Johannes Rainer’s Post

What are you willing to pay for something you likely will never use? Currently, a standard, run of the mill, best practices genomic sequencing pipeline creates not 1, not 2, but 3 copies of what is essentially the same data: BCLs, FASTQs, and BAMs. Sure, additional bins can be added. But what is the cost of adding those bins? Looking at the full resolution histogram (in the figure below), a clever person might notice that a majority of the data is above Q30, or even Q35. And, thusly, realize that, by adding a bin right in the middle of this distribution, they would increase the entropy of compression. In layman's terms, all 3 copies of your data would not compress well with the addition of a Q40 bin. In fact, you would increase the size of all 3 copies of the data up to 50% (according to the article below)! Given that most applications will not benefit from knowing a base call is Q40, what are you willing to spend on something that will (in most cases) not improve downstream data quality? * This post represents only my personal views and curiosity on the subject. https://lnkd.in/g7vbnyj3

edgeR 4.0: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets https://lnkd.in/exQniipA

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://lnkd.in/e7Asi-Km. Source: Nucleic Acids Res https://lnkd.in/efBjrMbn

🔬🚀 Excited to share our latest preprint: Ratios in Disguise, Truths Arise: Glycomics Meets Compositional Data Analysis! With the associated release of glycowork v1.3, we introduce a compositional data analysis framework specifically tailored to comparative glycomics. Here’s how our approach stands out: CLR and ALR Transformations: We use center log-ratio and additive log-ratio transformations to properly handle the compositional nature of glycomics data. Modeling Scale Uncertainty: Our model incorporates scale uncertainty/information, making our glycomics data analysis pipeline the most robust and sensitive yet. 📚 Dive into the details and let us know what you think! https://lnkd.in/gzTjDAC4 #glycotime #glycomics #dataanalysis #bioinformatics

New overview of the Mascot workflow for LC-MS/MS data, including links to tutorials on peak picking, database searching and quantitation. #proteomics #MassSpec #MassSpectrometry

A upgrade on PNNL PreProcessor from Prof. Bilbao and vendors (Agilent and MOBILion). Based on this preprocess tool/data convertion tool, LC-IM-MS data (from DT/SLIM) could be converted into a common DDA-like data then be processed in common lipidomics/proteomics tools.

[Uniformity of Raw Data Output] refers to the consistent amount of sequencing data generated by different samples throughout the sequencing process. Ideally, samples subjected to the same operation should yield approximately equal numbers of sequencing reads, ensuring a uniform data output. The closer the data volume output aligns with the theoretically expected volume, the higher the efficiency in processing multiple samples with a single sequencing kit, leading to cost reduction. In this video, we conduct a detailed analysis of the factors influencing sample variance, kit performance, operational procedures, and sequencing methodologies to clarify any uncertainties surrounding data volume uniformity. #NGS #Nanodigmbio #Wetlab

🎞 #ChatsWithChaudhrey S04E060 with Thermo Fisher Scientific's Simon Cubbon & Maximillian Hadfield on "Connecting your Lab, People, & Data" I caught up with Thermo Fisher Scientifics Max Hadfield, Senior Manager, Program Management Office and Simon Cubbon, Director, Strategy and Commercialization recently to talk about “Connecting your lab, your people, and your data” Both Max and Simon are members of the Chromatography and Mass Spectrometry Division (CMD) of Thermo Fisher Scientific, and more specifically live within the Software Business Unit I asked them both.... • Tell me more about the Chromatography and Mass Spectrometry Division (CMD) of Thermo Fisher Scientific, and more specifically the Software Business Unit. 01:14 • What do you mean when you say “Connecting your lab, your people, and your data” “What do you see and hear are the main issues facing those working within laboratories, especially when you think about things from a software perspective?” 02:58 • Si asked me what I hear are the may issues and challenges with regards software in the lab? 05:15 • Laboratories have a range of challenges, can you talk about the issues you see and hear about from a software perspective? 08:02 • Thermo already have quite a few different software tools; why do you have so many different products? Are these all different and for different markets? How do they talk to one another? 11:52 • How could Ardia could make a difference beyond what they might currently have such as LIMS and what makes it better than what’s already out there? 18:29 • And the data management side, how is it easy to manage for someone who hasn’t got an IT background? 24:00 • How much impact AI and ML had on the software development at Thermo Fisher Scientific and what do see in terms of software development? 26:14 • How do see the future for software in the Pharma sector in the future? 31:54 • How does the growth in Cell and Gene Therapy impact software development? 34:40 • Where can I go or who can I contact for more information on Ardia, and of course the related software that goes alongside Ardia? 37:37 If you have any questions or comments for Si or Max feel free to put them in the comments below. Learn more below about... ◉ The Ardia Platform • www.thermofisher.com/Ardia ◉ Chromeleon Software https://lnkd.in/epsvKge3 ◉ LSMS Software • https://lnkd.in/eVkZgsVg #RSKThermoFisher ##ardia, #RSKTFS #connectedlab, #massspec, #massspectrometry, #proteomics, #metabolomics, biopharma, #software, #ardiaplatform, #OrbitrapAstral, #PoweringWhatsNext, #omics https://lnkd.in/evYjfDDF

Take advantage of this Webinar today. Make Metabolomics library building easier with intelligent data acquisition and curation software. https://lnkd.in/gyzUh-eh

#Workshop Fundamentals of Next Generation Sequencing - Registration link - https://lnkd.in/dy5_6Zpv :Alternate time slot 6:30-7:30 pm also available For reviews and registration - https://lnkd.in/daXyJRvc As Next Generation Sequencing becomes a first stop for most analysis downstream - DNASeq, RNASeq, ChIPSeq, ATACSeq, BSSeq .... etc, it becomes imperative for every biotechnologist to have a clear understanding of the basics of NGA data generation and analysis.  Be my guest as I explain the basics of Next Generation Sequencing techniques, the necessary installations, the data formats and steps in DNASeq Analysis including Variant Calling and Variant Annotation with detailed explanation.