As treatments are increasingly tailored to an individual for greater efficacy, understanding the immune repertoire becomes more critical. B-cell receptor (BCR) reconstruction plays a crucial role in understanding the immune system’s response to various stimuli. Our immune system produces B cells, which carry unique receptors on their surfaces. These receptors act like keys, explicitly recognizing and binding to foreign invaders like viruses or bacteria. By reconstructing BCRs from single-cell RNA-seq (scRNA-seq) data, we can gain valuable information on the diversity and specificity of the immune response at the single-cell level.
Have you ever had to put a jigsaw puzzle together? That’s what happens when BCRs are reconstructed from scRNA-seq data. Choosing the right tool to assemble your numerous small pieces of data then becomes vital for achieving accurate results. But with so many software options out there, which one takes the crown?
A recent publication (1) compared the performance of several popular tools for BCR reconstruction with scRNA-seq data, namely:
Abdul R. Estalefi and Mathias Østergaard Mikkelsen, students of the Department of Biological and Chemical Engineering at Aarhus University, replicated the study and added QIAGEN CLC Genomics Workbench v.23.0.5 (CLC) to the mix, equipped with the Biomedical Genomics Analysis plugin. Specifically, the Immune Repertoire Analysis tool of CLC was used, which was developed for bulk RNA-seq data. Each single cell was treated as a separate sample. See the complete workflow.
The following dataset types were analyzed:
Real data: Datasets with BCR sequences from actual immune cells (plasmablasts) obtained from earlier studies
Simulated data: Datasets mimicking real-world scenarios with mutations in the BCR genes (heavy and light chains).
The results, detailed here, show that:
When working on BCR reconstruction (or NGS data in general), you want a tool with everything you might need. CLC offers a comprehensive toolset for immune repertoire analysis of single-cell data, among other applications.
You also need a software package that is easy to set up, does not require coding and works across various hardware. There would be no need to invest in new hardware and spend weeks or months learning a programming language. This makes it easier to get started and enables you to generate insights from your data right away. Remember – the right tools can take your research to the next level.
Learn more or request a trial of CLC Genomics Workbench, your all-in-one toolkit.
References:
Author acknowledgments: We thank Dr. Tommaso Andreani, Senior Principal Data Scientist at Sanofi, for continuous support and encouragement during this study.
Figure 1. CLC workflow used for dataset analysis.
The samples were handled in parallel using the Iterate element, and the results were aggregated for all samples using the Collect and Distribute elements. FASTQs pre-processed with the Trim Reads tool were used as workflow inputs. The per-sample part of the workflow consisted of three steps: (1) de-novo assembly, (2) consensus sequence extraction and (3) Immune Repertoire Analysis. IMGT Human BCR segments were used as reference segments. Compare Immune Repertoires produced the final output containing the clonotypes across all samples, which were exported to .csv to compare with the truth and compute the scores. A MacBook® Pro 2021 with an M1 Pro processor and 32 GB RAM was used to run the workflow.
Figure 2. Heat map showing each method's individual and average scores (y-axis) for the four datasets (x-axis). Leiden, Canzar and Upadhyay used plasmablast SMART-seq datasets with Sanger-sequenced ground truths. As previously described, SHM consisted of simulated datasets of somatic hypermutations in heavy and light chains (1).
When it comes to data-driven science, your results are only as good as your data and your software. Whether free or premium, your choice of software will greatly impact your research and reputation, and you shouldn’t make it lightly. It’s not enough for a tool to be cheap – it also needs to do its job.
You might think that all a pathway analysis program needs to do is just that: analyze genes and proteins in the context of pathways. But you’re analyzing more than a simple collection of genes. You need the causal relationships that connect them, including activation, inhibition, downstream outcomes and upstream factors.
While the free price tag might be enticing, barebones pathway analysis tools can't provide any depth beyond simple, non-directional relationships.
Is the basic package good enough? Let’s check out what premium gets you.
Your pathway analysis program should empower you to extract the insights you need for publishing. While premium analysis programs might seem like just a shiny interface, they are much more powerful. They’re backed by an extensive human-curated knowledge base, allowing you to analyze and compare vast amounts of data from the comfort of a single platform.
QIAGEN Ingenuity Pathway Analysis (IPA) pays for itself by giving you the tools you need, instead of forcing you to find them on the internet (or build them from scratch). Plus, if you're ever unsure of anything, IPA is supported by a team of PhD-level scientists who are ready to help out. IPA can perform a comprehensive set of over 20 unique analyses, which will help you:
When it comes to pathway analysis tools, the price reflects the value. Sometimes, you just need more.
You’re a data scientist working on drug discovery, watching the geography of your discipline shift dramatically with the introduction of accessible generative AI. It seems like everyone is talking about the incredible AI-powered future that awaits by using their tool. But is AI really that useful? What objective, concrete gains can you expect to see in your research?
You may be familiar with the QIAGEN Biomedical KB-HD, formerly known as the BKB. It’s the leading manually curated biomedical knowledge base, boasting over 24 million high-quality relationships created over 4000 human-years of manual work. But it takes time to deliver validated, full-context relationships.
Our new offering, the QIAGEN Biomedical KB-AI, uses generative AI data curation to create the largest biomedical knowledge graph on the market. Quality is great, but sometimes, you need the kind of quantity that only AI can enable. Let's get into it.
AI data curation helps create large, streamlined and timely knowledge graphs that cover more ground than any human-curated effort could. If you're looking for the whole picture, you might just find it in an AI-curated knowledge graph.
We’re excited to reveal many new improvements and enhancements to the latest release of QIAGEN CLC Genomics Workbench and its related plugins that significantly extend its value. Key improvements and new features in the new version (v24) include:
Figure 1. RNA-Seq volcano plot shows the relationship between fold changes and p-values. The reworked volcano plot allows for 1) different color gradients for positive and negative fold change values, 2) annotations, 3) legends and 4) customizable transparency of data points. Genes of interest can also be highlighted by setting thresholds.
Figure 2. Visualize and interact with spatial transcriptomics data.
Learn more about the applications supported by our portfolio of QIAGEN CLC Genomics software and request a consultation with one of our experts to help you find the right QIAGEN CLC toolset for your research goals.
Here's the scenario - You're a passionate scientist in a leading pharmaceutical company hoping to uncover a transformative drug candidate. Naturally, you use artificial intelligence (AI) to help you target the most promising leads. After weeks of dedicated work, you start to realize that something seems a little off with your results. Maybe you recognize that the algorithm-proposed drug candidate has a history of poor tolerance in human clinical trials. Or perhaps the drug candidate fails to reproduce even the most basic PK/PD modeling results in vitro. Just like you, many drug discovery researchers have found themselves misled by the results proposed by AI.
Even with state-of-the-art algorithms, outcomes of AI for drug discovery heavily depend on the data and context backing them up. Many researchers, just like you, are seeking ways to navigate the intricacies and challenges of this rapidly evolving field. The path to successful AI-driven drug discovery may appear complex, but with the right guidance, AI can significantly enhance both the efficiency and effectiveness of your drug discovery journey.
Here are 3 of our best secrets to help ensure your success when using AI for drug discovery:
1. Start with quality data
The foundation of any successful AI model lies in the quality of its training data. Inconsistent or noisy biomedical data can introduce biases, potentially making the AI model veer off course. Imagine trying to master a language using an inaccurate dictionary; the outcome would be a garbled mess.
Similarly, training an AI model on low-quality biomedical data can lead to misguided conclusions. Data quality, integrity and relevance are paramount. Using expert-curated databases ensures the model begins with accurate and comprehensive knowledge.
That's where our QIAGEN Biomedical Knowledge Base (BKB) database comes in. Curated by experts and continuously updated, QIAGEN BKB ensures you equip your AI models with the best possible start. It offers a strong foundation for building knowledge graphs and data models. Just as a building's strength depends on its foundation, your AI model's efficacy depends on starting with quality data.
2. Root AI inferences in real biological contexts
The power of AI lies in its ability to process vast amounts of information quickly. But it's worth remembering that an AI model, regardless of its sophistication, doesn't inherently understand the complexities of human biology. It sees numbers, patterns and correlations but not causations.
An AI model might draw associations that, at a glance, seem significant. However, without the biological context, these associations can be misleading. To avoid chasing after false positives, it's crucial to ensure the AI's conclusions are rooted within the biological realities.
Here's the good news: QIAGEN BKB and QIAGEN Ingenuity Pathway Analysis (IPA) have built-in causality. With IPA you can quickly check the conclusions your AI generates. IPA's intuitive GUI interface provides visual pathways, disease networks, upstream regulators/downstream effects and isoform-level differential expression analysis, all with the ability to bring in primary datasets for custom-tailored analyses.
3. Validate findings with peer-reviewed research
Science, at its core, thrives on collaboration, verification and iteration. A discovery today can be the stepping stone for a revolutionary breakthrough tomorrow. AI can be a potent tool in accelerating these discoveries, but its suggestions need validation.
While using AI for drug discovery can uncover potential candidates, it's essential to validate these findings using published, peer-reviewed studies. Not only does this process lend credibility to your findings, but it also provides invaluable insights. For instance, understanding which cell lines have been used in previous studies can guide your preclinical testing, ensuring you're on the right track.
For this crucial step, QIAGEN OmicSoft's curated omics data collection is your ally, especially for enterprises in need of high-quality multi-omics datasets. You can tap into a comprehensive landscape of sources, offering validation from published studies beyond just a single public repository. Such validation lends credibility to your discoveries and provides invaluable insights. QIAGEN OmicSoft's curated omics data collection facilitates this crucial step, bridging the gap between AI predictions and experimental data to construct disease models and digital twins of cells/organs/organisms.
Validating your cell line selection is also a critical factor for successful preclinical research. Using ATCC Cell Line Land, you can access authenticated cell line ‘omics data to make informed decisions before purchasing cell lines, helping to streamline your workflows, save time and resources, and enhance the predictability and reproducibility of your studies.
You can be confident in steering your research in the right direction with AI, provided you eliminate guesswork and maximize efficiency by using quality biomedical data, ensure biological soundness of AI results and validate your findings. By applying these three powerful tweaks to your AI, you'll surely revolutionize your drug discovery by spotting promising leads much quicker.
We design our QIAGEN Digital Insights knowledge and software with your success in mind.
After all, the future of new therapies is waiting, and we want to ensure you're well-equipped to lead the way. Want to uncover more secrets to drive drug discovery success from our experts?
Continue reading to see how QIAGEN can power your research.
Looking to collaborate further? Fast track your analysis with QIAGEN Discovery Bioinformatics Services.
If you're working in pharma or biotech, you probably rely on artificial intelligence (AI) to help you identify new drug targets or disease biomarkers within large datasets. As pharma scientists, we know AI is becoming standard practice. But what happens if your AI models are accidentally fed data from unreliable sources? Imagine wasting months of time and resources chasing a drug target that turns out to be a flop. Ouch.
The pitfalls of unreliable, unstructured biomedical data are not just hypotheticals - they're a stark reality we and other researchers face around the globe. For AI models to provide reliable insights, the underlying data must be 'high quality', meaning it's accurate, comprehensive, up-to-date and standardized.
Leave AI errors in the dust with expert-curated biomedical data
Enter QIAGEN Biomedical Knowledge Base (BKB), a repository of meticulously curated biomedical relationships data. We've tailored the BKB database with a keen, expert eye to ensure the molecular connections and relationships are only extracted from the highest quality sources like peer-reviewed scientific literature.
With continuous updates and enriched contextual information, such as tissue specificity and relationship directionality, we make sure you always have access to the most current and reliable data.
As a data scientist, you can mine our BKB through database files or directly from API integration. Our BKB data is particularly useful for creating graph-based models that handle interconnected heterogeneous biological data far better than simple relational databases do. With API integration, we can even help you customize BKB to seamlessly fit with your internal workflows.
What discoveries are waiting for you with the power of QIAGEN BKB?
Here are 3 ideas to get you started:
1. Identify high-quality drug targets using inferred causal interactions
Literature is full of publications showcasing associations between biomarkers, receptors and disease. But how do you know if that relationship is strong or weak within the context of finding a good target for drug development?
Using knowledge graphs powered by BKB data, you can thoroughly explore causal relationships between your target and disease, before you ever even set foot in the lab. Start with a high-level view of the pharmacological landscape of your target, then zoom in on the pathways directly linking target and diseases. From there, you can overlap pathways and functions to discover the exact mechanisms that connect a target to a disease, such as activation/inhibition signaling pathways or RNA binding.
2. Explore the current clinical landscape around your target
QIAGEN Biomedical Knowledge Base contains a wealth of clinical trial information connecting drug targets and their indications. Use knowledge graphs to intuitively navigate from your target to ongoing and completed drug trials, including whether the drug is an antagonist or agonist, the drug's indication of use and trial phase.
Using BKB, you can efficiently prioritize diseases for drug development within the current clinical landscape surrounding your target.
3. Build disease interactomes using protein-protein interactions
What if there are promising disease indications for your target that have not yet been discovered in the literature? You can easily use BKB data to build a knowledge graph based on the protein-protein interactome (PPI) surrounding your target. You can use the PPI graph like a roadmap, measuring and ranking the protein-protein relationships by distance, and determine which diseases are related to those proteins.
This may even lead you to discover less obvious disease indications associated with your target - creating perfect positioning for your new drug to accelerate your entry into the market.
"If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team" (1).
Andrew Ng, Founder & CEO of Landing AI
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Don't miss your chance to supercharge your AI toolbox and transform your drug discovery
During a recent webinar we hosted, a unique and talented group of experts came together to share their insights. They used gene-disease knowledge graphs created by combining AI with BKB to reveal promising novel drug targets for neuroinflammatory disease and other devastating autoimmune disorders.
This webinar offers a unique opportunity to learn directly from the experts how you can best use BKB to enhance and advance your drug discovery strategy. See BKB in action with an example of real-world drug discovery. Watch the webinar now.
Continue your journey with us and explore how you can combine AI and QIAGEN Biomedical Knowledge Base to maximize your drug discovery efforts while minimizing your timelines:
QIAGEN BKB powers the leading pathway analysis software in life science research
Looking for a transformative software suite that already integrates QIAGEN Biomedical Knowledge Base? Was that a ‘yes, please’?
QIAGEN Ingenuity Pathways Analysis (IPA) merges the advanced analytics of BKB with intuitive user design - all without ever having to write a line of code. Learn more.
References:
We've all been there. Huddled over a computer, sifting through research data and feeling slightly overwhelmed by the vast capabilities of a software tool. You might find yourself thinking, "Am I really using this to its full potential?"
As researchers, we know how easy it is to end up using only the surface features of a software, overlooking its hidden gems. So, let's dive deeper into five often overlooked but powerful features of QIAGEN Ingenuity Pathway Analysis (IPA).
1. Explore key molecules that impact a single disease using Machine Learning Disease Pathways
Disease networks are valuable for understanding disease drivers and discovering potential new molecular players. We used machine learning (ML) to mine the QIAGEN Knowledge Base, creating ~1500 disease, phenotype and function pathways all within the Ingenuity Pathway Analysis (IPA) software.
With the ML Disease Pathways tool, you're not just viewing data, but insights derived from sophisticated algorithms. These pathways offer a glance at key molecules influencing specific diseases. Beyond just the known molecular players, there are hidden influencers waiting for you to discover. Venturing into these inferred pathways can lead to groundbreaking findings, potentially revolutionizing disease understanding and treatment.
Sound interesting? Dive deeper into our Machine Learning Disease Pathways tool.
2. Take a closer look at the biological entities in your data with IPA's Activity Plot
Now, what about the opposite scenario? What if, instead of looking at all of the potential targets for a single disease, you want to see which diseases are impacted by your biological target?
With the Activity Plot feature from QIAGEN Ingenuity Pathway Analysis (IPA) you can visualize and explore the activity of a single IPA entity, such as an Upstream Regulator, Causal Network, Canonical Pathway, Disease or Function, across more than 100,000 OmicSoft Land analyses.
Using Activity Plot, you'll gain a deeper understanding of the potential target by exploring its predicted biological activity across thousands of datasets representing disease conditions, treatment conditions, knockouts and much more in our IPA Analysis Match database.
The Activity Plot feature is included with Analysis Match or Match Explorer licenses in IPA. Learn more about Activity Plot.
3. Splice variants and isoforms with IsoProfile
We know transcripts can be elusive. With numerous isoforms in RNA-seq datasets, identifying the significant ones can feel like searching for a needle in a haystack. Enter IsoProfiler.
Not only does IsoProfiler help you identify these crucial transcripts, it also provides context with fully integrated normal human tissue expression from the GTEx consortium. Whether it’s isoform switching patterns, known diseases or even tissue-specific expressions, the feature offers a comprehensive view. It’s like having a biologically astute detective by your side, guiding you through the maze of possibilities.
Let the IsoProfiler feature of QIAGEN IPA help you identify and prioritize isoforms with interesting biological properties relevant to your research.
4. Create causal hypotheses that explain biological outcomes with Regulator Effects
By this point, you probably know your data inside and out. But data in isolation can often be cryptic, especially when communicating the bigger picture to a broader audience.
The Regulator Effects feature in QIAGEN Ingenuity Pathway Analysis (IPA) provides unprecedented big-picture insights into your data. By integrating Upstream Regulator results with Downstream Effects results, Regulator Effects helps you to create causal hypotheses like:
Add depth and direction to your findings with Regulator Effects.
5. Make your IPA networks and pathways more striking with Path Designer
You’ve done the research - now it’s time to publish. First impressions matter, especially in the world of scientific peer review.
Path Designer is your tool to make a stellar first impression while maintaining biological accuracy. Transform the networks and pathways in QIAGEN IPA into publication-quality graphics rich with color, customized text and fonts, biological icons, organelles and custom backgrounds.
Expand and explore pathways using the high-quality content stored in QIAGEN IPA Path Designer.
In our pursuit of advancing science and enriching research, we’ve always believed in offering tools that are both sophisticated and user-friendly. With IPA, we have merged advanced analytics with intuitive design, ensuring that as you embark on your discovery journey, you're never alone.
Ready to unlock your data’s full potential? Dive deeper, explore the uncharted and make each element of your data count. If you feel like you’re on the edge of a breakthrough but need that extra push, reach out. We’re always there to help.
Ready to dig into other features of QIAGEN IPA that are more than worth your while? Try these out to give impactful context and depth to your biological data analyses.
What powerful partnerships come to mind when you think of great collaborations? The two big Steves (Jobs and Wozniak) who became digital giants? The unstoppable Williams sisters who teamed up on the tennis court to win 4 Grand Slam tournaments and three Olympic golds? Marie and Pierre Curie, one of the greatest science duos of all time, who discovered radioactivity?
Their inspirational legacies leave us all in awe and remind us of the magic and impact of combining two strong and innovative forces.
We've got another exciting partnership to share with you, and if you're a preclinical researcher, prepare to be thrilled. Our recent collaboration with ATCC combines our strength of collecting, integrating and manually curating high-quality 'omics data with ATCC's authenticated, traceable and highly trusted cell lines. The result? A powerful tool that the drug development industry can't afford to ignore.
Because as a preclinical researcher, you know choosing the right cell lines for your studies is crucial. Yet the selection process is often challenging due to the lack of access to genomic profiles before purchasing cell lines. Our new tool—ATCC Cell Line Land, which was born from our partnership with ATCC—revolutionizes cell line selection by providing authenticated cell line 'omics data.
So, let's explore what's so great about this partnership and how authenticated cell line ‘omics data will expedite cell line selection to enhance your preclinical studies.
Selecting appropriate cell lines for preclinical experiments is critical but is often a hit-or-miss process. Until recently, you couldn't reliably access the genomic profiles of specific cell lines before purchasing them. This led to uncertainties regarding gene expression, mutations and other essential characteristics needed for successful experiments. Cell lines can also undergo changes and mutations over time, further complicating the selection process. This 'identity crisis' of cell lines means you need to identify and characterize their genomic profiles before you invest time and resources using them in your experiments.
Authenticated cell line 'omics data provide traceable sequencing data linked to specific cell line lots. Our collaboration with ATCC enables you to access transcriptomic datasets from human and mouse cell lines, primary cells and primary tissues.
The genomic profiles of ATCC cell lines are traceable, so you can map them back to specific lots in the biorepository freezer. This information empowers you to make informed decisions when purchasing cell lines and eliminates the need for extensive sequencing and analysis work.
Authenticated 'omics data ensures reproducibility and predictability in studies. This means you can expect consistent and reliable results because the selected cell lines carry the desired genes and traits. Our collaboration with ATCC addresses concerns about the robustness and reliability of preclinical research, as evidenced by a high prevalence of irreproducible studies. By using authenticated data, you can mitigate risks, save time and more efficiently allocate resources.
So, how can you access authenticated cell line 'omics data? It's easy—you subscribe to the ATCC Cell Line Land database. Our partnership with ATCC allows seamless access to the data and identification of the exact cell lines you need for your experiments. The database is continually expanding with thousands of new samples each year, ensuring a wide range of cell line options. You can also request specific cell lines for priority addition to the database.
When choosing cell lines, it's essential to avoid common pitfalls. Relying solely on published journal articles or assuming that public data are vetted can lead to unreliable results. You've got to trust the source material and authenticate expressions, mutations, and other traits to establish a strong foundation for your work. While using ATCC Cell Line Land may involve a slight deviation from how you usually work, its advantages far outweigh any adjustments you need to make.
Cell line selection is a critical factor for successful preclinical studies. Thanks to our collaboration with ATCC, preclinical researchers like you can now access authenticated cell line 'omics data, ensuring informed decisions before purchasing cell lines. By leveraging ATCC Cell Line Land, you'll streamline your workflows, save time and resources, and enhance the predictability and reproducibility of your studies.
Our partnership is a significant milestone on the road to advancing preclinical research and supporting your success in drug development.
If you’re working in pharma or biotech, artificial intelligence (AI) is no stranger. You likely use it to help you identify new targets to explore for a therapeutic area, for drug repurposing or to identify plausible biomarkers for your disease of interest. You may think using AI is enough and will have all the answers if there are enough data. However, there’s a big problem with that assumption.
Limitations of AI-derived biomedical data
Biomedical data have errors and are mainly unstructured. So, removing errors and structuring the data to make them usable to address specific questions is essential, yet far beyond current natural language processing (NLP) approaches and generative AI models with large memories. So for AI models to provide insight, the underlying data must be based on ‘high-quality’ data. High-quality means it’s got to be accurate, yet also complete and comprehensive, up-to-date and standardized.
To complicate matters, scientific knowledge evolves daily, and the genetic basis of hundreds of diseases are identified each year. So the amount of biomedical data is constantly growing and, well… there’s a lot of it. Yet we still don’t know what 99% of our DNA even does. So with all the groundbreaking discoveries yet to be made, you don’t want to miss anything that will help you make your next big discovery.
Like panning for gold
Can you reconcile your need for data that’s accurate yet also complete? How do you find the needles in the haystack yet ensure you won’t miss valuable data that could give you unique insights? What’s the best way to convert biomedical data into biomedical knowledge?
And, even if the data you’ve got ticks all those boxes, there’s always the question of accessibility. How are you going to access it? And how much will you have access to? What if you only want a small slice of the data? Are there access models that will accommodate your specific needs, whether big or small?
To turn data into its usable form of information to create knowledge, it must be honed, fine-tuned and polished—by a human. This produces high-quality data and is the very core and backbone of our knowledge and database offerings, such as our premier QIAGEN Biomedical Knowledge Base. They are trusted by over 90,000 scientists worldwide, in over 4000 accounts, to make confident decisions.
As leaders of this augmented scientific data collection approach, we’re excited by the development of AI tools for curation and continue to evaluate and evolve our technology to take advantage of beneficial advancements. We apply state-of-the-art AI to maximize the completeness of evidence in our knowledge base. But for scientific interpretation, scalable content quality is ultimately essential.
AI + manual curation = Accurate and complete biomedical data
And it’s core to what we do best.
Our curation team scales with today’s growth in scientific publishing because we leverage NLP and other technologies to speed curation but still rely on human certification of biological findings to ensure quality. With domain-specific analytics, you can compute over our unparalleled knowledge base of high-quality evidence; something AI cannot infer.
Imagine having 25 years of curation experience and 200 experts at your disposal
Our experience and findings show the quality of AI and machine-generated content is not good enough for scientific purposes. We regularly identify many false positives and false negatives from machine-only curation. That’s why we’ve been perfecting our market-leading ‘augmented molecular intelligence’ approach for over two decades and leverage 200+ PhD scientists to work alongside machines to verify and improve the utility of the content to drive sound research hypotheses.
Our human curation team enables us to:
Access the data your way
Yet, having a collection of high-quality and reliable data alone isn’t enough. It’s got to be accessible when you need it, how you need it.
That’s why we’ve developed API access to QIAGEN Biomedical Knowledge Base. Now you can rest easy with data that’s not just reliable; it’s also available the way you want it, from the entire knowledge base to just the right slice for your project.
That’s all possible with data that’s easy to access any way you’d like it.
Learn about how flexible access to QIAGEN Biomedical Knowledge Base will open doors to reliable data that deliver true insights. With >35 million findings, >2.1 million entities and >24 million unique relationships, it’s got data that will fuel your data- and analytics-driven drug discovery, at whatever scale you need. Request a consultation to discover how this powerful tool will transform your drug discovery research.
"Download our free app for endless entertainment!" declared the app store. You eagerly install the app, expecting a world of joy and amusement. Soon realize, though, that the 'free' app has crashing issues, limited content and no one to contact for support. A lesson that paying a little for quality and reliability often pays off in the long run — and is worth every penny.
In our modern world, the allure of getting something for free is constant, and undeniably enticing. We get it; the promise of not having to spend a dime can be incredibly appealing. However, the age-old adage still holds true: You get what you pay for. While it may seem like a great deal at first, the reality is that free often comes with hidden costs that can outweigh the initial benefits.
Open source vs. commercial software: What's the better choice?
As a researcher, you also have many free options when it comes to bioinformatics software. You could invest in commercial tools or rely on open-source alternatives. Yet, settling for free options can often end up costing you more in the long run. Let's explore the risks of relying on open-source software and unravel how you'll benefit when you invest in value-driven bioinformatics software tools like ours from QIAGEN Digital Insights.
The risks of relying solely on open-source software
While open-source bioinformatics software has its advantages, it is crucial to consider the potential downsides when relying solely on such solutions:
Linux is only free if your time has no value.
Jamie Zawinski (1)
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Fragmented tools and compatibility issues
Open-source bioinformatics tools often require integrating multiple software packages to perform comprehensive analyses. This can lead to compatibility issues, as different tools may have conflicting dependencies or be designed for specific operating systems. As a result, you may spend considerable time and effort configuring and troubleshooting the software environment, detracting from your main research goals.
Limited support and documentation
Open-source software typically relies on community-driven support, which may vary in quality and availability. As a user, you often must rely on online forums or user groups for assistance, which can be time-consuming and unreliable. And though some open-source software might have excellent documentation and support, like, for example, the core R language system or Python, for many packages, documentation and help can be pretty limited. This makes it difficult to effectively harness the software's full potential, especially if you can't read source code, or don't have the time to try and understand someone else's source code.
Lack of validation and quality control
Though some open-source software may be professionally produced, many tools lack extensive validation and quality control processes which are core to commercial solutions. Without rigorous testing and ongoing maintenance, there may be a higher risk of encountering bugs, inaccuracies or inconsistent performance. So you've got to be ready to invest additional time and effort to validate open-source tools and ensure the reliability of your results. What's more, some open-source systems are produced without rigorous software engineering practices. That means it's up to you, the user, to validate and perform the QC yourself. Who's got the time to go down that rabbit hole?
Intellectual property and legal concerns
Using open-source software may raise intellectual property and legal concerns, especially if you work in commercial, regulated or proprietary environments. Open-source licenses may have "copyleft" effects, which could demand your work be open-sourced too, may exclude commercial use or may not be compliant with GDPR, HIPAA or other data security standards. These are significant concerns you should carefully consider to ensure compliance with legal and institutional requirements, as the onus is on you, the user, to find out what the license does or does not allow.
You get what you pay for
These potential risks of open-source bioinformatics software tools may seem overwhelming, but rest assured there's an entire platform of bioinformatics software tools that won't end up costing you nearly as much as free tools. Why? Because when you invest in quality, reliability and user-friendly functionality and support, you save time and effort. And that translates to money.
Comprehensive and integrated software tools
As a single company, we offer a suite of bioinformatics software tools designed to address a wide range of research needs. From genome analysis to transcriptomics, metagenomics and pathway analysis, our bioinformatics software provides comprehensive functionality, and works synergistically to enable you to seamlessly conduct diverse analyses. With a single integrated platform, you can streamline your workflow, and eliminate the need for piecing together multiple open-source tools.
User-friendly interface and workflow
Our software is developed with a focus on user experience and offers intuitive interfaces and workflows. You can easily navigate the software and perform complex analyses without extensive programming skills. Our user-friendly approach to designing our software empowers you to accelerate your data analyses, reduces the learning curve and enhances productivity so you can focus more on your biological questions rather than the technical complexities of bioinformatics.
High-quality data analysis and interpretation
Our bioinformatics software is built on robust algorithms and validated databases, ensuring accurate data analysis and interpretation. Our software leverages up-to-date reference databases and annotation resources so you can analyze your data and extract meaningful insights confidently. The reliability of our software means you can make informed decisions and generate high-quality results to more quickly drive your research forward.
Dedicated customer support and training
We provide excellent customer support, including technical assistance, training programs and resources. Our team of experts is readily available to address your queries, troubleshoot issues and guide you through our software's features and capabilities. This level of support ensures you'll maximize the potential of the software and be empowered to efficiently achieve your research goals.
When it comes down to it, free software will cost you
When it comes to your research, you can't afford to play around with cutting costs. While free may seem enticing, you've got to weigh the potential costs and quality before diving in. Because open-source tools compromise quality, have limited features, lack support and require you invest additional time and effort due to a lack of user-friendly interfaces or comprehensive documentation. So, as you evaluate which bioinformatics software you will rely on, keep this mantra in mind as a nugget of truth: Free isn't better. Better is better.
Price is what you pay. Value is what you get.
Warren Buffet (2)
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Learn more about QIAGEN Digital Insights bioinformatics tools
Explore our range of bioinformatics tools for research, clinical and pharma development applications.
Check out this quick guide to QIAGEN Digital Insights software to see which one might be right for you.
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