scholarly journals Integrative Bayesian Network Analysis of Genomic Data

2014 ◽  
Vol 13s2 ◽  
pp. CIN.S13786 ◽  
Author(s):  
Yang Ni ◽  
Francesco C. Stingo ◽  
Veerabhadran Baladandayuthapani
Keyword(s):  
Bayesian Network ◽  
Cancer Progression ◽  
Rapid Development ◽  
Genomic Data ◽  
Biological Knowledge ◽  
Network Approach ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
New Genes ◽  
Genome Wide

Rapid development of genome-wide profiling technologies has made it possible to conduct integrative analysis on genomic data from multiple platforms. In this study, we develop a novel integrative Bayesian network approach to investigate the relationships between genetic and epigenetic alterations as well as how these mutations affect a patient's clinical outcome. We take a Bayesian network approach that admits a convenient decomposition of the joint distribution into local distributions. Exploiting the prior biological knowledge about regulatory mechanisms, we model each local distribution as linear regressions. This allows us to analyze multi-platform genome-wide data in a computationally efficient manner. We illustrate the performance of our approach through simulation studies. Our methods are motivated by and applied to a multi-platform glioblastoma dataset, from which we reveal several biologically relevant relationships that have been validated in the literature as well as new genes that could potentially be novel biomarkers for cancer progression.

scholarly journals Computational Approaches to Prioritize Cancer Driver Missense Mutations

2018 ◽  
Vol 19 (7) ◽  
pp. 2113 ◽  
Author(s):  
Feiyang Zhao ◽  
Lei Zheng ◽  
Alexander Goncearenco ◽  
Anna Panchenko ◽  
Minghui Li
Keyword(s):  
Cancer Progression ◽  
Conformational Changes ◽  
Molecular Mechanisms ◽  
Complex Disease ◽  
Rapid Development ◽  
Genomic Data ◽  
Genetic Alterations ◽  
Driver Mutations ◽  
Missense Mutations ◽  
Computational Approaches

Cancer is a complex disease that is driven by genetic alterations. There has been a rapid development of genome-wide techniques during the last decade along with a significant lowering of the cost of gene sequencing, which has generated widely available cancer genomic data. However, the interpretation of genomic data and the prediction of the association of genetic variations with cancer and disease phenotypes still requires significant improvement. Missense mutations, which can render proteins non-functional and provide a selective growth advantage to cancer cells, are frequently detected in cancer. Effects caused by missense mutations can be pinpointed by in silico modeling, which makes it more feasible to find a treatment and reverse the effect. Specific human phenotypes are largely determined by stability, activity, and interactions between proteins and other biomolecules that work together to execute specific cellular functions. Therefore, analysis of missense mutations’ effects on proteins and their complexes would provide important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of cancer progression and facilitating treatment and prevention. Herein, we summarize the major computational approaches and tools that provide not only the classification of missense mutations as cancer drivers or passengers but also the molecular mechanisms induced by driver mutations. This review focuses on the discussion of annotation and prediction methods based on structural and biophysical data, analysis of somatic cancer missense mutations in 3D structures of proteins and their complexes, predictions of the effects of missense mutations on protein stability, protein-protein and protein-nucleic acid interactions, and assessment of conformational changes in protein conformations induced by mutations.

scholarly journals SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Verônica R. de Melo Costa ◽  
Julianus Pfeuffer ◽  
Annita Louloupi ◽  
Ulf A. V. Ørom ◽  
Rosario M. Piro
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Time Course ◽  
Progressive Increase ◽  
Rna Seq ◽  
Transcript Processing ◽  
Aberrant Transcript ◽  
Genome Wide ◽  
Splicing Efficiency ◽  
Nascent Rna

Abstract Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

scholarly journals Genome-wide methylation sequencing identifies progression-related epigenetic drivers in myelodysplastic syndromes

2020 ◽  
Vol 11 (11) ◽  
Author(s):  
Jing-dong Zhou ◽  
Ting-juan Zhang ◽  
Zi-jun Xu ◽  
Zhao-qun Deng ◽  
Yu Gu ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Myelodysplastic Syndromes ◽  
Bisulfite Sequencing ◽  
De Novo ◽  
Dna Hypermethylation ◽  
Reduced Representation ◽  
Targeted Bisulfite Sequencing ◽  
Specific Pcr ◽  
Genome Wide ◽  
Potential Biomarker

AbstractThe potential mechanism of myelodysplastic syndromes (MDS) progressing to acute myeloid leukemia (AML) remains poorly elucidated. It has been proved that epigenetic alterations play crucial roles in the pathogenesis of cancer progression including MDS. However, fewer studies explored the whole-genome methylation alterations during MDS progression. Reduced representation bisulfite sequencing was conducted in four paired MDS/secondary AML (MDS/sAML) patients and intended to explore the underlying methylation-associated epigenetic drivers in MDS progression. In four paired MDS/sAML patients, cases at sAML stage exhibited significantly increased methylation level as compared with the matched MDS stage. A total of 1090 differentially methylated fragments (DMFs) (441 hypermethylated and 649 hypomethylated) were identified involving in MDS pathogenesis, whereas 103 DMFs (96 hypermethylated and 7 hypomethylated) were involved in MDS progression. Targeted bisulfite sequencing further identified that aberrant GFRA1, IRX1, NPY, and ZNF300 methylation were frequent events in an additional group of de novo MDS and AML patients, of which only ZNF300 methylation was associated with ZNF300 expression. Subsequently, ZNF300 hypermethylation in larger cohorts of de novo MDS and AML patients was confirmed by real-time quantitative methylation-specific PCR. It was illustrated that ZNF300 methylation could act as a potential biomarker for the diagnosis and prognosis in MDS and AML patients. Functional experiments demonstrated the anti-proliferative and pro-apoptotic role of ZNF300 overexpression in MDS-derived AML cell-line SKM-1. Collectively, genome-wide DNA hypermethylation were frequent events during MDS progression. Among these changes, ZNF300 methylation, a regulator of ZNF300 expression, acted as an epigenetic driver in MDS progression. These findings provided a theoretical basis for the usage of demethylation drugs in MDS patients against disease progression.

Identification of Chromosome Inheritance Modifiers in Drosophila melanogaster

Genetics ◽  
2001 ◽  
Vol 157 (4) ◽  
pp. 1623-1637 ◽  
Author(s):  
Kenneth W Dobie ◽  
Cameron D Kennedy ◽  
Vivienne M Velasco ◽  
Tory L McGrath ◽  
Juliani Weko ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Biological Activity ◽  
Cancer Progression ◽  
Birth Defects ◽  
Mitotic Chromosome ◽  
P Element ◽  
Inverse Pcr ◽  
Gtpase Activating Protein ◽  
New Genes ◽  
Genomic Analyses

Abstract Faithful chromosome inheritance is a fundamental biological activity and errors contribute to birth defects and cancer progression. We have performed a P-element screen in Drosophila melanogaster with the aim of identifying novel candidate genes involved in inheritance. We used a “sensitized” minichromosome substrate (J21A) to screen ∼3,000 new P-element lines for dominant effects on chromosome inheritance and recovered 78 Sensitized chromosome inheritance modifiers (Scim). Of these, 69 decreased minichromosome inheritance while 9 increased minichromosome inheritance. Fourteen mutations are lethal or semilethal when homozygous and all exhibit dramatic mitotic defects. Inverse PCR combined with genomic analyses identified P insertions within or close to genes with previously described inheritance functions, including wings apart-like (wapl), centrosomin (cnn), and pavarotti (pav). Further, lethal insertions in replication factor complex 4 (rfc4) and GTPase-activating protein 1 (Gap1) exhibit specific mitotic chromosome defects, discovering previously unknown roles for these proteins in chromosome inheritance. The majority of the lines represent mutations in previously uncharacterized loci, many of which have human homologs, and we anticipate that this collection will provide a rich source of mutations in new genes required for chromosome inheritance in metazoans.

scholarly journals Computationally Efficient Magnetic Resonance Imaging Based Surface Contact Modeling as a Tool to Evaluate Joint Injuries and Outcomes of Surgical Interventions Compared to Finite Element Modeling

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Joshua E. Johnson ◽  
Phil Lee ◽  
Terence E. McIff ◽  
E. Bruce Toby ◽  
Kenneth J. Fischer
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Joint Mechanics ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Element Modeling ◽  
Joint Injuries ◽  
Surface Contact ◽  
Joint Contact ◽  
Contact Pressures

Joint injuries and the resulting posttraumatic osteoarthritis (OA) are a significant problem. There is still a need for tools to evaluate joint injuries, their effect on joint mechanics, and the relationship between altered mechanics and OA. Better understanding of injuries and their relationship to OA may aid in the development or refinement of treatment methods. This may be partially achieved by monitoring changes in joint mechanics that are a direct consequence of injury. Techniques such as image-based finite element modeling can provide in vivo joint mechanics data but can also be laborious and computationally expensive. Alternate modeling techniques that can provide similar results in a computationally efficient manner are an attractive prospect. It is likely possible to estimate risk of OA due to injury from surface contact mechanics data alone. The objective of this study was to compare joint contact mechanics from image-based surface contact modeling (SCM) and finite element modeling (FEM) in normal, injured (scapholunate ligament tear), and surgically repaired radiocarpal joints. Since FEM is accepted as the gold standard to evaluate joint contact stresses, our assumption was that results obtained using this method would accurately represent the true value. Magnetic resonance images (MRI) of the normal, injured, and postoperative wrists of three subjects were acquired when relaxed and during functional grasp. Surface and volumetric models of the radiolunate and radioscaphoid articulations were constructed from the relaxed images for SCM and FEM analyses, respectively. Kinematic boundary conditions were acquired from image registration between the relaxed and grasp images. For the SCM technique, a linear contact relationship was used to estimate contact outcomes based on interactions of the rigid articular surfaces in contact. For FEM, a pressure-overclosure relationship was used to estimate outcomes based on deformable body contact interactions. The SCM technique was able to evaluate variations in contact outcomes arising from scapholunate ligament injury and also the effects of surgical repair, with similar accuracy to the FEM gold standard. At least 80% of contact forces, peak contact pressures, mean contact pressures and contact areas from SCM were within 10 N, 0.5 MPa, 0.2 MPa, and 15 mm2, respectively, of the results from FEM, regardless of the state of the wrist. Depending on the application, the MRI-based SCM technique has the potential to provide clinically relevant subject-specific results in a computationally efficient manner compared to FEM.

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