene expression patterns in epithelium through microarray analysis. However, few robust specific biomarkers were identified and some inconsistent results were observed. Therefore, it is imperative to conduct a robust analysis to solve these problems. Herein, an integrated gene expression analysis of ten independent, publicly available microarray data of bronchial epithelial cells from 348 asthmatic patients and 208 healthy controls was performed. As a result, 78 up- and 75 down-regulated genes were identified in bronchial epithelium of asthmatics. Comprehensive functional enrichment and pathway analysis revealed that response to chemical stimulus, extracellular region, pathways in cancer, and arachidonic acid metabolism were the four most significantly enriched terms. In the protein-protein interaction network, three main communities associated with cytoskeleton, response to lipid, and regulation of response to stimulus were established, and the most highly ranked 6 hub genes (up-regulated CD44, KRT6A, CEACAM5, SERPINB2, and down-regulated LTF and MUC5B) were identified and should be considered as new biomarkers. Pathway cross-talk analysis highlights that signaling pathways mediated by IL-4/13 and transcription factor HIF-1α and FOXA1 play crucial roles in the pathogenesis of asthma. Interestingly, three chemicals, polyphenol catechin, antibiotic lomefloxacin, and natural alkaloid boldine, were predicted and may be potential drugs for asthma treatment. Taken together, our findings shed new light on the common molecular pathogenesis mechanisms of asthma and provide theoretical support for further clinical therapeutic studies. Full article

(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data)

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