Marker Annotations Research Articles

The updated mouse universal genotyping array bioinformatic pipeline improves genetic QC in laboratory mice.

The MiniMUGA genotyping array is a popular tool for genetic quality control of laboratory mice and genotyping samples from most experimental crosses involving laboratory strains, particularly for reduced complexity crosses. The content of the production version of the MiniMUGA array is fixed; however, there is the opportunity to improve the array's performance and the associated report's usefulness by leveraging thousands of samples genotyped since the initial description of MiniMUGA. Here, we report our efforts to update and improve marker annotation, increase the number and the reliability of the consensus genotypes for classical inbred strains and substrains, and increase the number of constructs reliably detected with MiniMUGA. In addition, we have implemented key changes in the informatics pipeline to identify and quantify the contribution of specific genetic backgrounds to the makeup of a given sample, remove arbitrary thresholds, include the Y Chromosome and mitochondrial genome in the ideogram, and improve robust detection of the presence of commercially available substrains based on diagnostic alleles. Finally, we have updated the layout of the report to simplify the interpretation and completeness of the analysis and added a section summarizing the ideogram in table format. These changes will be of general interest to the mouse research community and will be instrumental in our goal of improving the rigor and reproducibility of mouse-based biomedical research.

Development of a next generation SNP genotyping array for wheat.

High-throughput genotyping arrays have provided a cost-effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384-well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the 'Triticum aestivum Next Generation' array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins 'Core Collection'. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype-optimized novel SNPs and legacy cross-platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome-wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available.

Comparative analysis of 10X Chromium vs. BD Rhapsody whole transcriptome single-cell sequencing technologies in complex human tissues

The development of single-cell omics tools has enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, each of the different techniques may have its unique strengths and limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium vs. microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy.Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of cell populations differed. Cells with low mRNA content such as T cells were underrepresented in the droplet-based system, at least partly due to lower RNA capture rates. In contrast, microwell-based scRNA-seq recovered less cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation. Overall, our study provides important information for selection of the appropriate scRNA-seq platform and for the interpretation of published results.

Prediction of standard cell types and functional markers from textual descriptions of flow cytometry gating definitions using machine learning.

A key step in clinical flow cytometry data analysis is gating, which involves the identification of cell populations. The process of gating produces a set of reportable results, which are typically described by gating definitions. The non-standardized, non-interpreted nature of gating definitions represents a hurdle for data interpretation and data sharing across and within organizations. Interpreting and standardizing gating definitions for subsequent analysis of gating results requires a curation effort from experts. Machine learning approaches have the potential to help in this process by predicting expert annotations associated with gating definitions. We created a gold-standard dataset by manually annotating thousands of gating definitions with cell type and functional marker annotations. We used this dataset to train and test a machine learning pipeline able to predict standard cell types and functional marker genes associated with gating definitions. The machine learning pipeline predicted annotations with high accuracy for both cell types and functional marker genes. Accuracy was lower for gating definitions from assays belonging to laboratories from which limited or no prior data was available in the training. Manual error review ensured that resulting predicted annotations could be reused subsequently as additional gold-standard training data. Machine learning methods are able to consistently predict annotations associated with gating definitions from flow cytometry assays. However, a hybrid automatic and manual annotation workflow would be recommended to achieve optimal results.

Single-Cell Transcriptome Integration Analysis Reveals the Correlation Between Mesenchymal Stromal Cells and Fibroblasts.

Background: Mesenchymal stromal cells (MSCs) and fibroblasts show similar morphology, surface marker expression, and proliferation, differentiation, and immunomodulatory capacities. These similarities not only blur their cell identities but also limit their application. Methods: We performed single-cell transcriptome sequencing of the human umbilical cord and foreskin MSCs (HuMSCs and FSMSCs) and extracted the single-cell transcriptome data of the bone marrow and adipose MSCs (BMSCs and ADMSCs) from the Gene Expression Omnibus (GEO) database. Then, we performed quality control, batch effect correction, integration, and clustering analysis of the integrated single-cell transcriptome data from the HuMSCs, FMSCs, BMSCs, and ADMSCs. The cell subsets were annotated based on the surface marker phenotypes for the MSCs (CD105 + , CD90 +, CD73 +, CD45 −, CD34 −, CD19 −, HLA-DRA −, and CD11b −), fibroblasts (VIM +, PECAM1 −, CD34 −, CD45 −, EPCAM −, and MYH11 −), and pericytes (CD146 +, PDGFRB +, PECAM1 −, CD34 −, and CD45 −). The expression levels of common fibroblast markers (ACTA2, FAP, PDGFRA, PDGFRB, S100A4, FN1, COL1A1, POSTN, DCN, COL1A2, FBLN2, COL1A2, DES, and CDH11) were also analyzed in all cell subsets. Finally, the gene expression profiles, differentiation status, and the enrichment status of various gene sets and regulons were compared between the cell subsets. Results: We demonstrated 15 distinct cell subsets in the integrated single-cell transcriptome sequencing data. Surface marker annotation demonstrated the MSC phenotype in 12 of the 15 cell subsets. C10 and C14 subsets demonstrated both the MSC and pericyte phenotypes. All 15 cell subsets demonstrated the fibroblast phenotype. C8, C12, and C13 subsets exclusively demonstrated the fibroblast phenotype. We identified 3,275 differentially expressed genes, 305 enriched gene sets, and 34 enriched regulons between the 15 cell subsets. The cell subsets that exclusively demonstrated the fibroblast phenotype represented less primitive and more differentiated cell types. Conclusion: Cell subsets with the MSC phenotype also demonstrated the fibroblast phenotype, but cell subsets with the fibroblast phenotype did not necessarily demonstrate the MSC phenotype, suggesting that MSCs represented a subclass of fibroblasts. We also demonstrated that the MSCs and fibroblasts represented highly heterogeneous populations with distinct cell subsets, which could be identified based on the differentially enriched gene sets and regulons that specify proliferating, differentiating, metabolic, and/or immunomodulatory functions.

Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review.

BackgroundAntimicrobial resistance (AMR) is a rising health threat with 10 million annual casualties estimated by 2050. Appropriate treatment of infectious diseases with the right antibiotics reduces the spread of antibiotic resistance. Today, clinical practice relies on molecular and PCR techniques for pathogen identification and culture-based antibiotic susceptibility testing (AST). Recently, WGS has started to transform clinical microbiology, enabling prediction of resistance phenotypes from genotypes and allowing for more informed treatment decisions. WGS-based AST (WGS-AST) depends on the detection of AMR markers in sequenced isolates and therefore requires AMR reference databases. The completeness and quality of these databases are material to increase WGS-AST performance.MethodsWe present a systematic evaluation of the performance of publicly available AMR marker databases for resistance prediction on clinical isolates. We used the public databases CARD and ResFinder with a final dataset of 2587 isolates across five clinically relevant pathogens from PATRIC and NDARO, public repositories of antibiotic-resistant bacterial isolates.ResultsCARD and ResFinder WGS-AST performance had an overall balanced accuracy of 0.52 (±0.12) and 0.66 (±0.18), respectively. Major error rates were higher in CARD (42.68%) than ResFinder (25.06%). However, CARD showed almost no very major errors (1.17%) compared with ResFinder (4.42%).ConclusionsWe show that AMR databases need further expansion, improved marker annotations per antibiotic rather than per antibiotic class and validated multivariate marker panels to achieve clinical utility, e.g. in order to meet performance requirements such as provided by the FDA for clinical microbiology diagnostic testing.

Locally epistatic models for genome-wide prediction and association by importance sampling

BackgroundIn statistical genetics, an important task involves building predictive models of the genotype–phenotype relationship to attribute a proportion of the total phenotypic variance to the variation in genotypes. Many models have been proposed to incorporate additive genetic effects into prediction or association models. Currently, there is a scarcity of models that can adequately account for gene by gene or other forms of genetic interactions, and there is an increased interest in using marker annotations in genome-wide prediction and association analyses. In this paper, we discuss a hybrid modeling method which combines parametric mixed modeling and non-parametric rule ensembles.ResultsThis approach gives us a flexible class of models that can be used to capture additive, locally epistatic genetic effects, gene-by-background interactions and allows us to incorporate one or more annotations into the genomic selection or association models. We use benchmark datasets that cover a range of organisms and traits in addition to simulated datasets to illustrate the strengths of this approach.ConclusionsIn this paper, we describe a new strategy for incorporating genetic interactions into genomic prediction and association models. This strategy results in accurate models, with sometimes significantly higher accuracies than that of a standard additive model.

Training Nuclei Detection Algorithms with Simple Annotations

Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets.

Molecular marker database for efficient use in agricultural breeding programs.

The National Agricultural Biotechnology Information Center (NABIC) constructed a web-based molecular marker database to provide information about 7,847 sequence-tagged site (STS) markers identified in the 11 species using a next generation sequencing (NGS) technologies. The database consists of three major functional categories: keyword search, detailed viewer and download function. The molecular marker annotation table provides detailed information such as ownership information, basic information, and STS-related characterization information.AvailabilityThe database is available for free at http://nabic.rda.go.kr/Molecularmarker

Identification of genomic regions and candidate genes associated with growth ofEriocheir Sinensisby QTL mapping and marker annotation

Eriocheir sinensis is a valuable economic fishery resource in Asia and an invasive species of the world. A genome-wide quantitative trait loci (QTLs) scan for 10 growth traits was performed, resulting in 61 significant QTLs with phenotypic variance explained by a QTL from 11.2% to 42.2%. Most of the QTLs were restricted to narrow regions (<1 cM) and confirmed by genome wide association study. By blasting all markers on the linkage map with available transcriptomic and genomic sequences of E. sinensis, 15 markers on 14 linkage groups were found to be resided in gene sequences related to animal growth. A hypothetical protein gene (marker f1459) and a C-type lectin domain family 2 member D3 (marker m44) gene were detected to be located within the QTL intervals on LG11 and LG57 respectively. In addition, marker f2745 on LG34 was identified to be within intron 2 of Molting-inhibiting hormone 1, a moulting associated gene; two marker-containing genes, Adenomatous polyposis coli protein and Proto-oncogene tyrosine-protein kinase ROS were detected to be located on LG21 and LG44, which were involved in the growth-related pathways ‘Wnt signaling pathway’ and ‘mitogen-activated protein kinase signaling pathway’ respectively. The results are expected to be useful in marker-assisted selection and provide valuable information for the study of gene network regulation for growth of the crab.

NABIC marker database: A molecular markers information network of agricultural crops.

In 2013, National Agricultural Biotechnology Information Center (NABIC) reconstructs a molecular marker database for useful genetic resources. The web-based marker database consists of three major functional categories: map viewer, RSN marker and gene annotation. It provides 7250 marker locations, 3301 RSN marker property, 3280 molecular marker annotation information in agricultural plants. The individual molecular marker provides information such as marker name, expressed sequence tag number, gene definition and general marker information. This updated marker-based database provides useful information through a user-friendly web interface that assisted in tracing any new structures of the chromosomes and gene positional functions using specific molecular markers.AvailabilityThe database is available for free at http://nabic.rda.go.kr/gere/rice/molecularMarkers/

An annotated genetic map of loblolly pine based on microsatellite and cDNA markers

BackgroundPrevious loblolly pine (Pinus taeda L.) genetic linkage maps have been based on a variety of DNA polymorphisms, such as AFLPs, RAPDs, RFLPs, and ESTPs, but only a few SSRs (simple sequence repeats), also known as simple tandem repeats or microsatellites, have been mapped in P. taeda. The objective of this study was to integrate a large set of SSR markers from a variety of sources and published cDNA markers into a composite P. taeda genetic map constructed from two reference mapping pedigrees. A dense genetic map that incorporates SSR loci will benefit complete pine genome sequencing, pine population genetics studies, and pine breeding programs. Careful marker annotation using a variety of references further enhances the utility of the integrated SSR map.ResultsThe updated P. taeda genetic map, with an estimated genome coverage of 1,515 cM(Kosambi) across 12 linkage groups, incorporated 170 new SSR markers and 290 previously reported SSR, RFLP, and ESTP markers. The average marker interval was 3.1 cM. Of 233 mapped SSR loci, 84 were from cDNA-derived sequences (EST-SSRs) and 149 were from non-transcribed genomic sequences (genomic-SSRs). Of all 311 mapped cDNA-derived markers, 77% were associated with NCBI Pta UniGene clusters, 67% with RefSeq proteins, and 62% with functional Gene Ontology (GO) terms. Duplicate (i.e., redundant accessory) and paralogous markers were tentatively identified by evaluating marker sequences by their UniGene cluster IDs, clone IDs, and relative map positions. The average gene diversity, He, among polymorphic SSR loci, including those that were not mapped, was 0.43 for 94 EST-SSRs and 0.72 for 83 genomic-SSRs. The genetic map can be viewed and queried at http://www.conifergdb.org/pinemap.ConclusionsMany polymorphic and genetically mapped SSR markers are now available for use in P. taeda population genetics, studies of adaptive traits, and various germplasm management applications. Annotating mapped genes with UniGene clusters and GO terms allowed assessment of redundant and paralogous EST markers and further improved the quality and utility of the genetic map for P. taeda.

Fast, accurate, and robust automatic marker detection for motion correction based on oblique kV or MV projection image pairs

A robust and accurate method that allows the automatic detection of fiducial markers in MV and kV projection image pairs is proposed. The method allows to automatically correct for inter or intrafraction motion. Intratreatment MV projection images are acquired during each of five treatment beams of prostate cancer patients with four implanted fiducial markers. The projection images are first preprocessed using a series of marker enhancing filters. 2D candidate marker locations are generated for each of the filtered projection images and 3D candidate marker locations are reconstructed by pairing candidates in subsequent projection images. The correct marker positions are retrieved in 3D by the minimization of a cost function that combines 2D image intensity and 3D geometric or shape information for the entire marker configuration simultaneously. This optimization problem is solved using dynamic programming such that the globally optimal configuration for all markers is always found. Translational interfraction and intrafraction prostate motion and the required patient repositioning is assessed from the position of the centroid of the detected markers in different MV image pairs. The method was validated on a phantom using CT as ground-truth and on clinical data sets of 16 patients using manual marker annotations as ground-truth. The entire setup was confirmed to be accurate to around 1 mm by the phantom measurements. The reproducibility of the manual marker selection was less than 3.5 pixels in the MV images. In patient images, markers were correctly identified in at least 99% of the cases for anterior projection images and 96% of the cases for oblique projection images. The average marker detection accuracy was 1.4 +/- 1.8 pixels in the projection images. The centroid of all four reconstructed marker positions in 3D was positioned within 2 mm of the ground-truth position in 99.73% of all cases. Detecting four markers in a pair of MV images takes a little less than a second where most time is spent on the image preprocessing. The authors have developed a method to automatically detect multiple markers in a pair of projection images that is robust, accurate, and sufficiently fast for clinical use. It can be used for kV, MV, or mixed image pairs and can cope with limited motion between the projection images.

Gespeicherte Elektrogramme in Schrittmachern und ICDs der Firma St. Jude Medical

Stored electrograms (EGMs) significantly improve pacemaker and ICD therapy. In pacemaker systems, the main focus of stored EGMs concerns the manual control of device detection of atrial tachyarrhythmias, especially atrial fibrillation. In ICD therapy, stored EGMs allow the discrimination of adequate and inadequate detection of ventricular tachycardia. This review presents the implementation of stored EGMs in systems from St. Jude Medical and explains the mode of EGM storage and marker annotations, which are useful for interpretation of stored EGMs and to understand the way the device interprets the EGM. Clinical examples illustrate appropriate and inappropriate device classifications.

Gespeicherte Elektrogramme in Schrittmachern und ICDs der Fa. Sorin

Stored electrograms (EGMs) can improve therapy with pacemakers and ICDs by detecting technical problems (e.g., over- or undersensing) and medical problems (e.g., atrial tachyarrhythmias). Analysis of stored EGMs and their interpretation requires knowledge about EGM recording (e. g., channels, summation EGM) and marker annotations. This review presents stored EGMs in pacemaker and ICD systems from the Sorin Group with tips on the potential and interpretation of memory capabilities.

Gespeicherte Elektrogramme in Schrittmachern und ICDs

Stored electrograms (EGMs) represent an important development in pacemaker and ICD therapy. The most important issue in pacemaker EGMs is the confirmation of the detection of atrial tachyarrhythmias, especially atrial fibrillation. In ICD therapy, the discrimination between ventricular and supraventricular tachycardia (i.e., detection of inadequate therapy) is of central interest. Unfortunately, systematic"instructions" for interpreting stored EGMs in systems by different manufacturers are not available and the knowledge on this topic is limited to (too) few experts. The contributions in this issue aim at explaining the interpretation of stored EGMs in systems by different manufacturers, providing an understanding of marker annotations and EGM registrations in clinical examples. With the aim of improving pacemaker and ICD therapy, a broad distribution of knowledge on the usefulness and the practical use of stored EGMs is highly desirable.

Metabonomic Study of Ochratoxin A Toxicity in Rats after Repeated Administration: Phenotypic Anchoring Enhances the Ability for Biomarker Discovery

For early detection of toxicity and improved mechanistic understanding, GC/MS-, 1H NMR-, and LC/MS-based metabonomics were applied to urine samples from a rodent toxicity study on the mycotoxin and renal carcinogen ochratoxin A (OTA). OTA was administered at doses of 0, 21, 70, and 210 microg/kg body wt for up to 90 days. Urine samples were collected at 24 h intervals 14, 28, and 90 days after the start of treatment and analyzed with GC/MS, 1H NMR, and LC/MS. Principal component analysis and orthogonal projection to latent structures discriminate analysis (OPLS-DA) based on GC/MS and 1H NMR data discriminated controls from animals dosed with 210 microg/kg body wt OTA as early as 14 days and animals dosed with 70 microg/kg body wt 28 days after the start of treatment, correlating with mild histopathological changes in the kidney. Integration of histopathology scores as discriminators in OPLS-DA models resulted in better multivariate model predictivity and facilitated marker identification. Decreased 2-oxoglutarate and citrate excretion and increased glucose, creatinine, pseudouridine, 5-oxoproline, and myo-inositol excretion were detected with GC/MS. Decreased 2-oxoglutarate and citrate excretion and increased amino acid excretion were found with 1H NMR. Increased urinary glucose is a well-established indicator of kidney damage, and altered excretion of TCA cycle intermediates (citrate and 2-oxoglutarate) is found as a general response to toxic insult in many metabonomics studies. Other markers are associated with cell proliferation (pseudouridine), changes in renal osmolyte handling (myo-inositol), and oxidative stress (5-oxoproline), established mechanisms of OTA toxicity. LC/MS was also able to discriminate controls and treated animals but contained more noise, and marker annotation was only speculative due to lack of reference databases. Use of multiple analytical platforms for metabonomics analysis may result in a more comprehensive metabolite coverage and may be applied to obtain mechanistic information from conventional rodent toxicity studies.

Validation of Dual‐Chamber Pacemaker Diagnostic Data Using Dual‐Channel Stored Electrograms

Pacemaker diagnostic counters are used to guide device programming and patient management. However, these data are susceptible to inappropriate classification of events. The aim of this multicenter study was to evaluate pacemaker diagnostic data using stored intracardiac electrograms (EGMs). The study included 351 patients (191 males, aged 71 +/- 10 years) with standard indications for dual-chamber pacemaker implantation. EGM triggers were atrial tachycardia (AT), ventricular tachycardia (VT), sudden bradycardia response (SBR), and pacemaker-mediated tachycardia (PMT). For this study, the devices could store up to 5 EGMs of 8s each (with marker annotation and onset recording). After 3 months, the EGMs were analyzed and classified as "confirmed" if the EGM validated the trigger and as "false positive" if the EGM showed an event different from the trigger. Of the 1,003 EGMs available, the triggers were AT in 640 EGMs, VT in 76, SBR in 105, and PMT in 178 EGMs. Four EGMs were triggered by magnet application. The trigger was confirmed in 614 EGMs (62%): 62% of AT episodes, 18% of VT episodes, 100% of SBR episodes, and 54% of PMT episodes. In 385 cases (45%), the EGMs revealed false-positive events due to far-field sensing (39%), noise and myopotential sensing (26%), sinus tachycardias (21%), double counting (9%), exit block (4%), and undersensing (1%). This large-scale study of stored EGMs revealed their value in validating diagnostic counter data. Therapeutic decisions should not be based on diagnostic counters alone; they should be validated by sophisticated tools like stored EGMs.

A06-3 Diagnostic power of electrograms stored with onset recordings and marker annotations by a DDDR pacemaker

A06-3 Diagnostic power of electrograms stored with onset recordings and marker annotations by a DDDR pacemaker

A06-5 The prevalence of atrial arrhythmia is more accurate in patients implanted with a DDDR pacemaker with stored electrograms including the onset of the episode and the marker annotations

A06-5 The prevalence of atrial arrhythmia is more accurate in patients implanted with a DDDR pacemaker with stored electrograms including the onset of the episode and the marker annotations