Biology subjects

Dai, Y.

Publications and source records attributed to Dai, Y..

152 records · Page 9Linked to original sources

bioRxiv2019

M-TRACK: a platform for live cell multiplex imaging reveals cell phenotypic transition dynamics inherently missing in snapshot data

Recent advances in single-cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, in a step-by-step process. Two grand technical challenges, however, impede further development of the field. Fixed cell-based approaches can provide genome-wide snapshots of cell status but have fundamental limits on revealing temporal information, and fluorescence-based live cell imaging approaches provide temporal information but are technically challenging for multiplex long-term imaging. We first developed a live-cell imaging platform that tracks cellular status change through combining endogenous fluorescent labeling that minimizes perturbation to cell physiology, and/or live cell imaging of high-dimensional cell morphological and texture features. With our platform and an A549 VIM-RFP EMT reporter line, live cell trajectories reveal parallel paths of epithelial-to-mesenchymal transition missing from snapshot data due to cell-cell heterogeneity. Our results emphasize the necessity of extracting dynamical information of phenotypic transitions from multiplex live cell imaging.

systems biology↗

bioRxiv2019

The Study on Computer Vision-Assisted Cell Bank Construction and Screening & Classification

Computer vision technology plays an important role in screening and culturing cells. This paper proposes a method to construct a helper cell library based on cell image segmentation and screening. Firstly, cell culture and image acquisition were carried out. The main content is to use laboratory conditions to carry out different cell types. Through careful observation of the whole process of cell proliferation and passage, the representative pictures of different stages were taken. Analysis and summary of the relevant morphology, texture, color characteristics. Secondly, computer vision technology is used to segment cells and extract the main features such as cell perimeter and area. Explore the automatic information extraction method of cell bank, and complete the image segmentation of individual cell image from the whole picture. Finally, the cells were screened and identified. Investigate different pattern recognition methods and neural network structures, and prepare pictures of various cell pictures. The corresponding neural network and prediction program are constructed. This paper proposes an automatic image processing method for each image category in cell culture cycle, which improves the automation of production process. At the same time, compared with the design of a single algorithm for a certain type of cell, different algorithm design ideas are proposed for three types of pictures with different characteristics, which is closer to the dynamic change of cell morphology in the process of cell culture. This research has important application prospects in promoting cell factory research, cell bank construction and automatic screening.

bioinformatics↗

bioRxiv2019

Perinatal features of Prader-Willi syndrome: a Chinese cohort

BackgroundPrader-Willi syndrome (PWS) is a rare complex genetic disorder caused by an absence of expression of imprinted genes on the paternally derived chromosome 15q11-q13 region. This study aimed to characterize the perinatal features in a cohort of Chinese individuals with PWS.\n\nMethodsWe analyzed anonymous data of 134 patients from the PWS Registry in China. Perinatal and neonatal presentations were analyzed, and compared between the two PWS genetic subtypes. We also compared the perinatal features of PWS patients with the general population and other previous reported large cohorts from France, UK and USA.\n\nResultsThis study included 134 patients with PWS (115 patients with 15q11-q13 deletion and 19 with maternal UPD). High mean maternal age was found in this cohort (30.5 vs. 26.7) comparing with the general population. 88.6% of mothers reported a decrease of fetal movements. 42.5% and 18.7% of mothers had polyhydramnios and oligohydramnios during pregnancy, respectively. 82.8% of the patients were born by caesarean section. 32.1% of neonates had birth asphyxia, 98.5% had hypotonia and 97.8% had weak cry or even no cry at neonatal period. Feeding difficulty existed in 99.3% of the infants, and 94.8% of them had failure to thrive. 69.4% of the infants ever used feeding tube during hospitalization. However, 97.8% of them discontinued tube feeding after discharged home. Maternal age, maternal pre-pregnancy weight and BMI were significantly higher in the UPD group (all P<0.05).\n\nConclusionsPWS should be highlighted for differential diagnosis for infants with following perinatal factors including polyhydramnios, intrauterine decreased fetal movements, cesarean section, low birth weight, feeding difficulty, hypotonia, and failure to thrive. Higher maternal age may be a risk factor for PWS, especially for UPD, and further studies for the mechanism of PWS are required.\n\nAuthor SummaryEarly diagnosis and tailored multidisciplinary treatment are utmost important for better quality of life of the infants with Prader-willi syndrome. Genetic diagnosis for PWS is now easily available; and diagnosis can be made in most patients within the first months of life even prenatally if obstetricians or neonatologists can recognize the perinatal features of PWS well. However, most patients still had a late diagnosis because the early signs of PWS not recognized. Our study highlighted the perinatal features of a large cohort of Chinese patients with PWS, which will benefit for early diagnosis and treatment. We found high incidence of decreased fetal movements, polyhydramnios and delivery by cesarean section, and higher maternal age comparing with the general population. Neonatal features found in our cohort included low birth weight, birth asphyxia, failure to thrive, feeding difficulty, weak cry and hypotonia. We found mothers with UPD had higher maternal age, pre-pregnancy weight and BMI. Most patients required tube feeding during hospitalization, however tube feeding was discontinued by their parents after discharge at home. Nutrition deficiency was a serious problem in infants with PWS. Home tube-feeding instruction should be carried out for parents of PWS patients in China.

genetics↗

bioRxiv2018

Tissue-Specific Enrichment Analysis (TSEA) to decode tissue specificity

MotivationDiseases and traits are under dynamic tissue-specific regulation. However, heterogeneous tissues are often collected in biomedical studies, which reduce the power in the identification of disease-associated variants and gene expression profiles.\n\nResultsWe present TSEA, an R package to conduct Tissue-Specific Enrichment Analysis (TSEA) with two built-in reference panels. Statistical methods are developed and implemented for detecting tissue-specific genes and for enrichment test of different forms of query data. Our applications using multi-trait genome-wide association data and cancer expression data showed that TSEA could effectively identify the most relevant tissues for each query trait or sample, providing insights for future studies.\n\nAvailabilityhttps://github.com/bsml320/TSEA\n\nContactpeilin.jia@uth.tmc.edu or zhongming.zhao@uth.tmc.edu

bioinformatics↗

bioRxiv2018

Gene2Vec: Distributed Representation of Genes Based on Co-Expression

Existing functional description of genes are categorical, discrete, and mostly through manual process. In this work, we explore the idea of gene embedding, distributed representation of genes, in the spirit of word embedding. From a pure data-driven fashion, we trained a 300 dimension vector representation of all human genes, using gene co-expression patterns in 984 data sets from the GEO databases. These vectors capture functional relatedness of genes in terms of recovering known pathways - the average inner product (similarity) of genes within a pathway is 1.68X greater than that of random genes. Using t-SNE, we produced a gene co-expression map that shows local concentrations of tissue specific genes. We also illustrated the usefulness of the embedded gene vectors, laden with rich information on gene co-expression patterns, in tasks such as gene-gene interaction prediction. Overall, we believe that this distributed representation of genes may be useful for more bioinformatics applications.

bioinformatics↗

bioRxiv2018

Single-molecule optical mapping enables accurate molecular diagnosis of facioscapulohumeral muscular dystrophy (FSHD)

Facioscapulohumeral Muscular Dystrophy (FSHD) is a common adult muscular dystrophy in which the muscles of the face, shoulder blades and upper arms are among the most affected. FSHD is the only disease in which \"junk\" DNA is reactivated to cause disease, and the only known repeat array-related disease where fewer repeats cause disease. More than 95% of FSHD cases are associated with copy number loss of a 3.3kb tandem repeat (D4Z4 repeat) at the subtelomeric chromosomal region 4q35, of which the pathogenic allele contains less than 10 repeats and has a specific genomic configuration called 4qA. Currently, genetic diagnosis of FSHD requires pulsed-field gel electrophoresis followed by Southern blot, which is labor-intensive, semi-quantitative and requires long turnaround time. Here, we developed a novel approach for genetic diagnosis of FSHD, by leveraging Bionano Saphyr single-molecule optical mapping platform. Using a bioinformatics pipeline developed for this assay, we found that the method gives direct quantitative measurement of repeat numbers, can differentiate 4q35 and the highly paralogous 10q26 regions, can determine the 4qA/4qB allelic configuration, and can quantitate levels of post-zygotic mosaicism. We evaluated this approach on 5 patients (including two with post-zygotic mosaicism) and 2 patients (including one with post-zygotic mosaicism) from two separate cohorts, and had complete concordance with Southern blots, but with improved quantification of repeat numbers resolved between haplotypes. We concluded that single-molecule optical mapping is a viable approach for molecular diagnosis of FSHD and may be applied in clinical diagnostic settings once more validations are performed.

genomics↗

bioRxiv2018

PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolution Neural Networks for Metagenomic Data

MotivationAccurate prediction of the host phenotype from a metgenomic sample and identification of the associated bacterial markers are important in metagenomic studies. We introduce PopPhy-CNN, a novel convolutional neural networks (CNN) learning architecture that effectively exploits phylogentic structure in microbial taxa. PopPhy-CNN provides an input format of 2D matrix created by embedding the phylogenetic tree that is populated with the relative abundance of microbial taxa in a metagenomic sample. This conversion empowers CNNs to explore the spatial relationship of the taxonomic annotations on the tree and their quantitative characteristics in metagenomic data.\n\nResultsPopPhy-CNN is evaluated using three metagenomic datasets of moderate size. We show the superior performance of PopPhy-CNN compared to random forest, support vector machines, LASSO and a baseline 1D-CNN model constructed with relative abundance microbial feature vectors. In addition, we design a novel scheme of feature extraction from the learned CNN models and demonstrate the improved performance when the extracted features are used to train support vector machines.\n\nConclusionPopPhy-CNN is a novel deep learning framework for the prediction of host phenotype from metagenomic samples. PopPhy-CNN can efficiently train models and does not require excessive amount of data. PopPhy-CNN facilities not only retrieval of informative microbial taxa from the trained CNN models but also visualization of the taxa on the phynogenetic tree.\n\nContactyagndai@uic.edu\n\nAvailabilitySource code is publicly available at https://github.com/derekreiman/PopPhy-CNN\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

MeDEStrand: an improved method to infer genome-wide absolute methylation level from DNA enrichment experiment

BackgroundDNA methylation of dinucleotide CpG is an essential epigenetic modification that plays a key role in transcription. Bisulfite conversion method is a \"gold standard\" for DNA methylation profiling that provides single nucleotide resolution. However, whole-genome bisulfite conversion is very expensive. Alternatively, DNA enrichment-based methods offer high coverage of methylated CpG dinucleotides with the lowest cost per CpG covered genome-wide and have been used widely. They measure the DNA enrichment of methyl-CpG binding, therefore do not directly provide absolute methylation levels. Further, the enrichment is influenced by confounding factors besides the methylation status, e.g., CpG density. Computational models that can accurately derive the absolute methylation levels from the enrichment data are necessary.\n\nResultsWe present MeDEStrand, a method uses sigmoid function to estimate and correct the CpG bias from the numbers of reads that fell within bins that divide the genome. In addition, unlike the previous methods, which estimate CpG bias based on reads mapped at the same genomic loci, MeDEStrand processes the reads for the positive and negative DNA strands separately. We compare the performance of MeDEStrand with three other state-of-the-art methods MEDIPS, BayMeth and QSEA on four independent datasets generated using immortalized cell lines (GM12878 and K562) and human patient primary cells (foreskin fibroblast and mammary epithelial). Based on the comparison between the inferred absolute methylation levels from MeDIP-seq and the corresponding RRBS data, MeDEStrand shows the best performance at high resolution of 25, 50 and 100 base pairs.\n\nConclusions MeDEStrand benefits from the estimation of CpG bias with a sigmoid function and the procedure to process reads mapped to the positive and negative DNA strands separately. MeDEStrand is a tool to infer whole-genome absolute DNA methylation level at the cost of enrichment-based methods with adequate accuracy and resolution. R package MeDEStrand and its tutorial is freely available for download at https://github.com/jxu1234/MeDEStrand.git

bioinformatics↗