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cailab-tamu
scTenifoldKnk:In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks
A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.
Maintained by Daniel Osorio. Last updated 3 months ago.
functional-genomicsgene-functiongene-knockoutgene-regulatory-networkvirtual-knockout-experiments
44 stars 4.86 score 11 scripts
bioc
planttfhunter:Identification and classification of plant transcription factors
planttfhunter is used to identify plant transcription factors (TFs) from protein sequence data and classify them into families and subfamilies using the classification scheme implemented in PlantTFDB. TFs are identified using pre-built hidden Markov model profiles for DNA-binding domains. Then, auxiliary and forbidden domains are used with DNA-binding domains to classify TFs into families and subfamilies (when applicable). Currently, TFs can be classified in 58 different TF families/subfamilies.
Maintained by Fabrício Almeida-Silva. Last updated 5 months ago.
softwaretranscriptionfunctionalpredictiongenomeannotationfunctionalgenomicshiddenmarkovmodelsequencingclassificationfunctional-genomicsgene-familieshidden-markov-modelsplant-genomicsplantsprotein-domainstranscription-factors
4.00 score 5 scripts