VIPR HMM: A hidden Markov model for detecting recombination with microbial detection arrays
Ontology highlight
ABSTRACT:
ORGANISM(S): Peribunyaviridae Chlorocebus aethiops Alphavirus Flavivirus Togaviridae Flaviviridae
PROVIDER: GSE34490 | GEO | 2012/10/18
SECONDARY ACCESSION(S): PRJNA151325
REPOSITORIES: GEO
Similar Datasets
Project description:The development of rapid and sensitive assays capable of detecting a wide range of infectious agents is critical for the effective diagnosis of diseases that have multiple etiologies. In recent years, many microarray-based diagnostics have been developed to identify viruses present in clinical specimens in a highly parallel fashion. Unfortunately, the rate of development of algorithms to interpret data generated from such platforms has not been commensurate. In particular, none of the existing interpretive algorithms is capable of utilizing empirical training data in a Bayesian framework. We have developed an interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), to capitalize on our ability to generate positive control data for analysis of microbial diagnostic arrays. To illustrate this approach, we have focused on the analysis of viruses that cause hemorrhagic fever (HF). To assess the efficacy of VIPR, we hybridized 33 viruses to 100 microarrays and applied our algorithm to this dataset. A microarray composed of nearly 15,000 oligonucleotides was designed using a custom viral taxonomy-based strategy. The performance of VIPR was assessed by performing a leave-one-out cross validation. VIPR was able to identity the infecting virus with an accuracy of 94%. VIPR outperformed previously described algorithms for the set of HF viruses tested. Bayesian interpretative algorithms such as VIPR should be considered for diagnostic microarray applications.
2011-04-19 | GSE21407 | GEO
Project description:The development of rapid and sensitive assays capable of detecting a wide range of infectious agents is critical for the effective diagnosis of diseases that have multiple etiologies. In recent years, many microarray-based diagnostics have been developed to identify viruses present in clinical specimens in a highly parallel fashion. Unfortunately, the rate of development of algorithms to interpret data generated from such platforms has not been commensurate. In particular, none of the existing interpretive algorithms is capable of utilizing empirical training data in a Bayesian framework. We have developed an interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), to capitalize on our ability to generate positive control data for analysis of microbial diagnostic arrays. To illustrate this approach, we have focused on the analysis of viruses that cause hemorrhagic fever (HF). To assess the efficacy of VIPR, we hybridized 33 viruses to 100 microarrays and applied our algorithm to this dataset. A microarray composed of nearly 15,000 oligonucleotides was designed using a custom viral taxonomy-based strategy. The performance of VIPR was assessed by performing a leave-one-out cross validation. VIPR was able to identity the infecting virus with an accuracy of 94%. VIPR outperformed previously described algorithms for the set of HF viruses tested. Bayesian interpretative algorithms such as VIPR should be considered for diagnostic microarray applications. In this study, 33 viruses including virtually every known hemorrhagic fever virus and a selection of their close relatives were grown in culture and hybridized to 102 microarrays. In addition, 8 uninfected samples were hybridized (110 total hybridizations). These hybridizations were used to test a novel algorithm for diagnosing the infecting virus from a hybridization pattern.
2011-04-19 | E-GEOD-21407 | biostudies-arrayexpress
Project description:We set out to determine whether lncRNAs can contribute to additional antiviral immune strategies independently of canonical innate signaling. High-throughput genetic screening of 2200 human lncRNAs led to the identification of the cytoplasmic antiviral lncRNA Antiviral LncRNA Prohibiting Human Alphaviruses (ALPHA) which is transcriptionally induced by alphaviruses and functions independently of IFN to specifically inhibit the replication of CHIKV and its closest relative, O’nyong’nyong virus (ONNV), but not other viruses. Furthermore, we showed that ALPHA interacts with CHIKV genomic RNA and restrains viral RNA replication. Together, our findings reveal that ALPHA and potentially other lncRNAs can mediate non-canonical antiviral immune responses against specific viral pathogens.
2022-09-13 | GSE184306 | GEO
Project description:Most alphaviruses are transmitted by mosquito vectors and infect a wide range of vertebrate hosts, with a few exceptions. Eilat virus (EILV) in this genus is characterized by a host range restricted to mosquitoes. Its chimeric viruses have been developed as safe and effective vaccine candidates and diagnostic tools. Here, we investigated the interactions between these insect-specific viruses (ISVs) and mosquito cells, unveiling their potential roles in determining vector competence and arbovirus transmission. By RNA sequencing, we found that these ISVs profoundly modified host cell gene expression profiles. Two EILV-based chimeras, consisting of EILV’s nonstructural genes and the structural genes of Chikungunya virus (CHIKV) or Venezuelan equine encephalitis virus (VEEV), namely EILV/CHIKV (E/C) and EILV/VEEV (E/V), induced more intensive transcriptome regulation than parental EILV and activated different antiviral mechanisms in host cells. We demonstrated that E/C robustly promoted antimicrobial peptide production and E/V strongly upregulated the RNA interference pathway components. This also highlighted the intrinsic divergences between CHIKV and VEEV, representatives of the Old World and New World alphaviruses. In contrast, EILV triggered a limited antiviral response. We further showed that initial chimera infections efficiently inhibited subsequent pathogenic alphavirus replication, especially in the case of E/V infection, which almost prevented VEEV and Sindbis virus (SINV) superinfections. Altogether our study provided valuable information on developing ISVs as biological control agents.
2023-01-19 | GSE220562 | GEO
Project description:DamID LaminB1 data were generated in POU2F1-/- MEFs to study the potential role of POU2F1/Oct1 in genome - nuclear lamina interactions. DamID LaminA data were generated in NPCs and Astrocytes to study similarities/differences between LaminA and LaminB1 binding. The procedure to arrive at the provided Hidden Markov Model (HMM) state calls is as follows: We fitted a two-state HMM whereby emissions are distributed as Student's t variables. Mean and variance of DamID signals differ between states, but the degree of freedom (nu) is the same. Gaps in the probe coverage were filled by evenly spaced null probe-values. The parameters were estimated by an adaptation of the ECME algorithm to the HMM framework, showing faster convergence than regular EM when nu is unknown (Filion et al., Cell, 2010). State calls were derived through the Viterbi algorithm. This process was repeated separately for each cell type, yielding per-probe calls. Probes in the ‘bound’ (1) state are indicated as LAD-probes, probes in the ‘unbound’ (0) state as inter-LAD-probes.
2012-10-10 | GSE36132 | GEO
Project description:Segmented (HMM) copy number aberrations (CNA); discovery set
| EGAD00010000217 | EGA
Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors.
2015-04-29 | GSE57162 | GEO
Project description:<p><strong>BACKGROUND:</strong> Pulmonary tuberculosis (TB) and lung cancer (LC) have similar clinical symptoms and atypical imaging findings which are easily misdiagnosed. There is an urgent need for a noninvasive, rapid and accurate biomarker for distinguishing LC from TB.</p><p><strong>METHODS:</strong> A total of 694 subjects were enrolled and divided into discovery set (n=122), validation set (n=214) and another validation set (n=358). Metabolites were identified by multivariate and univariate analyses. The random forest (RF) algorithm and receiver operating characteristic curve analysis were used to evaluate the diagnostic efficacy of the biomarkers.</p><p><strong>RESULTS:</strong> Seven metabolites were identified and validated. The mean decrease accuracy of phenylalanylphenylalanine ranked first in RF analysis. The area under the curve of phenylalanylphenylalanine was 0.8885 (95% CI 0.8444-0.9326), the sensitivity was 71.43%, and the specificity was 92.13%. Compared with that in NC subjects, the level of phenylalanylphenylalanine was elevated in LC patients (fold change = 2.92, p value < 0.01) and reduced in TB patients (fold change = 0.80, p value < 0.05).</p><p><strong>CONCLUSIONS:</strong> The metabolomic profile of LC and TB was described and a key biomarker, phenylalanylphenylalanine, was identified. We produced a rapid and noninvasive method to supplement existing clinical diagnostic examinations for distinguishing LC from TB.</p>
2024-01-16 | MTBLS6990 | MetaboLights
Project description:In cancer management, early and accurate diagnosis of hepatocellular carcinoma (HCC) is important for enhancing survival rate of patients. Currently, serum alpha-fetoprotein (AFP) is the only one biomarker for detection of HCC. However, serum AFP is not satisfactory for diagnosis of HCC due to its low accuracy (about 60-70%). In this study, we collected 109 serum samples (discovery set) from healthy control (HC) and patients with chronic hepatitis B (CHB), liver cirrhosis (LC) and HCC, and analyzed them with custom lncRNA microarray. Profiling analysis shows 181 differentially expressed lncRNAs between HCs and patients with CHB, LC and HCC. Then a 48-lncRNA diagnostic signature was identified with 100% predictive accuracy for all subjects in the discovery set. This diagnostic signature was verified with a cross-validation analysis in the discovery set. To further corroborate the signature, we gathered another 66 serum samples (validation set) and also analyzed them with microarray. The result indicates that the same signature has similar diagnostic accuracy for HC (100%), CHB (73%), LC (88%) and HCC (95%), implying a reproducible diagnostic biomarker for HCC. Receiver operating characteristic (ROC) analysis exhibits that this signature has significantly higher diagnostic accuracy for HCC and non-cancerous subjects (area under curve [AUC]: 0.994) than AFP (AUC: 0.773) in the discovery set and this was also verified in the validation set (0.964 vs 0.792). More importantly, the signature detected small HCC (<3cm) with 100% (13/13) accuracy while AFP with only 61.5% (8/13). Altogether, this study demonstrates that the serum 48-lncRNA signature is not only a powerful and sensitive biomarker for diagnosis of HCC but also a potential biomarker for LC. ***************************************************************** Submitter declares these data are subject to patent number ZL 2016 1 0397094. *****************************************************************
2022-10-15 | GSE78160 | GEO
Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors. A total of 191 RCC FFPE samples are analyzed including 63 clear cell RCC (ccRCC), 57 papillary RCC (pRCC), 35 chromophobe RCC (chrRCC) and 36 oncocytoma (OC). Two-color array-comparative genomic hybdrization on custom designed using RCC DNA as test and normal sex-matched DNA as reference.
2015-04-29 | E-GEOD-57162 | biostudies-arrayexpress