Chemical and Biomedical Engineering - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 2 of 2
  • Item
    Thumbnail Image
    CoMet: a workflow using contig coverage and composition for binning a metagenomic sample with high precision
    Herath, D ; Tang, S-L ; Tandon, K ; Ackland, D ; Halgamuge, SK (BMC, 2017-12-28)
    BACKGROUND: In metagenomics, the separation of nucleotide sequences belonging to an individual or closely matched populations is termed binning. Binning helps the evaluation of underlying microbial population structure as well as the recovery of individual genomes from a sample of uncultivable microbial organisms. Both supervised and unsupervised learning methods have been employed in binning; however, characterizing a metagenomic sample containing multiple strains remains a significant challenge. In this study, we designed and implemented a new workflow, Coverage and composition based binning of Metagenomes (CoMet), for binning contigs in a single metagenomic sample. CoMet utilizes coverage values and the compositional features of metagenomic contigs. The binning strategy in CoMet includes the initial grouping of contigs in guanine-cytosine (GC) content-coverage space and refinement of bins in tetranucleotide frequencies space in a purely unsupervised manner. With CoMet, the clustering algorithm DBSCAN is employed for binning contigs. The performances of CoMet were compared against four existing approaches for binning a single metagenomic sample, including MaxBin, Metawatt, MyCC (default) and MyCC (coverage) using multiple datasets including a sample comprised of multiple strains. RESULTS: Binning methods based on both compositional features and coverages of contigs had higher performances than the method which is based only on compositional features of contigs. CoMet yielded higher or comparable precision in comparison to the existing binning methods on benchmark datasets of varying complexities. MyCC (coverage) had the highest ranking score in F1-score. However, the performances of CoMet were higher than MyCC (coverage) on the dataset containing multiple strains. Furthermore, CoMet recovered contigs of more species and was 18 - 39% higher in precision than the compared existing methods in discriminating species from the sample of multiple strains. CoMet resulted in higher precision than MyCC (default) and MyCC (coverage) on a real metagenome. CONCLUSIONS: The approach proposed with CoMet for binning contigs, improves the precision of binning while characterizing more species in a single metagenomic sample and in a sample containing multiple strains. The F1-scores obtained from different binning strategies vary with different datasets; however, CoMet yields the highest F1-score with a sample comprised of multiple strains.
  • Item
    No Preview Available
    Automated framework to reconstruct 3D model of cardiac Z-disk: an image processing approach
    Hanssen, E ; Rajagopal, V ; Khadankishandi, A ; Zheng, H ; Callejas, Z ; Griol, D ; Wang, H ; Hu, X ; Schmidt, H ; Baumbach, J ; Dickerson, J ; Zhang, L (IEEE, 2018)
    The Z-disk or Z-line is located at the lateral borders of sarcomere, the fundamental unit of striated muscle. They provide mechanical stability and can boost contractility of cardiac myocytes. In this paper, we propose to generate a 3D model of Z-disks within single adult cardiac cells from an automated segmentation of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The proposed fully automated segmentation scheme is comprised of three main modules including “pre-processing”, “segmentation” and “refinement”. We represent a timely-efficient, simple, yet effective model to perform segmentation and refinement steps. Contrast stretching, and Gaussian kernels are used to pre- process the dataset, and well-known “Sobel operators” are used in the segmentation module. We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. The results show that our model correctly detects Z-disks with 90.56% accuracy. Finally, the underlying network of Z-disks are rendered in 3D using ImageJ and IMARIS.