Research and experimental data from principal investigators and others at the University of British Columbia.


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UBC Research Data Collection
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Studies: 71 | Downloads: 3441
Description:

Dissipation Rate (\epsilon, units: [W/kg]) of Turbulent Kinetic Energy, derived from microstructure measurements of velocity shear and temperature gradients. Shear and temperature-gradient turbulence spectra are also included, as are CTD measurements and key glider variables. All measurements are taken from a Slocum G2 ocean glider.

Replication For:Scheifele et al 2018. Measuring the Dissipation Rate of Turbulent Kinetic Energy in Strongly Stratified, Low Energy Environments: A Case Study from the Arctic Ocean. Journal of Geophysical Research: Oceans.
hdl:11272/10613
0 downloads
Last Released: Jun 15, 2018
Monitoring changes in the Gene Ontology and their impact on genomic data analysis.by Jacobson, Matthew; Sedeño-Cortés, Adriana Estela; Pavlidis, Paul
Description:

Data and analysis of Gene Ontology annotations, to support reproducibility of results presented in the above cited preprint. There are two major parts to the data. The first is an analysis of the contents of the database supporting https://gotrack.msl.ubc.ca/ and represents direct downloads of files from that site at the time of our analysis. The second, concerning the analysis of the effects of changes in GO over time on enrichment analysis, includes python scripts and intermediate data and analysis files.

Production Date:May 10, 2018
Producer:Michael Smith Laboratories, University of British Columbia; Department of Psychiatry, University of British Columbia
Related Publications:Jacobson, Matthew; Sedeño-Cortés, Adriana Estela; Pavlidis, Paul (2018) Monitoring changes in the Gene Ontology and their impact on genomic data analysis
Link
hdl:11272/10596
1 download
Last Released: Jun 1, 2018
Description:

The SOG biogeochemical model of the Strait of Georgia is a 1-dimensional vertical model capable of predicting near-surface mixing, phytoplankton and zooplankton cycles, and carbonate chemistry. This dataset contains SOG model output data from 2001 to 2012 across 18 freshwater dissolved inorganic carbon (DIC) and total alkalinity (TA) scenarios. The 18 scenarios span a grid of 6 freshwater TA cases and 3 freshwater DIC:TA cases. The values for these freshwater scenarios were chosen based on a detail analysis of the Fraser River. Please refer to the methods section of the primary publication for more information.

Production Date:January 26, 2018
Producer:Ben Moore-Maley, UBC EOAS
Distribution Date:May 25, 2018
Distributor:Ben Moore-Maley, UBC EOAS
Replication For:Moore-Maley, B. L., Ianson, D., and Allen, S. E.: The sensitivity of estuarine aragonite saturation state and pH to the carbonate chemistry of a freshet-dominated river, Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-349, in review, 2017.
ID: DOI:10.5194/bg-2017-349
Link
Related Material:SOG model source code and documentation can by found at https://bitbucket.org/account/user/salishsea/projects/SOG.
Related Studies:Moore-Maley, B. L., Allen, S. E., and Ianson, D.: Locally-driven interannual variability of near-surface pH and ΩA in the Strait of Georgia, J. Geophys. Res. Oceans, 121, 1600–1625, doi:10.1002/2015JC011118, 2016.
hdl:11272/10606
1 download
Last Released: May 27, 2018
Description:

Abstract: An important goal in neuroscience is to understand gene expression patterns in the brain. The recent availability of comprehensive and detailed expression atlases for mouse and human creates opportunities to discover global patterns and perform cross-species comparisons. Recently we reported that the major source of variation in gene transcript expression in the adult normal mouse brain can be parsimoniously explained as reflecting regional variation in glia to neuron ratios, and is correlated with degree of connectivity and location in the brain along the anterior-posterior axis. Here we extend this investigation to two gene expression assays of adult normal human brains that consisted of over 300 brain region samples, and perform comparative analyses of brain-wide expression patterns to the mouse. We performed principal components analysis (PCA) on the regional gene expression of the adult human brain to identify the expression pattern that has the largest variance. As in the mouse, we observed that the first principal component is composed of two anti-correlated patterns enriched in oligodendrocyte and neuron markers respectively. However, we also observed interesting discordant patterns between the two species. For example, a few mouse neuron markers show expression patterns that are more correlated with the human oligodendrocyte-enriched pattern and vice-versa. In conclusion, our work provides insights into human brain function and evolution by probing global relationships between regional cell type marker expression patterns in the human and mouse brain.

Production Date:2013
Related Publications:Tan PPC, French L, Pavlidis P. "Neuron-Enriched Gene Expression Patterns are Regionally Anti-Correlated with Oligodendrocyte-Enriched Patterns in the Adult Mouse and Human Brain." Frontiers in Neuroscience. 2013;7:5. doi:10.3389/fnins.2013.00005.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/neuron-enriched-gene-expression-patterns/
hdl:11272/10578
0 downloads
Last Released: Mar 19, 2018
Altered Hippocampal Transcript Profile Accompanies an Age-Related Spatial Memory Deficit in Miceby Verbitsky, Miguel; Yonan, Amanda L.; Malleret, Gael; Kandel, Eric R; Gilliam, T. Conrad; Pavlidis, Paul
Description:

We have carried out a global survey of age-related changes in mRNA levels in the C57BL/6NIA mouse hippocampus and found a difference in the hippocampal gene expression profile between 2-month-old young mice and 15-month-old middle-aged mice correlated with an age-related cognitive deficit in hippocampal-based explicit memory formation. Middle-aged mice displayed a mild but specific deficit in spatial memory in the Morris water maze. By using Affymetrix GeneChip microarrays, we found a distinct pattern of age-related change, consisting mostly of gene overexpression in the middle-aged mice, suggesting that the induction of negative regulators in the middle-aged hippocampus could be involved in impairment of learning. Interestingly, we report changes in transcript levels for genes that could affect synaptic plasticity. Those changes could be involved in the memory deficits we observed in the 15-month-old mice. In agreement with previous reports, we also found altered expression in genes related to inflammation, protein processing, and oxidative stress.

Production Date:2004
Distributor:Columbia University (CU)
Related Publications:Verbitsky M, Yonan AL, Malleret G, Kandel ER, Gilliam TC, Pavlidis P. "Altered Hippocampal Transcript Profile Accompanies an Age-Related Spatial Memory Deficit in Mice." Learning & Memory. 2004;11(3):253-260. doi:10.1101/lm.68204.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/mouse-aging-data/
hdl:11272/10562
0 downloads
Last Released: Mar 19, 2018
Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platformsby Barnes, Michael; Freudenberg, Johannes; Thompson, Susan; Aronow, Bruce; Pavlidis, Paul
Description:

The growth in popularity of RNA expression microarrays has been accompanied by concerns about the reliability of the data especially when comparing between different platforms. Here, we present an evaluation of the reproducibility of microarray results using two platforms, Affymetrix GeneChips and Illumina BeadArrays. The study design is based on a dilution series of two human tissues (blood and placenta), tested in duplicate on each platform. The results of a comparison between the platforms indicate very high agreement, particularly for genes which are predicted to be differentially expressed between the two tissues. Agreement was strongly correlated with the level of expression of a gene. Concordance was also improved when probes on the two platforms could be identified as being likely to target the same set of transcripts of a given gene. These results shed light on the causes or failures of agreement across microarray platforms. The set of probes we found to be most highly reproducible can be used by others to help increase confidence in analyses of other data sets using these platforms.

Production Date:2005
Distributor:Columbia University (CU)
Related Publications:Barnes M, Freudenberg J, Thompson S, Aronow B, Pavlidis P. "Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms." Nucleic Acids Research. 2005;33(18):5914-5923. doi:10.1093/nar/gki890.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/data-files-and-supplementary-information-for-affymetrix-illumina-comparison/
hdl:11272/10556
0 downloads
Last Released: Mar 19, 2018
Description:

As public availability of gene expression profiling data increases, it is natural to ask how these data can be used by neuroscientists. Here we review the public availability of high-throughput expression data in neuroscience and how it has been reused, and tools that have been developed to facilitate reuse. There is increasing interest in making expression data reuse a routine part of the neuroscience tool-kit, but there are a number of challenges. Data must become more readily available in public databases; efforts to encourage investigators to make data available are important, as is education on the benefits of public data release. Once released, data must be better-annotated. Techniques and tools for data reuse are also in need of improvement. Integration of expression profiling data with neuroscience-specific resources such as anatomical atlases will further increase the value of expression data.

Production Date:2007
Producer:Department of Psychiatry (Dept of Psych)
Related Publications:Wan X, Pavlidis P. "Sharing and reusing gene expression profiling data in neuroscience." Neuroinformatics. 2007;5(3):161-175.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/web-supplement-to-sharing-and-reusing-gene-expression-profiling-data/
hdl:11272/10555
0 downloads
Last Released: Mar 19, 2018
Coexpression Analysis of Human Genes Across Many Microarray Data Setsby Lee, Homin K; Hsu, Amy K; Sajdak, Jon; Qin, Jie; Pavlidis, Paul
Description:

We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 “coexpression links” that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function.

Production Date:2004
Distributor:Columbia University (CU)
Related Publications:Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P. Coexpression Analysis of Human Genes Across Many Microarray Data Sets. Genome Research. 2004;14(6):1085-1094. doi:10.1101/gr.1910904.
Related Material:Pavlidis Lab supplementary information page: http://pavlab.msl.ubc.ca/coexpression-analysis-of-human-genes-across-many-microarray-data-sets-2/
hdl:11272/10580
2 downloads
Last Released: Mar 19, 2018
Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free textby French, Leon; Lane, Suzanne; Xu, Lydia; Siu, Celia; Kwok, Cathy; Chen, Yigi; Krebs, Claudia; Pavlidis, Paul
Description:

Motivation: Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large-scale connectivity resources. Unfortunately, the connectivity findings are not formally encoded and occur as natural language text. This hinders aggregation, indexing, searching and integration of the reports. We annotated a set of 1377 abstracts for connectivity relations to facilitate automated extraction of connectivity relationships from neuroscience literature. We tested several baseline measures based on co-occurrence and lexical rules. We compare results from seven machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features. Results: Co-occurrence based methods provided high recall with weak precision. The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision. Owing to its speed and simplicity, we applied the shallow linguistic kernel to a large set of new abstracts. To evaluate the results, we compared 2688 extracted connections with the Brain Architecture Management System (an existing database of rat connectivity). The extracted connections were connected in the Brain Architecture Management System at a rate of 63.5%, compared with 51.1% for co-occurring brain region pairs. We found that precision increases with the recency and frequency of the extracted relationships.

Production Date:2012
Related Publications:French L, Lane S, Xu L, et al. "Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free text." Bioinformatics. 2012;28(22):2963-2970. doi:10.1093/bioinformatics/bts542.
Related Material:http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/the-whitetext-project/application-and-evaluation-of-automated-methods-to-extract-connectivity-statements-from-free-text/
hdl:11272/10579
6 downloads
Last Released: Mar 19, 2018
Automated recognition of brain region mentions in neuroscience literatureby French, Leon; Lane, Suzanne; Xu, Lydia; Pavlidis, Paul
Description:

The ability to computationally extract mentions of neuroanatomical regions from the literature would assist linking to other entities within and outside of an article. Examples include extracting reports of connectivity or region-specific gene expression. To facilitate text mining of neuroscience literature we have created a corpus of manually annotated brain region mentions. The corpus contains 1,377 abstracts with 18,242 brain region annotations. Interannotator agreement was evaluated for a subset of the documents, and was 90.7% and 96.7% for strict and lenient matching respectively. We observed a large vocabulary of over 6,000 unique brain region terms and 17,000 words. For automatic extraction of brain region mentions we evaluated simple dictionary methods and complex natural language processing techniques. The dictionary methods based on neuroanatomical lexicons recalled 36% of the mentions with 57% precision. The best performance was achieved using a conditional random field (CRF) with a rich feature set. Features were based on morphological, lexical, syntactic and contextual information. The CRF recalled 76% of mentions at 81% precision, by counting partial matches recall and precision increase to 86% and 92% respectively. We suspect a large amount of error is due to coordinating conjunctions, previously unseen words and brain regions of less commonly studied organisms. We found context windows, lemmatization and abbreviation expansion to be the most informative techniques. The corpus is freely available at http://www.chibi.ubc.ca/WhiteText/.

Production Date:2009
Related Publications:French L, Lane S, Xu L, Pavlidis P. "Automated Recognition of Brain Region Mentions in Neuroscience Literature." Frontiers in Neuroinformatics. 2009;3:29. doi:10.3389/neuro.11.029.2009.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/the-whitetext-project/automated-recognition-of-brain-region-mentions-in-neuroscience-literature/
hdl:11272/10574
2 downloads
Last Released: Mar 19, 2018
 
 
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