YIGUIz

chessRevisions

chess revision scripts and notebooks

LyRic

Long RNA-seq analysis workflow

circrna_detection

circs_snake : a snakemake-based circRNA detection workflow

circrna_snakemake

find_circ

tools to find circRNAs in RNA-seq data

wf-transcriptomes

c_elegans_dRNAseq_analysis

Scripts used in the analysis of C elegans dRNAseq data

Phragmites-australis-transcriptome-optimal-assembly

Workflow and scripts used for the assembly of the Phragmites australis transcriptome.

illumina-array-protocols

processing illumina SNP arrays

Neuron

During brain development, neural stem cells (NSCs) undergo multiple fate-switches to generate various neuronal subtypes and glial cells, exhibiting distinct transcriptomic profiles at different stages. Despite the extensive transcriptomic characterization of human and mouse brain cells at bulk and single-cell levels, full-length transcriptomic datasets of NSCs across different neurodevelopmental stages under similar experimental settings are lacking, which is essential for uncovering stage-specific transcriptional and post-transcriptional mechanisms underlying the fate commitment of NSCs. Here, we report the full-length transcriptome of mouse NSCs at five different stages during embryonic and postnatal development. We used fluorescent-activated cell sorting (FACS) to isolate CD133+Blbp+ NSCs from C57BL/6 transgenic mice that express enhanced green fluorescent protein (EGFP) under the control of a Blbp promoter. Integrating Smart-seq2 RNA-seq and Oxford Nanopore full-length RNA-seq, we created a transcriptomic dataset of gene and isoform expression profiles in NSCs at embryonic days 15.5, 17.5, and postnatal days 1.5, 8, and 60. This dataset provides a detailed characterization of full-length transcripts in NSCs at distinct developmental stages, and could be used as a resource for the neuroscience community to study NSC fate determination, neural development, and disease.

TWAS

NA12878

Data and analysis for NA12878 genome on nanopore

blog_installing_packages

Blogpost about installing packages and getting package dependencies

scRNA-seq

single-cell-tutorial

Single cell current best practices tutorial case study for the paper:Luecken and Theis, "Current best practices in single-cell RNA-seq analysis: a tutorial"

scRNA-AHCA

Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs

scSeq_convCOVID

Custom code for data analysis from "A single-cell atlas of lymphocyte adaptive immune repertoires and transcriptomes reveals age-related differences in convalescent COVID-19 patients"

tabula-muris-vignettes

Examples analyses using the single-cell RNA-seq data from mouse cell atlases

CrossMap

CrossMap is a python program to lift over genome coordinates from one genome version to another.

fasta_SNP_extraction

Extract a section of a reference genome flanking an input locus with SNPs.

Scripts

Collection of various scripts

python-animal-td-model

A BLUPF90 wrapper written in Python implementing the Animal Model, as well as Test-Day Model for estimating breeding values (EBVs)

rd-imputation-accuracy

Phasing and genotype Imputation comparison. Have been evaluated: BEAGLE 5.4, EAGLE 2.4.1, SHAPEIT 4, MINIMAC 4, IMPUTE 5, using accuracy metrics like: IQS(Imputation Quality score), r2 (Pearson correlation), Concordance.

Blupf90TutorialStandard

A tutorial of the BLUPF90 family programs

cognito

🚀🤖 Cognito - Simplifies AutoML Data Preprocessing.

SNP_correlation_local_gEBV_variance

Rscript that conducts analysis of ‘corvar’, i.e., SNP correlation with variance of local gEBV

bayesR

Bayesian hierarchical model for complex trait analysis

simtrait

Simulate Complex Traits from Genotypes

KAML

:bicyclist: Kinship Adjusted Multi-Loci Best Linear Unbiased Prediction

nadiv

R package that constructs (non)additive genetic relationship matrices, and their inverses, from a pedigree to be used in linear mixed effect models (A.K.A. the 'animal model').