Module: Plant Transcriptomics

RNA sequencing is used to assess the expression levels of genes. This video introduces the important concepts related to RNA-seq analysis.

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This lecture goes over the concepts involved in assessing the quality of your sequencing data.

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• Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC
• Assess long reads FASTQ quality using Nanoplot and PycoQC
• Perform quality correction with Cutadapt (short reads)
• Summarise quality metrics MultiQC
• Process single-end and paired-end data

In this tutorial you will get some hand-on experience performing a quality assessment on sequencing data.

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• Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC
• Assess long reads FASTQ quality using Nanoplot and PycoQC
• Perform quality correction with Cutadapt (short reads)
• Summarise quality metrics MultiQC
• Process single-end and paired-end data

This lecture covers the basic concepts involved in mapping sequencing reads to a reference genome.

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• You will learn what mapping is
• A genome browser is shown that helps you to understand your data

In this tutorial you will map sequencing data to a reference genome, and explore the mapped reads in a genome browser.

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• You will learn what mapping is
• A genome browser is shown that helps you to understand your data

In plants, miRNAs can silence targets through RNA degradation as well as translational repression pathways, and unlike animals, a large proportion of miRNA and their targets have less than four mismatches. This feature has been exploited for developing miRNAs target prediction tools, providing an efficient approach to elucidate the miRNA-mediated regulatory networks, which can contribute to biotechnological solutions to improve crops productivity.

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• Perform miRNA differential expression analysis
• Understand the quasi-mapping-based Salmon method for quantifying the expression of transcripts using RNA-Seq data
• Idenfity potential miRNAs involved in brassinosteroid-mediated regulation networks

In plants, miRNAs can silence targets through RNA degradation as well as translational repression pathways, and unlike animals, a large proportion of miRNA and their targets have less than four mismatches. This feature has been exploited for developing miRNAs target prediction tools, providing an efficient approach to elucidate the miRNA-mediated regulatory networks, which can contribute to biotechnological solutions to improve crops productivity.

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• Perform miRNA differential expression analysis
• Understand the quasi-mapping-based Salmon method for quantifying the expression of transcripts using RNA-Seq data
• Idenfity potential miRNAs involved in brassinosteroid-mediated regulation networks

Single-cell RNA-seq analysis is a rapidly evolving field at the forefront of transcriptomic research, used in high-throughput developmental studies and rare transcript studies to examine cell heterogeneity within a populations of cells. The cellular resolution and genome wide scope make it possible to draw new conclusions that are not otherwise possible with bulk RNA-seq. Slides created by Mehmet Tekman.

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• To understand the pitfalls in scRNA-seq sequencing and amplification, and how they are overcome.
• Know the types of variation in an analysis and how to control for them.
• Grasp what dimension reduction is, and how it might be performed.
• Be familiarised with the main types of clustering techniques and when to use them.

This tutorial demonstrates the what barcodes in your sequencing data are, and how to handle them during analysis. Please work through the full tutorial yourself to get all the details.

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• Demultiplex FASTQ data via UMI-tools
• Understand and validate the extraction of barcodes

10x genomics has provided not only a cost-effective high-throughput solution to understanding sample heterogeneity at the individual cell level, but has defined the standards of the field that many downstream analysis packages are now scrambling to accommodate. Please work through the full tutorial yourself to get all the details.

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• Demultiplex single-cell FASTQ data from 10X Genomics
• Learn about transparent matrix formats
• Understand the importance of high and low quality cells

In this tutorial, we will investigate clustering of single-cell data from 10x Genomics, including preprocessing, clustering and the identification of cell types via known marker genes, using Scanpy. It will be illustrated using a dataset of Peripheral Blood Mononuclear Cells (PBMC), containing 2,700 single cells. Please work through the full tutorial yourself to get all the details.

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• Describe an AnnData object to store single-cell data
• Explain the preprocessing steps for single-cell data
• Evaluate quality of single-cell data and apply steps to select and filter cells and genes based on QC
• Execute data normalization and scaling
• Identify highly variable genes
• Construct and run a dimensionality reduction using Principal Component Analysis
• Perform a graph-based clustering for cells
• Identify marker genes for the clusters
• Construct and run a cell type annotation for the clusters

This tutorial replicates the paper “Spatiotemporal Developmental Trajectories in the Arabidopsis Root Revealed Using High-Throughput Single-Cell RNA Sequencing” (Denyer et al. 2019), where the major plant cell types are recovered in the data as well as distinguishing between QC and meristematic cells. The original paper used the Seurat analysis suite (Satija et al. 2015), but here we will use the ScanPy analysis suite (Wolf et al. 2018) integrated within the single-cell resources in Galaxy (Tekman et al. 2020).

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• Perform filtering, dimensionality reduction, and clustering
• Generate a DotPlot emulating the original paper using a different analysis tool
• Determine robust clusters across scRNA-seq pipelines