Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (9 results)

  • Thesis
  • Peer Reviewed
UC Riverside Electronic Theses and Dissertations (2019)

De novo genome assembly is one of the most critical problems in computational biology. Due to the limitations of current sequencing technologies, the de novo assembly is typically carried out in two stages, namely contig (sequence) assembly and scaffolding. The scaffolding process can vastly improve the assembly contiguity and can produce chromosome-level assemblies. Despite significant algorithmic progress, the scaffolding problem can be challenging due to the high repetitive content of eukaryotic genomes, possible mis-joins in assembled contigs and the inaccuracies of the linkage information.

Different types of linkage information such as paired-end/mate-pair/linked/Hi-C reads or genome-wide maps (optical, physical or genetic) are used to carry out the scaffolding process. Optical maps (in particular Bionano Genomics maps) have been extensively used in many recent large-scale genome assembly projects (e.g., goat, apple, barley, maize, quinoa, sea bass, among others).

In this dissertation, we address some of the computational issues associated with genome scaffolding when optical maps are used. We propose novel algorithms for scaffolding, chimeric detection, and assembly reconciliation. First, we introduce a novel chimeric removal tool called Chimericognizer. Chimericognizer takes advantage of one or more Bionano Genomics optical maps to accurately detect and correct chimeric contigs. Experimental results show that Chimericognizer is very accurate, and significantly better than the chimeric detection method offered by the Bionano Hybrid Scaffold pipeline. Chimericognizer can also detect and correct chimeric optical

molecules.

Second, we describe a novel method called Novo&Stitch that can take advantage of optical maps to accurately carry out assembly reconciliation. Experimental results demonstrate that Novo&Stitch can double the contiguity (N50) of the input assemblies without introducing mis-joins or reducing genome completeness.

Third, we introduce a scaffolding algorithm called OMGS that for the first time can take advantages of multiple optical maps. OMGS solves several optimization problems to generate scaffolds with optimal contiguity and correctness. Extensive experimental results demonstrate that our tool outperforms existing methods when multiple optical maps are available, and produces comparable scaffolds using a single optical map.

    Cover page: Optical Map-Based Genome Scaffolding
    • Article
    • Peer Reviewed
    UC Riverside Previously Published Works (2019)

    Summary

    A chimeric contig is contig that has been incorrectly assembled, i.e. a contig that contains one or more mis-joins. The detection of chimeric contigs can be carried out either by aligning assembled contigs to genome-wide maps (e.g. genetic, physical or optical maps) or by mapping sequenced reads to the assembled contigs. Here, we introduce a software tool called Chimericognizer that takes advantage of one or more Bionano Genomics optical maps to accurately detect and correct chimeric contigs. Experimental results show that Chimericognizer is very accurate, and significantly better than the chimeric detection method offered by the Bionano Hybrid Scaffold pipeline. Chimericognizer can also detect and correct chimeric optical molecules.

    Availability and implementation

    https://github.com/ucrbioinfo/Chimericognizer.

    Supplementary information

    Supplementary data are available at Bioinformatics online.
      Cover page: Accurate detection of chimeric contigs via Bionano optical maps.
      • Article
      • Peer Reviewed
      UC Riverside Previously Published Works (2020)
      Due to the current limitations of sequencing technologies, de novo genome assembly is typically carried out in two stages, namely contig (sequence) assembly and scaffolding. While scaffolding is computationally easier than sequence assembly, the scaffolding problem can be challenging due to the high repetitive content of eukaryotic genomes, possible mis-joins in assembled contigs, and inaccuracies in the linkage information. Genome scaffolding tools either use paired-end/mate-pair/linked/Hi-C reads or genome-wide maps (optical, physical, or genetic) as linkage information. Optical maps (in particular Bionano Genomics maps) have been extensively used in many recent large-scale genome assembly projects (e.g., goat, apple, barley, maize, quinoa, sea bass, among others). However, the most commonly used scaffolding tools have a serious limitation: they can only deal with one optical map at a time, forcing users to alternate or iterate over multiple maps. In this article, we introduce a novel scaffolding algorithm called OMGS (Optical Map-based Genome Scaffolding) that for the first time can take advantages of multiple optical maps. OMGS solves several optimization problems to generate scaffolds with optimal contiguity and correctness. Extensive experimental results demonstrate that our tool outperforms existing methods when multiple optical maps are available and produces comparable scaffolds using a single optical map.
        Cover page: OMGS: Optical Map-Based Genome Scaffolding
        • Article
        • Peer Reviewed
        UC Riverside Previously Published Works (2018)

        Motivation

        De novo genome assembly is a challenging computational problem due to the high repetitive content of eukaryotic genomes and the imperfections of sequencing technologies (i.e. sequencing errors, uneven sequencing coverage and chimeric reads). Several assembly tools are currently available, each of which has strengths and weaknesses in dealing with the trade-off between maximizing contiguity and minimizing assembly errors (e.g. mis-joins). To obtain the best possible assembly, it is common practice to generate multiple assemblies from several assemblers and/or parameter settings and try to identify the highest quality assembly. Unfortunately, often there is no assembly that both maximizes contiguity and minimizes assembly errors, so one has to compromise one for the other.

        Results

        The concept of assembly reconciliation has been proposed as a way to obtain a higher quality assembly by merging or reconciling all the available assemblies. While several reconciliation methods have been introduced in the literature, we have shown in one of our recent papers that none of them can consistently produce assemblies that are better than the assemblies provided in input. Here we introduce Novo&Stitch, a novel method that takes advantage of optical maps to accurately carry out assembly reconciliation (assuming that the assembled contigs are sufficiently long to be reliably aligned to the optical maps, e.g. 50 Kbp or longer). Experimental results demonstrate that Novo&Stitch can double the contiguity (N50) of the input assemblies without introducing mis-joins or reducing genome completeness.

        Availability and implementation

        Novo&Stitch can be obtained from https://github.com/ucrbioinfo/Novo_Stitch.
          Cover page: Novo&Stitch: accurate reconciliation of genome assemblies via optical maps
          • Article
          • Peer Reviewed
          UC Riverside Previously Published Works (2024)
          Pacific Biosciences (PacBio) HiFi sequencing technology generates long reads (>10 kbp) with very high accuracy (<0.01% sequencing error). Although several de novo assembly tools are available for HiFi reads, there are no comprehensive studies on the evaluation of these assemblers. We evaluated the performance of 11 de novo HiFi assemblers on (1) real data for three eukaryotic genomes; (2) 34 synthetic data sets with different ploidy, sequencing coverage levels, heterozygosity rates, and sequencing error rates; (3) one real metagenomic data set; and (4) five synthetic metagenomic data sets with different composition abundance and heterozygosity rates. The 11 assemblers were evaluated using quality assessment tool (QUAST) and benchmarking universal single-copy ortholog (BUSCO). We also used several additional criteria, namely, completion rate, single-copy completion rate, duplicated completion rate, average proportion of largest category, average distance difference, quality value, run-time, and memory utilization. Results show that hifiasm and hifiasm-meta should be the first choice for assembling eukaryotic genomes and metagenomes with HiFi data. We performed a comprehensive benchmarking study of commonly used assemblers on complex eukaryotic genomes and metagenomes. Our study will help the research community to choose the most appropriate assembler for their data and identify possible improvements in assembly algorithms.
            Cover page: Comprehensive assessment of 11 de novo HiFi assemblers on complex eukaryotic genomes and metagenomes.
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2021)
            Background: Recent studies from multiple countries have shown a high prevalence of coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) among severely ill patients. Despite providing valuable insight into the clinical management of CAPA, large-scale prospective studies are limited. Here, we report on one of the largest multicenter epidemiological studies to explore the clinical features and prevalence of COVID-19-associated pulmonary mold infections (CAPMIs) among mechanically ventilated patients. Methods: Bronchoalveolar lavage (BAL) and serum samples were collected for culture, galactomannan (GM), and β-D-glucan (BDG) testing. Patients were classified as probable CAPMI based on the presence of host factors, radiological findings, and mycological criteria. Results: During the study period, 302 COVID-19 patients were admitted to intensive care units (ICUs), among whom 105 were mechanically ventilated for ≥4 days. Probable CAPMI was observed among 38% of patients (40/105), among whom BAL culture of 29 patients turned positive for molds, while galactomannan testing on BAL (GM index ≥1) and serum (GM index >0.5) samples were positive for 60% (24/40) and 37.5% (15/39) of patients, respectively. Aspergillus (22/29; 75.8%) and Fusarium (6/29; 20.6%) constituted 96.5% of the molds isolated. Diaporthe foeniculina was isolated from a COVID-19 patient. None of the patients who presented with CAPMI were treated with antifungal drugs. Conclusion: Despite being prevalent, the absence of appropriate antifungal treatment highlights that CAPMI is a neglected complication among mechanically ventilated COVID-19 patients admitted to ICUs. CAPMI can be caused by species other than Aspergillus.
              Cover page: Pervasive but Neglected: A Perspective on COVID-19-Associated Pulmonary Mold Infections Among Mechanically Ventilated COVID-19 Patients
              • Article
              • Peer Reviewed
              UC Riverside Previously Published Works (2019)
              Genome-wide mutational screens are central to understanding the genetic underpinnings of evolved and engineered phenotypes. The widespread adoption of CRISPR-Cas9 genome editing has enabled such screens in many organisms, but identifying functional sgRNAs still remains a challenge. Here, we developed a methodology to quantify the cutting efficiency of each sgRNA in a genome-scale library, and in doing so improve screens in the biotechnologically important yeast Yarrowia lipolytica. Screening in the presence and absence of native DNA repair enabled high-throughput quantification of sgRNA function leading to the identification of high efficiency sgRNAs that cover 94% of genes. Library validation enhanced the classification of essential genes by identifying inactive guides that create false negatives and mask the effects of successful disruptions. Quantification of guide effectiveness also creates a dataset from which determinants of CRISPR-Cas9 can be identified. Finally, application of the library identified novel mutations for metabolic engineering of high lipid accumulation.
                Cover page: Validating genome-wide CRISPR-Cas9 function improves screening in the oleaginous yeast Yarrowia lipolytica
                • Article
                • Peer Reviewed
                UC San Diego Previously Published Works (2020)
                By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of 'iGlaucoma', a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets-200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834-0.877, with a sensitivity of 0.831-0.922 and a specificity of 0.676-0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953-0.979), 0.954 (0.930-0.977), and 0.873 (0.838-0.908), respectively. The 'iGlaucoma' is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input.
                  Cover page: Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection
                  • Article
                  • Peer Reviewed
                  UC Berkeley Previously Published Works (2017)
                  Cryptococcosis is a major fungal disease caused by members of the Cryptococcus gattii and Cryptococcus neoformans species complexes. After more than 15 years of molecular genetic and phenotypic studies and much debate, a proposal for a taxonomic revision was made. The two varieties within C. neoformans were raised to species level, and the same was done for five genotypes within C. gattii. In a recent perspective (K. J. Kwon-Chung et al., mSphere 2:e00357-16, 2017, https://doi.org/10.1128/mSphere.00357-16), it was argued that this taxonomic proposal was premature and without consensus in the community. Although the authors of the perspective recognized the existence of genetic diversity, they preferred the use of the informal nomenclature "C. neoformans species complex" and "C. gattii species complex." Here we highlight the advantage of recognizing these seven species, as ignoring these species will impede deciphering further biologically and clinically relevant differences between them, which may in turn delay future clinical advances.
                    Cover page: Importance of Resolving Fungal Nomenclature: the Case of Multiple Pathogenic Species in the Cryptococcus Genus
                    Top