TechValidate Research on QIAGEN Digital Insights

38 Charts – Page 1 of 2


QIAGEN Digital Insights Customer Research

Which of the following do you consider the biggest gap in the toolset of MGM?

Scalable phylogenetic analysis (large phylogenetic trees): 10%
Comparative phylogenetics (tree comparisons): 13%
Comparative (functional) metagenomics - analyze your sample in the context of many other samples and finding publications with similar profiles: 25%
Tools to infer phenotypes from genomic samples (e.g. predicting AMR, best growth medium for the given genotype or similar): 16%
Tools to infer observables from (functional) metagenomic samples (e.g. predicting the host health state, growth conditions of plants ...): 6%
Tools for building custom databases from private or public data: 13%
More elaborate visualizations, please specify: 3%
More dedicated reference databases, e.g. habitat specific taxonomic profiling databases or functional analysis databases: 7%
Other: 7%

QIAGEN Digital Insights Customer Research

Which databases for antimicrobial resistance detection do you use most frequently?

QMI-AR
31%
Arg-ANNOT
18%
CARD
39%
ResFinder
36%
PointFinder
21%
Other
20%

QIAGEN Digital Insights Customer Research

When you have identified a pathway from a metagenomic functional profiling analysis, which steps do you typically perform subsequently?

Identification of central nodes/enzymes as drug targets
25%
Identification of the phenotypic consequences of differential abundance/expression
69%
Identification of harmful/beneficial metabolites
35%
Other
8%

QIAGEN Digital Insights Customer Research

Which pathway databases would you like to see supported in CLC Microbial Genomics Module?

REACTOME
47%
MetaCyc
42%
Kegg Pathways
76%
WikiPathways
27%
SignaLink
16%
MACADAM
14%
Other
6%

QIAGEN Digital Insights Customer Research

Which terms for functional annotation would you like to see supported in CLC Microbial Genomics Module?

EggNOG
30%
COG
32%
Pfam
48%
EC numbers
37%
GO terms
62%
Kegg
62%
Other
5%

QIAGEN Digital Insights Customer Research

Which databases for metagenomic profiling do you use most frequently?

Kraken databases
22%
OTU databases, e.g. SILVA, Greengenes ...
45%
ProGenomes
6%
Custom-built with CLC
32%
Custom-built outside of CLC
20%
Pre-built databases in CLC (Optimized databases from Download Microbial Reference database)
21%
Other
9%

QIAGEN Digital Insights Customer Research

Which functional metagenomic analysis strategies do you use in your research?

Amplicon based (e.g. 16S, 18S ITS ...) sequencing and functional inference - e.g. PiCRUSt, Tax4Fun, Piphillin ...
58%
Whole metagenomic DNA sequencing and functional inference from read mappings to assemblies/genomes
55%
Whole metatranscriptomics (metagenomic RNA-Seq) sequencing
34%
Metagenomic single-cell RNA sequencing
9%
Other
3%

QIAGEN Digital Insights Customer Research

Which metagenomic analysis strategies do you use in your research?

Amplicon based (e.g. 16S, 18S ITS ...) sequencing
72%
Whole metagenomic DNA sequencing
59%
Metagenomic single-cell DNA sequencing
6%
Crosslinked DNA sequencing (e.g. Hi-C)
3%
Other
6%

QIAGEN Digital Insights Customer Research

For comparative metagenomics or functional metagenomics, which resources do you typically use?

Manually search SRA for relevant datasets and analyze them from scratch
23%
Manually search SRA for relevant datasets and use the results from relevant publications
31%
Search the web for relevant publications
61%
MGnify (https://www.ebi.ac.uk/metagenomics/)
6%
HMP (https://www.hmpdacc.org/)
5%
Use an internal data collection and comparison
34%
Other
6%

QIAGEN Customer Research

Would you be willing to spin down your sample, remove the stabilizing solution, and then transfer the sample to a tube to begin your nucleic acid isolation?

Yes: 83%
No: 15%
Other: 3%

QIAGEN Customer Research

What is the ideal length of time you would like to store stool or soil samples at each temperature, provided that the quality remained the same?

Up to 24 hours 24 - 72 hours 72 hours - 1 week 1 week-1 month More than 1 month
Room temp (15-25C)

27%

8%

24%

19%

22%

4C

8%

5%

8%

34%

45%

>35C

25%

15%

0%

15%

45%

QIAGEN Customer Research

How much would you be willing to pay for a collection device with stabilizing solution compared to just a bulk stabilizing solution?

10% more for collection device vs. bulk
62%
20% more for collection device vs. bulk
30%
50% more for collection device vs. bulk
0%
I would not use a bulk stabilization solution
3%
Other
5%

QIAGEN Customer Research

Using your current stool stabilization solution, do you face any of the following challenges?

The cost of the collection device/stabilization solution is too high
30%
DNA is degraded following storage
18%
RNA is degraded following storage
33%
Results of sequencing after storage differ from those of fresh samples
21%
I want to store stool samples at higher temperatures than recommended
6%
I want to store stool samples for longer than recommended
33%
None of the above
24%

QIAGEN Customer Research

Which of the following options would you prefer if you had the choice?

Standardized collection device with pre-aliquoted stabilization buffer
48%
Bulk solution so that I can choose my own collection device
48%
Other
5%

QIAGEN Customer Research

What method do you currently use for stabilization of DNA and/or RNA in stool or soil samples?

LifeGuard Soil Preservation Solution
0%
OmniGENE GUT
5%
DNA/RNA Shield
10%
RNAlater
28%
I do not use a stabilization solution
46%
Other
10%

QIAGEN Customer Research

How concerned are you about backlash from microbial contamination in NGS library prep reagents or master mixes?

Extremely concerned: 21%
Somewhat concerned: 74%
Not at all concerned: 6%

Qiagen Bioinformatics Commercial Solutions Customer Research

Computer Infrastructure

What computer infrastructure do you currently use to analyze your NGS data?

Desktop computer environment on premise
75%
Server side environment on premise
55%
In a virtual private cloud
10%
In a public cloud
6%

Variant Analysis users solving analysis bottlenecks and getting actionable data results.

How confident are you in the insights and results obtained from Variant Analysis for your experiments?

Extremely confident: 0%
Very confident: 43%
Confident: 57%
Not confident: 0%

Researchers love how Variant Analysis solves bottlenecks and accelerates their work.

What bottlenecks did deploying Ingenuity Variant Analysis solve for your laboratory or organization?

Workflow bottleneck due to the hours of data analysis and review required by other software products
82%
Delayed publication due to a lack of confidence in research findings
5%
Inability to identify the highest value variants resulting in extended experimental timelines and costs
27%
High IT infrastructure costs
14%

Benefits of accessing Ingenuity Variant Analysis through Ion Reporter

Which of the following benefits have you noticed since you began using the Ingenuity Variant Analysis plugin for Ion Reporter?

Helps reduce time to generate a short list of interesting variants
75%
Increases access to rich biological content
40%
Increases efficiency and team productivity
15%
Enables improved sharing and collaboration
15%
Other
5%

Ingenuity Variant Analysis used for both Gene Panel and Exome/Genome in Ion Reporter

For which sample types are you using the Ingenuity Variant Analysis plugin for Ion Reporter?

Gene Panel
62%
Exome / Genome
14%
Both Gene Panel and Exome/Genome
24%

Top Drivers for Selecting IPA for Human RNA-seq Analysis

What were the top drivers for selecting IPA for RNA seq analysis?

Quickly identify differentially expressed isoforms
23%
Interpret the impact of expression changes in the context of biological processes, disease and cellular phenotypes, and molecular interactions
75%
Visualize your RNA seq data in the context of Isoform View
21%
Compile targeted bibliographies with experimental evidence linking your differentially expressed isoforms to biological processes, disease, and molecular interactions
42%
Other
10%

IPA Capability Differentiation Compared to In-House Developed Software

Rate the following IPA capabilities in terms of how differentiated they are compared to the competition.

Extremely Differentiated Highly Differentiated Differentiated Not Differentiated

Faster time to insights
Ease of use
Novel insights
Deeper analysis

IPA Capability Differentiation Compared to GeneGo

Rate the following IPA capabilities in terms of how differentiated they are compared to GeneGo.

Extremely Differentiated Highly Differentiated Differentiated Not Differentiated

Faster time to insights
Ease of use
Novel insights
Deeper analysis

Top Drivers for Selecting IPA for RNA-seq Analysis over GeneGo

What were the top drivers for selecting IPA for RNA seq analysis?

Quickly identify differentially expressed isoforms
33%
Interpret the impact of expression changes in the context of biological processes, disease and cellular phenotypes, and molecular interactions
83%
Visualize your RNA seq data in the context of Isoform View
33%
Compile targeted bibliographies with experimental evidence linking your differentially expressed isoforms to biological processes, disease, and molecular interactions
44%

Top Drivers for Selecting IPA for RNA-seq Analysis over In-House Developed Software

What were the top drivers for selecting IPA for RNA seq analysis?

Quickly identify differentially expressed isoforms
33%
Interpret the impact of expression changes in the context of biological processes, disease and cellular phenotypes, and molecular interactions
71%
Visualize your RNA seq data in the context of Isoform View
21%
Compile targeted bibliographies with experimental evidence linking your differentially expressed isoforms to biological processes, disease, and molecular interactions
33%
Other
4%

Top Reasons for Deploying IPA for RNA-Seq

What types of challenges did deploying IPA for RNA sequencing analysis solve for your laboratory?

Understanding the structure of different isoforms
13%
More precise measurement of transcripts
34%
Ability to distinguish different isoforms
26%
Identifying biologically relevant isoforms
40%
Other
28%

Top Drivers for Selecting IPA for RNA-seq Analysis

What were the top drivers for selecting IPA for RNA seq analysis?

Quickly identify differentially expressed isoforms
22%
Interpret the impact of expression changes in the context of biological processes, disease and cellular phenotypes, and molecular interactions
69%
Visualize your RNA seq data in the context of Isoform View
19%
Compile targeted bibliographies with experimental evidence linking your differentially expressed isoforms to biological processes, disease, and molecular interactions
36%
Other
16%

Substantial Productivity Increase

By what factor did IPA increase the productivity of your bioinformatics staff for RNA seq analysis?

Greater than 10x: 11%
5 to 10x: 14%
2 to 5x: 31%
1 to 2x: 36%
Less than 1x: 8%

IPA RNA-Seq Customer Satisfaction

How satisfied are you with the value for identifying biologically relevant isoforms from RNA seq data using IPA?

Extremely satisfied: 14%
Very satisfied: 34%
Satisfied: 49%
Dissatisfied: 3%



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