SeqAcademy: an educational pipeline for RNA-Seq and anchor-Seq analysis

Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (anchor-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and anchor-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and anchor-Seq data: SeqAcademy ( source code:  https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in markdown language, emphasizes the use of publicly available RNA-Seq and anchor-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and anchor-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and anchor-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and anchor-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and anchor-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and anchor-Seq data.

SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis

Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy ( source code:  https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in Jupyter Notebook, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.

SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis

Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy ( source code:  https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in markdown language, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.

SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis

Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy ( source code:  https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in Jupyter Notebook, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.

m6aViewer: software for the detection, analysis, and visualization of N6-methyladenosine peaks from m6A-seq/ME-RIP sequencing data

Recent methods for transcriptome-wide N⁶-methyladenosine (m⁶A) profiling have facilitated investigations into the RNA methylome and established m⁶A as a dynamic modification that has critical regulatory roles in gene expression and may play a role in human disease. However, bioinformatics resources available for the analysis of m⁶A sequencing data are still limited. Here, we describe m6aViewer-a cross-platform application for analysis and visualization of m⁶A peaks from sequencing data. m6aViewer implements a novel m⁶A peak-calling algorithm that identifies high-confidence methylated residues with more precision than previously described approaches. The application enables data analysis through a graphical user interface, and thus, in contrast to other currently available tools, does not require the user to be skilled in computer programming. m6aViewer and test data can be downloaded here: http://dna2.leeds.ac.uk/m6a.

Assay for Transposase-Accessible Chromatin Using Sequencing (ATAC-seq) Data Analysis

The study of epigenetic properties of the human genome, including structural modifications of DNA and chromatin, has increased tremendously as mounting evidence has demonstrated how much epigenetics affects human gene expression. Buenrostro et al. have developed a rapid method, requiring low numbers of living cells as input, for examining chromatin accessibility across the epigenome, known as the assay for transposase-accessible chromatin using sequencing (ATAC-seq). The overall goal of this unit is to provide a thorough ATAC-seq data analysis plan, as well as describe how primary human blood samples can be processed for use in ATAC-seq. In addition, a number of quality control parameters are discussed to ensure the integrity and confidence in the ATAC-seq data. © 2017 by John Wiley & Sons, Inc.

bPeaks: a bioinformatics tool to detect transcription factor binding sites from ChIPseq data in yeasts and other organisms with small genomes

Peak calling is a critical step in ChIPseq data analysis. Choosing the correct algorithm as well as optimized parameters for a specific biological system is an essential task. In this article, we present an original peak-calling method (bPeaks) specifically designed to detect transcription factor (TF) binding sites in small eukaryotic genomes, such as in yeasts. As TF interactions with DNA are strong and generate high binding signals, bPeaks uses simple parameters to compare the sequences (reads) obtained from the immunoprecipitation (IP) with those from the control DNA (input). Because yeasts have small genomes (<20 Mb), our program has the advantage of using ChIPseq information at the single nucleotide level and can explore, in a reasonable computational time, results obtained with different sets of parameter values. Graphical outputs and text files are provided to rapidly assess the relevance of the detected peaks. Taking advantage of the simple promoter structure in yeasts, additional functions were implemented in bPeaks to automatically assign the peaks to promoter regions and retrieve peak coordinates on the DNA sequence for further predictions of regulatory motifs, enriched in the list of peaks. Applications of the bPeaks program to three different ChIPseq datasets from Saccharomyces cerevisiae, Candida albicans and Candida glabrata are presented. Each time, bPeaks allowed us to correctly predict the DNA binding sequence of the studied TF and provided relevant lists of peaks. The bioinformatics tool bPeaks is freely distributed to academic users. Supplementary data, together with detailed tutorials, are available online: http://bpeaks.gene-networks.net.

Using MACS to identify peaks from ChIP-Seq data

Model-based Analysis of ChIP-Seq (MACS) is a command-line tool designed by X. Shirley Liu and colleagues to analyze data generated by ChIP-Seq experiments in eukaryotes, especially mammals. MACS can be used to identify transcription factor binding sites and histone modification-enriched regions if the ChIP-Seq data, with or without control samples, are given. This unit describes two basic protocols that provide detailed information on how to use MACS to identify either the binding sites of a transcription factor or the enriched regions of a histone modification with broad peaks. Furthermore, the basic ideas for the MACS algorithm and its appropriate usage are discussed.