Wiley released a KnowItAll version with many new powerful electron ionization gas chromatography-mass spectrometry (EI GC-MS) capabilities. I have developed FREE training videos and associated handouts covering basic and advanced MS topics that will be helpful to the new user.
Also, a <a href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/Published_Know_It_Al_Start_QRG_2_7114366b93/Published-KnowItAl-Start-QRG-(2).pdf">Quick Reference Guide</a>. is available.
KnowItAll is a vendor neutral data processing solution for spectral analyses which will be very valuable in unknown identifications.
It processes IR, Raman, MS, NMR, and UV data employing a large number of file formats (&gt;170) and a total of 2.5 million reference spectra.
<ul>
<li>~264K IR</li>
<li>~25K Raman</li>
<li>~1.25M EI mass spec</li>
<li>~894K NMR</li>
<li>~30K UV</li>
</ul>
<h3>Highlights:</h3>
<ul>
<li>Deconvolution/very fast library searching of accurate/nominal mass GC-MS data</li>
<li>Easy manual processing of nominal and accurate mass GC-MS data</li>
<li>Fast EI searches of &gt;1.2 million spectra in &lt;0.5 seconds</li>
<li>Amazing adaptive search for unknown ID by extending usefulness of all libraries</li>
<li>Transfer structures and spectra between NIST search and KnowItAll</li>
<li>Easy structure drawing and structure searches</li>
<li>Conversion of NIST user libraries to Wiley format</li>
<li>Easy creation of libraries using links to MS interpreter and PubChem</li>
</ul>
<h2>In the ninth part we will look at:</h2>
Detailed Handouts including tables of content are available in the <a href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/structure_interchange_vs_2_7151aef59b/structure-interchange_vs_2.pdf">LabRulezGCMS library</a>.
<ul>
<li>Three Ways to Send Structures from NIST Search to KnowItall ChemWindow</li>
<li>Using NIST MS Interpreter to Determine Fragmentation in EI Spectra</li>
<li>Send Structures from ChemWindow to NIST Search</li>
<li>Small Problems When Sending Structures from ChemWindow to NIST Search</li>
<li>Advanced Topic: Using MS Interpreter to Understand EI Fragmentation</li>
<li>Conclusions</li>
</ul>
<h3>Future parts will focus on:</h3>
<ul>
<li>Part 1. <a href="https://gcms.labrulez.com/article/2974">KnowIt All Introduction</a></li>
<li>Part 2. <a href="https://gcms.labrulez.com/article/3059">ProcessIt for Manual Processing of EI GC-MS Data</a></li>
<li>Part 3. <a href="https://gcms.labrulez.com/article/3189">SearchIt/MineIt: Similarity Library Search</a></li>
<li>Part 4. <a href="https://gcms.labrulez.com/article/3269">SearchIt/MineIt; Novel New Adaptive Library Search</a></li>
<li>Part 5. <a href="https://gcms.labrulez.com/article/3288">SearchIt/MineIt: Unresolved Peak Analyses and Library Search</a></li>
<li>Part 6. <a href="https://gcms.labrulez.com/article/3302">MS Expert: Deconvolution of Mixtures with Library Search</a></li>
<li>Part 7. <a href="https://gcms.labrulez.com/article/3304">Interchange of Spectra Between KnowItAll and NIST Search</a></li>
<li>Part 8. <a href="https://gcms.labrulez.com/article/3306">Tips for Drawing Structures and Structure Searches in ChemWindow</a></li>
<li>Part 10. Converting NIST Libraries to Wiley Format</li>
<li>Part 11. Making a User Library Using NIST MS Interpreter and PubChem</li>
<li>Part 12. Real World Example with IR and MS Combined searches</li>
</ul>

Wiley KnowItAll Mass Spec Training - Part 9. Structure Interchange Between KnowItAll and NIST plus MS Interpreter

Wiley released a KnowItAll version with many new powerful electron ionization gas chromatography-mass spectrometry (EI GC-MS) capabilities. I have developed FREE training videos and associated handouts covering basic and advanced MS topics that will be helpful to the new user.
Also, a <a href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/Published_Know_It_Al_Start_QRG_2_7114366b93/Published-KnowItAl-Start-QRG-(2).pdf">Quick Reference Guide</a>. is available.
KnowItAll is a vendor neutral data processing solution for spectral analyses which will be very valuable in unknown identifications.
It processes IR, Raman, MS, NMR, and UV data employing a large number of file formats (&gt;170) and a total of 2.5 million reference spectra.
<ul>
<li>~264K IR</li>
<li>~25K Raman</li>
<li>~1.25M EI mass spec</li>
<li>~894K NMR</li>
<li>~30K UV</li>
</ul>
<h3>Highlights:</h3>
<ul>
<li>Deconvolution/very fast library searching of accurate/nominal mass GC-MS data</li>
<li>Easy manual processing of nominal and accurate mass GC-MS data</li>
<li>Fast EI searches of &gt;1.2 million spectra in &lt;0.5 seconds</li>
<li>Amazing adaptive search for unknown ID by extending usefulness of all libraries</li>
<li>Transfer structures and spectra between NIST search and KnowItAll</li>
<li>Easy structure drawing and structure searches</li>
<li>Conversion of NIST user libraries to Wiley format</li>
<li>Easy creation of libraries using links to MS interpreter and PubChem</li>
</ul>
<h2>In the eleventh part we will look at:</h2>
Detailed Handouts including tables of content are available in the <a href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/white_powder_talk_full_with_long_page_e591038cc5/white_powder_talk_full_with_long_page.pdf">LabRulezGCMS library</a>.
<ul>
<li>KnowItAll Attributes</li>
<li>Standard Identity MS Search Fails</li>
<li>KnowItAll Adaptive Search (Patented)</li>
<li>KnowItAll Adaptive Search Yields Very Useful Results</li>
<li>Shifted Reference Versus Unknown for Hit Quality Index Calculations</li>
<li>Top 3 Hits in Adaptive EI Mass Spec Search - Unknown White Powder</li>
<li>IR Analysis of Solder Using KnowItAll</li>
<li>Results of IR Search Using “Mixture 2” Option</li>
<li>Comparing IR Spectra of Unknown to Components 1 and 2 in Mixture Result</li>
<li>Conclusions</li>
</ul>
<h3>Previous parts were focused on:</h3>
<ul>
<li>Part 1. <a href="https://gcms.labrulez.com/article/2974">KnowIt All Introduction</a></li>
<li>Part 2. <a href="https://gcms.labrulez.com/article/3059">ProcessIt for Manual Processing of EI GC-MS Data</a></li>
<li>Part 3. <a href="https://gcms.labrulez.com/article/3189">SearchIt/MineIt: Similarity Library Search</a></li>
<li>Part 4. <a href="https://gcms.labrulez.com/article/3269">SearchIt/MineIt; Novel New Adaptive Library Search</a></li>
<li>Part 5. <a href="https://gcms.labrulez.com/article/3288">SearchIt/MineIt: Unresolved Peak Analyses and Library Search</a></li>
<li>Part 6. <a href="https://gcms.labrulez.com/article/3302">MS Expert: Deconvolution of Mixtures with Library Search</a></li>
<li>Part 7. <a href="https://gcms.labrulez.com/article/3304">Interchange of Spectra Between KnowItAll and NIST Search</a></li>
<li>Part 8. <a href="https://gcms.labrulez.com/article/3306">Tips for Drawing Structures and Structure Searches in ChemWindow</a></li>
<li>Part 9. <a href="https://gcms.labrulez.com/article/3357">Structure Interchange Between KnowItAll and NIST plus MS Interpreter</a></li>
<li>Part 10. <a href="https://gcms.labrulez.com/article/3359">Converting NIST Libraries to Wiley Format</a></li>
<li>Part 11. <a href="https://gcms.labrulez.com/article/3362">Making a User Library Using NIST MS Interpreter and PubChem</a></li>
</ul>

Wiley KnowItAll Mass Spec Training - Part 12. Real World Example with IR and MS Combined searches

<ul>
<li>🎤 Presenters: Lenny Kouwenhoven¹, Jop Bezuijen¹, Christina Kelly², Jon Sims³ (¹Petroleum Analyzer Company, ²LECO Corporation, ³Exxon.)</li>
</ul>
<blockquote>
💡 Book in your calendar: <a href="https://gcms.labrulez.com/events/436">15th Multidimensional Chromatography Workshop (MDCW) January 2024</a>
</blockquote>
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/15th_Multidimensional_Chromatography_MDC_Workshop_Main_Banner_45f9ba053d/15th-Multidimensional-Chromatography-(MDC)-Workshop-Main-Banner.jpg" alt="15th Multidimensional Chromatography (MDC) Workshop 2024"/>
<h3>Description</h3>
Legislation, commercial contracts or process control require standardized methods or procedures to ensure product results from all parties involved can be compared to meet contract requirements and resolve disputes.
Standardized method development and adoption is a collaborative process which requires buy-in from many competing interests before new methods are accepted. Innovative or unfamiliar technologies need to overcome market resistance to change, bureaucratic inertia within standardization committees, and the tendency of legacy market suppliers to protect their economic interest. All of these pre-existing stakeholders need to be brought into agreement to allow the new technology into the market space. With these difficulties as background, Lenny Kouwenhoven will talk about her real world experience in bringing the first GCxGC test method for the petroleum market through the ASTM method development process. In addition, the first preliminary ILS data for the GCxGC method will be presented and discussed. The path forward for European and Asian adoption will be overviewed and contrasted with ASTM procedures.

Guided Discussion 2: GC×GC, will it ever be in common usein the petroleum laboratories? (Lenny Kouwenhoven, Jop Bezuijen, Christina Kelly, Jon Sims, MDCW 2023)

<h4>Researchers will analyze materials from six disease study sites across US</h4>
Multi-omics is a research approach that leverages the power of several different “omics” data types at once to build a detailed picture of factors that contribute to human health and disease.
The National Institutes of Health (NIH) is channeling $50.3 million over the next five years into a new consortium dedicated to advancing the generation and analysis of multi-omics data for human health research. As part of this team, Washington University in St. Louis is establishing and will lead a central production center that functions as a hub for multi-omics analyses for materials from consortium members at each of six disease study sites identified by the NIH.
<a href="https://chemistry.wustl.edu/people/gary-patti?&amp;_ga=2.245277708.866671225.1710415924-433581621.1709556186">Gary Patti</a>, the Michael and Tana Powell Professor of Chemistry in Arts &amp; Sciences and a professor of medicine and of genetics at the School of Medicine, and <a href="https://genome.wustl.edu/people/ting-wang/?_ga=2.245277708.866671225.1710415924-433581621.1709556186">Ting Wang</a>, head of the Department of Genetics at the School of Medicine and the Sanford C. and Karen P. Loewentheil Distinguished Professor of Medicine, are principal investigators with the new consortium. Patti, who recently founded a multi-omics company called <a href="https://panomebio.com/">Panome Bio</a>, is an innovator in multi-omics research, and Wang is involved in multiple other NIH consortia, such as the <a href="https://medicine.wustl.edu/news/nih-funds-centers-to-improve-diversify-reference-human-genome/?_ga=2.20933809.866671225.1710415924-433581621.1709556186">Human Pangenome Reference Consortium</a>.
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Washington_University_in_St_Louis_Wash_U_leads_new_multi_omics_production_center_for_NIH_research_consortium_Patti_fa8e4a9e2a/Washington-University-in-St.-Louis-WashU-leads-new-multi-omics-production-center-for-NIH-research-consortium-Patti.jpg" alt="Washington University in St. Louis: WashU leads new multi-omics production center for NIH research consortium: Patti"/>
Collaborators will take advantage of cutting-edge resources at Washington University, such as the <a href="https://research.wustl.edu/core-facilities/center-for-metabolomics-and-isotope-tracing/?_ga=2.243156107.866671225.1710415924-433581621.1709556186">Center for Proteomics, Metabolomics, and Isotope Tracing</a> on the Danforth Campus. The university estimates the value of its portion of the new grant at $19.2 million.
“Most human diseases have complex origins that are influenced by a combination of genetic and environmental components such as diet, physical activity, exposure to pollutants and social determinants,” Patti said. “We are working to understand how disease develops in a person by studying the flow of molecular information at multiple levels of ‘omics’ in parallel.”
“*This consortium model eliminates one important barrier to progress by making it easier to collect multi-omics data from the same sets of samples and developing collaborative teams with the appropriate expertise to harmonize and integrate the results,*” he said.
<h4>A more holistic view</h4>
Multi-omics incorporates several “omics” data types, including genomics, epigenomics, transcriptomics, proteomics and metabolomics. Each of these data types reveals distinct information about different aspects of a biological system, and leveraging all these data types at once is becoming increasingly possible with advances in high-throughput technologies and data science.
The integration of multiple types of data from an individual participant’s biological sample can provide a more holistic view of the molecular factors and cellular processes involved in human health and disease, including untangling genetic and non-genetic factors in health and disease. This approach offers great promise in areas such as defining disease subtypes, identifying biomarkers and discovering drug targets.
<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Washington_University_in_St_Louis_Wash_U_leads_new_multi_omics_production_center_for_NIH_research_consortium_Wang_017d37d6a5/Washington-University-in-St.-Louis-WashU-leads-new-multi-omics-production-center-for-NIH-research-consortium-Wang.jpg" alt="Washington University in St. Louis: WashU leads new multi-omics production center for NIH research consortium: Wang"/>
“*With each additional layer of omics information comes more clues about biology,” Wang said. “I’m really excited about the work we will do in this consortium to integrate genomics, proteomics and metabolomics data. This is the future of systems biology.*”
“*Beyond gaining insights into individual diseases, the primary goal of this consortium is to develop scalable and generalizable multi-omics research strategies as well as methods to analyze these large and complex datasets,” said Joannella Morales, a National Human Genome Research Institute (NHGRI) program director involved in leading the consortium. “We expect these strategies will ultimately be adopted by other research groups, ensuring the consortium’s work will have broad and long-lasting impacts for clinical research.*”
<a href="https://www.nih.gov/news-events/news-releases/nih-awards-503-million-multi-omics-research-human-health-disease">Funding for the consortium</a> will support work at six disease study sites, which will examine conditions such as fatty liver diseases, hepatocellular carcinoma, asthma, chronic kidney disease and preeclampsia, among others.
The sites will enroll research participants, at least 75% of whom will be from ancestral backgrounds underrepresented in genomics research. The sites also will collect data on participants’ environments and social determinants of health to be used in conjunction with the multi-omics data. Combining the multi-omic and environmental data can offer an even more comprehensive view of the factors that contribute to disease risk and outcomes.
Specimens provided by participants will be processed at the omics production center at Washington University, which will use high-throughput molecular assays to generate genomics, epigenomics, transcriptomics, proteomics and metabolomics data that will be analyzed to generate molecular profiles of disease and non-disease states.
A data analysis and coordination center will then incorporate all of these data into large, organized datasets that will be made available to the scientific community for further studies.
“*This is a rare opportunity to bring together a bunch of scientists with highly complementary expertise. I expect it to inspire breakthroughs in leveraging multi-omics approaches to understand disease,*” said <a href="https://chemistry.wustl.edu/people/leah-shriver?_ga=2.10043580.866671225.1710415924-433581621.1709556186">Leah Shriver</a>, a research associate professor of chemistry in Arts &amp; Sciences and a co-investigator on the research team.
“*Multi-omics studies are at the forefront of biomedical research and promise to advance our understanding of disease onset and progression,” said Erin Ramos, deputy director of NHGRI’s Division of Genomic Medicine. “All while potentially providing important clues for treatment design and drug-discovery efforts. This new consortium is an important step in making those advances a reality.*”
The award is funded jointly by NHGRI, the National Cancer Institute and the National Institute of Environmental Health Sciences.

WashU leads new multi-omics production center for NIH research consortium

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