brentlab Goto Github PK

brentlabrnaseqtools

callvariants

A variant calling workflow suitable for both checking genotypes and processing data for BSA experiments

dockerize_rstudio_server

dual_threshold_optimization

Dual threshold optimization for identifying convergent evidence: TF binding locations and TF perturbation responses.

eval

Eval is a flexible tool for analyzing the performance of gene-structure prediction programs.

goeval

Gene Regulatory Network Evaluation Using Gene Ontology

iscan

net-evaluation

network evaluation package

netprophet_2.0

A “data light” TF-network mapping algorithm using only gene expression and genome sequence data.

netprophet_3.0

nscan

N-SCAN is a suite of software for gene-structure prediction. N-SCAN is available for human and Drosophila (fruitfly).

ppfinder

PPFINDER is a Perl-based procedure for finding processed pseudogenes in genome annotations (GTF format). PPFINDER identifies pseudogenes using two different methods, and creates a GTF file with pseudogene coordinates that can be used for masking the genome sequence.

rnaseq_databases_documentation

Documentation for all organisms with active databases

rnaseq_metadata_frontend

Front end for the RNASeq metadata database(s)

tfa-evaluation

tfpertrespexplainer

Predict genes' response to perturbations and explains which genomic factors determine the response predictions.

trace_recalling

Trace Recalling is a novel method for deconvoluting double traces that result from simultaneously sequencing two DNA templates. Trace Recalling identifies up to two bases at each position of such a trace.

twinscan

Twinscan is a suite of software for gene-structure prediction. Twinscan is currently available for Mammals, Caenorhabditis (worm), Dicot plants, and Cryptococci.

yeastcallingcardsdb

A Django managed shiny app for storing and visualizing Yeast Calling Cards data

yeastccdbshiny

Shiny front end to yeastCallingCardsDB

yeastdnnexplorer

A development environment to explore implementations of deep neural networks for predicting the relationship between transcription factor and target genes using binding and perturbation data