A route pruning algorithm for an automated geographic location graph construction.
Ontology highlight
ABSTRACT:
SUBMITTER: Schweimer C
PROVIDER: S-EPMC8172915 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundThe rapid advancement of sequencing technologies has made it possible to regularly produce millions of high-quality reads from the DNA samples in the sequencing laboratories. To this end, the de Bruijn graph is a popular data structure in the genome assembly literature for efficient representation and processing of data. Due to the number of nodes in a de Bruijn graph, the main barrier here is the memory and runtime. Therefore, this area has received significant attention in contemporary literature.ResultsIn this paper, we present an approach called HaVec that attempts to achieve a balance between the memory consumption and the running time. HaVec uses a hash table along with an auxiliary vector data structure to store the de Bruijn graph thereby improving the total memory usage and the running time. A critical and noteworthy feature of HaVec is that it exhibits no false positive error.ConclusionsIn general, the graph construction procedure takes the major share of the time involved in an assembly process. HaVec can be seen as a significant advancement in this aspect. We anticipate that HaVec will be extremely useful in the de Bruijn graph-based genome assembly.
| S-EPMC5591975 | biostudies-literature
Project description:PurposeTo develop a fully automated algorithm for accurate detection of fovea location in atrophic age-related macular degeneration (AMD), based on spectral-domain optical coherence tomography (SD-OCT) scans.MethodsImage processing was conducted on a cohort of patients affected by geographic atrophy (GA). SD-OCT images (cube volume) from 55 eyes (51 patients) were extracted and processed with a layer segmentation algorithm to segment Ganglion Cell Layer (GCL) and Inner Plexiform Layer (IPL). Their en face thickness projection was convolved with a 2D Gaussian filter to find the global maximum, which corresponded to the detected fovea. The detection accuracy was evaluated by computing the distance between manual annotation and predicted location.ResultsThe mean total location error was 0.101±0.145mm; the mean error in horizontal and vertical en face axes was 0.064±0.140mm and 0.063±0.060mm, respectively. The mean error for foveal and extrafoveal retinal pigment epithelium and outer retinal atrophy (RORA) was 0.096±0.070mm and 0.107±0.212mm, respectively. Our method obtained a significantly smaller error than the fovea localization algorithm inbuilt in the OCT device (0.313±0.283mm, p <.001) or a method based on the thinnest central retinal thickness (0.843±1.221, p <.001). Significant outliers are depicted with the reliability score of the method.ConclusionDespite retinal anatomical alterations related to GA, the presented algorithm was able to detect the foveal location on SD-OCT cubes with high reliability. Such an algorithm could be useful for studying structural-functional correlations in atrophic AMD and could have further applications in different retinal pathologies.
| S-EPMC9203415 | biostudies-literature
Project description:Most gene prediction methods detect coding sequences from transcriptome assemblies in the absence of closely related reference genomes. Such methods are of limited application due to high transcript fragmentation and extensive assembly errors, which may lead to redundant or false coding sequence predictions. We present inGAP-CDG, which can construct full-length and non-redundant coding sequences from unassembled transcriptomes by using a codon-based de Bruijn graph to simplify the assembly process and a machine learning-based approach to filter false positives. Compared with other methods, inGAP-CDG exhibits a significant increase in predicted coding sequence length and robustness to sequencing errors and varied read length.
| S-EPMC5114782 | biostudies-literature
Project description:BackgroundEncoded by the var gene family, highly variable Plasmodium falciparum erythrocyte membrane protein-1 (PfEMP1) proteins mediate tissue-specific cytoadherence of infected erythrocytes, resulting in immune evasion and severe malaria disease. Sequencing and assembling the 40-60 var gene complement for individual infections has been notoriously difficult, impeding molecular epidemiological studies and the assessment of particular var elements as subunit vaccine candidates.MethodsWe developed and validated a novel algorithm, Exon-Targeted Hybrid Assembly (ETHA), to perform targeted assembly of var gene sequences, based on a combination of Pacific Biosciences and Illumina data.ResultsUsing ETHA, we characterized the repertoire of var genes in 12 samples from uncomplicated malaria infections in children from a single Malian village and showed them to be as genetically diverse as vars from isolates from around the globe. The gene var2csa, a member of the var family associated with placental malaria pathogenesis, was present in each genome, as were vars previously associated with severe malaria.ConclusionETHA, a tool to discover novel var sequences from clinical samples, will aid the understanding of malaria pathogenesis and inform the design of malaria vaccines based on PfEMP1. ETHA is available at: https://sourceforge.net/projects/etha/ .
| S-EPMC5368897 | biostudies-literature
Project description:Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation-including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.
| S-EPMC8423278 | biostudies-literature
Project description:OBJECTIVE Lower-extremity amputation (LEA) is common among persons with diabetes. The goal of this study was to identify geographic variation and the influence of location on the incidence of LEA among U.S. Medicare beneficiaries with diabetes. RESEARCH DESIGN AND METHODS We conducted a cohort study of beneficiaries of Medicare. The geographic unit of analysis was hospital referral regions (HRRs). Tests of spatial autocorrelation and geographically weighted regression were used to evaluate the incidence of LEA by HRRs as a function of geographic location in the U.S. Evaluated covariates covered sociodemographic factors, risk factors for LEA, diabetes severity, provider access, and cost of care. RESULTS Among persons with diabetes, the annual incidence per 1,000 of LEA was 5.0 in 2006, 4.6 in 2007, and 4.5 in 2008 and varied by the HRR. The incidence of LEA was highly concentrated in neighboring HRRs. High rates of LEA clustered in contiguous portions of Texas, Oklahoma, Louisiana, Arkansas, and Mississippi. Accounting for geographic location greatly improved our ability to understand the variability in LEA. Additionally, covariates associated with LEA per HRR included socioeconomic status, prevalence of African Americans, age, diabetes, and mortality rate associated with having a foot ulcer. CONCLUSIONS There is profound "region-correlated" variation in the rate of LEA among Medicare beneficiaries with diabetes. In other words, location matters and whereas the likelihood of an amputation varies dramatically across the U.S. overall, neighboring locations have unexpectedly similar amputation rates, some being uniformly high and others uniformly low.
| S-EPMC3198303 | biostudies-literature
Project description:BackgroundThis resident application cycle posed academic leadership and applicants with an unprecedented challenge: how to virtually match applicants to mutually beneficial programs. The authors sought to refer to previous years' data, specifically geographic trends, to better inform both program directors and applicants. The authors hypothesized that geography, as it pertains to the transition from medical school to residency, impacts match patterns.MethodsThe study was designed as a cross-sectional analysis including all current integrated plastic surgery residents. The independent websites of all accredited integrated plastic surgery programs were then queried for the desired demographic resident information. Additionally, as an illustrative endpoint, geospatial heat maps were generated to better understand geographic trends.ResultsAll (n = 78) integrated plastic surgery programs and 953 residents were included in the study. Nearly half (47.2%) of current residents remain in the same geographic region in which they obtained their medical degree, with 26% and 17% remaining in the same state and institution, respectively. Students within all regions (North, South, Midwest, West) were more likely to stay within that region for residency (OR 2.59, 2.39, 2.09, 3.80, respectively). Students attending medical schools with affiliated integrated plastic surgery residencies have matched to programs with significantly higher Doximity rankings (p < 0.0001).ConclusionsMatched integrated plastic surgery applicants are more likely to continue their training at institutions in closer geographic proximity to their medical schools. Students graduating from medical schools without affiliated integrated plastic surgery programs appear to be at a disadvantage during the match process.
| S-EPMC8057751 | biostudies-literature
Project description:ObjectiveThis paper aims to identify periods of depression using geolocation movements recorded from mobile phones in a prospective community study of individuals with bipolar disorder (BD).MethodsAnonymized geographic location recordings from 22 BD participants and 14 healthy controls (HC) were collected over 3 months. Participants reported their depressive symptomatology using a weekly questionnaire (QIDS-SR16). Recorded location data were preprocessed by detecting and removing imprecise data points and features were extracted to assess the level and regularity of geographic movements of the participant. A subset of features were selected using a wrapper feature selection method and presented to 1) a linear regression model and a quadratic generalized linear model with a logistic link function for questionnaire score estimation; and 2) a quadratic discriminant analysis classifier for depression detection in BD participants based on their questionnaire responses. R esults: HC participants did not report depressive symptoms and their features showed similar distributions to nondepressed BD participants. Questionnaire score estimation using geolocation-derived features from BD participants demonstrated an optimal mean absolute error rate of 3.73, while depression detection demonstrated an optimal (median ± IQR) [Formula: see text] score of 0.857 ± 0.022 using five features (classification accuracy: 0.849 ± 0.016; sensitivity: 0.839 ± 0.014; specificity: 0.872 ± 0.047).ConclusionThese results demonstrate a strong link between geographic movements and depression in bipolar disorder. S ignificance: To our knowledge, this is the first community study of passively recorded objective markers of depression in bipolar disorder of this scale. The techniques could help individuals monitor their depression and enable healthcare providers to detect those in need of care or treatment.
| S-EPMC5947818 | biostudies-literature
Project description:Motivation:We present Faucet, a two-pass streaming algorithm for assembly graph construction. Faucet builds an assembly graph incrementally as each read is processed. Thus, reads need not be stored locally, as they can be processed while downloading data and then discarded. We demonstrate this functionality by performing streaming graph assembly of publicly available data, and observe that the ratio of disk use to raw data size decreases as coverage is increased. Results:Faucet pairs the de Bruijn graph obtained from the reads with additional meta-data derived from them. We show these metadata-coverage counts collected at junction k-mers and connections bridging between junction pairs-contain most salient information needed for assembly, and demonstrate they enable cleaning of metagenome assembly graphs, greatly improving contiguity while maintaining accuracy. We compared Fauceted resource use and assembly quality to state of the art metagenome assemblers, as well as leading resource-efficient genome assemblers. Faucet used orders of magnitude less time and disk space than the specialized metagenome assemblers MetaSPAdes and Megahit, while also improving on their memory use; this broadly matched performance of other assemblers optimizing resource efficiency-namely, Minia and LightAssembler. However, on metagenomes tested, Faucet,o outputs had 14-110% higher mean NGA50 lengths compared with Minia, and 2- to 11-fold higher mean NGA50 lengths compared with LightAssembler, the only other streaming assembler available. Availability and implementation:Faucet is available at https://github.com/Shamir-Lab/Faucet. Contact:rshamir@tau.ac.il or eranhalperin@gmail.com. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC5870852 | biostudies-literature
Project description:Modern neuroscientific research stands on the shoulders of countless giants. PubMed alone contains more than 21 million peer-reviewed articles with 40-50,000 more published every month. Understanding the human brain, cognition, and disease will require integrating facts from dozens of scientific fields spread amongst millions of studies locked away in static documents, making any such integration daunting, at best. The future of scientific progress will be aided by bridging the gap between the millions of published research articles and modern databases such as the Allen brain atlas (ABA). To that end, we have analyzed the text of over 3.5 million scientific abstracts to find associations between neuroscientific concepts. From the literature alone, we show that we can blindly and algorithmically extract a "cognome": relationships between brain structure, function, and disease. We demonstrate the potential of data-mining and cross-platform data-integration with the ABA by introducing two methods for semi-automated hypothesis generation. By analyzing statistical "holes" and discrepancies in the literature we can find understudied or overlooked research paths. That is, we have added a layer of semi-automation to a part of the scientific process itself. This is an important step toward fundamentally incorporating data-mining algorithms into the scientific method in a manner that is generalizable to any scientific or medical field.
| S-EPMC3376233 | biostudies-literature