Ingenuity http://www.ingenuity.com Intuitive web-based applications for quickly analyzing and accurately interpreting the biological meaning in your genomics data Wed, 19 Nov 2014 18:24:29 +0000 en-US hourly 1 http://wordpress.org/?v=4.0.1 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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S_4225_BI_Clinical

 

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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S_4225_BI_Clinical

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.

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Craniofacial Disorder Study Yields New Clues for Mount Sinai Researcher!http://www.ingenuity.com/blog/science/craniofacial-disorder-study-yields-new-clues-mount-sinai-researcher http://www.ingenuity.com/blog/science/craniofacial-disorder-study-yields-new-clues-mount-sinai-researcher#comments Fri, 07 Nov 2014 23:25:37 +0000 http://www.ingenuity.com/?p=5312 Physician-scientist Bryn Webb is using Ingenuity Variant Analysis to interpret sequence data from patients with rare congenital facial paralysis disorders. Just a few labs around the world specialize in these disorders, which include Moebius syndrome and other forms of facial … Read More

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Bryn Webb, Instructor in Genomic sciences, & Pediatrician at the Icahn School of Medicine at Mount Sinai

Bryn Webb, Instructor in Genomic sciences, & Pediatrician at the Icahn School of Medicine at Mount Sinai

Physician-scientist Bryn Webb is using Ingenuity Variant Analysis to interpret sequence data from patients with rare congenital facial paralysis disorders. Just a few labs around the world specialize in these disorders, which include Moebius syndrome and other forms of facial palsy. One of these labs is led by Bryn Webb, an instructor in genetics and genomic sciences as well as pediatrics at the Icahn School of Medicine at Mount Sinai and at the Icahn Institute for Genomics and Multiscale Biology. Though she is still early in her career, Webb has already won awards for her genetic research in craniofacial disorders. With Ingenuity Variant Analysis from QIAGEN in her arsenal, she is poised to add even more to the body of knowledge around these disorders.

She is on a mission to make it more straightforward to diagnose people with developmental congenital facial paralysis and associated conditions. Webb is taking a candidate gene approach with next-gen sequencing to elucidate the genetic underpinnings of these disorders. Her gene panel is 2.5 Mb and includes 436 genes she chose based on candidates from animal models in the literature, variants known to be involved in these disorders, and results from her own research that merited additional interrogation. So far, she has sequenced nearly 100 probands, running new samples as they arrive and confirming next-gen results with Sanger.

Webb interprets her sequencing results using Ingenuity Variant Analysis. She uses the platform to exclude the most variable exonic regions and focus on changes that are predicted to be deleterious. “Not only can I sort variants, but then I can also start to look at more complex features,” she says. “I look at whether any variants are common to multiple persons, and then use burden analysis and association features.” She notes that being able to upload sequence results for all 98 probands and analyze variants across them has been especially helpful. “I can see how many variants are common to two people, three people, four people, and so on,” she adds.

Because she used Ingenuity Variant Analysis, Webb also had access to a built-in database of control samples. She used genomic data from the Personal Genome Project and the 1,000 Genomes Project, both available within the QIAGEN platform, to help with the burden analysis interpretation and provide more power than just using dbSNP frequencies. Now she is conducting follow-up studies on the promising variants highlighted in her analysis.

Ultimately, establishing clear genetic markers for these disorders will not only enable physicians to clearly diagnose them — and therefore provide better treatment for patients — but may even contribute to the discovery of prenatal treatments that could detect anomalies and help steer cells toward healthy development.

For more on Bryn Webb’s work on these rare disorders and her use of Ingenuity Variant Analysis, check out the case study here.Headshot0914

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Clinical Lab Director’s View Part 1: Launching a New Genetic Testhttp://www.ingenuity.com/blog/news/clinical-lab-directors-view-part-1-launching-new-genetic-test http://www.ingenuity.com/blog/news/clinical-lab-directors-view-part-1-launching-new-genetic-test#comments Thu, 06 Nov 2014 22:58:36 +0000 http://www.ingenuity.com/?p=5325   Today we kick off a new series of blog posts focusing on the challenges associated with the interpretation and reporting of NGS-based diagnostic tests. We’ve been thinking about this a lot lately as clinical labs engage with our new … Read More

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S_4225_BI_Clinical

 

Today we kick off a new series of blog posts focusing on the challenges associated with the interpretation and reporting of NGS-based diagnostic tests. We’ve been thinking about this a lot lately as clinical labs engage with our new Clinical Decision Support platform through the early access program. The platform is designed to help clinical labs scale their NGS-based test offerings, saving time and accelerating results.

Junaid Shabbeer, QIAGEN Bioinformatics’ Clinical Science Director

Junaid Shabbeer, QIAGEN Bioinformatics’ Clinical Science Director

In this arena we rely heavily on people like Junaid Shabbeer, a board-certified lab director and QIAGEN Bioinformatics’ Clinical Science Director, to gather a more comprehensive understanding of clinical labs’ unmet needs, requirements, and challenges — and make sure that our new platform addresses those. We recently sat down with Junaid to get a better handle on the opportunities and challenges related to NGS genetic testing interpretation and reporting , and throughout this blog series we’ll relate the great insights he shared with us. In today’s post we take a look at the components that go into designing, building, and launching a new genetic test for a lab. Junaid is a good guide for us, given his experience in directing clinical labs at Mount Sinai Hospital in New York, Myriad Genetics, and most recently Ariosa Diagnostics.

Launching the Test

Setting up a new assay is certainly not easy, Junaid tells us — but there are established strategies and steps that labs can follow, as well as guidelines from professional organizations such as ACMG and CAP. At the early test development stage, lab directors gather information on genes known to be associated with a disease they want to test for. Next, they’ll design an assay and choose a platform that will ensure the best performance for the test, for example one that provides uniform coverage of the genes. The aim is for the lab to be able to offer a test with good sensitivity and specificity to find important genetic variants.

“When it comes to designing the assay, you need to make sure it covers enough of the gene, or targets specific regions of the gene where the majority of relevant variants are known to be located,” Junaid says. Next-gen sequencing enables full coverage of multiple genes in a single test, ensuring that variants will be detected no matter where in the genes they may fall. Such tests, though, have unique challenges when it comes to ensuring the robustness and quality of results. For example, depth of sequencing is a very important factor in determining whether a variant found in the assay is real or not.

“There are several commercially available ways of amplifying and sequencing a gene,” Junaid says. “The onus is on the clinical lab to validate the assay.” This entails running a number of samples with known variants to make sure the new assay is picking up everything it should be — and nothing it shouldn’t.

The Variant Universe

Of course, part of laying the groundwork for the new test is getting a sense of the number and types of variants this assay will detect in order to plan for result interpretation needs. For well-established cancer driver mutations, like BRAF V600E or EGFR exon 19 deletions, lab directors could conceivably automate variant interpretation and reporting for these mutations. But for rarer cancer variants, the interpretation and reporting will necessarily be customized for each and every sample that comes through the door. Junaid says that while it is ideal to have a variant analyst who specializes in the genes and diseases the new assay will evaluate, the reality is that, for the most part, “a gene is a gene. The same core principles and techniques would be used to score variants for a new assay.” Smaller reference labs in particular, he notes, are unlikely to have the luxury of hiring analysts with expertise in each and every disease their assays cover.

The Report

Before the assay can be run for patients, the lab director must make important decisions about what the test report will look like — what information it will include, how pathogenic or actionable variants will be highlighted, and more. For diseases associated with a small number of genes, such as Lynch syndrome, directors will usually include all variants found in the final report, Junaid says. Assays screening larger number of genes may not be conducive to that kind of report, so in those cases lab directors may choose not to include variants that are commonly seen in the general population (and therefore likely benign) or that are already well-known not to confer risk for the disease. Variants that are known or likely to be pathogenic or actionable will be highlighted and annotated with information about the risk classification.

Of course, clinical lab regulations dictate the minimal information that will appear on the report, such as details about the test performed or important notes on the assay or platform used. Reports may also include guidance notes on the clinical significance of pathogenic variants and advice on treatment options.

Validation

Extensive validation studies must be conducted by the laboratory before a new test is offered for patient testing. These include internal concordance tests to ensure the right result is obtained for samples with known variants. The lab must also perform proficiency testing in accordance with clinical lab standards at least annually to make sure the assay continues to perform as expected. Junaid notes that ACMG, CAP, and other professional organizations offer extensive guidelines and support for laboratories about test requirements and what to include in a validation. “Fortunately, there are a lot of guidance documents,” he says.

Check back soon as we post the next couple of installments of our series on how clinical labs handle genetic tests and variant interpretation.  Be sure to visit our booth (# 707 and #1023) and workshop next week at the upcoming annual meeting of the Association for Molecular Pathology in National Harbor, Md. Junaid Shabbeer will be at AMP 2014 and would be happy to answer any questions you may have.

 

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Modeling Systems: How IPA Helps Manchester Scientisthttp://www.ingenuity.com/blog/news/modeling-systems-ipa-helps-manchester-scientist http://www.ingenuity.com/blog/news/modeling-systems-ipa-helps-manchester-scientist#comments Wed, 05 Nov 2014 17:21:59 +0000 http://www.ingenuity.com/?p=5281 At The University of Manchester, network modeling expert Adam Stevens uses QIAGEN’s Ingenuity Pathway Analysis to predict upstream regulators, molecular activity, and more for integrated ’omics data sets. A senior research associate in endocrine sciences at Manchester’s Institute of Human … Read More

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Adam Stevens, Senior Research Associate at The University of Manchester’s Institute of Human Development.

At The University of Manchester, network modeling expert Adam Stevens uses QIAGEN’s Ingenuity Pathway Analysis to predict upstream regulators, molecular activity, and more for integrated ’omics data sets.

A senior research associate in endocrine sciences at Manchester’s Institute of Human Development, Stevens uses his background in drug discovery on certain projects related to growth development. He deploys IPA for systems modeling, including predicting molecular activity and the function of upstream regulators. “My job is almost entirely in silico now,” Stevens says, “and I’m loving every minute of it.”

A publication in The Pharmacogenomics Journal describes a large study in which Stevens and his team pulled together metabolomic and transcriptomic data to create a detailed view of how growth rates differ for children born smaller than normal. “Insights into the pathophysiology of catch-up compared with non-catch-up growth in children born small for gestational age: an integrated analysis of metabolic and transcriptomic data,” a paper for which Stevens was lead author, reports biological differences between kids who later caught up to normal size and those who remained small for their age. In addition to being a useful source of information about differences in growth rates, the project was important because children who exhibit catch-up growth are more likely to develop cardiometabolic diseases later in life.

For this work, Stevens says, IPA played a key role in data analysis. “We used IPA because it has fantastic metabolomics features,” he notes. “It helped me decode what was going on in these two data sets.” The paper demonstrates Stevens’ first use of the new Molecule Activity Predictor tool in IPA, which helped reveal the primary functional relevance of the data. He also found the Upstream Regulator Analysis to be very powerful. “It’s elegantly accessed in IPA and is tied in with Mechanistic Networks and the Molecule Activity Predictor,” he says.

Stevens’ appreciation for in silico science means that he reserves the relatively expensive bench work for procedures that can’t be done any other way, such as validating computational observations. “Bench work is expensive and time-consuming,” he says. “An Ingenuity Pathway Analysis license is a lot more affordable than somebody who’s working with cell cultures and running all sorts of transfections.”

For more on Adam Stevens, including his scientific career path and how he uses IPA, check out this case study.

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Product Update: QIAGEN’s IPA and Variant Analysis 2014 Fall Releaseshttp://www.ingenuity.com/blog/news/product-update-qiagens-ipa-variant-analysis-2014-fall-releases http://www.ingenuity.com/blog/news/product-update-qiagens-ipa-variant-analysis-2014-fall-releases#comments Thu, 30 Oct 2014 19:07:03 +0000 http://www.ingenuity.com/?p=5288 We’ve had a busy fall here at QIAGEN Bioinformatics, culminating in significant product updates for both the IPA and Ingenuity Variant Analysis applications. These updates are part of our ongoing commitment to innovation in data interpretation and keeping pace with … Read More

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We’ve had a busy fall here at QIAGEN Bioinformatics, culminating in significant product updates for both the IPA and Ingenuity Variant Analysis applications. These updates are part of our ongoing commitment to innovation in data interpretation and keeping pace with the rapid advances happening in biomedical research.

Here are some of the highlights: 

Ingenuity Variant Analysis:Ingenuity Variant Analysis 2014 Fall Release

One of the powerful new capabilities for Variant Analysis released this fall is the ability to pre-filter data during the pre-analysis step. This convenient feature allows you to speed loading and optimize system resources by focusing on exonic regions, high-quality variants or likely causal variants typically absent in a “normal” population. You can also filter on Copy Number Variants which are specified as a range of bases along with corresponding copy number in VCFS files.

The Ingenuity Variant Analysis Fall Release also includes:

  • Splice site prediction for calculating the effects of single nucleotide variations (SNVs) on splicing events
  • Tighter integration with HGMD which now includes HGMD findings directly within Ingenuity Variant Analysis, no need for a separate HGMD Pro subscription
  • Search for only variants that are also listed in HGMD Pro
  • Pre-filtering to speed up the analysis of vary large cohort studies

Watch the features highlight in Variant Analysis Fall Release video.

Ingenuity Pathway Analysis:

New to IPA is the ability to predict the activity of Canonical Pathways. Now IPA calculates whether Canonical Pathway activity is likely increased or decreased based on differentially expressed genes or proteins in your dataset.

The IPA Fall Release also includes:FallRelease_300x250_101414

  • PathTracer: A quick way to highlight relationships and nodes of interest within networks and pathways
  • BioProfiler: A new way to explore the detailed relationships between molecules and their associated diseases, functions, or phenotypes
  • Relationship Export: Export the structural information contained within IPA networks or pathways

Watch the features highlight in IPA Fall Release video.

 

The post Product Update: QIAGEN’s IPA and Variant Analysis 2014 Fall Releases appeared first on Ingenuity.

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