Junction Mapper is a novel computer vision tool to decipher cell-cell contact phenotypes.
Ontology highlight
ABSTRACT:
SUBMITTER: Brezovjakova H
PROVIDER: S-EPMC7034980 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Chromosomes of all species studied so far display a variety of higher-order organisational features, such as self-interacting domains or loops. These structures, which are often associated to biological functions, form distinct, visible patterns on genome-wide contact maps generated by chromosome conformation capture approaches such as Hi-C. Here we present Chromosight, an algorithm inspired from computer vision that can detect patterns in contact maps. Chromosight has greater sensitivity than existing methods on synthetic simulated data, while being faster and applicable to any type of genomes, including bacteria, viruses, yeasts and mammals. Our method does not require any prior training dataset and works well with default parameters on data generated with various protocols.
| S-EPMC7670471 | biostudies-literature
Project description:With the rapid aging of the U.S. population, the mobility impairment is becoming a more and more challenging issue that affects a large number of individuals. The research presented in this paper aims at helping the mobility-challenged individuals with a novel robotic companion, which is a walker-type mobile robot capable of accompanying the human user and keeping user at the center for protection and possible power assistance. The robotic companion is equipped with a 3D computer vision system, which provides a unique capability of sensing the human-robot relative position/orientation without physical contact or the need for wearable sensors. As such, the robotic companion enables the user to walk freely with minimum disturbance to his/her normal gait, relieving the user from the physical and cognitive loads associated with the use of traditional assistive devices. For the development of the robotic companion, the authors designed and fabricated a low-cost, differentially steered mobile robotic platform, and also developed a unique image processing system to extract the position/orientation information from the 3D camera-captured images. Furthermore, an advanced motion control system was developed for the robotic companion, which provides novel solutions to the unique challenges such as sway reduction and noise reduction in digital differentiation. To quantify the performance, component and system-level experimentation was conducted, and the results demonstrated that robotic companion and its key components function as desired and the system is expected to reduce the user load and improve the user mobility in real-world scenarios.
| S-EPMC7990042 | biostudies-literature
Project description:MotivationGenetic loci associated with the variance of phenotypic traits have been of recent interest as they can be signatures of genetic interactions, gene by environment interactions, parent of origin effects and canalization. We present a fast efficient tool to map loci affecting variance of gene expression and other molecular phenotypes in cis. Results: Applied to the publicly available Geuvadis gene expression dataset, we identify 816 loci associated with variance of gene expression using an additive model, and 32 showing differences in variance between homozygous and heterozygous alleles, signatures of parent of origin effects.Availability and implementationDocumentation and links to source code and binaries for linux can be found at https://funpopgen.github.io/veqm/ .Contactandrew.brown@unige.ch.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5860057 | biostudies-literature
Project description:Current robots can manipulate only surface-attached cells seriously limiting the fields of their application for single cell handling. We developed a computer vision-based robot applying a motorized microscope and micropipette to recognize and gently isolate intact individual cells for subsequent analysis, e.g., DNA/RNA sequencing in 1-2 nanoliters from a thin (~100??m) layer of cell suspension. It can retrieve rare cells, needs minimal sample preparation, and can be applied for virtually any tissue cell type. Combination of 1??m positioning precision, adaptive cell targeting and below 1?nl liquid handling precision resulted in an unprecedented accuracy and efficiency in robotic single cell isolation. Single cells were injected either into the wells of a miniature plate with a sorting speed of 3 cells/min or into standard PCR tubes with 2 cells/min. We could isolate labeled cells also from dense cultures containing ~1,000 times more unlabeled cells by the successive application of the sorting process. We compared the efficiency of our method to that of single cell entrapment in microwells and subsequent sorting with the automated micropipette: the recovery rate of single cells was greatly improved.
| S-EPMC4746594 | biostudies-literature
Project description:BackgroundMitochondrial respiration is central to cellular and organismal health in eukaryotes. In baker's yeast, however, respiration is dispensable under fermentation conditions. Because yeast are tolerant of this mitochondrial dysfunction, yeast are widely used by biologists as a model organism to ask a variety of questions about the integrity of mitochondrial respiration. Fortunately, baker's yeast also display a visually identifiable Petite colony phenotype that indicates when cells are incapable of respiration. Petite colonies are smaller than their Grande (wild-type) counterparts, and their frequency can be used to infer the integrity of mitochondrial respiration in populations of cells. Unfortunately, the computation of Petite colony frequencies currently relies on laborious manual colony counting methods which limit both experimental throughput and reproducibility.ResultsTo address these problems, we introduce a deep learning enabled tool, petiteFinder, that increases the throughput of the Petite frequency assay. This automated computer vision tool detects Grande and Petite colonies and computes Petite colony frequencies from scanned images of Petri dishes. It achieves accuracy comparable to human annotation but at up to 100 times the speed and outperforms semi-supervised Grande/Petite colony classification approaches. Combined with the detailed experimental protocols we provide, we believe this study can serve as a foundation to standardize this assay. Finally, we comment on how Petite colony detection as a computer vision problem highlights ongoing difficulties with small object detection in existing object detection architectures.ConclusionColony detection with petiteFinder results in high accuracy Petite and Grande detection in images in a completely automated fashion. It addresses issues in scalability and reproducibility of the Petite colony assay which currently relies on manual colony counting. By constructing this tool and providing details of experimental conditions, we hope this study will enable larger-scale experiments that rely on Petite colony frequencies to infer mitochondrial function in yeast.
| S-EPMC9930278 | biostudies-literature
Project description:MotivationMapping high-throughput sequencing data to a reference genome is an essential step for most analysis pipelines aiming at the computational analysis of genome and transcriptome sequencing data. Breaking ties between equally well mapping locations poses a severe problem not only during the alignment phase but also has significant impact on the results of downstream analyses. We present the multi-mapper resolution (MMR) tool that infers optimal mapping locations from the coverage density of other mapped reads.ResultsFiltering alignments with MMR can significantly improve the performance of downstream analyses like transcript quantitation and differential testing. We illustrate that the accuracy (Spearman correlation) of transcript quantification increases by 15% when using reads of length 51. In addition, MMR decreases the alignment file sizes by more than 50%, and this leads to a reduced running time of the quantification tool. Our efficient implementation of the MMR algorithm is easily applicable as a post-processing step to existing alignment files in BAM format. Its complexity scales linearly with the number of alignments and requires no further inputs.Availability and implementationOpen source code and documentation are available for download at http://github.com/ratschlab/mmr Comprehensive testing results and further information can be found at http://bioweb.me/mmr.Contactandre.kahles@ratschlab.org or gunnar.ratsch@ratschlab.orgSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4795617 | biostudies-literature
Project description:MotivationIn order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing.ResultsWe have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted P-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches.Availability and implementationSource code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/Contactemmanouil.dermitzakis@unige.ch or olivier.delaneau@unige.chSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4866519 | biostudies-literature
Project description:PurposeTo evaluate the prevalence of computer vision syndrome (CVS)-related symptoms in a presbyopic population using the computer as the main work tool, as well as the relationship of CVS with the electronic device use habits and the ergonomic factors.MethodsA sample of 198 presbyopic participants (aged 45-65 years) who regularly work with a computer completed a customised questionnaire divided into: general demographics, optical correction commonly used and for work, habits of electronic devices use, ergonomic conditions during the working hours and CVS-related symptoms during work performance. A total of 10 CVS-related symptoms were questioned indicating the severity with which they occurred (0-4) and the median total symptom score (MTSS) was calculated as the sum of the symptoms.ResultsThe MTSS in this presbyopic population is 7 ± 5 symptoms. The most common symptoms reported by participants are dry eyes, tired eyes and difficulties in refocusing. MTSS is higher in women (p < 0.05), in laptop computer users (p < 0.05) and in teleworkers compared to office workers (p < 0.05). Regarding ergonomic conditions, MTSS is higher in participants who do not take breaks while working (p < 0.05), who have an inadequately lighting in the workspace (p < 0.05) and in the participants reporting neck (p < 0.01) or back pain (p < 0.001).ConclusionThere is a relationship between CVS-related symptoms, the use of electronic devices and the ergonomic factors, which indicates the importance of adapting workplaces, especially for home-based teleworkers, and following basic visual ergonomics rules.
| S-EPMC10133911 | biostudies-literature
Project description:Fluorescence-activated cell sorting (FACS) applying flow cytometry to separate cells on a molecular basis is a widespread method. We demonstrate that both fluorescent and unlabeled live cells in a Petri dish observed with a microscope can be automatically recognized by computer vision and picked up by a computer-controlled micropipette. This method can be routinely applied as a FACS down to the single cell level with a very high selectivity. Sorting resolution, i.e., the minimum distance between two cells from which one could be selectively removed was 50-70 micrometers. Survival rate with a low number of 3T3 mouse fibroblasts and NE-4C neuroectodermal mouse stem cells was 66 ± 12% and 88 ± 16%, respectively. Purity of sorted cultures and rate of survival using NE-4C/NE-GFP-4C co-cultures were 95 ± 2% and 62 ± 7%, respectively. Hydrodynamic simulations confirmed the experimental sorting efficiency and a cell damage risk similar to that of normal FACS.
| S-EPMC3548191 | biostudies-other
Project description:Extracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer vision (CV) guided by deep learning (DL) has made significant strides in recognizing a specific identity despite highly variable attributes. This is the same challenge faced by the nervous system and partially addressed by the concept cells-neurons exhibiting selective firing in response to specific persons/places, described in the human medial temporal lobe (MTL) . Yet, access to neurons representing a particular concept is limited due to these neurons' sparse coding. It is conceivable, however, that the information required for such decoding is present in relatively small neuronal populations. To evaluate how well neuronal populations encode identity information in natural settings, we recorded neuronal activity from multiple brain regions of nine neurosurgical epilepsy patients implanted with depth electrodes, while the subjects watched an episode of the TV series "24". First, we devised a minimally supervised CV algorithm (with comparable performance against manually-labeled data) to detect the most prevalent characters (above 1% overall appearance) in each frame. Next, we implemented DL models that used the time-varying population neural data as inputs and decoded the visual presence of the four main characters throughout the episode. This methodology allowed us to compare "computer vision" with "neuronal vision"-footprints associated with each character present in the activity of a subset of neurons-and identify the brain regions that contributed to this decoding process. We then tested the DL models during a recognition memory task following movie viewing where subjects were asked to recognize clip segments from the presented episode. DL model activations were not only modulated by the presence of the corresponding characters but also by participants' subjective memory of whether they had seen the clip segment, and by the associative strengths of the characters in the narrative plot. The described approach can offer novel ways to probe the representation of concepts in time-evolving dynamic behavioral tasks. Further, the results suggest that the information required to robustly decode concepts is present in the population activity of only tens of neurons even in brain regions beyond MTL.
| S-EPMC9837190 | biostudies-literature