Here we provides comprehensive and step wise tutorials to analyse different datasets:
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.
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?
RNAseq data analysis
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
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
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
SCell: single-cell RNA-seq analysis software
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
Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis
SC3 – consensus clustering of single-cell RNA-Seq data
Citrus: A toolkit for single cell sequencing analysis
Single-Cell Resolution of Temporal Gene Expression during Heart Development
Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects
Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes
SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation
SCOUP is a probabilistic model to analyze single-cell expression data during differentiation
scLVM is a modelling framework for single-cell RNA-seq data
Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories
SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
Visualization and cellular hierarchy inference of single-cell data using SPADE
OEFinder: Identify ordering effect genes in single cell RNA-seq data
SCANPY: large-scale single-cell gene expression data analysis
BC2 Single Cell Tutorial (Single Cell Analysis Tutorial )
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
Visualizes data in R
Gene Set Enrichment Analysis (GSEA)
Gene co-expression network and Gene regulatory network
GENIE3: GEne Network Inference with Ensemble of trees
CoExpNetViz Comparative Co-Expression Network Construction and Visualization
GeneNetworkBuilder Guide: Build Regulatory Network from ChIP-chip/ChIP-seq and Expression Data
CoRegNet: Reconstruction and integrated analysis of Co-Regulatory Networks
ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks
ARACNe-inferred gene networks from TCGA tumor datasets
WGCNA: an R package for weighted correlation network analysis
MERLIN: An approach to capture per-gene and per-module regulatory information
coexnet: An R package to build CO-EXpression NETworks from Microarray Data
RTN: reconstruction of transcriptional networks and analysis of master regulators.
GWAS/ Variant Calling Pipeline
Pipeline for variant calling
Machine learning in Genomics
Bioinformatics Training Courses
Other useful links
This document is summarised in the table below. It shows the linear models underlying common parametric and “non-parametric” tests.