Current Issue : October - December Volume : 2018 Issue Number : 4 Articles : 5 Articles
Background: The rapid development of next-generation sequencing (NGS) technology has continuously been refreshing\nthe throughput of sequencing data. However, due to the lack of a smart tool that is both fast and accurate, the analysis\ntask for NGS data, especially those with low-coverage, remains challenging.\nResults: We proposed a decision-tree based variant calling algorithm. Experiments on a set of real data indicate that our\nalgorithm achieves high accuracy and sensitivity for SNVs and indels and shows good adaptability on low-coverage data. In\nparticular, our algorithm is obviously faster than 3 widely used tools in our experiments.\nConclusions: We implemented our algorithm in a software named Fuwa and applied it together with 4 well-known\nvariant callers, i.e., Platypus, GATK-UnifiedGenotyper, GATK-HaplotypeCaller and SAMtools, to three sequencing data\nsets of a well-studied sample NA12878, which were produced by whole-genome, whole-exome and low-coverage\nwhole-genome sequencing technology respectively. We also conducted additional experiments on the WGS data of 4\nnewly released samples that have not been used to populate dbSNP....
Meniscus injuries are very common and still pose a challenge for the orthopedic surgeon.Meniscus injuries in the inner two-thirds\nof themeniscus remain incurable.Tissue-engineered meniscus strategies seemto offer a newapproach for treating meniscus injuries\nwith a combination of seed cells, scaffolds, and biochemical or biomechanical stimulation. Cell- or scaffold-based strategies play\na pivotal role in meniscus regeneration. Similarly, biochemical and biomechanical stimulation are also important. Seed cells and\nscaffolds can be used to construct a tissue-engineered tissue; however, stimulation to enhance tissue maturation and remodeling\nis still needed. Such stimulation can be biomechanical or biochemical, but this review focuses only on biochemical stimulation.\nGrowth factors (GFs) are one of the most important forms of biochemical stimulation. Frequently used GFs always play a critical\nrole in normal limb development and growth. Further understanding of the functional mechanism of GFs will help scientists to\ndesign the best therapy strategies. In this review, we summarize some of the most important GFs in tissue-engineered menisci, as\nwell as other types of biological stimulation....
Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify\nChemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results\nof Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription,\nDNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool\nfor the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were\nscreened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with\na better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs\n(DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668DEGs and 5 DEMs were extracted from\nGSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients.The Database for Annotation,\nVisualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify\npotential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper\nunderstanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma....
As a versatile nanofiber manufacturing technique, electrospinning has been widely employed for the fabrication of tissue\nengineering scaffolds. Since the structure of natural extracellular matrices varies substantially in different tissues, there has\nbeen growing awareness of the fact that the hierarchical 3D structure of scaffolds may affect intercellular interactions, material\ntransportation, fluid flow, environmental stimulation, and so forth. Physical blending of the synthetic and natural polymers to form\ncomposite materials better mimics the composition and mechanical properties of natural tissues. Scaffolds with element gradient,\nsuch as growth factor gradient, have demonstrated good potentials to promote heterogeneous cell growth and differentiation.\nCompared to 2D scaffolds with limited thicknesses, 3D scaffolds have superior cell differentiation and development rate. The\nobjective of this review paper is to review and discuss the recent trends of electrospinning strategies for cartilage tissue engineering,\nparticularly the biomimetic, gradient, and 3D scaffolds, along with future prospects of potential clinical applications....
Background: Metabolomics has the promise to transform the area of personalized medicine with the rapid\ndevelopment of high throughput technology for untargeted analysis of metabolites. Open access, easy to use,\nanalytic tools that are broadly accessible to the biological community need to be developed. While technology\nused in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access\nplatform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving\nhistories and enabling the sharing workflows among scientists.\nResults: SECIMTools (SouthEast Center for Integrated Metabolomics) is a set of Python applications that are\navailable both as standalone tools and wrapped for use in Galaxy. The suite includes a comprehensive set of quality\ncontrol metrics (retention time window evaluation and various peak evaluation tools), visualization techniques\n(hierarchical cluster heatmap, principal component analysis, modular modularity clustering), basic statistical analysis\nmethods (partial least squares - discriminant analysis, analysis of variance, t-test, Kruskal-Wallis non-parametric test),\nadvanced classification methods (random forest, support vector machines), and advanced variable selection tools\n(least absolute shrinkage and selection operator LASSO and Elastic Net).\nConclusions: SECIMTools leverages the Galaxy platform and enables integrated workflows for metabolomics data\nanalysis made from building blocks designed for easy use and interpretability. Standard data formats and a set of\nutilities allow arbitrary linkages between tools to encourage novel workflow designs. The Galaxy framework enables\nfuture data integration for metabolomics studies with other omics data....
Loading....