161 research outputs found
Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline
No full text<p>This upload contains the HZV029 Plasma and HZV029 Lipidomics datasets for reviewers of the "Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline" submission. </p>
<p>Both datasets will be uploaded to metabolomics workbench and the upload completed before final publication of the manuscript. For the he HZV029 Plasma datasets only the final run is included for any sample (i.e., failed injections or other samples with data quality issues that were reran during acquisition were omitted).</p>
<p>Also included in the upload is the source code for the MetDataModel and the pcpfm at the time of manuscript submission and the pcpfm itself. If you find this upload in the future, please check out the github repos for more updated versions:</p>
<p>https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics</p>
<p>https://github.com/shuzhao-li-lab/metDataModel</p>
<p>The github repo does not store the input the data for space reasons, they only have the notebooks. However, the .zip here has both the notebooks by themselves in the notebook subdirectory and a separate directory with the notebooks and the data used to generate all the figures and results in the manuscript.</p>
<p><strong>Some information that is needed to rerun this analysis:</strong></p>
<p>Sequence files are critical to the functioning of the pipeline. The sequence files for all analyses are provided under sequence_files.zip. These can be used to recapitulate the analysis by eitehr changing the filepath to each acquisition to where you put it on your sytem or by placing the sequence file in the same directory as the mzml or raw. In the latter case, the pipeline will search for filenames matching the sample names. The sequence files also store some sample metadata such as the type of sample a given acquisition is (unknown, pooled, qc, etc...)</p>
<p>.raw to .mzML conversion works well on MacOS but may not work well on other systems. You will need to use the ability to specify your own conversion command or convert files outside of the pipeline. </p>
<p>To replicate the results, you do need to have the annotation sources downloaded which can be done using the pipeline. MS2 annotation requires the files in the AcquireX directory which is MS2 acquisitions on pooled HZV029 plasma samples.</p>
<p>For the comparison between MetaboAnalystR and the pcpfm, subsets of the datasets were used. These subsets and the sequence files are in Subsets_for_performance_testing.zip. The sequences are also in the sequence_files directory as well</p>
<p> </p>
<p><strong>Version History:</strong></p>
<p>This updates v1.0.0 of the upload which was missing a sequence file.</p>
<p> </p>
<p><strong>Contributions:</strong></p>
<p>Joshua M Mitchell implemented the pipeline and was first author on the manuscript. Shuzhao Li is the corresponding author on the manuscript. </p>
<p>Maheshwor Thapa performed the experiments to collect the HZV029 data. Yuanye Chi helped with testing and documenting the pipeline. </p>
<p>Jiangou (Jeff) Xia and Zhiqiang Pang provided the R portion of the analysis. </p>
Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline
No full text<p>This upload contains the HZV029 Plasma and HZV029 Lipidomics datasets for reviewers of the "Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline" submission. </p>
<p>Both datasets will be uploaded to metabolomics workbench and the upload completed before final publication of the manuscript. For the he HZV029 Plasma datasets only the final run is included for any sample (i.e., failed injections or other samples with data quality issues that were reran during acquisition were omitted).</p>
<p>Also included in the upload is the source code for the MetDataModel and the pcpfm at the time of manuscript submission and the pcpfm itself. If you find this upload in the future, please check out the github repos for more updated versions:</p>
<p>https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics</p>
<p>https://github.com/shuzhao-li-lab/metDataModel</p>
<p>The github repo does not store the input the data for space reasons, they only have the notebooks. However, the .zip here has both the notebooks by themselves in the notebook subdirectory and a separate directory with the notebooks and the data used to generate all the figures and results in the manuscript.</p>
<p><strong>Some information that is needed to rerun this analysis:</strong></p>
<p>Sequence files are critical to the functioning of the pipeline. The sequence files for all analyses are provided under ./sequence_files. These can be used to recapitulate the analysis by eitehr changing the filepath to each acquisition to where you put it on your sytem or by placing the sequence file in the same directory as the mzml or raw. In the latter case, the pipeline will search for filenames matching the sample names. The sequence files also store some sample metadata such as the type of sample a given acquisition is (unknown, pooled, qc, etc...)</p>
<p>.raw to .mzML conversion works well on MacOS but may not work well on other systems. You will need to use the ability to specify your own conversion command or convert files outside of the pipeline. </p>
<p>To replicate the results, you do need to have the annotation sources downloaded which can be done using the pipeline.</p>
Using genome-scale metabolic networks for analysis, visualization, and integration of targeted metabolomics data
No full textInterpretation of metabolomics data in the context of biological pathways is important to gain knowledge about underlying metabolic processes. In this chapter we present methods\ua0to analyze genome-scale models (GSMs) and metabolomics data together. This includes reading and mining of GSMs using the SBTab format to retrieve information on genes, reactions, and metabolites. Furthermore, the chapter showcases the generation of metabolic pathway maps using the Escher tool, which can be used for data visualization. Lastly, approaches to constrain flux balance analysis (FBA) by metabolomics data are presented
Large reversible capacity of high quality graphene sheets as an anode material for lithium-ion batteries
No full textHigh quality graphene sheets were prepared from graphite powder through oxidation followed by rapid thermal expansion in nitrogen atmosphere. The preparation process was systematically investigated by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy and Brunauer-Emmett-Teller (BET) measurements. The morphology and structure of graphene sheets were characterized by scanning electron microscope (SEM) and high-resolution transmission electron microscopy (HRTEM). The electrochemical performances were evaluated in coin-type cells versus metallic lithium. It is found that the graphene sheets possess a curled morphology consisting of a thin wrinkled paper-like structure, fewer layers (similar to 4 layers) and large specific surface area (492.5 m(2) g(-1)). The first reversible specific capacity of the prepared graphene sheets was as high as 1264 mA h g(-1) at a current density of 100 mA g(-1). Even at a high current density of 500 mA g(-1), the reversible specific capacity remained at 718 mA h g(-1). After 40 cycles, the reversible capacity was still kept at 848 mA h g(-1) at the current density of 100 mA g(-1). These results indicate that the prepared high quality graphene sheets possess excellent electrochemical performances for lithium storage. (C) 2010 Elsevier Ltd. All rights reserved
Bridging Functional Genomics and Toxicogenomics Through DNA Microarrays in a Fish Model
No full textIn a case study of finding gene expression signatures for environmental stressors in Cyprinodon variegatus, this dissertation examines several important issues of applying DNA microarray technology to fish toxicogenomics. The most relevant disciplines, fish toxicogenomics and computational systems biology are reviewed in Chapter 1. Chapter 2 reviews major aspects of DNA microarray technology.
On DNA microarrays, even for probes that target the same transcript, large variations are seen in the probe signals. These variations are partly dependent and partly independent on probe sequences. Chapter 3 estimates the sequence independent variation by combining experimental and computational approaches. Chapter 4 and 5 take on the central problem of sequence dependent variations, that is, modeling the physiochemistry of microarray hybridization. I propose a new competitive hybridization model, which demonstrates good success on publically available benchmark data. This new model leads the way to quantification of absolute target concentration, and brings critical insights into probe design and data interpretation on DNA microarrays. Our model relies on the accuracy of computing duplexing energy, which does yet not take into account secondary structures of probes and targets. I further explore the structural effects in Chapter 6.
In order to see the complete Abstract, please download the dissertation
Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data
No full textIn untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions in relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu) and provides a JSON format for easy data exchange and software interoperability. By generalized preannotation, khipu makes it feasible to connect metabolomics data with common data science tools and supports flexible experimental designs
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