All data-types described above can be easily converted into a Feature Abundance Matrix suitable as input to our method. In the future we also plan to provide converters for data generated by commonly-used analysis tools.
Constructing Feature Abundance Matrix
All data-types described above can be easily converted into a Feature Abundance Matrix suitable as input to our method. In the future we also plan to provide converters for data generated by commonly-used analysis tools.
Corresponding Organization :
Other organizations : University of Maryland, College Park
Protocol cited in 283 other protocols
Variable analysis
- The choice of software package used to construct the Feature Abundance Matrix from 16S rRNA and random shotgun data, e.g., RDP Bayesian classifier, Greengenes SimRank, DOTUR, MEGAN, CARMA, MG-RAST
- The Feature Abundance Matrix, which is the input to the authors' method
- No control variables were explicitly mentioned.
- No positive or negative controls were specified by the authors.
Annotations
Based on most similar protocols
As authors may omit details in methods from publication, our AI will look for missing critical information across the 5 most similar protocols.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!