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Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.
<p>This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms </p> <p>We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references). </p> <p>This repository contains 3 different files:</p> <p>TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions</p> <p>TableS2_physical_priors.xlsx - contains three tabs:<br> ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) + ChIP-seq datasets (GSE28350, GSE81521) from (Hochbaum et. al, 2011, Li et. al, 2016).</p> <p>eY1HATAC- contains 3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016).</p> <p>motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1</p> <p>TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources)</p> <p>---</p> <p>Description of methodology to obtain regulatory interactions in TableS2:</p> <p>Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations.</p> <p>For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands.</p> <p>An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs.</p> <p>For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair.</p> <p>For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).</p>
Prediction of ice duration along large rivers
<p>Contains two files:</p> <p>shp files: contain river centerline points and its associated downstream distance:</p> <p>* "name": name of the river</p> <p>* "dsDistKm": downstream distance in km</p> <p>* "dsPointId": unique id for each centerline point for a river, together with river name uniquely identify a point in the dataset</p> <p>"river_ice_duration_winter_temperature" (csv file)—contains river ice duration and winter mean temperature prediction:</p> <p>* "name" : name of the river </p> <p>* "dsPointId": unique id for each centerline point for a river, together with river name uniquely identify a point in the dataset</p> <p>* "year": year (from 2019–2099) </p> <p>* "tasmean": winter (Dec, Jan, and Feb) mean surface air temperature (from ERA5, in degree Celsius) </p> <p>* "iceDuration": ice duration in days</p> <p>Temperature and ice duration were based on CESM1-BGC under RCP8.5.</p>
Fig. 11 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 11. Native vegetation (pine forest and montane shrubland) at the top of Cerro del Águila, Mixteca, Oaxaca.
Fig. 10 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 10. Projection of potential distribution for Geotrupes viridiobscurus Jekel, 1866 and G. pecki Howden, 1974 in the state of Oaxaca, representing
Fig. 9. Geotrupes viridiobscurus Jekel, 1866 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 9. Geotrupes viridiobscurus Jekel, 1866: a – male dorsal view (17.44361°N, -96.50361°W); b – female dorsal view (16.160961°N, -96.244523°W); c–f – male genitalia: c – in dorsal view, d – in ventral view; e–f – lateral views. Scale bars = 5 mm (a–b), 2 mm (c–f).
Fig. 8. Geotrupes pecki Howden, 1974 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 8. Geotrupes pecki Howden, 1974: a – male dorsal view (16.17325°N, -96.24708°W); b – female dorsal view (16.12138°N, -96.45222°W); c–f – male genitalia: c – in dorsal view, d – in ventral view; e–f – lateral views. Scale bars = 5 mm (a–b), 2 mm (c–f).
Fig. 5 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 5. Map showing the known distribution of Geotrupes lobatus Howden, 1974, G. sallei Jekel, 1866, and G. nebularum Howden, 1974 (upper map) and the known distribution of G. nuntiatus Kohlmann & Arriaga-Jiménez, sp. nov., G. viridiobscurus Jekel, 1866, and G. pecki Howden, 1974 (lower map).
Fig. 7. Geotrupes nebularum Howden, 1974 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 7. Geotrupes nebularum Howden, 1974: a – male dorsal view (19.03638°N, -97.17138°W); b – female dorsal view (19.2533°N, -97.98433°W); c–f – male genitalia: c – in dorsal view, d – in ventral view; e–f – lateral views. Scale bars = 5 mm (a–b), 2 mm (c–f).
Fig. 4 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 4. Comparison of male genitalia: a – Ceratotrupes bolivari Halffter & Martínez, 1962; and b – Geotrupes lobatus Howden, 1974. Scale bar = 2 mm.
Fig. 3 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 3. Morphological detail of male protibiae: a – Geotrupes nuntiatus Kohlmann & Arriaga-Jiménez, sp. nov.; b – G. lobatus Howden, 1974; arrow pointing at the apical tooth on the profemur. Scale bar = 2 mm.
Fig. 2. Geotrupes lobatus Howden, 1974 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 2. Geotrupes lobatus Howden, 1974: a – male dorsal view (16.16096°N, -96.24452°W); b – female dorsal view (16.16096°N, -96.24452°W); c–f – male genitalia: c – in dorsal view, d – in ventral view; e–f – lateral views. Scale bars = 5 mm (a–b), 2 mm (c–f).
Fig. 1 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 1. Geotrupes nuntiatus Kohlmann & Arriaga-Jiménez, sp. nov.: a – male dorsal view (17.13087°N, -97.66402°W); b – female dorsal view (17.129932°N, -97.663673°W); c–f – male genitalia: c – in dorsal view, d – in ventral view; e–f – lateral views. Scale bars = 5 mm (a–b), 2 mm (c–f).
Fig. 6. Geotrupes sallei Jekel, 1866 in A predicted new Geotrupes from the mountains of Oaxaca, Mexico, and a description of the male of Geotrupes lobatus (Coleoptera: Geotrupidae)
Fig. 6. Geotrupes sallei Jekel, 1866: a – male dorsal view (16.19231°N, -96.45711°W); b – female dorsal view (holotype Howden CMN); c–f – male genitalia: c – in dorsal view, d – in ventral view; e–f – lateral views. Scale bars = 5 mm (a–b), 2 mm (c–f).
Flaky Test Dataset to Accompany "FlakeFlagger: Predicting Flakiness Without Rerunning Tests"
<p>When developers make changes to their code, they typically run regression tests to detect if their recent changes (re)introduce any bugs. However, many tests are flaky, and their outcomes can change non-deterministically, failing without apparent cause. Flaky tests are a significant nuisance in the development process, since they make it more difficult for developers to trust the outcome of their tests. The traditional approach to identify flaky tests is to rerun them multiple times: if a test is observed both passing and failing on the same code, it is definitely flaky. We conducted a very large empirical study looking for flaky tests by rerunning the test suites of 24 projects 10,000 times each, and found that even with this many reruns, some flaky tests were still not detected. We propose FlakeFlagger, a novel approach that collects a set of features describing the behavior of each test, and then predicts tests that are likely to be flaky based on similar behavioral features. We found that FlakeFlagger correctly labeled at least as many tests as flaky as a state-of-the-art flaky test classifier, but that FlakeFlagger reported far fewer false positives (an increase in precision from just 11% to 60%). This lower false positive rate translates directly to saved time for researchers and developers who use the classification result to guide more expensive flaky test detection processes. By investigating the information gain of each feature, we conclude that test execution time, overall test coverage, coverage of recently changed lines and usage of third party libraries are effective predictors of test flakiness. We did not find any keywords or tokens in the source code of tests that were effective in predicting test flakiness, and did not find the presence of test smells to be effective in predicting test flakiness.</p> <p>This archive contains the dataset that we collected of flaky tests, along with the features that we collected from each test.</p> <p>Contents:<br> Project_Info.csv: List of projects and their revisions studied<br> build-logs-<project-slug>.tgz: An archive of all of the maven build logs from each of the 10,000 runs of that project's test suite. <br> failing-test-reports-<project-slug>.tgz An archive of all of the surefire XML reports for each failing test of each build of each project.<br> test_results.csv: Summary of the number of passing and failing runs for each test in each project. <br> "Run ID" is a key into the <project-slug>.tgz archive also in this artifact, which refers to the run that we observed the test fail on.<br> test_features.csv: Summary of the features that each test had, as per our feature detectors described in the paper<br> flakeflagger-code.zip: All scripts used to generate and process these results. These scripts are also located at https://github.com/AlshammariA/FlakeFlagger</p>
Mapping present and future predicted distribution patterns for a meso-grazer guild in the Baltic Sea
<p>Baltic Sea communities consisting of key and endemic species are threatened by climate change. Using Ecological niche modelling, we map predicted distribution patterns under recent and future climate change scenarios (2050) for a food-web consisting of a guild of meso-grazers (Idotea spp.), their host algae (Fucus vesiculosus and F. radicans) and their fish predator (Gasterosteus aculeatus). Brackish water species depend on two important abiotic factors: temperature and salinity. We assess which of these environmental factors determines the distribution limits of the grazers in the Baltic Sea today. For species in a semi-enclosed sea area such as the Baltic Sea, climate-induced changes may lead to dramatic food-web effects. We assess the consequences of the predicted climate-induced habitat range changes for this unique Baltic community.<br /> </p>
MicroRNA Panel Predicts Lung Adenocarcinoma in Patients Presenting with Ground-Glass Nodules
<p>MicroRNA (miRNA) expression is correlated with tumor histology, differentiation, invasiveness and treatment outcome. We aimed to identify miRNAs whose differential expression might enable early diagnosis of lung adenocarcinoma in patients presenting with ground-glass nodules (GGNs). To identify potential miRNAs of interest, we analyzed the miRNA expression profile of tumor and adjacent non para-tumor tissue in 3 participants by next-generation sequencing (NGS). We then assessed the expression levels of the miRNAs of interest in 73 lung adenocarcinoma presenting with GGNs with matched adjacent non-tumor tissue by quantitative real-time polymerase chain reaction (qRT-PCR). Target genes of our selected miRNA panel were predicted using Miranda with default parameters. Twenty-three miRNAs showed differential expression between tumor and adjacent non-tumor tissue by NGS. Five miRNAs exhibited higher expression in tumor tissue compared to adjacent non-tumor tissue (P＜0.05), eighteen miRNAs demonstrated lower expression in tumor tissue versus adjacent non-tumor tissue (P＜0.05). When qRT-PCR was performed for the 23 miRNAs identified by NGS in the pilot stage, seven were found to have statistically significant expression in tumor versus adjacent non-tumor tissue (P＜0.05). The predicted targets of our miRNAs of interest are frequently associated with cancer signaling pathways. We developed a miRNA panel that could potential predict the presence of lung adenocarcinoma in patients presenting with GGNs. </p>
Accompanying dataset for: Predicting Species Emergence in Simulated Complex Pre-Biotic Networks
<p>This is the accompanying data and code for the publication [Markovitch & Krasnogor: Predicting Species Emergence in Simulated Complex Pre-Biotic Networks] containing the full set of 10,000 lognormal networks studied, their network communities and the compotype species observed during simulations with the GARD model. Details are given in the aforementioned paper. Please see also: http://ico2s.org/</p> <p>This work was funded by the UK's Engineering and Physical Sciences Research Council (EPSRC) under projects (EP/J004111/2) "Towards a Universal Biological-Cell Operating System (AUdACiOuS)" and (EP/N031962/1) "Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies"</p> <p> </p>
Internal and Predictive Validity of the French Health of the Nation Outcome Scales Dataset
<p>This dataset is related to Golay P, Basterrechea L, Conus P, Bonsack C (2016). Internal and Predictive Validity of the French Health of the Nation Outcome Scales: Need for Future Directions. PLoS ONE 11(8): e0160360. doi:10.1371/journal.pone.0160360.</p> <p>http://journals.plos.org/plosone/article/related?id=10.1371%2Fjournal.pone.0160360</p> <p><br /> It includes 19 variables and 2722 cases.</p>
A Queueing Network Model for Performance Prediction of Apache Cassandra
<p>The dataset consists in several csv files containing Cassandra and ScyllaDB performance.</p> <p>The experiments are organized in folders. There are three main folders containing:<br> - Cassandra 4 nodes: the files related to the Cassandra experiments conducted on a cluster composed of four nodes.<br> - ScyllaDB 4 nodes: The files related to the ScyllaDB experiments conducted on a cluster composed of four nodes. <br> - Cassandra QUORUM variant: the simulation data where a different kind of QUORUM is implemented in Cassandra.<br> <br> "Cassandra 4 nodes" and "ScyllaDB 4 nodes" include some subfolders, each one containing the files of the Consistency Level applied for those experiments. Each experiment is composed by three files (data*.csv) with the data reported by Yahoo! Cloud System Benchmark (YCSB) in the end of the experiment execution. Each folder contains also a sim.csv file with the data gathered from the simulation of the model inside Java Modeling Tool.</p> <p>The data*.csv files are composed by:<br> -Number of threads or clients<br> -Overall Throughput<br> -Number of Read requests<br> -Overall Read Response Time<br> -95 percentile Read Response Time<br> -99 percentile Read Response Time<br> -99.9 percentile Read Response Time<br> <br> Differently, the sim.csv files are composed by:<br> -Number of threads or clients<br> -Overall Throughput<br> -Overall Read Response Time</p>
LiDAR-derived forest structure data and predictions of the locations of old-growth forests for Central Finland.
<p><strong>INTRO</strong><br> This archive contains data and analysis code for the Biodiversity Map -project conducted by Open Knowledge Finland (http://fi.okfn.org/projects/biodiversity-map/)</p> <p><strong>LICENCE</strong><br> The files listed below are all released to the public domain under a CC0 public domain dedication (https://creativecommons.org/publicdomain/zero/1.0/)</p> <p><strong>FILE DESCRIPTIONS</strong></p> <p><em><strong>FILE 1:</strong></em> background.zip<br> Inside the archive is a comma-separated file "background.csv" containing LiDAR-derived forest structure variables for 2/3 of Central Finland. These were derived from 3 raster data sets describing forest canopy maximum height (mh), forest canopy cover (cc) and lidar return intensity (in). The rasters had resolutions of 6 metres, 6 metres and 2 metres, respectfully. An 18 m resolution grid was then used to aggregate the rasters into average, minimum and maximum values + standard deviations of the original variables. The original LiDAR data was made available by the National Land Survey of Finland.</p> <p><br> <em><strong>FILE 2:</strong></em> conservation.lambdas<br> This file contains fitted parameters for the maxent model. For more information, check maxent documentation at https://www.cs.princeton.edu/~schapire/maxent/</p> <p><strong><em>FILE 3:</em></strong> conserved_swd.csv<br> Forest structure variables at 18 meter resolution for old-growth conservation areas in Central Finland. A subset of background.csv. This file still has a header, the variables are the same as in background.csv</p> <p><em><strong>FILE 4:</strong></em> grass_create_forest_rasters_from_las.sh<br> A shell script used to convert LiDAR files to raster maps of forest structure with GRASS 7.</p> <p><em><strong>FILE 5:</strong></em> lidar_coverage.png<br> A map showing the extent of LiDAR data available for Central Finland when we did the analyses.</p> <p><em><strong>FILE 6:</strong></em> maxent_model_run_product.sh<br> A shell script used to fit the maximum entropy model to predict the locations of conservation-area-like forests in Central Finland.</p> <p><em><strong>FILE 7:</strong></em> projection_product.csv<br> The results of the maxent model in a comma separated file. The first row has the variable names: x,y,product_fit. x and y are coordinates in the CRS ETRS-TM35FIN (EPSG:3067). product_fit is "the probablility that this 18*18 meter grid cell is old-growth conservation area".</p> <p><em><strong>FILE 8:</strong></em> README<br> A file with a description of the dataset in human-readable form.</p> <p><strong>VALIDATION FILES</strong><br> The data in these files was collected to validate the results of the aforementioned maxent model. The data were collected in a hierarchical sampling scheme: six randomly determinded unintersecting 9 km * 9 km landscape windows were chosen for sampling. From each window, three samples were taken. One sample from conservation areas, one sample from the "best" 10 % of forests as determined by the maxent model excluding conservation areas and one random sample. Not all windows contained conservation areas, and not all areas were accessible (islands, for example). In addition a few areas were skipped due to time constraints.</p> <p>The sampled points are identified by their lanscape window (suuralue), their sample (otos) and their sample number (mittauspiste).</p> <p><em><strong>FILE 9:</strong></em> validation_felled.csv<br> A comma separated list of those points that were not measured because they were felled.</p> <p><em><strong>FILE 10:</strong></em> validation_gps_results_2016-09-07.csv<br> A list of gps coordinates for all the sample points. product_fit is the value of the geographically closest prediction from the maxent model described above.</p> <p><em><strong>FILE 11:</strong></em> validation_lying_deadwood_transects_2016-08-30.csv<br> A comma separated file with data from deadwood transects. From each validation point, three 30 m long transects were made with 120 degree angles between them, and all lying deadwood more than 2 cm in diameter were measured. For some validation points, there were geographical obstructions which prevented the full 90 m of transect being surveyed, this is also recorded in the data. Each row holds measurements from one lying trunk.<br> </p> <p><em><strong>FILE 12:</strong></em> validation_relascope_2016-08-30.csv<br> Relascope measurements from the validation points. Each row is measurements for one species from one validation point. Dead and alive trees are counted separately.<br> </p> <p><strong>MORE INFORMATION</strong></p> <p>For more in-depth descritions of the files, read the file named README.<br> For some auxilliary files and information, check our old hackathon repository on github: https://github.com/Koalha/bdm_hackathon</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.