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6,467 results for “process”
Datasets and Scripts: Full-length mRNA sequencing uncovers a widespread coupling between transcription and mRNA processing
<p>The multilayered control of gene expression requires tight coordination of regulatory mechanisms at the transcriptional and post-transcriptional level. In this study, we studied the interdependence of transcription, splicing and polyadenylation events on single mRNA molecules by full-length mRNA sequencing. In MCF-7 breast cancer cells and three human tissues, we found an unforeseen number of genes that demonstrate mutually inclusive or exclusive alternative transcription and mRNA processing events, which can span the entire length of mRNA molecules. Furthermore, alternative poly(A) sites that are coupled with alternative splicing events are depleted for known poly(A) signals and enriched for MBNL binding motifs, supporting a dual role of MBNL proteins in regulating splicing and polyadenylation. We predict thousands of open-reading frames from the sequence of full-length mRNAs, allowing for a more sensitive proteogenomics analysis of MCF-7 mass-spectrometry data. Our findings demonstrate that our understanding of transcriptome complexity is far from complete and provides a framework to reveal largely unresolved mechanisms that coordinate transcription and mRNA processing.</p>
The process of HDAC11 Assay Development: using kit
<p>Preliminary tests to check if the Fluor-de-Lys-green HDAC activity kit could be used for developing a high-throughput HDAC11 assay have been performed here using controls and recombinantly purified HDAC11.</p>
Links between Gestures and Multisensory Processing: Individual Differences Suggest a Compensation Mechanism
<p>Dataset associated with the following publication:</p> <p>Schmalenbach, S.B., Billino, J., Kircher, T., van Kemenade, B.M.*, Straube, B*. (2017). Links between Gestures and Multisensory Processing: Individual Differences Suggest a Compensation Mechanism. <em>Frontiers in Psychology</em> 8:1828.</p>
Time-generalized multivariate analysis of EEG responses reveals a cascading architecture of semantic mismatch processing
<p>This entry includes the required data to conduct the analysis from our paper. It includes recordings, a look-up csv for target word onsets and montage file. Github page for analysis code: https://github.com/heikele/GAT_n4-p6</p> <p> </p> <p>Abstract from submitted paper:</p> <p>Event-related brain potentials have a strong impact on neurocognitive models, as they inform about the temporal sequence of cognitive processes. Nevertheless, their value for deciding among alternative cognitive architectures is partly limited by component overlap and the possibility of ambiguity regarding component identity. Here, we apply temporally-generalized multivariate pattern analysis – a recently-proposed machine learning method capable of tracking the evolution of neurocognitive processes over time – to constrain possible alternative architectures underlying the processing of semantic incongruency in sentences. In a spoken sentence paradigm, we replicate established N400/P600 correlates of semantic mismatch. Time-generalized decoding indicatesthat early vs. late mismatch-sensitive processes are (i) distinct in their neural substrate, arguing against recurrent or latency-shifted single process architectures, and (ii) partially overlapping in time, inconsistent withpredictions of strictly serial models. These results are in accordance withan incremental-cascading neurocognitive organization of semantic mismatch processing. We propose time-generalized multivariate decoding as a valuable tool for neurocognitive language studies.</p> <p> </p> <p>Keywords: EEG; ERP; semantic mismatch; N400; P600; multivariate pattern analysis; generalization across time decoding</p>
Processed read counts from macrophage RNA-seq and ATAC-seq experiments
<p><strong>RNA-seq files:</strong></p> <ol> <li>RNA_count_matrix.txt.gz - raw read counts</li> <li>RNA_cqn_matrix.txt.gz - read counts quantile normalised with the cqn R package</li> <li>RNA_gene_metadata.txt.gz - information about the genes</li> <li>RNA_sample_metadata.txt.gz - information about the samples</li> </ol> <p><strong>ATAC-seq files:</strong></p> <ol> <li>ATAC_count_matrix.txt.gz - raw read counts</li> <li>ATAC_cqn_matrix.txt.gz - read counts quantile normalised with the cqn R package</li> <li>ATAC_peak_metadata.txt.gz - peak coordinates and other metadata</li> <li>ATAC_sample_metadata.txt.gz - sample metadata</li> <li>ATAC_consensus_peaks.gff3.gz - GFF3 file containing the GRCh38 coordinates of the peaks</li> </ol>
Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018
<p>Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018</p>
Precipitating electron fluxes of the magnetosphere-ionosphere coupling processes
<p>Numerical data for he precipitating electron fluxes, at altitude of 800 km, of the magnetosphere-ionosphere coupling processes, calculated for both Maxwell and Kappa distributions - used in Khazanov et al. (2018), in review.</p>
An audit of some processing effects in aggregated occurrence records
<p>These files include original and processed data from a study of data filtering by the Atlas of Living Australia (ALA) and the Global Biodiversity Information Facility (GBIF). The study has been submitted for publication on 2018-03-04 as the paper "An audit of some filtering effects in aggregated occurrence records" and details of data sources and processing are in that manuscript. ALA and GBIF sourced occurrence records from the Australian Museum (AM) [Malacology], Museums Victoria (MV) [Entomology] and the New Zealand Arthropod Collection (NZAC). The unprocessed ALA and GBIF data are in the zipped files AM-ALA_download.zip, MV-ALA_download.zip, AM-GBIF_verbatim.download.zip, AM-GBIF_occurrence.download.zip, MV-GBIF_verbatim.download.zip, MV-GBIF_occurrence.download.zip, NZAC-GBIF_verbatim.download.zip and NZAC-GBIF_occurrence.download.zip. The processed data are in a single zip file name_change_tables_and_data_notes_ver_.zip. The processed data are my own work and are here made available under a CCA 4.0 licence. Full citations for the ALA and GBIF data are as follows:<br> AM Malacology from ALA<br> Atlas of Living Australia occurrence download at https://biocache.ala.org.au/occurrences/search?&q=collection_uid%3Aco114 accessed on Wed Feb 14 18:44:13 AEDT 2018<br> AM from GBIF<br> Australian Museum (2017). Australian Museum provider for OZCAM. Occurrence Dataset https://doi.org/10.15468/e7susi accessed via GBIF.org on 2018-02-14 [Malacology section records extracted and archived here]<br> MV from ALA<br> Atlas of Living Australia occurrence download at https://biocache.ala.org.au/occurrences/search?&q=data_resource_uid%3Adr342 accessed on Wed Jan 31 06:42:40 AEDT 2018 [Entomology section records extracted and archived here]<br> MV from GBIF<br> Museums Victoria (2017). Museums Victoria provider for OZCAM. Occurrence Dataset https://doi.org/10.15468/lp1ctu accessed via GBIF.org on 2018-01-30 [Entomology section records extracted and archived here]<br> NZAC from GBIF<br> Wilton A (2018). New Zealand Arthropod Collection (NZAC). Version 1.67. Landcare Research. Occurrence Dataset https://doi.org/10.15468/lrgzz9 accessed via GBIF.org on 2018-01-08</p>
The process of HDAC11 Assay Development: enzyme stability
<p>Before performing the kinetic study for calculating the Km for HDAC11, it is important to know the duration of the stability of the protein under the assay conditions. This is being analyzed here.</p> <p> </p> <p><strong>Note: </strong>1. In the assay buffer, BSA conc. is 0.5 mg/ml (instead of 0.5%).</p> <p> 2. In the 7.5 ul developer solution, 40 uM of TSA (Trichostatin A) is also included.</p>
The process of HDAC11 Assay Development: buffer screening
<p>A buffer screen at various pH was performed to pick the buffer that gives the best activity for HDAC11. </p> <p>Note: 1. In the assay buffer, BSA conc. is 0.5 mg/ml (instead of 0.5%).</p> <p> 2. In the 7.5 ul developer solution, 40 uM of TSA (Trichostatin A) is also included.</p>
VoiceHome-2 corpus : A corpus dedicated to distant-microphone speech processing in domestic environments
<p><strong>Purpose: </strong></p> <p>This corpus includes reverberated, noisy speech signals spoken by 12 native French talkers in 4 houses (3 rooms per house) and recorded by an 8-microphone device at various angles and distances and in various noise conditions.</p> <p>This corpus stands apart from other corpora in the field by the number of rooms and homes considered by the diversity of acoustic conditions recorded and by the facts that it is publicly available at no cost.</p> <p><strong>Other materials:</strong></p> <ul> <li>Article : N. Bertin, E. Camberlein, R. Lebarbenchon, E. Vincent, S. Sivasankaran, I. Illina and F. Bimbot: <a href="https://hal.inria.fr/hal-01923108"><strong>VoiceHome-2, an extended corpus for multichannel speech processing in real homes</strong></a>, <em>Speech Communication</em>, Elsevier : North-Holland, 2019, 106, pp.68-78. <a href="https://dx.doi.org/10.1016/j.specom.2018.11.002">⟨10.1016/j.specom.2018.11.002⟩</a>.</li> <li>Code baseline to reproduce article's results: <ul> <li><a href="https://hal.inria.fr/hal-02963528">Localization and speech enhancement</a></li> <li><a href="https://doi.org/10.5281/zenodo.4079314">Acoustic models</a> and <a href="https://hal.inria.fr/hal-02963802">recognition scripts</a> for automatic speech recognition</li> </ul> </li> <li>Related software to execute the baseline: <ul> <li><a href="https://gitlab.inria.fr/bass-db/mbss_locate">MBSS Locate (v2.0)</a></li> <li><a href="https://gitlab.inria.fr/bass-db/fasst">FASST</a></li> </ul> </li> </ul> <p><strong>Documentation:</strong></p> <p>The corpus documentation is both available into the archive and hereafter by clicking on voiceHome-2_corpus_v1.0_documentation.pdf .</p> <p><strong>Terms of use</strong></p> <p>You may exploit the corpus for a non-commercial scientific purpose provided you mention it in any written work or software you derive from its use. Within a published article, paper or report, the corpus must appear in the bibliographical references.</p> <p><strong>Speaker records diffusion consent</strong></p> <p>All participants have given an informed and signed consent about public diffusion of recorded sentences.</p> <p><strong>Contact:</strong></p> <p>nancy [dot] bertin [at] irisa [dot] fr</p>
SCID Multiomics Post-Processed Data and Analysis
<p>In this repository are the post-processed datasets and analytical code for the SCID Multiomics paper. The repository is structured as an installable R package for dependency management and dataset loading; it does not export any functions.</p> <p><strong>Installation</strong></p> <p>The easiest way to install this is to download the repository and install using `devtools::install()`. This will allow the import of various datasets using the `data()` function, upon which many of the analysis scripts depend.</p> <p><strong>Datasets</strong></p> <p>In no particular order, the important datasets are described below:</p> <p>- <strong>intsites</strong>: summary statistics from (Wang et al, Blood, 2010) for timepoints used in this study<br> - <strong>tcr</strong>: Aggregate TCR data from Adaptive Biotechnology's ImmunoSeq pipeline.<br> - <strong>mb</strong>: Metadata for the microbiome sampling timepoints, as well as species data from Metaphlan (not used)<br> - <strong>agg.mb.kz</strong>: Kraken species data for the microbiome samples, after low-complexity filtering<br> - <strong>agg.vp.kz</strong>: Kraken species data for the virome samples, after low-complexity filtering<br> - <strong>card</strong>: Antibiotic resistance gene data from CARD<br> - <strong>subject_ids.csv</strong>: Provides a mapping from the original sample IDs used in the datasets to the ones used in the manuscript.</p> <p>The code for creating these datasets from the original data files are in the `data-raw` directory.</p> <p><strong>Analysis/Figures</strong></p> <p>The analysis code is broken apart by subject and is largely concerned with figure generation. The R<br> scripts are all located in the `inst` folder. To generate all figures, you should run each script in the<br> order specified by the `GenerateFigures.R` file.</p> <p>Figures are output to the `figures` directory, while tables are output to the `tables` directory.</p> <p>Please note: many of the figures used in the manuscript were aesthetically modified after generation (text size, color palette, orientation), precluding exact figure replication</p>
The process of HDAC11 Assay Development: linearity and Km calculation
<p>With previously optimized conditions, the linearity range for HDAC11 was obtained and the Km for the substrate was calculated.</p> <p> </p> <p><strong>Note: </strong>1. In the assay buffer, BSA conc. is 0.5 mg/ml (instead of 0.5%).</p> <p> 2. In the 7.5 ul developer solution, 40 uM of TSA (Trichostatin A) is also included.</p>
The process of HDAC11 Assay Development: Buffer optimization again
<p>The pH range used earlier was expanded for further optimization to check if the activity could improve.</p> <p> </p> <p><strong>Note: </strong>1. In the assay buffer, BSA conc. is 0.5 mg/ml (instead of 0.5%).</p> <p> 2. In the 7.5 ul developer solution, 40 uM of TSA (Trichostatin A) is also included.</p>
Figure 3. Stylopallene tubirostris, A, B, male, dorsal and lateral views of trunk; C, female, anterior view of cephalon; D, scape process; E, live specimen; F, juveniles on male; G, juvenile; H, exuviae attached to male oviger.
Figure 3. Stylopallene tubirostris, A, B, male, dorsal and lateral views of trunk; C, female, anterior view of cephalon; D, scape process; E, live specimen; F, juveniles on male; G, juvenile; H, exuviae attached to male oviger.
The process of HDAC11 Assay Development: Effect of DMSO
<p>For the purpose of compound library screenings, the compounds would be made available in DMSO and thus, when they would be incubated with the protein, the protein solution will now contain DMSO as well. It is important to determine the concentration of DMSO that could be tolerated by the protein such that the protein stays active. Hence, the effect of varying concentration of DMSO was analyzed on HDAC11 activity.</p>
The process of HDAC11 Assay Development: Effect of Detergent
<p>The presence of detergent can prevent the sticking of the fluorophore to the sides of the wells of the plate and thus improve the signals. Hence, the effect of detergent was observed on the overall signals and the activity of HDAC11.</p>
The process of HDAC11 Assay Development: Check for linearity and Km-repeat
<p>The linearity for HDAC11 activity for Boc-Lys-(TFA)-AMC substrate was monitored using the newly optimized buffer conditions. This was followed upon by Km calculation.</p>
The process of HDAC11 Assay Development: time-course study follow-up-effect of additives
<p>Since the activity of HDAC11 has been observed to be quite low as per the previous data, a preliminary test was performed to check any drastic increase in the activity in the presence of an additive. </p> <p>version2 (v2) has updated reaction recipe.</p>
The process of HDAC11 Assay Development: time-course study follow-up
<p>The time of incubation and conc. of the developer to be used are important parameters during HDAC11 assay optimization and thus, have been considered here.</p> <p>Version2 (v2) has updated reaction recipe.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.