Here we provides comprehensive and step wise tutorials to analyse different datasets:
Analysis Pipeline For 3’RNA-seq
Single Cell RNAseq data analysis
Gene co-expression network and Gene regulatory network
Visualizes data in a circular plots
Gene Set Enrichment Analysis (GSEA)
GWAS/ Variant Calling Pipeline
Machine Learning/Deep Learning in Genomics
Bioinformatics Training Courses/ Books
An Introduction to Machine Learning with R
ChIPseq data analysis
Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data
ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia
ChIP-seq analysis notes from Tommy Tang
Q & A ChIP-seq technologies and the study of gene regulation
A survey of tools for variant analysis of next-generation genome sequencing data
CGAT: a model for immersive personalized training in computational genomics
NGS data analysis with R / Bioconductor: ChIP-Seq workflow
Introduction to ChIP-Seq data and analysis
Hands-on introduction to ChIP-seq analysis – VIB Training
Pipeline for ChIP-seq preprocessing
A comprehensive comparison of tools for differential ChIP-seq analysis
Analysis of ChIP-seq data using Galaxy
ChIP-Seq data analysis ( Galaxy Training)
Analysis of ChIP-seq Data in R/Bioconductor
ATAC-Seq data analysis
Assessment of computational methods for the analysis of single-cell ATAC-seq data
From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis
RNAseq data analysis
A survey of best practices for RNA-seq data analysis
RNA-seq workflow: gene-level exploratory analysis and DE
RNAseq analysis notes from Tommy Tang
Informatics for RNA-seq: A web resource for analysis on the cloud
RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
An open RNA-Seq data analysis pipeline tutorial with an example
Transcript-level expression analysis of RNA-seq experiments
quanTIseq is a computational pipeline for the quantification of the Tumor Immune contexture from human RNA-seq data
RNAseq analysis in R
RNA-seq Tutorial (with Reference Genome)
Reference-based RNA-Seq data analysis (Galaxy Training)
BIOCORE RNAseq course 2019
Informatics for RNA-seq: A web resource for analysis on the cloud
Analysis Pipeline For 3’RNA-seq
QuantSeq 3′ mRNA sequencing for RNA quantification
Analysis of Lexogen Quantseq Data: Part 1
3′-seq computational analyses Lianoglou et al 2013
3ʹ-seq computational analyses Irtisha et al 2018
Single Cell RNAseq data analysis
bigSCale is a complete framework for the analysis and visualization of single cell data.
Orchestrating Single-Cell Analysis with Bioconductor
Downstream single-cell RNA analysis with RaceID
Single Cell RNAseq data analysis Tutorial
A step-by-step workflow for low-level analysis of single-cell RNA-seq data
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
SCell: single-cell RNA-seq analysis software
https://github.com/diazlab/SCell
Seurat R package designed for QC, analysis, and exploration of single cell RNA-seq data
Beta-Poisson model for single-cell RNA-seq data analyses
https://github.com/nghiavtr/BPSC
Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis
https://research.cchmc.org/pbge/sincera.html
SC3 – consensus clustering of single-cell RNA-Seq data
http://biorxiv.org/content/early/2016/09/02/036558
Citrus: A toolkit for single cell sequencing analysis
http://biorxiv.org/content/early/2016/09/14/045070
Single-Cell Resolution of Temporal Gene Expression during Heart Development
http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7
Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects
http://biorxiv.org/content/early/2016/11/15/087775
Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes
http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract
SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation
http://biorxiv.org/content/early/2016/11/21/088856
SCOUP is a probabilistic model to analyze single-cell expression data during differentiation
https://github.com/hmatsu1226/SCOUP
scLVM is a modelling framework for single-cell RNA-seq data
https://github.com/PMBio/scLVM
Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories
https://github.com/jw156605/SLICER
SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality
http://www.morgridge.net/SinQC.html
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
https://github.com/zji90/TSCAN
Visualization and cellular hierarchy inference of single-cell data using SPADE
http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html
OEFinder: Identify ordering effect genes in single cell RNA-seq data
https://github.com/lengning/OEFinder
SCANPY: large-scale single-cell gene expression data analysis
https://github.com/theislab/Scanpy
BC2 Single Cell Tutorial (Single Cell Analysis Tutorial )
https://ppapasaikas.github.io/BC2_SingleCell/
Proteomics/Mass Spectrometry/Data Analysis/ Interpretation
- RMassBank: The work ow by example
- Proteomics/Protein Identification -MS/Data Analysis/ Interpretation
- Mass spectrometry and proteomics data analysis
- Visualisation of proteomics data using R and Bioconductor
- Computational Proteomics
- R guide: Analysis of Proteomics Data
- Bioinformatics for Proteomics
Microarray data analysis
Using Bioconductor for Microarray Analysis
An end to end workflow for differential gene expression using Affymetrix microarrays
A Tutorial Review of Microarray Data Analysis
Microarray analysis exercises 1 – with R
Analyze your own microarray data in R/Bioconductor
Analysing microarray data in BioConductor
Processing Affymetrix Gene Expression Arrays
R package for Meta-Analysis of MicroArray
Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets
Visualizes data in R
Dendrograms in R, a lightweight approach
Introduction to Circos plot ppt
RCircos: an R package for Circos 2D track plots
Gene Set Enrichment Analysis (GSEA)
fgsea: An R-package for fast preranked gene set enrichment analysis (GSEA)
Gene co-expression network and Gene regulatory network
GENIE3: GEne Network Inference with Ensemble of trees
https://bioconductor.org/packages/devel/bioc/vignettes/GENIE3/inst/doc/GENIE3.html
CoExpNetViz Comparative Co-Expression Network Construction and Visualization
http://bioinformatics.psb.ugent.be/webtools/coexpr/
GeneNetworkBuilder Guide: Build Regulatory Network from ChIP-chip/ChIP-seq and Expression Data
https://bioconductor.org/packages/release/bioc/vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html
CoRegNet: Reconstruction and integrated analysis of Co-Regulatory Networks
https://bioconductor.org/packages/release/bioc/vignettes/CoRegNet/inst/doc/CoRegNet.html
ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks
http://califano.c2b2.columbia.edu/aracne/
ARACNe-inferred gene networks from TCGA tumor datasets
https://bioconductor.org/packages/release/data/experiment/html/aracne.networks.html
WGCNA: an R package for weighted correlation network analysis
https://horvath.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/
MERLIN: An approach to capture per-gene and per-module regulatory information
http://pages.discovery.wisc.edu/~sroy/merlin/index.html
coexnet: An R package to build CO-EXpression NETworks from Microarray Data
https://www.bioconductor.org/packages/3.7/bioc/vignettes/coexnet/inst/doc/coexnet.pdf
RTN: reconstruction of transcriptional networks and analysis of master regulators.
http://bioconductor.org/packages/devel/bioc/vignettes/RTN/inst/doc/RTN.html
GWAS/ Variant Calling Pipeline
Genome-Wide Association Studies
Variant Calling Pipeline: FastQ to Annotated SNPs in Hours
Hands-on Tutorial on SNP Calling
Pipeline for variant calling
Machine learning in Genomics
Machine learning applications in genetics and genomics
Deep learning, genomics, and precision medicine
Deep Learning for Cancer Immunotherapy
Bioinformatics Training Courses
Next-Generation Sequencing Analysis Resources
rstudio-conf 2019 Workshop materials
Cancer Research UK Cambridge Bioinformatics core facility
HarvardX Biomedical Data Science Open Online Training
SIB Virtual Computational Biology Seminar Series
Training Courses by the Babraham Institute the Bioinformatics group
Next-Gen Sequence Analysis Workshop (2013)
Bioinformatics Resources by Obi Griffith
Tutorials developed and maintained by the worldwide Galaxy community
An Introduction to Machine Learning with R
- caret
- ggplot2
- mlbench
- class
- caTools
- randomForst
- impute
- ranger
- kernlab
- class
- glmnet
- naivebayes
- rpart
- rpart.plot