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What_molecular_aspect_was_attempting_to_be_identified_with_this_data?

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ATAC-seq is a method for determining chromatin accessibility across the genome, and the guidelines provide recommendations for experimental design, sequencing, and data analysis.

ATAC-seq is a method for determining chromatin accessibility across the genome, and the guidelines provide recommendations for experimental design, sequencing, and data analysis.

This is a slide deck that explains the concepts behind ATAC-seq

This is a slide deck that explains the concepts behind ATAC-seq

This standard ENCODE ATAC-seq pipeline shows how ATAC-seq data can be analyzed

The mint pipeline analyzes single-end reads coming from sequencing assays measuring DNA methylation and hydroxymethylation. The pipeline analyzes reads from both bisulfite-converted assays such as WGBS and RRBS, and from pulldown assays such as MeDIP-seq, hMeDIP-seq, and hMeSeal. Moreover, with data measuring both 5-methylcytosine (5mc) and 5-hydroxymethylcytosine (5hmc), the mint pipeline integrates the two data types to classify genomic regions of 5mc, 5hmc, a mixture, or neither.

This pipeline in Galaxy shows how methylation sequence data can be analyzed using the Galaxy GUI cloud.

These slides describe the concept behind bulk RNA-seq data

This GitHub training RMarkdown based files show how one can analyze RNA-seq data using R

This Data Carpentries lesson walks through the steps that need to take place for analyzing RNA-seq data

These Galaxy slides describe how RNA-seq counts can be converted to gene-level data

These Galaxy slides describe how RNA-seq reads can be converted to counts

The EdgeR package helps downstream RNA-seq be analyzed. Differential expression and count normalization are things that EdgeR can do.

This GitHub repository has files related to a workshop that describes how ChIP-seq data can be processed.

This webpage describes considerations one should think of when analyzing ChIP-seq data

These slides describe epigenomics assays: ChIP-seq and ATAC-seq

The GATK4 tool set allows you to process whole genome sequencing

This webpage also discusses how to use the GATK4 variant calling pipeline.

CNVnator is a tool for CNV discovery and genotyping from depth-of-coverage by mapped reads

This Galaxy training material discusses sequence analysis on a conceptual level.

This Galaxy training material discusses variant analysis on a conceptual level.

This webpage describes bioinformatics workflow for Whole Genome Sequencing

DNAseq Workflow in a Diagnostic Context and an Example of a User Friendly Implementation

A description of what a standard DNA-sequencing entails at each step

From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline

This training material from Alex's Lemonade Stand Foundation describes how to process microarray data from soup to nuts

The limma package helps analyze gene expression microarray data

This bioconductor tutorial shows how to normalize and process microarray data.

The snakePipes HiC workflow allows users to process their HiC data from raw fastq files to corrected HiC matrices and TADs.

This bioconductor tutorial shows how to normalize and process methylation array data

This set of Galaxy training slides show information about proteomic data

This Bioconductor pacakge shows how to analyze proteomic data

This training material from Alex's Lemonade Stand Foundation describes how to process RNA-seq data from soup to nuts

Evaluate the performance of depth normalization methods in microRNA sequencing.

DANA is an approach for assessing the performance of normalization for microRNA-Seq data based on biology-motivated and data-driven metrics.

A guide to creating design matrices for gene expression experiments

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

This bioconductor tutorial shows how to use EdgeR to perform differential expression analysis on microarray data

This webpage discusses how to properly handle bulk RNA-seq data

This webpage describes the underlying principles behind RNA-seq data

This bioconductor tutorial shows how to use R to analyze single cell RNA-seq data

This bioconductor tutorial shows advanced techniques for analyzing single cell RNA-seq data

SCGV is an interactive graphical tool for single-cell genomics data, with emphasis on single-cell genomics of cancer

This set of slides from Alex's Lemonade Stand Foundation describe the concepts behind single-cell RNA-seq data

This training material from Alex's Lemonade Stand Foundation describes how to process single cell RNA-seq data from soup to nuts

This training material from NYU shows how to analyze single cell RNA0-seq data

These documentation guides from Salmon show how to analyze single-cell data using the Alevin package.

This Galaxy tutorial describes the concepts underlying single-cell RNA-seq data

This Galaxy tutorial shows the basics of analyzing single-cell RNA-seq data

This book walks through the details of using single-cell RNA-seq data

Seurat is a useful R package for downstream analysis of single-cell RNA-seq data

scater is a useful R package for visualization of single-cell RNA-seq data

scran is a useful R package for normalization of single-cell RNA-seq data