VIPR HMM: A hidden Markov model for detecting recombination with microbial detection arrays
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PROVIDER: PRJNA151325 | ENA |
REPOSITORIES: ENA
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Project description:In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data using a training set of empirical hybridizations of infected and uninfected samples. We have expanded up our previous implementation by incorporating a hidden Markov model (HMM) to detect recombination. We trained our HMM on a set of nonrecombinant parental viruses and applied our method to 11 recombinant alphaviruses and 4 recombinant flaviviruses hybridized to a diagnostic microarray in order to evaluate performance of the HMM. VIPR HMM identified 95% of the 62 inter-species recombinant breakpoints in the validation set and only two false positive breakpoints were predicted. This study represents the first description and validation of an algorithm capable of identifying recombination in viruses based on diagnostic microarray hybridization patterns.
2012-10-18 | GSE34490 | GEO
Project description:MotivationCurrent methods in diagnostic microbiology typically focus on the detection of a single genomic locus or protein in a candidate agent. The presence of the entire microbe is then inferred from this isolated result. Problematically, the presence of recombination in microbial genomes would go undetected unless other genomic loci or protein components were specifically assayed. Microarrays lend themselves well to the detection of multiple loci from a given microbe; furthermore, the inherent nature of microarrays facilitates highly parallel interrogation of multiple microbes. However, none of the existing methods for analyzing diagnostic microarray data has the capacity to specifically identify recombinant microbes. In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data.ResultsWe have expanded upon our previous implementation of VIPR by incorporating a hidden Markov model (HMM) to detect recombinant genomes. We trained our HMM on a set of non-recombinant parental viruses and applied our method to 11 recombinant alphaviruses and 4 recombinant flaviviruses hybridized to a diagnostic microarray in order to evaluate performance of the HMM. VIPR HMM correctly identified 95% of the 62 inter-species recombination breakpoints in the validation set and only two false-positive breakpoints were predicted. This study represents the first description and validation of an algorithm capable of detecting recombinant viruses based on diagnostic microarray hybridization patterns.AvailabilityVIPR HMM is freely available for academic use and can be downloaded from http://ibridgenetwork.org/wustl/vipr.Contactdavewang@borcim.wustl.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3496345 | biostudies-literature
Project description:Motivation: Identification of eQTL, the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model simultaneously accounts for the effects of genotypes while learning hidden factors, where we make use of the complete likelihood of a unified multivariate regression and factor analysis model to derive a ridge estimator for combined factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, no iterative model selection, it provides p-values, and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of SNPs on a standard 8 core 2.6G desktop. Results: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and outperforms related hidden factor methods for eQTL analysis, where the improved performance is particularly evident in the detection of eQTL with multivariate effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in human lung tissue for a study that included presumptive hidden factors. The analysis identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer. We have developed HEFT, a fast multivariate method that detect eQTLs by analyzing thousands of traits simultaneously in the presence of hidden factors. HEFT employs a combined regression factor analysis approach to analyze the gene expression and genotype data sets, looking for univariate or multivariate eQTLs that regulate gene expression, while simultaneously controlling for both orthogonal or non-orthogonal hidden factors. We show by extensive simulation that HEFT outperforms competing methods, and by applying HEFT to a study that included presumptive hidden factors, we identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis. HEFT analysis results file (includes all results, not just top hits) linked below as supplementary file.
2014-01-07 | E-GEOD-40364 | biostudies-arrayexpress
Project description:Motivation: Identification of eQTL, the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model simultaneously accounts for the effects of genotypes while learning hidden factors, where we make use of the complete likelihood of a unified multivariate regression and factor analysis model to derive a ridge estimator for combined factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, no iterative model selection, it provides p-values, and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of SNPs on a standard 8 core 2.6G desktop. Results: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and outperforms related hidden factor methods for eQTL analysis, where the improved performance is particularly evident in the detection of eQTL with multivariate effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in human lung tissue for a study that included presumptive hidden factors. The analysis identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer.
2014-01-07 | GSE40364 | GEO
Project description:Isolated deletions of the long arm of chromosome 5 (del(5q)) are observed in 10% of myelodysplastic syndromes (MDS) and are associated with a more favorable prognosis, although the clinical course varies considerably. If one or more additional chromosomal aberration/s are present this correlates with a significant shorter overall survival. To assess the frequency of hidden abnormalities in cases with an isolated cytogenetic del(5q), we have performed a genome wide high resolution 44K 60mer oligonucleotide array CGH study using DNA from bone marrow cells of 12 MDS and one AML patient. Additional chromosomal aberrations were identified in three of 13 cases (23%): in one case, a single additional hidden 5.6 Mb deletion of 13q14 and in two cases, multiple larger aberrations involving many chromosomes. Fluorescence in situ hybridization (FISH) demonstrated that aberrations present in only 10% of the bone marrow cells were detectable by aCGH. Furthermore with oligonucleotide aCGH the deletion end points in 5q were mapped precisely, revealing a cluster of proximal break points in band q14.3 (n=8) and a distal cluster between bands q33.2 to q34 (n=11). This study shows the high resolution of oligonucleotide CGH arrays for precisely mapping genomic alterations and for refinement of deletion end points. In addition the high sensitivity of this method enables the study of whole bone marrow cells from MDS patients, a disease with a low blast count. Future studies of more patients with isolated del(5q) for hidden abnormalities will be necessary to evaluate the impact of these on the variable prognosis in these patients and to further define new genetic subgroups. Keywords: Myelodysplastic syndromes, MDS, array CGH, del(5q), hidden aberrations
2007-09-11 | GSE8804 | GEO
Project description:Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently find genes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these methods are expensive and thus are not easily applicable, e.g., to finding genes that are expressed only in very specific conditions. We demonstrate a method for finding novel genes with sparse arrays, applying it on the 33.9 Mb genome of the filamentous fungus Trichoderma reesei. Our computational method does not require normalisations between arrays and it takes into account the multiple-testing problem typical for analysis of microarray data. In contrast to tiling arrays, that use overlapping probes, only one 25mer microarray oligonucleotide probe was used for every 100 b. Thus, only relatively little space on a microarray slide was required to cover the intergenic regions of a genome. The analysis was done as a by-product of a conventional microarray experiment with no additional costs. We found at least 23 good candidates for novel transcripts that could code for proteins and all of which were expressed at high levels. Candidate genes were found to neighbour ire1 and cre1 and many other regulatory genes. Our simple, low-cost method can easily be applied to finding novel species-specific genes without prior knowledge of their sequence properties.
2010-08-13 | GSE12960 | GEO
Project description:This study aimed to use pan-viral detection microarrays to identify viruses in serum from cases of acute pediatric febrile illness in a tropical setting. Patient clinical data and serum samples were collected between 2005 and 2009 as part of an ongoing pediatric dengue virus study at the Hospital Infantil Manuel de Jesús Rivera in Managua, Nicaragua. This study focused on patients who presented with dengue-like illness but who tested negative for dengue-virus infection. We hypothesized that non-dengue viruses or previously uncharacterized viruses might be causing these illnesses. The Virochip microarray is capable of detecting known viruses and discovering novel viruses. This series includes 153 arrays corresponding to 148 cases and 5 HeLa controls. Keywords: viral detection, tropical febrile illness, dengue virus, Nicaragua, Virochip
2011-12-31 | GSE28142 | GEO
Project description:In meiotic prophase, chromosomes are organized into compacted loop arrays to promote homolog pairing and recombination. Here, we probe the architecture of the mouse spermatocyte genome in early and late meiotic prophase using Hi-C. We show that early-prophase chromosomes are arranged as linear arrays of 0.8-1 Mb loops, which extend to 1.5-2 Mb in late prophase as chromosomes compact and homologs undergo synapsis. Topologically associating domains (TADs) are lost in meiotic prophase, suggesting that assembly of the meiotic chromosome axis dramatically reduces the dynamics of chromosome-associated cohesin complexes. While TADs are lost, physically-separated A and B compartments are maintained in meiotic prophase. Moreover, meiotic DNA breaks and inter-homolog crossovers preferentially form in the gene-dense A compartment, revealing a role for chromatin organization in meiotic recombination. Finally, direct detection of inter-homolog contacts genome-wide reveals the structural basis for homolog alignment and juxtaposition by the synaptonemal complex.
2018-11-17 | GSE122622 | GEO
Project description:The goals of the study were to assess array platform performance and analysis algorithm performance. The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: ChIP-chip, competition, artificial DNA For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2008-01-09 | GSE10076 | GEO
Project description:The goals of the study were to assess array platform performance and analysis algorithm performance. The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: ChIP-chip, competition For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2008-01-09 | GSE10090 | GEO