Similar Datasets
Project description:The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduced their respective read counts by more than 85%, resulting in an increase in sequencing depth of up to 93% for long non-coding RNAs. We also applied RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as a significant increase in non-globin reads. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER’s modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser.
2024-03-29 | GSE262284 | GEO
Project description:The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduced their respective read counts by more than 85%, resulting in an increase in sequencing depth of up to 93% for long non-coding RNAs. We also applied RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as a significant increase in non-globin reads. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER’s modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser.
2024-03-29 | GSE262283 | GEO
Project description:Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER
| PRJNA1090959 | ENA
Project description:Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER (cell lines)
| PRJNA1090960 | ENA
Project description:Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER (whole blood)
| PRJNA1090961 | ENA
Project description:This dataset offers a comprehensive compilation of attention-related features captured during online classes. The dataset is generated through the integration of key components including face detection, hand tracking, head pose estimation, and mobile phone detection modules. The data collection process involves leveraging a web interface created using the Django web framework. Video frames of participating students are collected following institutional guidelines and informed consent through their webcams, subsequently decomposed into frames at a rate of 20 FPS, and transformed from BGR to RGB color model. The aforesaid modules subsequently process these video frames to extract raw data. The dataset consists of 16 features and one label column, encompassing numerical, categorical, and floating-point values. Inherent to its potential, the dataset enables researchers and practitioners to explore and examine attention-related patterns and characteristics exhibited by students during online classes. The composition and design of the dataset offer a unique opportunity to delve into the correlations and interactions among face presence, hand movements, head orientations, and phone interactions. Researchers can leverage this dataset to investigate and develop machine learning models aimed at automatic attention detection, thereby contributing to enhancing remote learning experiences and educational outcomes. The dataset in question also constitutes a highly valuable resource for the scientific community, enabling a thorough exploration of the multifaceted aspects pertaining to student attention levels during online classes. Its rich and diverse feature set, coupled with the underlying data collection methodology, provides ample opportunities for reuse and exploration across multiple domains including education, psychology, and computer vision research.
| S-EPMC10694051 | biostudies-literature
Project description:A principal goal of molecular biophysics is to show how protein structural transitions explain physiology. We have developed a strategic tool, transient time-resolved FRET [(TR)(2)FRET], for this purpose and use it here to measure directly, with millisecond resolution, the structural and biochemical kinetics of muscle myosin and to determine directly how myosin's power stroke is coupled to the thermodynamic drive for force generation, actin-activated phosphate release, and the weak-to-strong actin-binding transition. We find that actin initiates the power stroke before phosphate dissociation and not after, as many models propose. This result supports a model for muscle contraction in which power output and efficiency are tuned by the distribution of myosin structural states. This technology should have wide application to other systems in which questions about the temporal coupling of allosteric structural and biochemical transitions remain unanswered.
| S-EPMC4655543 | biostudies-literature
Project description:We describe the direct detection of DNA methylation, without bisulfite conversion, through single-molecule, real-time (SMRT) sequencing. In SMRT sequencing, DNA polymerases catalyze the incorporation of fluorescently labeled nucleotides into complementary nucleic acid strands. The arrival times and durations of the resulting fluorescence pulses yield information about polymerase kinetics and allow direct detection of modified nucleotides in the DNA template, including N6-methyladenine, 5-methylcytosine and 5-hydroxymethylcytosine. Measurement of polymerase kinetics is an intrinsic part of SMRT sequencing and does not adversely affect determination of primary DNA sequence. The various modifications affect polymerase kinetics differently, allowing discrimination between them. We used these kinetic signatures to identify adenine methylation in genomic samples and found that, in combination with circular consensus sequencing, they can enable single-molecule identification of epigenetic modifications with base-pair resolution. This method is amenable to long read lengths and will likely enable mapping of methylation patterns in even highly repetitive genomic regions.
| S-EPMC2879396 | biostudies-literature
Project description:In recent decades "saliva" has emerged as an important non-invasive biofluid for diagnostic purposes in both human and animal health sectors. However, with the rapid evolution of molecular detection technologies, the limitation has been the lack of an efficient method for the facile amplification of target RNA from such a complex matrix. Herein, we demonstrate the novel application of hydrogel microparticles of primer-immobilized networks (PIN) for direct quantitative reverse transcription PCR (dirRT-qPCR) of viral RNA from saliva samples without prior RNA purification. Each of these highly porous PIN particles operates as an independent reactor. They filter in micro-volumes of the analyte solution. Viral RNA is captured and converted to complementary DNA (cDNA) through the RT step using covalently incorporated RT primers. The PIN with cDNA of the viral target will be ready for subsequent highly specific qPCR. Preceded by heat-treatment for viral lysis, we were able to conduct PIN dirRT-qPCR with 95% efficiency of the matrix (M) gene for influenza A virus (IAV) and 5' untranslated region (5' UTR) for chicken coronavirus spiked into saliva samples. The addition of reverse transcriptase enzyme (RTase) and 10% dilution of the matrix improved the assay sensitivity considerably. PIN particles' compatibility with microfluidic PCR chip technology has significantly reduced total sample processing time to 50 min, instead of an average of 120 min that are normally used by other assays. We anticipate this technology will be useful for other viral RNA targets by changing the incorporated RT primer sequences and can be adapted for onsite diagnostics.Supplementary informationThe online version contains supplementary material available at 10.1007/s13206-022-00065-0.
| S-EPMC9358062 | biostudies-literature
Project description:We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than DNA passes through the pore, allowing real-time classification and read ejection. Using 1 s of sequencing data, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than alignment-based approaches. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.
| S-EPMC8548853 | biostudies-literature