共查询到20条相似文献,搜索用时 15 毫秒
1.
Background
The American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants.Results
Using 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation (false concordance) where concordant in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables such as disease mechanism, gene constraint, and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011–2017).Conclusions
Our analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines.2.
Background
Whole exome sequencing (WES) has provided a means for researchers to gain access to a highly enriched subset of the human genome in which to search for variants that are likely to be pathogenic and possibly provide important insights into disease mechanisms. In developing countries, bioinformatics capacity and expertise is severely limited and wet bench scientists are required to take on the challenging task of understanding and implementing the barrage of bioinformatics tools that are available to them.Results
We designed a novel method for the filtration of WES data called TAPER? (Tool for Automated selection and Prioritization for Efficient Retrieval of sequence variants).Conclusions
TAPER? implements a set of logical steps by which to prioritize candidate variants that could be associated with disease and this is aimed for implementation in biomedical laboratories with limited bioinformatics capacity. TAPER? is free, can be setup on a Windows operating system (from Windows 7 and above) and does not require any programming knowledge. In summary, we have developed a freely available tool that simplifies variant prioritization from WES data in order to facilitate discovery of disease-causing genes.3.
Gordon K C Leung Christopher C Y Mak Jasmine L F Fung Wilfred H S Wong Mandy H Y Tsang Mullin H C Yu Steven L C Pei K S Yeung Gary T K Mok C P Lee Amelia P W Hui Mary H Y Tang Kelvin Y K Chan Anthony P Y Liu Wanling Yang P C Sham Anita S Y Kan Brian H Y Chung 《BMC medical genomics》2018,11(1):93
Background
Whole-exome sequencing (WES) has become an invaluable tool for genetic diagnosis in paediatrics. However, it has not been widely adopted in the prenatal setting. This study evaluated the use of WES in prenatal genetic diagnosis in fetuses with structural congenital anomalies (SCAs) detected on prenatal ultrasound.Method
Thirty-three families with fetal SCAs on prenatal ultrasonography and normal chromosomal microarray results were recruited. Genomic DNA was extracted from various fetal samples including amniotic fluid, chorionic villi, and placental tissue. Parental DNA was extracted from peripheral blood when available. We used WES to sequence the coding regions of parental-fetal trios and to identify the causal variants based on the ultrasonographic features of the fetus.Results
Pathogenic mutations were identified in three families (n?=?3/33, 9.1%), including mutations in DNAH11, RAF1 and CHD7, which were associated with primary ciliary dyskinesia, Noonan syndrome, and CHARGE syndrome, respectively. In addition, variants of unknown significance (VUSs) were detected in six families (18.2%), in which genetic changes only partly explained prenatal features.Conclusion
WES identified pathogenic mutations in 9.1% of fetuses with SCAs and normal chromosomal microarray results. Databases for fetal genotype-phenotype correlations and standardized guidelines for variant interpretation in prenatal diagnosis need to be established to facilitate the use of WES for routine testing in prenatal diagnosis.4.
5.
Korey J. Brownstein Mahmoud Gargouri William R. Folk David R. Gang 《Metabolomics : Official journal of the Metabolomic Society》2017,13(11):133
Introduction
Botanicals containing iridoid and phenylethanoid/phenylpropanoid glycosides are used worldwide for the treatment of inflammatory musculoskeletal conditions that are primary causes of human years lived with disability, such as arthritis and lower back pain.Objectives
We report the analysis of candidate anti-inflammatory metabolites of several endemic Scrophularia species and Verbascum thapsus used medicinally by peoples of North America.Methods
Leaves, stems, and roots were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and partial least squares-discriminant analysis (PLS-DA) was performed in MetaboAnalyst 3.0 after processing the datasets in Progenesis QI.Results
Comparison of the datasets revealed significant and differential accumulation of iridoid and phenylethanoid/phenylpropanoid glycosides in the tissues of the endemic Scrophularia species and Verbascum thapsus.Conclusions
Our investigation identified several species of pharmacological interest as good sources for harpagoside and other important anti-inflammatory metabolites.6.
Zhan Zhou Shanshan Wu Jun Lai Yuan Shi Chixiao Qiu Zhe Chen Yufeng Wang Xun Gu Jie Zhou Shuqing Chen 《BMC medical genomics》2017,10(1):49
Background
Intratumor heterogeneity (ITH) poses an urgent challenge for cancer precision medicine because it can cause drug resistance against cancer target therapy and immunotherapy. The search for trunk mutations that are present in all cancer cells is therefore critical for each patient.Case presentation
In this study, we aimed to evaluate the efficiency of multiregional sequencing for the identification of trunk mutations present in all regions of a tumor as a case study. We applied multiregional whole-exome sequencing (WES) to investigate the genetic heterogeneity and homogeneity of a case of gastric carcinoma. Approximately 83% of common missense mutations present in two samples and approximately 89% of common missense mutations present in three samples were trunk mutations. Notably, trunk mutations appeared to have higher variant allele frequencies (VAFs) than non-trunk mutations.Conclusions
Our results indicate that small-scale multiregional sampling and subsequent screening of low VAF somatic mutations might be a cost-effective strategy for identifying the majority of trunk mutations in gastric carcinoma.7.
Po-Jung Huang Chi-Ching Lee Ling-Ya Chiu Kuo-Yang Huang Yuan-Ming Yeh Chia-Yu Yang Cheng-Hsun Chiu Petrus Tang 《BMC genomics》2018,19(2):86
Background
High throughput sequencing technologies have been an increasingly critical aspect of precision medicine owing to a better identification of disease targets, which contributes to improved health care cost and clinical outcomes. In particular, disease-oriented targeted enrichment sequencing is becoming a widely-accepted application for diagnostic purposes, which can interrogate known diagnostic variants as well as identify novel biomarkers from panels of entire human coding exome or disease-associated genes.Results
We introduce a workflow named VAReporter to facilitate the management of variant assessment in disease-targeted sequencing, the identification of pathogenic variants, the interpretation of biological effects and the prioritization of clinically actionable targets. State-of-art algorithms that account for mutation phenotypes are used to rank the importance of mutated genes through visual analytic strategies. We established an extensive annotation source by integrating a wide variety of biomedical databases and followed the American College of Medical Genetics and Genomics (ACMG) guidelines for interpretation and reporting of sequence variations.Conclusions
In summary, VAReporter is the first web server designed to provide a “one-stop” resource for individual’s diagnosis and large-scale cohort studies, and is freely available at http://rnd.cgu.edu.tw/vareporter.8.
Background
Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.Methods
In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.Results
Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.Conclusions
Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.9.
Wesley W. Ingwersen Ezra Kahn Joyce Cooper 《The International Journal of Life Cycle Assessment》2018,23(11):2266-2270
Introduction
New platforms are emerging that enable more data providers to publish life cycle inventory data.Background
Providing datasets that are not complete LCA models results in fragments that are difficult for practitioners to integrate and use for LCA modeling. Additionally, when proxies are used to provide a technosphere input to a process that was not originally intended by the process authors, in most LCA software, this requires modifying the original process.Results
The use of a bridge process, which is a process created to link two existing processes, is proposed as a solution.Discussion
Benefits to bridge processes include increasing model transparency, facilitating dataset sharing and integration without compromising original dataset integrity and independence, providing a structure with which to make the data quality associated with process linkages explicit, and increasing model flexibility in the case that multiple bridges are provided. A drawback is that they add additional processes to existing LCA models which will increase their size.Conclusions
Bridge processes can be an enabler in allowing users to integrate new datasets without modifying them to link to background databases or other processes they have available. They may not be the ideal long-term solution but provide a solution that works within the existing LCA data model.10.
Background
High-throughput technologies, such as DNA microarray, have significantly advanced biological and biomedical research by enabling researchers to carry out genome-wide screens. One critical task in analyzing genome-wide datasets is to control the false discovery rate (FDR) so that the proportion of false positive features among those called significant is restrained. Recently a number of FDR control methods have been proposed and widely practiced, such as the Benjamini-Hochberg approach, the Storey approach and Significant Analysis of Microarrays (SAM).Methods
This paper presents a straight-forward yet powerful FDR control method termed miFDR, which aims to minimize FDR when calling a fixed number of significant features. We theoretically proved that the strategy used by miFDR is able to find the optimal number of significant features when the desired FDR is fixed.Results
We compared miFDR with the BH approach, the Storey approach and SAM on both simulated datasets and public DNA microarray datasets. The results demonstrated that miFDR outperforms others by identifying more significant features under the same FDR cut-offs. Literature search showed that many genes called only by miFDR are indeed relevant to the underlying biology of interest.Conclusions
FDR has been widely applied to analyzing high-throughput datasets allowed for rapid discoveries. Under the same FDR threshold, miFDR is capable to identify more significant features than its competitors at a compatible level of complexity. Therefore, it can potentially generate great impacts on biological and biomedical research.Availability
If interested, please contact the authors for getting miFDR.11.
Nicholas J. Bond Albert Koulman Julian L. Griffin Zoe Hall 《Metabolomics : Official journal of the Metabolomic Society》2017,13(11):128
Introduction
Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.Objectives
We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.Methods
massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.Results
Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.Conclusion
massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.12.
Justin Y. Lee Mark P. Styczynski 《Metabolomics : Official journal of the Metabolomic Society》2018,14(12):153
Introduction
A common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection of the analytical instrumentation.Objectives
Here, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations.Methods
We compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data.Results
Our results show that NS-kNN typically outperforms kNN when at least 20–30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR.Conclusion
Accounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.13.
Jamie V. de Seymour Stephanie Tu Xiaoling He Hua Zhang Ting-Li Han Philip N. Baker Karolina Sulek 《Metabolomics : Official journal of the Metabolomic Society》2018,14(6):79
Introduction
Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.Objective
Investigate the maternal hair metabolome for predictive biomarkers of ICP.Methods
The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.Results
Of 105 metabolites detected in hair, none were significantly associated with ICP.Conclusion
Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.14.
Introduction
Sickle cell disease has been associated with many renal structural and functional abnormalities. Collapsing glomerulopathy or the collapsing variant of focal segmental glomerulosclerosis is a rare clinicopathologic entity in patients with sickle cell disease that requires timely diagnosis and aggressive management.Case presentation
In this case report we describe a 21-year-old African-American woman with a medical history of significant sickle cell disease and asthma. She was admitted for pain, decreased urine output, bilateral leg swelling and reported weight gain. During her period of hospitalisation she developed acute renal failure requiring dialysis. Further investigation revealed the collapsing variant of focal segmental glomerulosclerosis.Conclusions
Although focal segmental glomerulosclerosis is a common feature of sickle cell nephropathy, the collapsing variant of focal segmental glomerulosclerosis or collapsing glomerulopathy has been rarely documented. Even when other risk factors are controlled, collapsing glomerulopathy has a very poor prognosis. This is a rare case of a patient with massive proteinuria presenting as acute renal failure with a very poor response to corticosteroids and a much faster rate of progression to end-stage renal disease.15.
Background
The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.Methods
Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.Results
Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.Conclusions
Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.16.
Daniel Cañueto Josep Gómez Reza M. Salek Xavier Correig Nicolau Cañellas 《Metabolomics : Official journal of the Metabolomic Society》2018,14(3):24
Introduction
Adoption of automatic profiling tools for 1H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices.Objectives
To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.Methods
rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.Results
The information and quality achieved in two public datasets of complex matrices are maximized.Conclusion
rDolphin is an open-source R package (http://github.com/danielcanueto/rDolphin) able to provide the best balance between accuracy, reproducibility and ease of use.17.
Background
Amyloidosis cutis dyschromica is a rarely documented variant of cutaneous amyloidosis. To date, only 26 cases have been reported.Objective
The purpose of this study was to improve the clinical and histopathological data for this variant of amyloidosis and to highlight the immunohistochemical features of the disease. The published cases were also reviewed.Methods
We performed a retrospective review of patients with amyloidosis cutis dyschromica in a single centre. The clinical, histopathological and immunohistochemical features were documented and analysed.Observations
We described 10 cases of amyloidosis cutis dyschromica. Six of them were female. Five patients were from the same family, and the other 5 were sporadic. The distinguishing features of the clinical presentation included generalised mottled hyper- and hypopigmented macules, which were asymptomatic or mild pruritic. The typical onset of the lesions occurred in childhood (n?=?7) and occasionally after puberty (n?=?3). No evidence of systemic amyloidosis deposition was observed in these cases of amyloidosis cutis dyschromica. Amyloid deposits were observed in the papillary dermis and were positive for the Congo red stain. An immunohistochemical study showed that the amyloid expresses cytokeratins CK34βE12 and CK5/6.Conclusions
We described the largest series of amyloidosis cutis dyschromica to date and reviewed the published patients. This rare disease is featured by generalised mottled hyper- and hypopigmented lesions, and it is a rare variant of primary cutaneous amyloidosis without evidence of systemic amyloid deposition. Positive staining for the cytokeratins CK34βE12 and CK5/6 in amyloidosis cutis dyschromica suggests that the amyloid is derived from keratinocytes.18.
Ester Coutinho Ana M Silva Cláudia Freitas Ernestina Santos 《Journal of medical case reports》2011,5(1):68
Introduction
Pseudotumor cerebri is an entity characterized by elevated intracranial pressure with normal cerebrospinal fluid and no structural abnormalities detected on brain MRI scans. Common secondary causes include endocrine pathologies. Hyperthyroidism is very rarely associated and only three case reports have been published so far.Case presentation
We report the case of a 31-year-old Luso-African woman with clinical symptoms and laboratory confirmation of Graves' disease that presented as pseudotumor cerebri.Conclusion
This is a rare form of presentation of Graves' disease and a rare cause of pseudotumor cerebri. It should be remembered that hyperthyroidism is a potential cause of pseudotumor cerebri.19.
Ingo Meinshausen Peter Müller-Beilschmidt Tobias Viere 《The International Journal of Life Cycle Assessment》2016,21(9):1231-1235
Purpose
This paper introduces the new EcoSpold data format for life cycle inventory (LCI).Methods
A short historical retrospect on data formats in the life cycle assessment (LCA) field is given. The guiding principles for the revision and implementation are explained. Some technical basics of the data format are described, and changes to the previous data format are explained.Results
The EcoSpold 2 data format caters for new requirements that have arisen in the LCA field in recent years.Conclusions
The new data format is the basis for the Ecoinvent v3 database, but since it is an open data format, it is expected to be adopted by other LCI databases. Several new concepts used in the new EcoSpold 2 data format open the way for new possibilities for the LCA practitioners and to expand the application of the datasets in other fields beyond LCA (e.g., Material Flow Analysis, Energy Balancing).20.
Johra Muhammad Moosa Rameen Shakur Mohammad Kaykobad Mohammad Sohel Rahman 《BMC medical genomics》2016,9(2):47