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Book ; Online: Computational Methods for the Analysis of Genomic Data and Biological Processes

Gómez Vela, Francisco A. / Divina, Federico / García-Torres, Miguel

2021

Keywords	Research & information: general ; Biology, life sciences ; HIGD2A ; cancer ; DNA methylation ; mRNA expression ; miRNA ; quercetin ; hypoxia ; eQTL ; CRISPR-Cas9 ; single-cell clone ; fine-mapping ; power ; RNA N6-methyladenosine site ; yeast genome ; methylation ; computational biology ; deep learning ; bioinformatics ; hepatocellular carcinoma ; transcriptomics ; proteomics ; bioinformatics analysis ; differentiation ; Gene Ontology ; Reactome Pathways ; gene-set enrichment ; meta-analysis ; transcription factor ; binding sites ; genomics ; chilling stress ; CBF ; DREB ; CAMTA1 ; pathway ; text mining ; infiltration tactics optimization algorithm ; classification ; clustering ; microarray ; ensembles ; machine learning ; infiltration ; computational intelligence ; gene co-expression network ; murine coronavirus ; viral infection ; immune response ; data mining ; systems biology ; obesity ; differential genes expression ; exercise ; high-fat diet ; pathways ; potential therapeutic targets ; DNA N6-methyladenine ; Chou's 5-steps rule ; Convolution Neural Network (CNN) ; Long Short-Term Memory (LSTM) ; machine-learning ; chromatin interactions ; prediction ; genome architecture ; n/a
Size	1 electronic resource (222 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Publishing place	Basel, Switzerland
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT021044707
ISBN	9783039437726 ; 3039437720
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Computational Methods for the Analysis of Genomic Data and Biological Processes.

Gómez-Vela, Francisco / Divina, Federico / García-Torres, Miguel

Genes

2020 Volume 11, Issue 10

Abstract: ... more genomic data [...]. ... Today, new technologies, such as microarrays or high-performance sequencing, are producing more and ...

Abstract	Today, new technologies, such as microarrays or high-performance sequencing, are producing more and more genomic data [...].
MeSH term(s)	Animals ; Biological Phenomena ; Computational Biology/methods ; Gene Expression Profiling ; Humans ; Oligonucleotide Array Sequence Analysis/methods ; Sequence Analysis, DNA/methods
Keywords	covid19
Language	English
Publishing date	2020-10-20
Publishing country	Switzerland
Document type	Editorial ; Introductory Journal Article
ZDB-ID	2527218-4
ISSN	2073-4425 ; 2073-4425
ISSN (online)	2073-4425
ISSN	2073-4425
DOI	10.3390/genes11101230
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Computational Methods for the Analysis of Genomic Data and Biological Processes

Gómez-Vela, Francisco / Divina, Federico / García-Torres, Miguel

Genes. 2020 Oct. 20, v. 11, no. 10

2020

Abstract: ... more genomic data [...] ... Today, new technologies, such as microarrays or high-performance sequencing, are producing more and ...

Abstract	Today, new technologies, such as microarrays or high-performance sequencing, are producing more and more genomic data [...]
Keywords	computational methodology ; genomics ; microarray technology
Language	English
Dates of publication	2020-1020
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2527218-4
ISSN	2073-4425
ISSN	2073-4425
DOI	10.3390/genes11101230
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Statistical and Computational Methods for Proteogenomic Data Analysis.

Song, Xiaoyu

Methods in molecular biology (Clifton, N.J.)

2023 Volume 2629, Page(s) 271–303

Abstract: ... discoveries, especially with an integration with genomics and transcriptomics. These methods cover topics like ... these methods, we will use the proteogenomic data from the lung squamous cell carcinoma study of the Clinical ... Then, we will describe several statistical methods that leverage well-processed proteomic data to generate scientific ...

Abstract	Proteins are the functional molecules for almost all cellular and biological processes. They are also the targets of most drugs. Proteins employ complex, multilevel regulations, so their abundance levels do not well correlated with their mRNA expression levels. The structure, activity, and functional roles of proteins are affected by posttranslational modifications (PTM), which are even less correlated with mRNA expression levels than protein abundances. Comprehensive characterization of the proteomics data is critical for understanding the molecular and cellular mechanisms of biological systems and developing news therapeutics. Current large-scale proteomic profiling technologies, such as mass spectrometry, provide relative identification of peptides and proteins, with data vulnerable to outliers, batch effects, and nonrandom missingness. In order to perform high-quality proteomic data analysis, we will first introduce a data preprocessing and quality control pipeline that includes normalization, outlier detection and removal, batch effect identification and handling, and missing data imputation. Then, we will describe several statistical methods that leverage well-processed proteomic data to generate scientific discoveries, especially with an integration with genomics and transcriptomics. These methods cover topics like association analysis, network construction, clustering, and cell-type deconvolution. To demonstrate these methods, we will use the proteogenomic data from the lung squamous cell carcinoma study of the Clinical Proteomic Tumor Analysis Consortium and provide sample codes for data access and analyses.
MeSH term(s)	Proteomics/methods ; Proteogenomics ; Genomics/methods ; Peptides/metabolism ; RNA, Messenger
Chemical Substances	Peptides ; RNA, Messenger
Language	English
Publishing date	2023-03-16
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-2986-4_13
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Big Data Analysis in Computational Biology and Bioinformatics.

Kumar, Prakash / Paul, Ranjit Kumar / Roy, Himadri Shekhar / Yeasin, Md / Ajit / Paul, Amrit Kumar

Methods in molecular biology (Clifton, N.J.)

2023 Volume 2719, Page(s) 181–197

Abstract: ... of big data analysis in computational biology and bioinformatics. We discuss the various aspects of big ... data analysis presents significant opportunities like development of efficient and fast computing algorithms ... data analysis, including data acquisition, storage, processing, and analysis. We also highlight ...

Abstract	Advancements in high-throughput technologies, genomics, transcriptomics, and metabolomics play an important role in obtaining biological information about living organisms. The field of computational biology and bioinformatics has experienced significant growth with the advent of high-throughput sequencing technologies and other high-throughput techniques. The resulting large amounts of data present both opportunities and challenges for data analysis. Big data analysis has become essential for extracting meaningful insights from the massive amount of data. In this chapter, we provide an overview of the current status of big data analysis in computational biology and bioinformatics. We discuss the various aspects of big data analysis, including data acquisition, storage, processing, and analysis. We also highlight some of the challenges and opportunities of big data analysis in this area of research. Despite the challenges, big data analysis presents significant opportunities like development of efficient and fast computing algorithms for advancing our understanding of biological processes, identifying novel biomarkers for breeding research and developments, predicting disease, and identifying potential drug targets for drug development programs.
MeSH term(s)	Computational Biology/methods ; Genomics/methods ; Metabolomics ; Algorithms ; Big Data
Language	English
Publishing date	2023-10-05
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3461-5_11
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Computational Approaches to Assess Abnormal Metabolism in Alzheimer's Disease Using Transcriptomics.

Lüleci, Hatice Büşra / Uzuner, Dilara / Çakır, Tunahan / Thambisetty, Madhav

Methods in molecular biology (Clifton, N.J.)

2022 Volume 2561, Page(s) 173–189

Abstract: ... available for mapping transcriptome data on GEMs, we focus here on an Integrative Metabolic Analysis Tool ... of metabolic reactions that are differentially inactivated in the tissue of interest. Among the methods ... iMAT), which we have recently applied to the analysis of Alzheimer's disease (AD). We provide ...

Abstract	Transcriptome-integrated human genome-scale metabolic models (GEMs) have been used widely to assess alterations in metabolism in response to disease. Transcriptome integration leads to identification of metabolic reactions that are differentially inactivated in the tissue of interest. Among the methods available for mapping transcriptome data on GEMs, we focus here on an Integrative Metabolic Analysis Tool (iMAT), which we have recently applied to the analysis of Alzheimer's disease (AD). We provide a detailed protocol for applying iMAT to create models of personalized metabolic networks, which can be further processed to identify reactions associated with abnormal metabolism.
MeSH term(s)	Humans ; Transcriptome ; Alzheimer Disease/diagnosis ; Alzheimer Disease/genetics ; Alzheimer Disease/metabolism ; Models, Biological ; Metabolic Networks and Pathways/genetics ; Genome, Human
Language	English
Publishing date	2022-11-18
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Intramural
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-2655-9_9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Emerging Trends in Big Data Analysis in Computational Biology and Bioinformatics in Health Informatics: A Case Study on Epilepsy and Seizures.

Chouhan, Usha / Sahu, Rakesh Kumar / Bhatt, Shaifali / Kurmi, Sonu / Choudhari, Jyoti Kant

Methods in molecular biology (Clifton, N.J.)

2023 Volume 2719, Page(s) 99–119

Abstract: ... interdisciplinary fields that develop and apply computational methods (e.g., analytical methods ... sequencing, microarrays, and mass spectrometry). Since technology has been developed, massive biological data ... mathematical modeling, and simulation) to analyze large collections of biological data, such as genetic sequences, cell ...

Abstract	Advanced technology innovations allow cost-effective, high-throughput profiling of biological systems. It enabled genome sequencing in days using advanced technologies (e.g., next-generation sequencing, microarrays, and mass spectrometry). Since technology has been developed, massive biological data (e.g., genomics, proteomics) has been produced cheaply, allowing the "big data" era to create new opportunities to solve medical and biological complications in many disciplines-preventive medicine, biology, Personalized Medicine, gene sequencing, healthcare, and industry. Computational biology and bioinformatics are interdisciplinary fields that develop and apply computational methods (e.g., analytical methods, mathematical modeling, and simulation) to analyze large collections of biological data, such as genetic sequences, cell populations, or protein samples, to make new predictions or discover new biology. Biological data storage, mining, and analysis have challenges because data is much more heterogeneous. In this study, the big data resources of genomics, proteomics, and metabolomics have been explored to solve biological problems using big data analysis approaches. The goal is to build a network of relationship-based gene-disease associations to prioritize phenotypes common to epilepsy and seizure disease. Through network analysis, The 10 seed genes, 22 associated genes, 132 microRNAs, and 38 transcription factors have been identified that have a direct effect on all forms of epilepsy and seizures. The majority of seed genes, according to the results of a functional analysis of seed genes, are involved in the acetylcholine-gated channel complex (10%) and the heterotrimeric G-protein complex (10%) pathways related to cellular components, followed by a role in the regulation of action potential (20%) and positive regulation of vascular endothelial growth factor production (20%) in Epilepsy and Seizures pathways related to biological processes. This study might provide insight into the workings of the disease and shows the importance of continued research into epilepsy and other conditions that can trigger seizure activity.
MeSH term(s)	Humans ; Big Data ; Vascular Endothelial Growth Factor A ; Computational Biology/methods ; Medical Informatics ; Epilepsy/genetics ; Seizures
Chemical Substances	Vascular Endothelial Growth Factor A
Language	English
Publishing date	2023-10-06
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3461-5_6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Computational Protocol for DNA Methylation Profiling in Plants Using Restriction Enzyme-Based Genome Reduction.

Pereira, Wendell Jacinto / de Castro Rodrigues Pappas, Marília / Pappas, Georgios Joannis

Methods in molecular biology (Clifton, N.J.)

2023 Volume 2638, Page(s) 23–36

Abstract: ... we describe a general overview of the technique and a computational protocol to process the generated data ... for population-scale studies, genomic reduced representation methods emerged as viable alternatives ... but is costly for population-scale studies. Genomic reduction methods emerged as viable alternatives ...

Abstract	Epigenetics can be described as heritable phenotype changes that do not involve alterations in the underlying DNA sequence. Having widespread implications in fundamental biological phenomena, there is an increased interest in characterizing epigenetic modifications and studying their functional implications. DNA methylation, particularly 5-methylcytosine (5mC), stands out as the most studied epigenetic mark and several methodologies have been created to investigate it. With the development of next-generation sequencing technologies, several approaches to DNA methylation profiling were conceived, with differences in resolution and genomic scope. Besides the gold standard whole-genome bisulfite sequencing, which is costly for population-scale studies, genomic reduced representation methods emerged as viable alternatives to investigate methylation loci. Whole-genome bisulfite sequencing provides single-base methylation resolution but is costly for population-scale studies. Genomic reduction methods emerged as viable alternatives to investigate a fraction of methylated loci. One of such approaches uses double digestion with the restriction enzymes PstI and one of the isoschizomers, MspI and HpaII, with differential sensitivity to 5mC at the restriction site. Statistical comparison of sequencing reads counts obtained from the two libraries for each sample (PstI-MspI and PstI-HpaII) is used to infer the methylation status of thousands of cytosines. Here, we describe a general overview of the technique and a computational protocol to process the generated data to provide a medium-scale inventory of methylated sites in plant genomes. The software is available at https://github.com/wendelljpereira/DArTseqMet .
MeSH term(s)	DNA Methylation ; Genomics/methods ; Sulfites ; Epigenesis, Genetic ; DNA Restriction Enzymes/genetics ; Sequence Analysis, DNA/methods
Chemical Substances	hydrogen sulfite (OJ9787WBLU) ; Sulfites ; DNA Restriction Enzymes (EC 3.1.21.-)
Language	English
Publishing date	2023-02-13
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3024-2_3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A computational framework to explore large-scale biosynthetic diversity.

Nature chemical biology

2019 Volume 16, Issue 1, Page(s) 60–68

Abstract: ... analyzing datasets of this size and complexity. In the present study, a streamlined computational workflow ... prospecting engine' (BiG-SCAPE), which facilitates fast and interactive sequence similarity network analysis ... of biosynthetic gene clusters and gene cluster families; and the 'core analysis of syntenic orthologues ...

Abstract	Genome mining has become a key technology to exploit natural product diversity. Although initially performed on a single-genome basis, the process is now being scaled up to mine entire genera, strain collections and microbiomes. However, no bioinformatic framework is currently available for effectively analyzing datasets of this size and complexity. In the present study, a streamlined computational workflow is provided, consisting of two new software tools: the 'biosynthetic gene similarity clustering and prospecting engine' (BiG-SCAPE), which facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families; and the 'core analysis of syntenic orthologues to prioritize natural product gene clusters' (CORASON), which elucidates phylogenetic relationships within and across these families. BiG-SCAPE is validated by correlating its output to metabolomic data across 363 actinobacterial strains and the discovery potential of CORASON is demonstrated by comprehensively mapping biosynthetic diversity across a range of detoxin/rimosamide-related gene cluster families, culminating in the characterization of seven detoxin analogues.
MeSH term(s)	Actinobacteria/genetics ; Algorithms ; Biological Products ; Biosynthetic Pathways/genetics ; Cluster Analysis ; Computational Biology/methods ; Data Mining/methods ; Genome, Bacterial ; Genomics ; Metabolomics ; Microbiota ; Multigene Family ; Phylogeny ; Reproducibility of Results ; Software
Chemical Substances	Biological Products
Language	English
Publishing date	2019-11-25
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2202962-X
ISSN	1552-4469 ; 1552-4450
ISSN (online)	1552-4469
ISSN	1552-4450
DOI	10.1038/s41589-019-0400-9
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Article ; Online: Computational methods for ribosome profiling data analysis.

Kiniry, Stephen J / Michel, Audrey M / Baranov, Pavel V

Wiley interdisciplinary reviews. RNA

2019 Volume 11, Issue 3, Page(s) e1577

Abstract: ... to the biological questions it can address. This review describes the computational methods and tools that have been ... routine processing of raw data and follows with more specific tasks such as the identification ... and expand the bioinformatician's toolbox for ribosome profiling data analysis. This article is ...

Abstract	Since the introduction of the ribosome profiling technique in 2009 its popularity has greatly increased. It is widely used for the comprehensive assessment of gene expression and for studying the mechanisms of regulation at the translational level. As the number of ribosome profiling datasets being produced continues to grow, so too does the need for reliable software that can provide answers to the biological questions it can address. This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process. It starts with initial routine processing of raw data and follows with more specific tasks such as the identification of translated open reading frames, differential gene expression analysis, or evaluation of local or global codon decoding rates. The review pinpoints challenges associated with each step and explains the ways in which they are currently addressed. In addition it provides a comprehensive, albeit incomplete, list of publicly available software applicable to each step, which may be a beneficial starting point to those unexposed to ribosome profiling analysis. The outline of current challenges in ribosome profiling data analysis may inspire computational biologists to search for novel, potentially superior, solutions that will improve and expand the bioinformatician's toolbox for ribosome profiling data analysis. This article is characterized under: Translation > Ribosome Structure/Function RNA Evolution and Genomics > Computational Analyses of RNA Translation > Translation Mechanisms Translation > Translation Regulation.
MeSH term(s)	Computational Biology ; Data Analysis ; Gene Expression Profiling ; Humans ; Ribosomes/genetics ; Ribosomes/metabolism ; Sequence Analysis, RNA ; Software
Language	English
Publishing date	2019-11-24
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2634714-3
ISSN	1757-7012 ; 1757-7004
ISSN (online)	1757-7012
ISSN	1757-7004
DOI	10.1002/wrna.1577
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