TAD fusion score: discovery and ranking the contribution of deletions to genome structure.
Ontology highlight
ABSTRACT:
SUBMITTER: Huynh L
PROVIDER: S-EPMC6427865 | biostudies-literature | 2019 Mar
REPOSITORIES: biostudies-literature
Publications
TAD fusion score: discovery and ranking the contribution of deletions to genome structure.
Huynh Linh L Hormozdiari Fereydoun F
Genome biology 20190321 1
Deletions that fuse two adjacent topologically associating domains (TADs) can cause severe developmental disorders. We provide a formal method to quantify deletions based on their potential disruption of the three-dimensional genome structure, denoted as the TAD fusion score. Furthermore, we show that deletions that cause TAD fusion are rare and under negative selection in the general population. Finally, we show that our method correctly gives higher scores to deletions reported to cause variou ...[more]
Similar Datasets
Project description:A fusion gene is the physical juxtaposition of two different genes resulting in a structure consisting of the head of one gene and the tail of the other. Gene fusion is often a primary neoplasia-inducing event in leukemias, lymphomas, solid malignancies as well as benign tumors. Knowledge about fusion genes is crucial not only for our understanding of tumorigenesis, but also for the diagnosis, prognostication, and treatment of cancer. Balanced chromosomal rearrangements, in particular translocations and inversions, are the most frequent genetic events leading to the generation of fusion genes. In the present review, we summarize the existing knowledge on chromosome deletions as a mechanism for fusion gene formation. Such deletions are mostly submicroscopic and, hence, not detected by cytogenetic analyses but by array comparative genome hybridization (aCGH) and/or high throughput sequencing (HTS). They are found across the genome in a variety of neoplasias. As tumors are increasingly analyzed using aCGH and HTS, it is likely that more interstitial deletions giving rise to fusion genes will be found, significantly impacting our understanding and treatment of cancer.
| S-EPMC8126330 | biostudies-literature
Project description:The topology of most experimentally determined protein domains is defined by the relative arrangement of secondary structure elements, i.e. α-helices and β-strands, which make up 50-70% of the sequence. Pairing of β-strands defines the topology of β-sheets. The packing of side chains between α-helices and β-sheets defines the majority of the protein core. Often, limited experimental datasets restrain the position of secondary structure elements while lacking detail with respect to loop or side chain conformation. At the same time the regular structure and reduced flexibility of secondary structure elements make these interactions more predictable when compared to flexible loops and side chains. To determine the topology of the protein in such settings, we introduce a tailored knowledge-based energy function that evaluates arrangement of secondary structure elements only. Based on the amino acid C(β) atom coordinates within secondary structure elements, potentials for amino acid pair distance, amino acid environment, secondary structure element packing, β-strand pairing, loop length, radius of gyration, contact order and secondary structure prediction agreement are defined. Separate penalty functions exclude conformations with clashes between amino acids or secondary structure elements and loops that cannot be closed. Each individual term discriminates for native-like protein structures. The composite potential significantly enriches for native-like models in three different databases of 10,000-12,000 protein models in 80-94% of the cases. The corresponding application, "BCL::ScoreProtein," is available at www.meilerlab.org.
| S-EPMC3500277 | biostudies-literature
Project description:BackgroundCo-expression based Cancer Modules (CMs) are sets of genes that act in concert to carry out specific functions in different cancer types, and are constructed by exploiting gene expression profiles related to specific clinical conditions or expression signatures associated to specific processes altered in cancer. Unfortunately, genes involved in cancer are not always detectable using only expression signatures or co-expressed sets of genes, and in principle other types of functional interactions should be exploited to obtain a comprehensive picture of the molecular mechanisms underlying the onset and progression of cancer.ResultsWe propose a novel semi-supervised method to rank genes with respect to CMs using networks constructed from different sources of functional information, not limited to gene expression data. It exploits on the one hand local learning strategies through score functions that extend the guilt-by-association approach, and on the other hand global learning strategies through graph kernels embedded in the score functions, able to take into account the overall topology of the network. The proposed kernelized score functions compare favorably with other state-of-the-art semi-supervised machine learning methods for gene ranking in biological networks and scales well with the number of genes, thus allowing fast processing of very large gene networks.ConclusionsThe modular nature of kernelized score functions provides an algorithmic scheme from which different gene ranking algorithms can be derived, and the results show that using integrated functional networks we can successfully predict CMs defined mainly through expression signatures obtained from gene expression data profiling. A preliminary analysis of top ranked "false positive" genes shows that our approach could be in perspective applied to discover novel genes involved in the onset and progression of tumors related to specific CMs.
| S-EPMC3439680 | biostudies-literature
Project description:While next-generation sequencing (NGS) has become the primary technology for discovering gene fusions, we are still faced with the challenge of ensuring that causative mutations are not missed while minimizing false positives. Currently, there are many computational tools that predict structural variations (SV) and gene fusions using whole genome (WGS) and transcriptome sequencing (RNA-seq) data separately. However, as both WGS and RNA-seq have their limitations when used independently, we hypothesize that the orthogonal validation from integrating both data could generate a sensitive and specific approach for detecting high-confidence gene fusion predictions. Fortunately, decreasing NGS costs have resulted in a growing quantity of patients with both data available. Therefore, we developed a gene fusion discovery tool, INTEGRATE, that leverages both RNA-seq and WGS data to reconstruct gene fusion junctions and genomic breakpoints by split-read mapping. To evaluate INTEGRATE, we compared it with eight additional gene fusion discovery tools using the well-characterized breast cell line HCC1395 and peripheral blood lymphocytes derived from the same patient (HCC1395BL). The predictions subsequently underwent a targeted validation leading to the discovery of 131 novel fusions in addition to the seven previously reported fusions. Overall, INTEGRATE only missed six out of the 138 validated fusions and had the highest accuracy of the nine tools evaluated. Additionally, we applied INTEGRATE to 62 breast cancer patients from The Cancer Genome Atlas (TCGA) and found multiple recurrent gene fusions including a subset involving estrogen receptor. Taken together, INTEGRATE is a highly sensitive and accurate tool that is freely available for academic use.
| S-EPMC4691743 | biostudies-literature
Project description:BackgroundAging in postmitotic tissues is associated with clonal expansion of somatic mitochondrial deletions, the origin of which is not well understood. Such deletions are often flanked by direct nucleotide repeats, but this alone does not fully explain their distribution. Here, we hypothesized that the close proximity of direct repeats on single-stranded mitochondrial DNA (mtDNA) might play a role in the formation of deletions.ResultsBy analyzing human mtDNA deletions in the major arc of mtDNA, which is single-stranded during replication and is characterized by a high number of deletions, we found a non-uniform distribution with a "hot spot" where one deletion breakpoint occurred within the region of 6-9 kb and another within 13-16 kb of the mtDNA. This distribution was not explained by the presence of direct repeats, suggesting that other factors, such as the spatial proximity of these two regions, can be the cause. In silico analyses revealed that the single-stranded major arc may be organized as a large-scale hairpin-like loop with a center close to 11 kb and contacting regions between 6-9 kb and 13-16 kb, which would explain the high deletion activity in this contact zone. The direct repeats located within the contact zone, such as the well-known common repeat with a first arm at 8470-8482 bp (base pair) and a second arm at 13,447-13,459 bp, are three times more likely to cause deletions compared to direct repeats located outside of the contact zone. A comparison of age- and disease-associated deletions demonstrated that the contact zone plays a crucial role in explaining the age-associated deletions, emphasizing its importance in the rate of healthy aging.ConclusionsOverall, we provide topological insights into the mechanism of age-associated deletion formation in human mtDNA, which could be used to predict somatic deletion burden and maximum lifespan in different human haplogroups and mammalian species.
| S-EPMC10166460 | biostudies-literature
Project description:Reorganization of topologically associated domain (TAD) is considered to be a novel mechanism for cell fate transitions. Here, we present a protocol to manipulate TAD via abscisic acid (ABA)-dependent genome linking. We use this protocol to merge two adjacent TADs and evaluate the influence on cell fate transitions. The advantages are that the manipulation does not change the genome and is reversible by withdrawing ABA. The major challenge is how to select linking loci for efficient TAD reorganization. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).
| S-EPMC8433250 | biostudies-literature
Project description:A novel human coronavirus, Middle East respiratory syndrome coronavirus (MERS-CoV), has caused outbreaks of a SARS-like illness with high case fatality rate. The reports of its person-to-person transmission through close contacts have raised a global concern about its pandemic potential. Here we characterize the six-helix bundle fusion core structure of MERS-CoV spike protein S2 subunit by X-ray crystallography and biophysical analysis. We find that two peptides, HR1P and HR2P, spanning residues 998-1039 in HR1 and 1251-1286 in HR2 domains, respectively, can form a stable six-helix bundle fusion core structure, suggesting that MERS-CoV enters into the host cell mainly through membrane fusion mechanism. HR2P can effectively inhibit MERS-CoV replication and its spike protein-mediated cell-cell fusion. Introduction of hydrophilic residues into HR2P results in significant improvement of its stability, solubility and antiviral activity. Therefore, the HR2P analogues have good potential to be further developed into effective viral fusion inhibitors for treating MERS-CoV infection.
| S-EPMC7091805 | biostudies-literature
Project description:MotivationWhen identifying differentially expressed (DE) genes from high-throughput gene expression measurements, we would like to take both statistical significance (such as P-value) and biological relevance (such as fold change) into consideration. In gene set enrichment analysis (GSEA), a score that can combine fold change and P-value together is needed for better gene ranking.ResultsWe defined a gene significance score π-value by combining expression fold change and statistical significance (P-value), and explored its statistical properties. When compared to various existing methods, π-value based approach is more robust in selecting DE genes, with the largest area under curve in its receiver operating characteristic curve. We applied π-value to GSEA and found it comparable to P-value and t-statistic based methods, with added protection against false discovery in certain situations. Finally, in a gene functional study of breast cancer profiles, we showed that using π-value helps elucidating otherwise overlooked important biological functions.Availabilityhttp://gccri.uthscsa.edu/Pi_Value_Supplementary.aspContactxy@ieee.org, cheny8@uthscsa.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3957066 | biostudies-literature
Project description:BACKGROUND:Mycobacterium tuberculosis encodes 11 putative serine-threonine proteins Kinases (STPK) which regulates transcription, cell development and interaction with the host cells. From the 11 STPKs three kinases namely PknA, PknB and PknG have been related to the mycobacterial growth. From previous studies it has been observed that PknB is essential for mycobacterial growth and expressed during log phase of the growth and phosphorylates substrates involved in peptidoglycan biosynthesis. In recent years many high affinity inhibitors are reported for PknB. Previously implementation of data fusion has shown effective enrichment of active compounds in both structure and ligand based approaches .In this study we have used three types of data fusion ranking algorithms on the PknB dataset namely, sum rank, sum score and reciprocal rank. We have identified reciprocal rank algorithm is capable enough to select compounds earlier in a virtual screening process. We have also screened the Asinex database with reciprocal rank algorithm to identify possible inhibitors for PknB. RESULTS:In our work we have used both structure-based and ligand-based approaches for virtual screening, and have combined their results using a variety of data fusion methods. We found that data fusion increases the chance of actives being ranked highly. Specifically, we found that the ranking of Pharmacophore search, ROCS and Glide XP fused with a reciprocal ranking algorithm not only outperforms structure and ligand based approaches but also capable of ranking actives better than the other two data fusion methods using the BEDROC, robust initial enhancement (RIE) and AUC metrics. These fused results were used to identify 45 candidate compounds for further experimental validation. CONCLUSION:We show that very different structure and ligand based methods for predicting drug-target interactions can be combined effectively using data fusion, outperforming any single method in ranking of actives. Such fused results show promise for a coherent selection of candidates for biological screening.
| S-EPMC3600029 | biostudies-literature
Project description:Array-based genome-wide segmental aneuploidy screening detects both de novo and inherited copy number variations (CNVs). In sporadic patients de novo CNVs are interpreted as potentially pathogenic. However, a deletion, transmitted from a healthy parent, may be pathogenic if it overlaps with a mutated second allele inherited from the other healthy parent. To detect such events, we performed multiplex enrichment and next-generation sequencing of the entire coding sequence of all genes within unique hemizygous deletion regions in 20 patients (1.53 Mb capture footprint). Out of the detected 703 non-synonymous single-nucleotide variants (SNVs), 8 represented variants being unmasked by a hemizygous deletion. Although evaluation of inheritance patterns, Grantham matrix scores, evolutionary conservation and bioinformatic predictions did not consistently indicate pathogenicity of these variants, no definitive conclusions can be drawn without functional validation. However, in one patient with severe mental retardation, lack of speech, microcephaly, cheilognathopalatoschisis and bilateral hearing loss, we discovered a second smaller deletion, inherited from the other healthy parent, resulting in loss of both alleles of the highly conserved heat shock factor binding protein 1 (HSBP1) gene. Conceivably, inherited deletions may unmask rare pathogenic variants that may exert a phenotypic impact through a recessive mode of gene action.
| S-EPMC3376260 | biostudies-literature