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Muse

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Most cited protocols related to «Muse»

1
Benchmarking Somatic Variant Callers
For the virtual-tumor benchmarking data, we measured sensitivity and specificity by applying MuSE and MuTect [5 (link)] to the combination of 24 spike-in BAMs (4 different variant allele fractions × 6 distinct depths) with the same depth non-spike-in WGS BAMs. The matched-normal WGS BAM was fixed at 30× depth. We considered any missed calls from our in silico spike-in ground truth as false negatives, and any calls from the non-spike-in WGS BAMs as false positives. The denominator for the FPR calculation is the total length of the hg19 reference genome from chromosome 1 to chromosome X.
For the DREAM challenge IS1, IS2, and IS3 data, we took the organizer provided script and the truth VCF files to compute sensitivity and specificity [23 ]. We extracted the sensitivity and specificity of SomaticSniper, Strelka, and VarScan2 from the DREAM challenge leaderboards. The denominator for the FPR calculation is the total length of the hg19 reference genome from chromosome 1 to chromosome X.
For the multi-region lung adenocarcinoma data, we calculated sensitivity and the positive predictive value (PPV) based on an artificial truth set for the reason that the known validation set was extracted and compiled from the paper’s supplementary document and was biased toward Caller A. The artificial truth set included shared calls (Fig. 3c; black in ovals 1, 2, and 3), validated calls (Fig. 3c; orange in oval 1), and unique-not-validated calls that helped the recognition of trunk mutations (Fig. 3c; red in oval 2 and blue in oval 3). Here, a trunk mutation was a somatic variant call that all tumor regions of one patient had at the same genomic locus. All the other calls were considered as false positives (Fig. 3c; red in five-pointed star 4 and blue in five-pointed star 5). We evaluated accuracy using the F1 and F2 scores, which were defined as
Fβ=1+β2PPV×TPRβ2×PPV+TPRβ=1or2.
To compare the performance of multiple callers in the ACC WES data and the ICGC Pilot-63 WGS data, we also made the artificial truth sets by taking calls that were shared by at least three callers, and computed sensitivity. We regarded other calls as false positives to calculate PPVs. We calculated the F1 and F2 scores by following the same equation above.
Fan Y., Xi L., Hughes D.S., Zhang J., Zhang J., Futreal P.A., Wheeler D.A, & Wang W. (2016). MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. Genome Biology, 17(1), 178.
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Publication 2016
Adenocarcinoma of Lung Alleles Arhinia, choanal atresia, and microphthalmia Chromosomes, Human, Pair 1 Diploid Cell Dreams Genome Hypersensitivity Muse Mutation Neoplasms Patients stella blue
2
Targeted Validation of Somatic Variants
To validate variants identified by MuSE and Caller A in the ACC data, we selected 550 patient-specific positions and designed NimbleGen probes correspondingly for the purpose of targeted capture enrichment and deep sequencing. Paired-end Illumina resequencing was carried out to an average sequencing depth at 1500×. After mapping the reads against the hg19 reference genome using BWA, we considered a somatic variant as validated if its p value calculated from Fisher’s exact test comparing the tumor and matched-normal samples was not larger than 0.05. The validation rates of MuSE and Caller A were calculated as
validation rate of MuSE unique calls
=1139+2303811·(11+141)+739·(39+221)+534·(34+345)+825·(25+494)+730·(30+1102)≈0.2634,
validation rate of MuSE shared calls
=1290+9584125125·(125+8900)+99111·(111+472)+2529·(29+109)+1217·(17+52)+78·(8+51)≈0.9889,
validation rate of MuSE total calls
=1139+290+2303+9584×0.2634·(139+2303)+0.9889·(290+9584)≈0.8450, validation rate of Caller A unique calls=30121≈0.2479, validation rate of Caller A total calls=1121+290+1693+9584×0.2479·(121+1693)+0.9889·(290+9584)≈0.8739.
Fan Y., Xi L., Hughes D.S., Zhang J., Zhang J., Futreal P.A., Wheeler D.A, & Wang W. (2016). MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. Genome Biology, 17(1), 178.
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Publication 2016
Diploid Cell Genome Muse Neoplasms Patients
3
Wheat SNP Discovery and Genotyping
A set of 135 SNPs discovered in a panel of 32 lines of tetraploid and hexaploid wheat by sequencing of 92 gene fragments was downloaded from the Wheat SNP Database (http://wheat.pw.usda.gov/SNP/new/index.shtml). The total length of sequenced regions was 51,493 bp. The discovery panel included 10 accessions of wild emmer, 13 accessions of hexaploid wheat represented by landraces and 9 accessions of synthetic wheats (http://wheat.pw.usda.gov/SNP/new/index.shtml and Supplementary Table S1). The list of SNPs is provided in the Supplementary materials. Repetitive elements in the sequences detected by comparing them with the TREP (http://wheat.pw.usda.gov/ITMI/Repeats/) and GIRI (www.girinst.org) databases were masked. The SNP-harboring sequences were then submitted to Illumina for processing by Illumina® Assay Design Tool (ADT). ADT generates scores for each SNP that could vary from 0 to 1; SNPs with the scores above 0.6 have a high probability to be converted into a successful genotyping assay. In a set of 135 submitted SNPs, the ADT score varied from 0.18 to 0.99 with mean 0.85 (Table S2). A total of 96 SNP sites that were present at the frequency above 2 in the discovery panel and having ADT scores above 0.6 were selected for OPA design (Tables S2, S3). Out of 96 SNPs, 26 were in the wheat D-genome and 70 SNPs were in the A-genome.
A total of 150 ng of genomic DNA per plant was used for Illumina SNP genotyping at the UC Davis Genome center (www.genomecenter.ucdavis.edu/dna_technologies) using the Illumina BeadArray platform and GoldenGate Assay following the manufacturer’s protocol. The fluorescence images of an array matrix carrying Cy3- and Cy5-labeled beads were generated with the two-channel scanner. Raw hybridization intensity data processing, clustering and genotype calling were performed using the genotyping module in the BeadStudio package (Illumina, San Diego, CA, USA). Illumina developed a self-normalization algorithm that relies on information contained in each array. This algorithm adjusts for channel-dependent intensity variations, differences in the background between the channels, and possible crosstalk between the dyes. The normalization procedure implemented in the BeadStudio genotyping module includes outlier removal and background correction and scaling (details of this proprietary normalization algorithm could be obtained from Illumina, San Diego, CA). Before genotype calling, the trimmed mean intensities were calculated from the normalized intensity values obtained for each bead type on the array by rejecting outliers to ensure high quality of genotype data. Genotype calls were generated using the GenCall software incorporated into the BeadStudio package. This algorithm uses a Bayesian model to assign normalized intensity values to one of the three possible homozygous and heterozygous genotype clusters. In the presence of only two homozygous clusters, GenCall computes the location of a missing heterozygous cluster by simulating data using the artificial neural network (Shen et al. 2005 (link)). Since only two clusters were expected for homozygous polyploid wheat lines (see “Discussion” for details), the genotype clusters generated for each SNP locus by GenCall were edited manually after visual inspection of Cy3 and Cy5 fluorescence intensity clustering on two-dimensional Cartesian plots. SNPs that failed to show two-group clustering were excluded from the analysis.
Genotyping error rate was assessed by comparing SNP genotypes determined with the GoldenGate assay with those determined by Sanger sequencing. Trace files for 56 SNP-harboring gene loci were downloaded from the Wheat SNP project database (http://wheat.pw.usda.gov/SNP/new/index.shtml). Base calling and sequence assembly were performed using the phred/phrap and consed programs (Ewing and Green 1998 (link); Ewing et al. 1998 (link); Gordon et al. 1998 (link)). SNP discovery was performed with the polyphred program using the default settings (Stephens et al. 2006 (link)) followed by visual inspection of sequence trace files and manual verification of each discovered SNP. Genetic diversity, defined as the probability that two randomly chosen alleles from the population are different (Weir 1996 , p. 150, 151), was calculated using the PowerMarker program (Liu and Muse 2005 (link)).
Akhunov E., Nicolet C, & Dvorak J. (2009). Single nucleotide polymorphism genotyping in polyploid wheat with the Illumina GoldenGate assay. TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik, 119(3), 507-517.
Publication 2009
Alleles Biological Assay Crossbreeding Cross Reactions DNA, A-Form Dyes Fluorescence Genes Genetic Diversity Genetic Loci Genome Genotype Heterozygote Homozygote Muse Plants Polyploidy Repetitive Region Tetraploidy Triticum aestivum
4
Quantifying DHME-Induced Apoptosis in CRC
DHME-induced apoptosis was quantitatively evaluated by flow cytometry analysis to identify the levels of annexin V-stained (apoptotic) cells on the Muse Cell Analyzer, using Muse annexin V and a Dead Cell Assay Kit (Millipore; Burlington, MA, USA), in accordance with the procedures reported previously [35 (link)]. To elucidate the significance of apoptosis induction to DHME-induced cytotoxicity, CRC cells were pre-treated for 1 h with 50 μM of z-VAD-fmk to block apoptosis, followed by 24 h incubation with DHME for subsequent apoptosis analysis.
Fan H.C., Hsieh Y.C., Li L.H., Chang C.C., Janoušková K., Ramani M.V., Subbaraju G.V., Cheng K.T, & Chang C.C. (2020). Dehydroxyhispolon Methyl Ether, A Hispolon Derivative, Inhibits WNT/β-Catenin Signaling to Elicit Human Colorectal Carcinoma Cell Apoptosis. International Journal of Molecular Sciences, 21(22), 8839.
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Publication 2020
Annexin A5 Apoptosis benzyloxycarbonylvalyl-alanyl-aspartyl fluoromethyl ketone Biological Assay Cardiac Arrest Cells Cytotoxin Flow Cytometry Muse
5
BUSTED[S]: Detecting Positive Selection in the Presence of SRV
We adapted the existing BUSTED test of positive selection (Murrell et al. 2015 (link)) to account for the presence of SRV and call the new method BUSTED[S]. To explore the generality of our findings about FPRs in the presence of SRV we also investigated a second existing test of selection, the M1a versus M2a comparison from Wong et al. (2004) (link), modified slightly to employ MG94 substitution models.
BUSTED[S] is a straightforward extension of BUSTED (Murrell et al. 2015 (link)). The nucleotide substitution process is modeled using the standard finite state continuous time Markov process approach of Muse and Gaut (1994) (link), with entries of the instantaneous rate matrix Q corresponding to substitutions between sense codons i and j denoted as
qij={αsθijπjp1-step synonymous change,αsωbsθijπjp1-step nonsynonymous change,0otherwise.
The θij (with θij=θji ) are parameters governing nucleotide substitution biases. For example, θACT,AGT=θCG and because we incorporate the standard nucleotide GTR model there are five identifiable θij parameters: θAC, θAT, θCG, θCT , and θGT , with θAG≡1 . The position-specific equilibrium frequency of the target nucleotide of a substitution is πjp ; for example, it is πG2 for the second-position change associated with qACT,AGT . The πjp and the stationary frequencies of codons under this model are estimated using the CF3 × 4 procedure (Kosakovsky Pond et al. 2010 (link)), adding nine parameters to the model. The ratio of nonsynonymous to synonymous substitution rates for site s along branch b is ωbs, and this ratio is modeled using a 3-bin general discrete distribution (GDD) with five estimated hyperparameters: 0≤ω1≤ω2≤1≤ω3, p1=P(ωbs=ω1) , and p2=P(ωbs=ω2) . The procedure for efficient computation of the phylogenetic likelihood function for these models was described in Kosakovsky Pond et al. (2011) (link). The quantity αs is a site-specific synonymous substitution rate (no branch-to-branch variation is modeled) drawn from a separate 3-bin GDD. The mean of this distribution is constrained equal to one to maintain statistical identifiability, resulting in four estimated hyperparameters: 0≤cα1<α2=c≤cα3, f1=P(αs=α1) , and f2=P(αs=α2) , with c chosen to ensure that E{αs}=1 . Typical implementations, including ours, allow the number of α and ω rate categories to be separately adjusted by the user, for example, to minimize AICc or to optimize some other measure of model fit. The default setting of three categories generally provides a good balance between fit and performance when using this GDD approach for modeling. Our HyPhy implementation of BUSTED[S] will warn the user if there is evidence of model overfitting, such as the appearance of rate categories with very similar estimated rate values or very low frequencies.
The BUSTED[S] procedure for identifying positive selection is the likelihood ratio test comparing the full model described above to the constrained model formed when ω3 is set equal to 1 (i.e., no positively selected sites). Critical values of the test are derived from a 50:50 mixture distribution of χ02 and χ22 . Note that this asymptotic statistic differs from the 3-component mixture used by Murrell et al. (2015) (link); the simulation studies performed in the current study suggest that this less conservative mixture is sufficient to maintain nominal Type I errors. Both BUSTED[S] and BUSTED analyses in the current work use the same 50:50 mixture test statistic. BUSTED[S] reduces to BUSTED by setting αs=1 , that is, by placing all the mass of the synonymous rate heterogeneity distribution at α = 1. The method is implemented as a part of HyPhy (version 2.5.1 or later). BUSTED[S] is available for free public use on the Datamonkey webserver (Weaver et al. 2018 (link)) at https://www.datamonkey.org/BUSTED (last accessed February 24, 2020).
Wisotsky S.R., Kosakovsky Pond S.L., Shank S.D, & Muse S.V. (2020). Synonymous Site-to-Site Substitution Rate Variation Dramatically Inflates False Positive Rates of Selection Analyses: Ignore at Your Own Peril. Molecular Biology and Evolution, 37(8), 2430-2439.
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Publication 2020
Codon FPR1 protein, human Genetic Heterogeneity Muse Nucleotides Sense Codon

Most recents protocols related to «Muse»

1
Evaluating Cell Metabolic Activity and Apoptosis

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Publication 2023
A549 Cells Annexin A5 Apoptosis Biological Assay Cells Histones Muse Phenol Serum Sulfoxide, Dimethyl
2
Validation of AI Model for Predicting Heart Failure
We develop and validate our model using datasets from two different hospitals. Our first dataset consists of 7121 records from 3767 unique patients who underwent cardiac catheterization at Massachusetts General Hospital (Hospital 1). All patients had a diagnosis of HF (according to ICD 9/10 codes in their medical record) within the 1 year prior to their catheterization date.
This dataset is split into an 80% development set, used to train predictive models, and a 20% internal holdout test set, used for model evaluation. Datasets are constructed such that no data from a single patient appears in different data splits; i.e., all data splits are done on a per-patient basis. We further split the development set on a per-patient level using an 80–20 split into training and “dev” sets. The training set is used to train the model and the dev set is used to determine when training is completed.
Our second dataset consists of 2725 records from 1249 unique patients who underwent cardiac catheterization at the Brigham and Women’s Hospital (Hospital 2). As with data from MGH, these patients all had a diagnosis of heart failure (according to ICD 9/10 codes in their medical record) within the 1 year prior to their catheterization date. We used this entire dataset as an external validation set for model evaluation.
Each record in the datasets consists of: the mean Pulmonary Capillary Wedge Pressure (as measured by cardiac catheterization), a 10-s, 12-lead ECG recorded by the same system (GE Healthcare MUSE) on the same day as the catheterization procedure, and basic demographic information (age/sex). Dataset details are summarized in Table 2.

Model performance (AUROC) on test data. HFNet significantly outperforms the baseline logistic regression (LR) model.

ModelAUROC
Internal test setExternal holdout set
LR0.71 + − 0.010.67 + − 0.01
HFNet0.82 + − 0.01 *0.81 + − 0.01 *

Significant values are in bold.

Key: *: p value < 1e − 10.

Raghu A., Schlesinger D., Pomerantsev E., Devireddy S., Shah P., Garasic J., Guttag J, & Stultz C.M. (2023). ECG-guided non-invasive estimation of pulmonary congestion in patients with heart failure. Scientific Reports, 13, 3923.
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Publication 2023
Catheterization Catheterizations, Cardiac Diagnosis Electrocardiography, 12-Lead Heart Failure Muse Patients Pulmonary Wedge Pressure Woman
3
Apoptosis and Cell Cycle Profiling in 2D/3D Cultures
The apoptotic profiles and cell cycle progression of A549 cells cultured in 2D/3D culturing microenvironments were assessed using Muse® Cell Analyzer (Luminex) as previously described (21 (link)). Briefly, the cells were harvested, dissociated into single-cell suspensions and stained with Muse Annexin V & Dead Cell kit or Muse Cell Cycle kit (Luminex), according to the manufacturer's instructions. Then, the cells were subjected to apoptotic detection or cell cycle analysis. Results are expressed as the mean values of total apoptosis (percentage of early + late apoptotic cells) or proportion of the cells in each phase of cell cycle (G0/G1, S and G2/M).
Keeratichamroen S., Sornprachum T., Ngiwsara L., Ornnork N, & Svasti J. (2023). p‑STAT3 influences doxorubicin and etoposide resistance of A549 cells grown in an in vitro 3D culture model. Oncology Reports, 49(4), 71.
Publication 2023
A549 Cells Annexin A5 Apoptosis Cell Cycle Cells Disease Progression Muse
4
Characterization of Tea Cultivar Diversity
A total of 36 SSR loci from 30 CsLAC genes were selected for designing primers. To validate the primers, 45 tea cultivars or varieties were used for PCR amplification and subsequent resolution by electrophoresis. The reaction mixtures, thermocycling conditions and protocols for PCR product separation were performed based on a previous study [38 (link)]. The amplified fragments were separated on a 96-capillary automated DNA fragment analyzer (Fragment Analyzer™ 96, Advanced Analytical Technologies, Inc., Ames, IA). The separated DNA bands were visually scored using PROSize™ 2.0 software, which was included in the advanced Fragment Analyzer™ 96 system. Only one or two fragments were collected for each individual [37 (link)].
The number of alleles (Na), Shannon’s information index (I), observed heterozygosity (Ho), expected heterozygosity (He), genetic diversity (GD) and polymorphism information content (PIC) values were calculated with PowerMarker version 3.25 (http://statgen.ncsu.edu/powermarker/downloads.htm) (Liu and Muse 2005).
Zhu J., Zhang H., Huang K., Guo R., Zhao J., Xie H., Zhu J., Gu H., Chen H., Li G., Wei C, & Liu S. (2023). Comprehensive analysis of the laccase gene family in tea plant highlights its roles in development and stress responses. BMC Plant Biology, 23, 129.
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Publication 2023
Alleles Capillaries Electrophoresis Genes Genetic Diversity Genetic Polymorphism Heterozygote Mineralocorticoid Excess Syndrome, Apparent Muse Oligonucleotide Primers
5
Apoptosis and Necrosis Quantification
The quantitative analysis of apoptotic and necrotic dead cells was conducted using the flow cytometer Muse ™ Cell Analyzer (Merck Millipore, Billerica, MA, USA) and the Muse™ Annexin V and Dead Cell Assay Kit (MCH100105; Merck Millipore) according to the manufacturer’s instructions. The mock and irradiated C. reinhardtii cells were harvested at 6, 24, and 48 h after X-irradiation, washed twice with Dulbecco’s PBS, stained with Muse™ Annexin V and Dead Cell Reagent at a final concentration of 5 × 106 cells mL −1, and finally subjected to the apoptotic cell death assay using 5,000 cells for each sample. Apoptotic cell death was expressed as the proportion of living, early/late apoptotic, and dead cells, which were determined using Muse™ Cell Analyzer software (Muse 1.1.2; Merck Millipore).
Kim J.H., Dubey S.K., Hwangbo K., Chung B.Y., Lee S.S, & Lee S. (2023). Application of ionizing radiation as an elicitor to enhance the growth and metabolic activities in Chlamydomonas reinhardtii. Frontiers in Plant Science, 14, 1087070.
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Publication 2023
Annexin A5 Apoptosis Biological Assay Cells Muse Necrosis Roentgen Rays

Top products related to «Muse»

1
Muse cell analyzer by Merck Group
Sourced in United States, Germany, Italy, Poland, Singapore, Australia, France
The Muse Cell Analyzer is a compact, fully automated cell analysis system designed for sample preparation and high-throughput analysis. The instrument utilizes the principles of flow cytometry to provide accurate and reliable cell counts, viability, and cell population analysis.
2
Muse cell cycle kit by Merck Group
Sourced in United States, Germany, Italy, France, Poland
The Muse Cell Cycle Kit is a lab equipment product that enables the assessment of cell cycle stages through flow cytometry analysis. It provides a standardized method for quantifying the percentage of cells in G0/G1, S, and G2/M phases.
3
Muse annexin 5 dead cell kit by Merck Group
Sourced in United States, Germany, Italy
The Muse™ Annexin V & Dead Cell Kit is a flow cytometry-based assay that measures the percentage of cells undergoing apoptosis (programmed cell death) and the percentage of dead cells in a sample. The kit uses Annexin V, a protein that binds to phosphatidylserine, and a cell-impermeable dye to distinguish between viable, early apoptotic, late apoptotic, and dead cells.
4
Muse oxidative stress kit by Merck Group
Sourced in United States, Germany, France
The Muse Oxidative Stress Kit is a lab equipment product designed to measure oxidative stress in biological samples. It provides quantitative analysis of oxidized proteins, a key indicator of oxidative stress levels.
5
Muse cell cycle assay kit by Merck Group
Sourced in United States, Germany
The Muse Cell Cycle Assay Kit is a flow cytometry-based tool used to analyze the cell cycle distribution of a cell population. The kit utilizes propidium iodide (PI) staining to quantify the DNA content in cells, which is then used to determine the percentage of cells in the G0/G1, S, and G2/M phases.
6
Muse annexin 5 and dead cell assay kit by Merck Group
Sourced in United States, Germany, Poland
The Muse Annexin V and Dead Cell Assay Kit is a lab equipment product designed for the quantitative analysis of apoptosis and cell death. It provides a rapid, accurate, and reproducible method for the detection and enumeration of live, early apoptotic, late apoptotic, and dead cells within a sample.
7
Muse cell analyser by Merck Group
Sourced in Germany, United States, Australia
The Muse Cell Analyser is a compact, automated cell analysis instrument designed for cell counting and viability assessment. It utilizes flow cytometry technology to provide rapid, reliable, and accurate data on cell populations. The core function of the Muse Cell Analyser is to analyze and quantify various cell characteristics, such as cell count and percentage of viable cells, within a sample.
8
Muse annexin 5 and dead cell kit by Merck Group
Sourced in United States, Germany, France
The Muse Annexin V and Dead Cell Kit is a lab equipment product that enables the detection and quantification of apoptotic and dead cells in a sample. It provides a rapid and reliable method for cell health assessment using flow cytometry technology.

More about "Muse"

Muse is a cutting-edge writing assistant that utilizes intelligent algorithms to enhance the quality and clarity of your written content.
This versatile tool analyzes your text in real-time, providing valuable feedback to improve grammar, spelling, and sentence structure.
Muse also offers suggestions for more effective word choices and tone, empowering writers to refine their work and deliver polished, professional-quality pieces.
Whether you're crafting a blog post, an academic paper, or a captivating story, Muse can help you bring your ideas to life with eloquence and impact.
Explore the capabilities of this innovative technology and take your writing to new heights.
Muse can be especially useful for researchers and scientists, as it can help optimize research protocols and ensure reproducibility and accuracy in your work.
Beyond its writing assistance features, Muse is also associated with a range of analytical tools, such as the Muse Cell Analyzer, Muse Cell Cycle Kit, Muse™ Annexin V & Dead Cell Kit, Muse Oxidative Stress Kit, Muse Cell Cycle Assay Kit, Muse Annexin V and Dead Cell Assay Kit, and Muse Cell Analyser.
These specialized tools can provide valuable insights and data to support your research and experiments.
Discover the power of Muse and unlock your full writing potential today.
Leveraging the latest advancements in natural language processing and machine learning, this cutting-edge technology can help you create content that stands out and resonates with your audience.