Geneious Biologics has a new round of features to announce. This article covers the main highlights from the last few months:
- Analysis Workflows for batching NGS Antibody Annotation
- New Representative Sequence option: Most Common
- Graphing and plots:
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New Reference Databases: Mouse TCR and Rabbit IgG
Analysis Workflows for batching NGS Antibody Annotation
If you regularly run Set & Merge Paired Reads before running NGS Antibody Annotator, the NGS Antibody Analysis workflow lets you configure and run both steps as a single operation. Rather than waiting for each step to complete before setting up the next, you configure all your options upfront and receive a result once all the steps are finished.
You can also use the workflow to batch analyze multiple datasets in a single run. If you select multiple datasets (for example R1 and R2 read pairs across multiple sequencing runs), you can produce a separate Annotator Result for each dataset using the same settings across all of them.
To learn more, see Workflow: NGS Antibody Analysis
New Representative Sequence option: Most Common
When extracting a representative sequence from a cluster you can now choose Most Common as well as Best Liability Score.
The Most Common Representative is the most abundant full V(D)J within the cluster, measured by the number of input sequences it represents. The option to select the Most Common representative sequence is available on exact and inexact cluster tables, and for Antibody Annotator, NGS Antibody Annotator and Peptide Annotator results.
Note: A full V(D)J region is required, so the option is unavailable if your dataset's Sequence region of interest was not set to FR1 → FR4 at annotation.
To learn more, see Representative Sequences.
Graphing and plots:
Interactive graphs, plus colorblind-friendly palettes
You can now click a cluster node in the Cluster Similarity Network or Cluster Similarity Tree to filter the Sequences Table by those clusters. Select several clusters at once with shift-click or ctrl/cmd-click, then use the Filter by selected clusters button to filter by all of them.
We've also updated the color palettes used across the interactive graphs to make them easier to read, especially for colorblind viewers.
For categorical data, you can now choose colorblind-friendly palettes based on Paul Tol's color schemes, alongside an accessible replacement for the previous rainbow scheme. Large categorical palettes now place contrasting colors next to each other by default, so neighboring clusters are easier to tell apart.
For continuous data, we have added the Viridis, Inferno and Cividis scales and all continuous palettes can now have the colors inverted.
See Using Graphs to interpret Clusters and Clonotypes to learn more.
Color clusters in Networks and Trees by label
You can now color the Cluster Similarity Network and Cluster Similarity Tree graphs by the labels you've assigned to your sequences.
This makes it much easier to tie the graph back to your data: clusters you've flagged in the cluster table are now visually distinguishable in the graph, so you can connect a high-level overview of a large dataset to the individual sequences you care about.
To learn more, see Using Custom Labels.
New Reference Databases: Mouse TCR and Rabbit IgG
We've released two new curated germline reference databases:
- Mouse TCR
- Rabbit Ig
Both are curated from publicly available germline genes following our existing reference database conventions, and are up to date with the records deposited at NCBI. These should both be available for all users, and can be found as an option across all our Antibody Annotators.
Note: Our new Rabbit Ig reference database has some genes that couldn't be matched to standard IMGT-named genes. To learn more about this specific database, see our Rabbit IgG reference database article.