Ingenuity http://www.ingenuity.com Intuitive web-based applications for quickly analyzing and accurately interpreting the biological meaning in your genomics data Wed, 17 Dec 2014 16:48:09 +0000 en-US hourly 1 http://wordpress.org/?v=4.0.1 2014 in Review: A Year of Great Sciencehttp://www.ingenuity.com/blog/news/2014-review-year-great-science http://www.ingenuity.com/blog/news/2014-review-year-great-science#comments Wed, 17 Dec 2014 16:48:09 +0000 http://www.ingenuity.com/?p=5545 As we approach the holidays and our thoughts drift to eggnog and shopping lists, we’re taking a moment to look back at 2014. It has certainly been a busy year here at QIAGEN Bioinformatics! In this blog, we round up … Read More

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As we approach the holidays and our thoughts drift to eggnog and shopping lists, we’re taking a moment to look back at 2014. It has certainly been a busy year here at QIAGEN Bioinformatics! In this blog, we round up some of the highlights.

Ingenuity Product Citation

It was another banner year for publications from our users. In fact, there are now 12,113 papers citing our tools, and that number is growing all the time. You can search our site to see many of them; start here to check citations mentioning Ingenuity Products.

Customer Stories

We are constantly impressed by our customers’ remarkable scientific findings and it has been very rewarding for us, to have the opportunity, to share some of their stories. Here are the scientists whose work we featured in 2014:

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Conferences

We also got to meet many existing and prospective customers at a number of excellent scientific and clinical conferences this year. To our delight, genome interpretation and data analysis were key themes at many of the meetings and really highlighted the exceptional work our users are already doing with Ingenuity Variant Analysis and Ingenuity Pathway Analysis. We also got a great glimpse of how our newest product, the Ingenuity Clinical Decision Support platform, will solve many users’ problems. Check out the logo links below for our coverage of these events:

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Deep Dives

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This year we posted a few blog series as a way to take deeper dives into some topics that our users are most interested in. We looked in detail at the Ingenuity Knowledge Base, the engine that powers all of our web applications, including how it was built, how we integrate new content, and more. You can check out the first post here, or view this cool infographic.

Leading up to the commercial launch of our new clinical tool, we also spent a lot of time exploring how genetic variants are currently interpreted in clinical labs. We relied on our in-house experts Tara Love and Junaid Shabbeer, who proved to be excellent guides for this complex topic. Check out blog post Q&As with Tara and Junaid, or view the first post of the clinical series here.

Product Updates

Finally, we made some big leaps with our Ingenuity applications this year that will no doubt have our users conducting even more impressive work in the near future. For one thing, we integrated important new sources into our Knowledge Base. BIOBASE’s expert-curated content, including HGMD, is now fully integrated for Ingenuity users. We also integrated with the InSilico DB open data management platform to make life easier for scientists working with public and private samples.

We also added a host of new features to our core products, IPA and Ingenuity Variant Analysis. IPA now allows for exploring endpoints and predicting activity of canonical pathways; connecting diseases or functions of interest; viewing detailed relationships between molecules and associated functions or phenotypes; and more. Ingenuity Variant Analysis was updated to permit filtering by family/kindred; nested searches; pre-filtering of data for faster analysis; and splice site predictions.

 

We wish you all a wonderful holiday season, and best wishes for insightful analysis in 2015!

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Doug Bassett Talks Data Interpretation with Front Line Genomicshttp://www.ingenuity.com/blog/events/doug-bassett-talks-data-interpretation-front-line-genomics http://www.ingenuity.com/blog/events/doug-bassett-talks-data-interpretation-front-line-genomics#comments Wed, 10 Dec 2014 17:37:07 +0000 http://www.ingenuity.com/?p=5532 Data interpretation was a big topic of discussion at this year’s ASHG meeting, especially as it relates to translational research and clinical applications. The program was packed with population studies, cancer studies, and other massive-scale hunts for causal variants driving … Read More

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Screenshot 2014-12-08 10.13.21

Data interpretation was a big topic of discussion at this year’s ASHG meeting, especially as it relates to translational research and clinical applications. The program was packed with population studies, cancer studies, and other massive-scale hunts for causal variants driving biological change. We were excited to catch up with many of our existing customers to hear about what they are doing; we also met with many new researchers who told us about their challenges in looking for the meaningful variation in bigger and bigger data sets.

Doug Bassett, QIAGEN Bioinformatics CSO & CTO, was among the many team members in San Diego. Front Line Genomics caught up with Doug to get his perspective on addressing the many challenges and exciting opportunities in interpreting next-generation sequencing data. Listen to the full interview here.

“Interpretation is really all about leveraging the content that came before. All the translational research, the clinical studies that have been done, and putting the genome into that context, into that framework,” said Bassett. “So that when you see a particular mutation, or constellation of mutations in a given patient and one or more of those have been observed before, or effects a pathway that has been linked to disease before, you can very quickly identify that association and do the right thing.  Whether that’s identifying a novel causal variant for disease, or a novel driver variant of cancer or identifying the right treatment for a given patient.”

Helping clinicians and translational researchers look at their data in context and get to answers faster is a major priority at QIAGEN Bioinformatics. Over the past two decades, for example, we have built the Ingenuity Knowledge Base as a horizontally and vertically structured database that pulls in relevant scientific and medical information and describes it consistently, making this data interoperable and computable so you can interpret your results. Ingenuity Knowledge Base scours scientific journals, publicly available molecular content databases, textbooks, and more to gather data. Our expert curators manually review top-tier scientific literature, pulling out key details to ensure that data is captured with full context. Information is gleaned from the entire paper, including figures. Once curated, data is integrated into the Ingenuity Knowledge Base using our proprietary ontology to ensure that information is represented consistently. The integration process also structures data so you can query, visualize, and compute across it.

You can learn more about the Ingenuity Knowledge Base here and in a four-part blog series we recently ran on this invaluable resource.

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At AMP 2014, Variant Interpretation in the Spotlighthttp://www.ingenuity.com/blog/news/amp-2014-variant-interpretation-spotlight http://www.ingenuity.com/blog/news/amp-2014-variant-interpretation-spotlight#comments Wed, 03 Dec 2014 18:35:56 +0000 http://www.ingenuity.com/?p=5451 Whew! After last month’s AMP 2014 conference in National Harbor, Md., the QIAGEN Bioinformatics team could use a vacation. Between the steady interest in demos of our new Ingenuity Clinical Decision Support platform, the jam-packed exhibit hall booth, and attending world-class … Read More

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Whew! After last month’s AMP 2014 conference in National Harbor, Md., the QIAGEN Bioinformatics team could use a vacation. Between the steady interest in demos of our new Ingenuity Clinical Decision Support platform, the jam-packed exhibit hall booth, and attending world-class talks, the meeting was a veritable marathon.

We couldn’t be happier about how it all turned out, and it was a real honor to be able to show off our newest application to this savvy group of clinical lab professionals. The Ingenuity Clinical Decision Support platform was designed to accelerate variant interpretation and reporting of clinically relevant variants, so the AMP crowd was exactly the right audience for it.

At times it really felt like variant interpretation was the key theme of the entire conference. It popped up in all sorts of presentations, from those early-bird concurrent sessions at 7 am (ouch) to the plenary talks in front of nearly 2,000 attendees. During the opening keynote from NIH Director Francis Collins, he said that a major obstacle in getting to genomic medicine is our lack of knowledge about which genetic variants are associated with what outcome or disease. Delving into these variants, learning more about them, and accurately linking them to phenotypes are critical needs to achieve the community’s goal of precision medicine, Collins said, highlighting two public databases attempting to help with this: ClinVar and ClinGen.

In an award presentation, Uta Francke, professor emerita at Stanford University, spoke about her long-term research interest in mosaicism. She reported that filtering down a long list of variants to find causative ones is a crucial part of understanding unusual phenotypes — but cautioned against over-filtering, since variants that seem benign may turn out to be important in understanding certain disorders. She also predicted that whole genome sequencing will become widely available (both through the healthcare system and outside of it), and said she anticipates the development of an automated, machine-learning approach to deal with variant interpretation at the massive scale that will be required.

Eric Green, director of the National Human Genome Research Institute, talked about how genomics has become a big data field for the first time. Now, he said, the real bottleneck is data analysis; the community needs a robust, reliable way to quickly get a list of important variants and their functions from all of this sequence data. “We need systems to get us from variants to what they mean,” he said.

It was gratifying to see that this part of the genome interpretation process is important enough to have gotten the attention of the Genome in a Bottle consortium, an industry-wide group trying to nail down best practices for DNA sequencing. In a presentation from NIST’s Marc Salit, he said that the consortium aims to address everything from sample prep through variant calling and confidence estimates, eventually providing reference materials to ensure that everybody is getting comparable results with a high-quality benchmarking system. They’re also working with the Global Alliance (as are we) to support confidence calls in variant scoring for medical use.

AMP was a terrific experience for the QIAGEN Bioinformatics team, and we’re glad that our newest application could make a significant difference for clinical geneticists and other medical professionals as they work to achieve the goals outlined in presentations from some of the field’s greatest leaders.

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Infectious Disease Research Using Ingenuity Pathway Analysis (IPA)http://www.ingenuity.com/blog/webinar/infectious-disease-research-using-ingenuity-pathway-analysis-ipa http://www.ingenuity.com/blog/webinar/infectious-disease-research-using-ingenuity-pathway-analysis-ipa#comments Mon, 01 Dec 2014 19:35:39 +0000 http://www.ingenuity.com/?p=5472 This week QIAGEN Bioinformatics Principal Scientist Jean-Noel Billaud, PhD, will host a live webinar on how IPA can be used to look at viral pathogenesis.  For this particular presentation, he will look at two West Nile Virus datasets that were created … Read More

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Jean-Noel Billaud, QIAGEN Bioinformatics Principal Scientist

Jean-Noel Billaud, QIAGEN Bioinformatics Principal Scientist

This week QIAGEN Bioinformatics Principal Scientist Jean-Noel Billaud, PhD, will host a live webinar on how IPA can be used to look at viral pathogenesis.  For this particular presentation, he will look at two West Nile Virus datasets that were created using different cell culture methods, one a microarray study on retinal pigment epithelial cells and the second an RNAseq on primary macrophages.

Infection by West Nile Virus is a worldwide public health concern and is the most common cause of epidemic viral encephalitis in the United States. Yet, the viral pathogenesis of this disease is not well understood. In this webinar, Dr. Billaud will look at how IPA was used to shed light on canonical pathways, biological processes, and transcription regulators involved in West Nile Virus infection.

Dr. Billaud is principal scientist for in silico research program in oncology and infectious diseases at QIAGEN. He holds a PhD in Blood Cell Biology from Paris VII, and conducted his post-doctoral work at the Scripps Research Institute in San Diego, CA.

This free webinar will take place on Wednesday, December 3, 2014 at 5:00 PM–6:00 PM (CET). Visit our webinar webpage to register.

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With New Ebola Model, UW Scientists Track Genetics of Host Resistancehttp://www.ingenuity.com/blog/news/new-ebola-model-uw-scientists-track-genetics-host-resistance http://www.ingenuity.com/blog/news/new-ebola-model-uw-scientists-track-genetics-host-resistance#comments Wed, 19 Nov 2014 18:24:29 +0000 http://www.ingenuity.com/?p=5420 Scientists around the world are racing to learn more about the ongoing Ebola virus outbreak in West Africa, which as of November 2014 had claimed nearly 5,000 lives and infected more than 13,000. Thanks to remarkable new work from researchers … Read More

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Angela Rasmussen, Research Assistant professor at the University of Washington

Angela Rasmussen, Research Assistant professor at the University of Washington

Scientists around the world are racing to learn more about the ongoing Ebola virus outbreak in West Africa, which as of November 2014 had claimed nearly 5,000 lives and infected more than 13,000. Thanks to remarkable new work from researchers at the University of Washington and collaborators at the University of North Carolina and Rocky Mountain Laboratories, there’s a new tool in the battle against this virus: a collection of lab mice that closely model the types and progression of Ebola seen in humans.

Until now, scientists seeking to understand virus biology or screening drug compounds, among other activities, were limited in their ability to use model organisms. The mouse model for Ebola did not display similar phenotypes to humans infected with the virus. Conducting research on the non-human primate that best models human responses to Ebola — the rhesus macaque — is expensive, challenging, and ethically complicated, says Angela Rasmussen, a research assistant professor in the microbiology department at the University of Washington and lead author on this new project.

Rasmussen and her colleagues aimed to design a better model, and they started with a community resource that has successfully delivered improved mouse models for other research areas: the Collaborative Cross program, which is currently managed at the University of North Carolina. She also pulled in another tool that has worked well for her in the past: Ingenuity Pathway Analysis (IPA) from QIAGEN Bioinformatics, which she deploys to analyze complex transcriptomic data. Between these tools, Rasmussen not only developed an important new model for Ebola research, but she also discovered significant details about effects of host genetics on infection outcomes.

The new mice, featured in the paper just published in Science, display all three major phenotypes seen among humans infected with Ebola: hemorrhagic fever, death, and resistance to lethal infection. “We can now use mice to really model the full breadth of Ebola outcomes that we see in human populations — for example, in the current outbreak,” Rasmussen says. Her team’s findings are reported in a paper entitled “Host genetic diversity enables Ebola hemorrhagic fever pathogenesis and resistance.”

To learn more about how Rasmussen used IPA to uncover important information about the host response to Ebola virus, check out the full case study in our featured researcher section.

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Benchmarking Project Shows CLC Genomics Server Setup for HighSeq X Ten Halves Number of Compute Nodes Neededhttp://www.ingenuity.com/blog/news/benchmarking-project-shows-clc-genomics-server-setup-highseq-x-ten-halves-number-compute-nodes-needed http://www.ingenuity.com/blog/news/benchmarking-project-shows-clc-genomics-server-setup-highseq-x-ten-halves-number-compute-nodes-needed#comments Mon, 17 Nov 2014 17:45:45 +0000 http://www.ingenuity.com/?p=5408 For many of us who spend much of our time interpreting sequencing data for novel biological insights, you maybe interested in hearing about some of the dramatic improvements being made on upstream data processing and analysis before the data is ready … Read More

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CLC Genomics Server

CLC Genomics Server

For many of us who spend much of our time interpreting sequencing data for novel biological insights, you maybe interested in hearing about some of the dramatic improvements being made on upstream data processing and analysis before the data is ready for interpretation.  These improvements lower the cost, increase data through-put and overall improve the accuracy of the data we receive for interpretation.  Our QIAGEN Bioinformatics colleague, Mikael Flensborg, Director of Global Partner Relations at CLC bio, recently acquired by QIAGEN, wrote an interesting post for the Intel Health & Life Science blog on a benchmarking study they recently performed using publically available HiSeq X Ten data.

The $1,000 genome sequence has generated a lot of excitement but as a community we are still tackling the cost of processing and analyzing next-generation sequencing data.  This benchmarking study is important because it shows how innovation driven by our colleagues at CLC bio has essentially halved the number of compute nodes originally specified for supporting a HighSeq X Ten setup paving the way to lower the costs of data analysis for labs working with this exciting sequencing platform.

According to Illumina’s “HiSeq X Ten Lab Setup and Site Prep Guide (15050093 E)”, the requirements for data analysis are specified to be a compute cluster with 134 compute nodes (16 CPU cores @ 2.0 GHz, 128 GB of memory, 6 x 1 terabyte (TB) hard drives) based on an analysis pipeline consisting of the tools BWA+GATK.

This benchmarking study was based on a workflow (Trim, QC for sequencing reads, Read Mapping to Reference, Indels and Structural Variants, Local Re-alignment, Low Frequency Variant Detection, QC for Read Mapping) of tools on CLC Genomics Server running on a compute cluster with Intel® Lustre® filesystem, InfiniBand®, Intel® Xeon® Processor E5-2697 v3 @ 2.60GHz, 14 CPU cores, 64GB of memory, SSD DC S3500 Series 800GB.

Based on these specifications, they were able to create a compute cluster infrastructure using just 61 compute nodes, less than half the 134 recommended by Illumina.  Congratulations!

QIAGEN Bioinformatics is presenting these results at Super Computing 14 (http://sc14.supercomputing.org) in New Orleans next week at the Enterprise Community Hub Session on Tuesday Nov. 18 from 3PM-4PM in the INTEL booth area. They will also have a Community Hub Session about Cancer Research on Wednesday Nov. 19 from 3PM-4PM (INTEL booth area) and a theatre presentation about Cancer Research tools on Tuesday Nov. 18 at 2:30PM at the INTEL Theater in the exhibition area.

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Clinical Lab Director’s View Part 3: Somatic and Hereditary Cancerhttp://www.ingenuity.com/blog/events/clinical-lab-directors-view-part-3-somatic-hereditary-cancer http://www.ingenuity.com/blog/events/clinical-lab-directors-view-part-3-somatic-hereditary-cancer#comments Wed, 12 Nov 2014 20:44:44 +0000 http://www.ingenuity.com/?p=5387   Recently we’ve been looking at the challenges of interpreting and reporting variants found in genetic tests by clinical labs. In this final post in our miniseries, QIAGEN Bioinformatics’ Clinical Science Director Junaid Shabbeer walks us through the intricacies of … Read More

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Recently we’ve been looking at the challenges of interpreting and reporting variants found in genetic tests by clinical labs. In this final post in our miniseries, QIAGEN Bioinformatics’ Clinical Science Director Junaid Shabbeer walks us through the intricacies of handling data from genetic tests related to cancer.

Having been involved in directing the clinical lab at Myriad Genetics, Junaid has a lot of experience with variants detected in the BRCA1 and BRCA2 genes. When it comes to hereditary cancer, he says, the number of never-before-seen variants was always remarkably high. “For the BRCA genes, even after so many years of testing, we were still finding new variants,” Junaid tells us. Another example is Lynch syndrome, which is associated with a hereditary form of colon cancer.

Junaid Shabbeer, QIAGEN Bioinformatics’ Clinical Science Director

Junaid Shabbeer, QIAGEN Bioinformatics’ Clinical Science Director

For germline variants associated with hereditary cancer, the variant interpretation process aims to determine whether a variant is causative for the disease. In simple terms, lab directors, medical directors, and variant analysts are trying to determine if an observed variant in a specific gene from a patient is causing the observed phenotype or disease. Analysts track the variants reported from each test because the more often any given variant is observed, the more information can be collected for that variant to help accurately classify it. Over time, particular variants may be seen often enough that analysts become comfortable classifying them as benign or likely pathogenic. But for variants that are not well characterized, analysts must conduct a time-intensive and laborious variant scoring process to apply existing evidence toward an evaluation of whether the new variants are implicated in disease.

One of the big challenges for labs launching new hereditary cancer tests is dealing with soaring interest from patients and physicians. “For labs that had never offered hereditary cancer testing before, such as for BRCA genes, many find that they are not prepared to deal with the volume of demand when they launch a new test,” Junaid says. High demand means scores of novel variants observed, so analysts must determine whether those variants — most of which they are not yet familiar with, having just launched the test — are causative.

Interpreting variants for somatic cancer is a different process, as analysts are looking at DNA variants in a tumor to determine whether these variants are actionable or targetable, either with approved therapies or through prognostic or diagnostic data. The variants may also indicate how certain therapies are likely to affect a patient’s outcome. “In somatic cancer, where you’re sequencing to look for the molecular profile of a tumor, you’re seeking variants that are already well known to have a role in driving cancer,” Junaid says. Because scoring new variants is less important in this workflow, interpretation can be faster — although the rapidly expanding use of NGS-based tests for tumor assessment means that many clinical lab teams still face substantial demand for variant analysis services for this application.

Here at QIAGEN, we’re hoping to help alleviate the interpretation process with our newest application, the Ingenuity Clinical Decision Support platform. Currently available to early access customers, the tool will be showcased at this week’s AMP meeting in Maryland, where attendees are welcome to stop by our booths (#707 and #1023) for a demo. We hope to see you there!

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Clinical Lab Director’s View Part 2: Scoring a Varianthttp://www.ingenuity.com/blog/products/clinical-lab-directors-view-part-2-scoring-variant http://www.ingenuity.com/blog/products/clinical-lab-directors-view-part-2-scoring-variant#comments Mon, 10 Nov 2014 23:02:37 +0000 http://www.ingenuity.com/?p=5363 In this blog miniseries, we’ve asked Junaid Shabbeer, Clinical Science Director at QIAGEN Bioinformatics, to guide us through the process of analyzing DNA variants for genetic tests. In our last post we walked through what it takes to launch a … Read More

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In this blog miniseries, we’ve asked Junaid Shabbeer, Clinical Science Director at QIAGEN Bioinformatics, to guide us through the process of analyzing DNA variants for genetic tests. In our last post we walked through what it takes to launch a new genetic test. Today, we look at the steps involved in scoring a variant.

“All labs go about gathering evidence to classify variants in more or less the same manner,” Junaid says, noting that the process of scoring a germline (hereditary disease) variant fundamentally differs from the process of scoring a somatic (tumor) variant. The goal of scoring a germline variant is to get at whether the variant is causal or not causal for a disease, and guidelines from organizations such as ACMG have helped make this process very consistent. The goal of scoring a somatic variant is to get at whether there are targeted treatments that correspond to the variant, as well as any prognostic or diagnostic information pertinent to that variant that could aid in the cancer patient’s outcome.

The process begins, naturally, with DNA sequencing data generated from the test taken by the patient. This data goes to the clinical lab director, who has to decide what information to report. Each lab has a variant interpretation workflow — involving a variant committee or a dedicated variant analyst — drawing upon external data as well as internal data from variants processed by that lab in the past.

Once the list of variants has been sifted out from the test data, the analysis process kicks off with a search of what is already known about each variant. Analysts comb through COSMIC, HGMD, and other public databases (including gene-specific databases) relevant to the particular disease or condition being tested for. They will use modeling tools as well to get a better sense of how each variant might affect gene expression or the structure or stability of the protein.

Publications are very important to variant scoring, so analysts will also query a search engine such as Google Scholar or PubMed to find information about each variant in the literature (both clinical and research papers). While research papers generally cannot be relied upon entirely in the variant scoring process, Junaid says, “they might be useful if the scientists did functional studies, say, in a model system like yeast cells.” The literature search can be very time-consuming for analysts and is not a completely reproducible science, though standard search strings can be used across variants to ensure consistency in the publication search process. “What if you miss that one critical paper?” Junaid says, noting that analysts are acutely aware that their reports might sway a physician and patient to go ahead with a drastic surgery or treatment.

For somatic variants, analysts will consult drug labels and focus heavily on guidelines articles in the literature, for example from NCCN and ASCO, as well as search for clinical trials that pertain to the variant. Additional literature containing treatment studies and prognostic data are also consulted.

For germline variants, the analysts will also consult pedigree information for variant scoring. “The more often you see a variant in affected individuals in families, the greater the weight of evidence that the variant tracks with disease,” Junaid says. “But if you’re seeing it in unaffected family members or broadly through the population, then it is more likely to be benign. Lab directors wouldn’t rely only on observations within families, but that’s certainly an important factor.”

The goal of all the research is to find enough evidence to classify a variant as pathogenic or as benign, with as few variants as possible in the “unknown significance” category that lies between (for somatic variants, analysts aim to further classify them in terms of their actionability in the patient’s particular cancer or in another cancer). “Once a lab has enough evidence to confidently classify a variant, that’s very good,” Junaid says. “That’s what labs try to do: understand as accurately as possible the role of that variant in disease causation or what targeted treatments are available.”

When a particular variant has been characterized well enough to be definitively classified , it can be included in automated reporting pipelines for that test in the future — an important step in reducing both the test turnaround time and the time spent by analysts and lab directors in the analysis process.

Junaid Shabbeer, QIAGEN Bioinformatics’ Clinical Science Director

Junaid Shabbeer, QIAGEN Bioinformatics’ Clinical Science Director

Particularly for new variants, however, such automated reporting has not been possible and the interpretation process remains a customized, hands-on effort. “We’re dealing with the information that makes that individual unique,” Junaid says, “so every result has to be examined and reviewed.”

Because of that, variant scoring can be an incredibly tedious effort. “It might take a person a whole day to classify a single difficult variant,” Junaid says. On average, he adds, analysts can typically expect to spend several hours to classify each variant. “You have to search through all this information, read lengthy clinical research papers, analyze clinical data, and weigh the importance and relevance of each study. Then there are the modeling programs, such as RNA splicing predictions. It’s a very time-consuming process,” he notes.

As NGS-based genetic testing becomes more commonplace, and as labs see increasing test volumes, having such a hands-on interpretation workflow will not be sustainable. Accelerating that process is one of the key goals of our newest product, Ingenuity Clinical, currently being evaluated by early access users. Our team has worked hard on this new application and we’re eager to get it out to a broader customer base so we can help clinical teams save time and perform high-quality variant interpretation for these important tests.

If you’ll be attending this week’s AMP conference, visit the QIAGEN booths (#707 and #1023) for a demo of the new platform. Junaid will be at the conference to answer any of your questions. In the meantime, check back for the last blog in our series, which will focus on interpreting cancer variants.

 

Next blog: Clinical Lab Director’s View Part 3: Somatic and Hereditary Cancer

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