Visuals
Explore the job’s output with interactive plots, charts, and other visualization tools.
Volcano plot of Transcripts and Heatmap of Differentially Expressing Genes
The Volcano Plot is -Log10(Padj) vs. Log2(Fold Change). A positive Log2(Fold Change) indicates an up-regulation of a gene in condition A vs. condition B. A Log2(Fold Change) of 1 means that the gene expressed two-fold higher in condition A vs. condition B. -Log10(Padj) is a scaling metric that allows higher interpretability for the significance of results. A smaller adjusted P-value indicates higher significance. The heatmap contains centered and normalized raw read count data of Differentially Expressing Genes (DEGs) and allows for a quick overview of expression patterns by condition and gene.
NOTE: For -Log10(Padj), NaN and infinite values are automatically set to -1.
Gene Ontology Enrichment
NOTE: Initial gene ontology scan of DEGs. Not every gene has a result from our standard gene ontology process, so we provide a secondary scan in addition to this one below of all genes that did not get annotations in the first pass.
Acronym's in Category: MF - Molecular Function
Acronym's in Evidence: EXP - Experimental Evidence, IDA - Inferred from Direct Assay, IPI - Inferred from Physical Interaction, IMP - Inferred from Mutant Phenotype, IGI - Inferred from Genetic Interaction, IEP - Inferred from Expression Pattern, ISS - Inferred from Sequence or Structural Similarity, ISO - Inferred from Sequence Orthology, ISA - Inferred from Sequence Alignment, ISM - Inferred from Sequence Model, IGC - Inferred from Genomic Context, RCA - Inferred from Reviewed Computational Analysis, TAS - Traceable Author Statement, NAS - Non-traceable Author Statement, IC - Inferred by Curator, ND - No Biological Data Available
Secondary Gene Ontology Enrichment
NOTE: Secondary Gene Ontology Scan, some genes still may not have results and will require further investigation.
AI models produce responses and outputs through sophisticated algorithms and learning techniques, which may result in inaccuracies. By engaging with this model, you accept responsibility for any potential harm resulting from its responses or outputs.
Config
View the configuration and settings that were used to produce this job.
| Configuration Setting | Set Value |
|---|
Files
Access the input and generated output files associated with this job.
Input Files
Download all as a zip file:
Output Files
Download all as a zip file:
Citations
Reference these works when publishing findings derived from this job.
|
Muzellec, B., Telenczuk, M., Cabeli, V., & Andreux, M. (2022). PyDESeq2: a python package for bulk RNA-seq differential expression analysis. bioRxiv. |
|
Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |