scholarly journals Clinical Functional Genomics

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4627
Author(s):  
Seren Carpenter ◽  
R. Steven Conlan
Keyword(s):  
Functional Genomics ◽  
Ethnic Diversity ◽  
Personalized Treatment ◽  
Biological Processes ◽  
Functional Variation ◽  
Clinical Genomics ◽  
Whole Genomes ◽  
Treatment Plans ◽  
Functional Genome

Functional genomics is the study of how the genome and its products, including RNA and proteins, function and interact to affect different biological processes. The field of functional genomics includes transcriptomics, proteomics, metabolomics and epigenomics, as these all relate to controlling the genome leading to expression of particular phenotypes. By studying whole genomes—clinical genomics, transcriptomes and epigenomes—functional genomics allows the exploration of the diverse relationship between genotype and phenotype, not only for humans as a species but also in individuals, allowing an understanding and evaluation of how the functional genome ‘contributes’ to different diseases. Functional variation in disease can help us better understand that disease, although it is currently limited in terms of ethnic diversity, and will ultimately give way to more personalized treatment plans.

Toward Personalized Treatment for Patients with Low von Willebrand Factor and Quantitative von Willebrand Disease

2021 ◽  
Vol 47 (02) ◽  
pp. 192-200
Author(s):  
James S. O'Donnell
Keyword(s):  
Von Willebrand Factor ◽  
Bleeding Risk ◽  
Von Willebrand Disease ◽  
Personalized Treatment ◽  
Treatment Plans ◽  
Type 3 ◽  
Von Willebrand ◽  
Willebrand Factor

AbstractThe biological mechanisms involved in the pathogenesis of type 2 and type 3 von Willebrand disease (VWD) have been studied extensively. In contrast, although accounting for the majority of VWD cases, the pathobiology underlying partial quantitative VWD has remained somewhat elusive. However, important insights have been attained following several recent cohort studies that have investigated mechanisms in patients with type 1 VWD and low von Willebrand factor (VWF), respectively. These studies have demonstrated that reduced plasma VWF levels may result from either (1) decreased VWF biosynthesis and/or secretion in endothelial cells and (2) pathological increased VWF clearance. In addition, it has become clear that some patients with only mild to moderate reductions in plasma VWF levels in the 30 to 50 IU/dL range may have significant bleeding phenotypes. Importantly in these low VWF patients, bleeding risk fails to correlate with plasma VWF levels and inheritance is typically independent of the VWF gene. Although plasma VWF levels may increase to > 50 IU/dL with progressive aging or pregnancy in these subjects, emerging data suggest that this apparent normalization in VWF levels does not necessarily equate to a complete correction in bleeding phenotype in patients with partial quantitative VWD. In this review, these recent advances in our understanding of quantitative VWD pathogenesis are discussed. Furthermore, the translational implications of these emerging findings are considered, particularly with respect to designing personalized treatment plans for VWD patients undergoing elective procedures.

Neurobiology

Depression ◽  
2019 ◽  
pp. 17-32
Author(s):  
Sonia Israel ◽  
David Benrimoh ◽  
Sylvanne Daniels ◽  
Gustavo Turecki
Keyword(s):  
Resource Allocation ◽  
Stress Response ◽  
Functional Genomics ◽  
Diagnostic Markers ◽  
Personalized Treatment ◽  
Current Evidence ◽  
Pathophysiological Mechanism ◽  
Neurotransmitter Systems ◽  
New Treatments ◽  
Etiological Heterogeneity

This chapter explores the evidence of disturbances in various neurobiological pathways in depression. No unifying pathophysiological mechanism has yet been discovered. Depression is more than simply a deficiency in a single neurotransmitter or pathway, as neurobiological correlates of depression have been identified in diverse studies. This chapter reviews depression-related changes in neurotransmitter systems, neurogenesis, inflammation, stress response, and functional genomics including epigenetics, and how these might contribute to the depressive phenotype. The diverse neurobiological findings of depression reflect the nature of its symptomatology, and likely etiological heterogeneity. Current evidence suggests that depression is not a single condition, but rather multiple overlapping phenotypes with converging and diverging underlying pathophysiological processes. New treatments may be identified with a better understanding of depression neurobiology. Such advances could also lead to the development of prognostic and diagnostic markers, which would allow for more personalized treatment and resource allocation.

A Multi-agent Planning Approach for the Generation of Personalized Treatment Plans of Comorbid Patients

2013 ◽  
pp. 23-27 ◽  
Author(s):  
Inmaculada Sánchez-Garzón ◽  
Juan Fdez-Olivares ◽  
Eva Onaindía ◽  
Gonzalo Milla ◽  
Jaume Jordán ◽  
...  
Keyword(s):  
Personalized Treatment ◽  
Planning Approach ◽  
Treatment Plans ◽  
Multi Agent ◽  
Multi Agent Planning

scholarly journals accuMUlate: A mutation caller designed for mutation accumulation experiments

2017 ◽  
Author(s):  
David J. Winter ◽  
Steven H. Wu ◽  
Abigail A. Howell ◽  
Ricardo B. R. Azevedo ◽  
Rebecca A. Zufall ◽  
...  
Keyword(s):  
Source Code ◽  
Mutation Accumulation ◽  
Molecular Spectra ◽  
Whole Genome ◽  
Biological Processes ◽  
Summary Statistics ◽  
Spontaneous Mutations ◽  
Heuristic Rules ◽  
Sequencing Technologies ◽  
Whole Genomes

AbstractMotivationMutation accumulation (MA) is the most widely used method for directly studying the effects of mutation. Modern sequencing technologies have led to an increased interest in MA experiments. By sequencing whole genomes from MA lines, researchers can directly study the rate and molecular spectra of spontaneous mutations and use these results to understand how mutation contributes to biological processes. At present there is no software designed specifically for identifying mutations from MA lines. Studies that combine MA with whole genome sequencing use custom bioinformatic pipelines that implement heuristic rules to identify putative mutations.ResultsHere we describe accuMUlate, a program that is designed to detect mutations from MA experiments. accuMUlate implements a probabilistic model that reflects the design of a typical MA experiments while being flexible enough to accommodate properties unique to any particular experiment. For each putative mutation identified from this model accuMUlate calculates a set of summary statistics that can be used to filter sites that may be false positives. A companion tool, denominate, can be used to apply filtering rules based on these statistics to simulated mutations and thus identify the number of callable sites per sample.AvailabilitySource code and releases available from https://github.com/dwinter/accuMUlate.

The development of complete personalized treatment plans for colon cancer patients utilizing three gene prediction models.

2014 ◽  
Vol 32 (15_suppl) ◽  
pp. e14553-e14553
Author(s):  
Jeng-Kai Jiang ◽  
Hung-Shin Lin ◽  
Jen-Kou Lin ◽  
Yu-Chung Wu ◽  
Teh-Ying Chou ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Prediction Models ◽  
Gene Prediction ◽  
Personalized Treatment ◽  
Treatment Plans

scholarly journals Computational functional genomics-based reduction of disease-related gene sets to their key components

2018 ◽  
Vol 35 (14) ◽  
pp. 2362-2370 ◽  
Author(s):  
Catharina Lippmann ◽  
Alfred Ultsch ◽  
Jörn Lötsch
Keyword(s):  
Functional Genomics ◽  
Directed Acyclic Graph ◽  
R Package ◽  
Quantitative Information ◽  
Knowledge Bases ◽  
Supplementary Information ◽  
Biological Processes ◽  
Gene Set ◽  
Gene Sets ◽  
Disease Related Gene

Abstract Motivation The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction are applied to laboratory-derived genetic data, the analysis of topical sets of genes gathered from knowledge bases requires a modified approach as no quantitative information about gene expression is available. Results We propose a computational functional genomics-based approach at reducing sets of genes to the most relevant items based on the importance of the gene within the polyhierarchy of biological processes characterizing the disease. Knowledge bases about the biological roles of genes can provide a valid description of traits or diseases represented as a directed acyclic graph (DAG) picturing the polyhierarchy of disease relevant biological processes. The proposed method uses a gene importance score derived from the location of the gene-related biological processes in the DAG. It attempts to recreate the DAG and thereby, the roles of the original gene set, with the least number of genes in descending order of importance. This obtained precision and recall of over 70% to recreate the components of the DAG charactering the biological functions of n=540 genes relevant to pain with a subset of only the k=29 best-scoring genes. Conclusions A new method for reduction of gene sets is shown that is able to reproduce the biological processes in which the full gene set is involved by over 70%; however, by using only ∼5% of the original genes. Availability and implementation The necessary numerical parameters for the calculation of gene importance are implemented in the R package dbtORA at https://github.com/IME-TMP-FFM/dbtORA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals An integrated functional and clinical genomics approach reveals genes driving aggressive metastatic prostate cancer

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rajdeep Das ◽  
Martin Sjöström ◽  
Raunak Shrestha ◽  
Christopher Yogodzinski ◽  
Emily A. Egusa ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Functional Genomics ◽  
Large Scale ◽  
Metastatic Prostate Cancer ◽  
Driver Genes ◽  
Aggressive Prostate Cancer ◽  
Clinical Genomics ◽  
Cancer Phenotypes

AbstractGenomic sequencing of thousands of tumors has revealed many genes associated with specific types of cancer. Similarly, large scale CRISPR functional genomics efforts have mapped genes required for cancer cell proliferation or survival in hundreds of cell lines. Despite this, for specific disease subtypes, such as metastatic prostate cancer, there are likely a number of undiscovered tumor specific driver genes that may represent potential drug targets. To identify such genetic dependencies, we performed genome-scale CRISPRi screens in metastatic prostate cancer models. We then created a pipeline in which we integrated pan-cancer functional genomics data with our metastatic prostate cancer functional and clinical genomics data to identify genes that can drive aggressive prostate cancer phenotypes. Our integrative analysis of these data reveals known prostate cancer specific driver genes, such as AR and HOXB13, as well as a number of top hits that are poorly characterized. In this study we highlight the strength of an integrated clinical and functional genomics pipeline and focus on two top hit genes, KIF4A and WDR62. We demonstrate that both KIF4A and WDR62 drive aggressive prostate cancer phenotypes in vitro and in vivo in multiple models, irrespective of AR-status, and are also associated with poor patient outcome.

scholarly journals MasterPATH: network analysis of functional genomics screening data

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Natalia Rubanova ◽  
Guillaume Pinna ◽  
Jeremie Kropp ◽  
Anna Campalans ◽  
Juan Pablo Radicella ◽  
...  
Keyword(s):  
Dna Damage ◽  
Functional Genomics ◽  
Oxidative Dna Damage ◽  
Transcriptome Profiling ◽  
Molecular Pathways ◽  
Biological Processes ◽  
Loss Of Function ◽  
Damage Recognition ◽  
Dna Damage Recognition

Abstract Background Functional genomics employs several experimental approaches to investigate gene functions. High-throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list). Several computational methods exist to analyze and interpret such lists, the most widespread of which aim either at investigating of significantly enriched biological processes, or at extracting significantly represented subnetworks. Results Here we propose a novel network analysis method and corresponding computational software that employs the shortest path approach and centrality measure to discover members of molecular pathways leading to the studied phenotype, based on functional genomics screening data. The method works on integrated interactomes that consist of both directed and undirected networks – HIPPIE, SIGNOR, SignaLink, TFactS, KEGG, TransmiR, miRTarBase. The method finds nodes and short simple paths with significant high centrality in subnetworks induced by the hit genes and by so-called final implementers – the genes that are involved in molecular events responsible for final phenotypic realization of the biological processes of interest. We present the application of the method to the data from miRNA loss-of-function screen and transcriptome profiling of terminal human muscle differentiation process and to the gene loss-of-function screen exploring the genes that regulates human oxidative DNA damage recognition. The analysis highlighted the possible role of several known myogenesis regulatory miRNAs (miR-1, miR-125b, miR-216a) and their targets (AR, NR3C1, ARRB1, ITSN1, VAV3, TDGF1), as well as linked two major regulatory molecules of skeletal myogenesis, MYOD and SMAD3, to their previously known muscle-related targets (TGFB1, CDC42, CTCF) and also to a number of proteins such as C-KIT that have not been previously studied in the context of muscle differentiation. The analysis also showed the role of the interaction between H3 and SETDB1 proteins for oxidative DNA damage recognition. Conclusion The current work provides a systematic methodology to discover members of molecular pathways in integrated networks using functional genomics screening data. It also offers a valuable instrument to explain the appearance of a set of genes, previously not associated with the process of interest, in the hit list of each particular functional genomics screening.

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