Here we provides comprehensive and step wise tutorials to analyse different datasets:

ChIPseq data analysis

ATAC-Seq data analysis

RNAseq data analysis

Analysis Pipeline For 3’RNA-seq

Single Cell RNAseq data analysis

Proteomics/Mass Spectroscopy

Microarray 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

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a central method in epigenomic research. ChIP-seq is a powerful method for identifying genome-wide DNA binding sites for transcription factors and other proteins. Here we provides complete guidelines about ChIP and ChIP-seq.

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

The Analysis of ChIP-Seq Data

EBI ChIP-seq analysis

Introduction to ChIP-Seq data and analysis

Hands-on introduction to ChIP-seq analysis – VIB Training

ENCODE: TF ChIP-seq pipeline

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)

ChIP-seq data analysis in R

Analysis of ChIP-seq Data in R/Bioconductor

ATAC-Seq data analysis

What is ATAC-Seq?
ATAC-Seq stands for Assay for Transposase-Accessible Chromatin with high-throughput sequencing. It is a method for determining chromatin accessibility across the genome and widely used in studying chromatin biology.. The ATAC-Seq method relies on next-generation sequencing (NGS) library construction using the hyperactive transposase Tn5.

How does ATAC-Seq work?

In ATAC-Seq, genomic DNA is exposed to Tn5, a highly active transposase. Tn5 simultaneously fragments DNA, preferentially inserts into open chromatin sites, and adds sequencing primers (a process known as tagmentation). The sequenced DNA identifies the open chromatin and data analysis can provide insight into gene regulation.

What is the difference between ChIP-seq and ATAC-seq?
ATAC-seq is a direct measure of open chromatin by measuring the accessibility of transposition. ChIP-seq is a more indirect method for measuring open chromatin based upon presence of certain histone marks or other protein factors.

Applications of ATAC-Seq?
Chromatin accessibility analysis with ATAC-Seq can provide valuable insights into the regulatory landscape of the genome. Popular applications include:
• Nucleosome mapping
• Transcription factor binding analysis
• Novel enhancer identification
• Cell type–specific regulation analysis
• Evolutionary studies
• Comparative epigenomics

How to analyse ATAC-Seq raw data?

ATAC-seq workshop

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

ATACseqQC Guide

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

Analysis of RNA ‐ Seq Data

RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

EBI RNA-Seq exercise

An open RNA-Seq data analysis pipeline tutorial with an example

RNA-Seq Analysis Workflow

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

quanTIseq documentation

RNAseq analysis in R

Click here

RNA-seq Tutorial (with Reference Genome)

Click here

Reference-based RNA-Seq data analysis (Galaxy Training)

Click here

BIOCORE RNAseq course 2019

Click here

Informatics for RNA-seq: A web resource for analysis on the cloud

click here

Introduction to RNA Seq Course : Tutorial by The University of Texas at Austin

This is a course designed to give you an overview of RNA-Sequencing in a hands-on manner. It will comprise of lectures and guided tutorials. For the tutorials, they used a canned dataset. This course has the following objectives:

To teach you about the different options that are available to you when setting up a RNA-Seq study.
To teach you about the different options that are available to you when analyzing a RNA-Seq dataset.
To familiarize you with some of the typically used RNA-Seq analyses methods.
To provide a vocabulary to understand NGS and RNA-Seq terminology and to provide give you a starting point of where to begin you own data analysis, and enough experience that you can begin that analysis on your own. Click here

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

Introduction to Single-cell RNA-seq

Single-cell RNA-seq analysis workshop

bigSCale is a complete framework for the analysis and visualization of single cell data.

Link

Orchestrating Single-Cell Analysis with Bioconductor

Link

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

Getting Started with Seurat

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/

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

A simple SNP calling pipeline

Hands-on Tutorial on SNP Calling

Genome Analysis Toolkit

Pipeline for variant calling

click here

Machine learning in Genomics

Machine learning applications in genetics and genomics

Deep learning, genomics, and precision medicine

Deep Learning for Cancer Immunotherapy

Deep learning Biology

Bioinformatics Training Courses

Bioconductor workshop 2019

Next-Generation Sequencing Analysis Resources

Computational Genomics With R

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

JCBioinformatics

Bioinformatics Crash Course

Next-Gen Sequence Analysis Workshop (2013)

R and NGS tutorial

Bioinformatics Resources by Obi Griffith

Tutorials developed and maintained by the worldwide Galaxy community

Other useful links

Common statistical tests are linear models

This document is summarised in the table below. It shows the linear models underlying common parametric and “non-parametric” tests.

An Introduction to Machine Learning with R

caret
ggplot2
mlbench
class
caTools
randomForst
impute
ranger
kernlab
class
glmnet
naivebayes
rpart
rpart.plot

BeGenomics

Tutorial

Here we provides comprehensive and step wise tutorials to analyse different datasets:

ChIPseq data analysis

ATAC-Seq data analysis

RNAseq data analysis

Analysis Pipeline For 3’RNA-seq

Single Cell RNAseq data analysis

Proteomics/Mass Spectrometry/Data Analysis/ Interpretation

Microarray data analysis

Visualizes data in R

Gene Set Enrichment Analysis (GSEA)

Gene co-expression network and Gene regulatory network

GWAS/ Variant Calling Pipeline

Machine learning in Genomics

Bioinformatics Training Courses

Common statistical tests are linear models

An Introduction to Machine Learning with R

An extensive resource for Bioinformatics, Epigenomics, Genomics and Metagenomics