A tandem simulation framework for predicting mapping quality.
Ontology highlight
ABSTRACT:
SUBMITTER: Langmead B
PROVIDER: S-EPMC5557537 | biostudies-literature | 2017 Aug
REPOSITORIES: biostudies-literature
Publications
A tandem simulation framework for predicting mapping quality.
Genome biology 20170810 1
Read alignment is the first step in most sequencing data analyses. Because a read's point of origin can be ambiguous, aligners report a mapping quality, which is the probability that the reported alignment is incorrect. Despite its importance, there is no established and general method for calculating mapping quality. I describe a framework for predicting mapping qualities that works by simulating a set of tandem reads. These are like the input reads in important ways, but the true point of orig ...[more]
Similar Datasets
Project description:MotivationExtra-long tandem repeats (ETRs) are widespread in eukaryotic genomes and play an important role in fundamental cellular processes, such as chromosome segregation. Although emerging long-read technologies have enabled ETR assemblies, the accuracy of such assemblies is difficult to evaluate since there are no tools for their quality assessment. Moreover, since the mapping of error-prone reads to ETRs remains an open problem, it is not clear how to polish draft ETR assemblies.ResultsTo address these problems, we developed the TandemTools software that includes the TandemMapper tool for mapping reads to ETRs and the TandemQUAST tool for polishing ETR assemblies and their quality assessment. We demonstrate that TandemTools not only reveals errors in ETR assemblies but also improves the recently generated assemblies of human centromeres.Availability and implementationhttps://github.com/ablab/TandemTools.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC7355294 | biostudies-literature
Project description:Accurate mapping of next-generation sequencing (NGS) reads to reference genomes is crucial for almost all NGS applications and downstream analyses. Various repetitive elements in human and other higher eukaryotic genomes contribute in large part to ambiguously (non-uniquely) mapped reads. Most available NGS aligners attempt to address this by either removing all non-uniquely mapping reads, or reporting one random or "best" hit based on simple heuristics. Accurate estimation of the mapping quality of NGS reads is therefore critical albeit completely lacking at present. Here we developed a generalized software toolkit "AlignerBoost", which utilizes a Bayesian-based framework to accurately estimate mapping quality of ambiguously mapped NGS reads. We tested AlignerBoost with both simulated and real DNA-seq and RNA-seq datasets at various thresholds. In most cases, but especially for reads falling within repetitive regions, AlignerBoost dramatically increases the mapping precision of modern NGS aligners without significantly compromising the sensitivity even without mapping quality filters. When using higher mapping quality cutoffs, AlignerBoost achieves a much lower false mapping rate while exhibiting comparable or higher sensitivity compared to the aligner default modes, therefore significantly boosting the detection power of NGS aligners even using extreme thresholds. AlignerBoost is also SNP-aware, and higher quality alignments can be achieved if provided with known SNPs. AlignerBoost's algorithm is computationally efficient, and can process one million alignments within 30 seconds on a typical desktop computer. AlignerBoost is implemented as a uniform Java application and is freely available at https://github.com/Grice-Lab/AlignerBoost.
| S-EPMC5051939 | biostudies-literature
Project description:Health system stewards have the critical task to identify quality of care deficiencies and resolve underlying system limitations. Despite a growing evidence-base on the effectiveness of certain mechanisms for improving quality of care, frameworks to facilitate the oversight function of stewards and the use of mechanisms to improve outcomes remain underdeveloped. This review set out to catalogue a wide range of quality of care mechanisms and evidence on their effectiveness, and to map these in a framework along two dimensions: (i) governance subfunctions; and (ii) targets of quality of care mechanisms. To identify quality of care mechanisms, a series of searches were run in Health Systems Evidence and PubMed. Additional grey literature was reviewed. A total of 128 quality of care mechanisms were identified. For each mechanism, searches were carried out for systematic reviews on their effectiveness. These findings were mapped in the framework defined. The mapping illustrates the range and evidence for mechanisms varies and is more developed for some target areas such as the health workforce. Across the governance sub-functions, more mechanisms and with evidence of effectiveness are found for setting priorities and standards and organizing and monitoring for action. This framework can support system stewards to map the quality of care mechanisms used in their systems and to uncover opportunities for optimization backed by systems thinking.
| S-EPMC6946442 | biostudies-literature
Project description:Using bioimaging technology, biologists have attempted to identify and document analytical interpretations that underlie biological phenomena in biological cells. Theoretical biology aims at distilling those interpretations into knowledge in the mathematical form of biochemical reaction networks and understanding how higher level functions emerge from the combined action of biomolecules. However, there still remain formidable challenges in bridging the gap between bioimaging and mathematical modeling. Generally, measurements using fluorescence microscopy systems are influenced by systematic effects that arise from stochastic nature of biological cells, the imaging apparatus, and optical physics. Such systematic effects are always present in all bioimaging systems and hinder quantitative comparison between the cell model and bioimages. Computational tools for such a comparison are still unavailable. Thus, in this work, we present a computational framework for handling the parameters of the cell models and the optical physics governing bioimaging systems. Simulation using this framework can generate digital images of cell simulation results after accounting for the systematic effects. We then demonstrate that such a framework enables comparison at the level of photon-counting units.
| S-EPMC4509736 | biostudies-literature
Project description:In computational evolutionary biology, verification and benchmarking is a challenging task because the evolutionary history of studied biological entities is usually not known. Computer programs for simulating sequence evolution in silico have shown to be viable test beds for the verification of newly developed methods and to compare different algorithms. However, current simulation packages tend to focus either on gene-level aspects of genome evolution such as character substitutions and insertions and deletions (indels) or on genome-level aspects such as genome rearrangement and speciation events. Here, we introduce Artificial Life Framework (ALF), which aims at simulating the entire range of evolutionary forces that act on genomes: nucleotide, codon, or amino acid substitution (under simple or mixture models), indels, GC-content amelioration, gene duplication, gene loss, gene fusion, gene fission, genome rearrangement, lateral gene transfer (LGT), or speciation. The other distinctive feature of ALF is its user-friendly yet powerful web interface. We illustrate the utility of ALF with two possible applications: 1) we reanalyze data from a study of selection after globin gene duplication and test the statistical significance of the original conclusions and 2) we demonstrate that LGT can dramatically decrease the accuracy of two well-established orthology inference methods. ALF is available as a stand-alone application or via a web interface at http://www.cbrg.ethz.ch/alf.
| S-EPMC3341827 | biostudies-other
Project description:Precision medicine's potential to transform complex autoimmune-disease treatment is often challenged by limited data availability and inadequate sample size when compared to the number of molecular features found in high-throughput multi-omics datasets. Addressing this issue, the novel framework PRoBeNet (Predictive Response Biomarkers using Network medicine) was developed. ProBeNet operates under the hypothesis that the therapeutic effect of a drug propagates through a protein–protein interaction network to reverse disease states. ProBeNet prioritizes biomarkers by considering (1) therapy-targeted proteins, (2) disease-specific molecular signatures, and (3) an underlying network of interactions among cellular components (the human interactome). With ProBeNet, biomarkers were discovered predicting patient responses to both an established autoimmune therapy (infliximab) and an investigational compound (a MAPK3/1 inhibitor). Predictive power of ProBeNet biomarkers was validated with retrospective gene-expression data from ulcerative-colitis and rheumatoid-arthritis patients and prospective data from ulcerative-colitis and Crohn’s disease patient-derived tissues. Machine-learning models using ProBeNet biomarkers significantly outperformed models using either all genes or randomly selected genes, especially when data were limited (fewer than 20 samples). These results illustrate the value of ProBeNet for reducing features and for constructing robust machine-learning models when limited data are available. ProBeNet may be used to develop companion and complementary diagnostic assays for complex autoimmune-disease therapies, which may help stratify suitable patient subgroups in clinical trials, approve new drugs, and improve patient outcomes.
2024-03-13 | GSE261205 | GEO
Project description:There are various fantastic biological phenomena in biological pattern formation. Mathematical modeling using reaction-diffusion partial differential equation systems is employed to study the mechanism of pattern formation. However, model parameter selection is both difficult and time consuming. In this paper, a visual feedback simulation framework is proposed to calculate the parameters of a mathematical model automatically based on the basic principle of feedback control. In the simulation framework, the simulation results are visualized, and the image features are extracted as the system feedback. Then, the unknown model parameters are obtained by comparing the image features of the simulation image and the target biological pattern. Considering two typical applications, the visual feedback simulation framework is applied to fulfill pattern formation simulations for vascular mesenchymal cells and lung development. In the simulation framework, the spot, stripe, labyrinthine patterns of vascular mesenchymal cells, the normal branching pattern and the branching pattern lacking side branching for lung branching are obtained in a finite number of iterations. The simulation results indicate that it is easy to achieve the simulation targets, especially when the simulation patterns are sensitive to the model parameters. Moreover, this simulation framework can expand to other types of biological pattern formation.
| S-EPMC5321435 | biostudies-literature
Project description:BACKGROUND:Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs. METHODS:In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori. RESULTS:We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis. CONCLUSIONS:The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.
| S-EPMC6300887 | biostudies-other
Project description:Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) identifies regulated chromatin accessibility modules at the single-cell resolution. Robust evaluation is critical to the development of scATAC-seq pipelines, which calls for reproducible datasets for benchmarking. We hereby present the simATAC framework, an R package that generates scATAC-seq count matrices that highly resemble real scATAC-seq datasets in library size, sparsity, and chromatin accessibility signals. simATAC deploys statistical models derived from analyzing 90 real scATAC-seq cell groups. simATAC provides a robust and systematic approach to generate in silico scATAC-seq samples with known cell labels for assessing analytical pipelines.
| S-EPMC7934446 | biostudies-literature
Project description:Covalent organic frameworks (COFs) are ideal host matrices for biomolecule immobilization and biocatalysis due to their high porosity, various functionalities, and structural robustness. However, the porosity of COFs is limited to the micropore dimension, which restricts the immobilization of enzymes with large volumes and obstructs substrate flow during enzyme catalysis. A hierarchical 3D nanostructure possessing micro-, meso-, and macroporosity could be a beneficial host matrix for such enzyme catalysis. In this study, we employed an in situ CO2 gas effervescence technique to induce disordered macropores in the ordered 2D COF nanostructure, synthesizing hierarchical TpAzo COF-foam. The resulting TpAzo foam matrix facilitates the immobilization of multiple enzymes with higher immobilization efficiency (approximately 1.5 to 4-fold) than the COF. The immobilized cellulolytic enzymes, namely β-glucosidase (BGL), cellobiohydrolase (CBH), and endoglucanase (EG), remain active inside the TpAzo foam. The immobilized BGL exhibited activity in organic solvents and stability at room temperature (25 °C). The enzyme-immobilized TpAzo foam exhibited significant activity towards the hydrolysis of p-nitrophenyl-β-d-glucopyranoside (BGL@TpAzo-foam: Km and Vmax = 23.5 ± 3.5 mM and 497.7 ± 28.0 μM min-1) and carboxymethylcellulose (CBH@TpAzo-foam: Km and Vmax = 18.3 ± 4.0 mg mL-1 and 85.2 ± 9.6 μM min-1 and EG@TpAzo-foam: Km and Vmax = 13.2 ± 2.0 mg mL-1 and 102.2 ± 7.1 μM min-1). Subsequently, the multi-enzyme immobilized TpAzo foams were utilized to perform a one-pot tandem conversion from carboxymethylcellulose (CMC) to glucose with high recyclability (10 cycles). This work opens up the possibility of synthesizing enzymes immobilized in TpAzo foam for tandem catalysis.
| S-EPMC10283510 | biostudies-literature