Next-Generation Sequencing

• Chromatin Structure Analysis
• Use Cases
• Summary and Methods
• Inputs
• Output
• Workflow Walkthrough
• Results Walkthrough
• Citations
• Built with
• Epigenomics: DNA Methylation Detection
• Use Cases
• Summary and Methods
• Inputs
• Outputs
• Workflow Walkthrough
• Results Walkthrough
• Citations
• Built with
• Expression Analysis in RNASeq
• Use Cases
• Summary and Methods
• Inputs
• Outputs
• Workflow Walkthrough
• Results Walkthrough
• Citations
• Built with
• Genomic Variant Analysis
• Use Cases
• Summary and Methods
• Inputs
• Outputs
• Workflow Walkthrough
• Results Walkthrough
• Citations
• Built with
• Human Haplotype
• Summary
• Workflow Walkthrough
• Citations
• Built with
• Single-Cell Analysis
• Use Cases
• Summary
• Methods
• Inputs
• Outputs
• Workflow Walkthrough
• Results Walkthrough
• Citations
• Built with

Chromatin Structure Analysis

Analyze data to determine chromatin accessibility and protein-DNA interactions.

Version 1.0.3

Use Cases

This workflow analyzes data to determine chromatin accessibility and protein-DNA interactions

Summary and Methods

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Epigenomics: DNA Methylation Detection

Determine patterns of methylation in bisulfite sequencing applications and determine methylation events and frequencies for ONT-based assemblies.

Version 0.0.3

Use Cases

• Determine patterns of methylation in Bisulfite-Seq applications
• Calculate methylation events and frequencies for ONT-based assemblies

Summary and Methods

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Expression Analysis in RNASeq

This workflow can be used to determine gene expression, splice variants and differential expression analysis.

Version 1.1.1

Use Cases

• Determine differentially expressed genes between two or more groups of samples (treated vs untreated, knock-out vs wildtype, cell type A vs cell type B)
• Determine differentially expressed transcripts between two or more groups of samples
• Compare the gene expression profiles of samples

Summary and Methods

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Genomic Variant Analysis

Identify single-nucleotide variants (SNVs), indels, and structural variants in a diploid genome resequencing projects by comparison to a reference genome.

Version 1.6.1

Use Cases

• Determine variants in DNA samples compared to a reference genome including single nucleotide variants (SNVs), insertions, deletions and structural variants
• Germline Variant Calling
• Variant Calling in Ancient DNA
• Somatic Mutation Detection
• Determine variants in DNA samples compared to a custom reference genome for small or synthetic genomes
• Plasmid
• Virus
• Bacteria
• Synthetic Genome
• Sequencing Platform supported include Illumina, Pacbio and Oxford Nanopore (ONT)

Summary and Methods

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Human Haplotype

Determine the haplotype of certain human genes, include HLA, RBG, and Codis

Version 0.0.2

Summary

Sequence reads are aligned and their haplotype is predicted using Hisat-Genotype [1].

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Single-Cell Analysis

Analyze single-cell RNASeq data using 10X assays.

Version 1.0.0

Use Cases

• Determine clustering, differential expression, and annotation of cells in a single-cell experiment
• Create a H5AD file to allow for visualization using CellxGene

Summary

Single-cell RNA sequencing (scRNA-seq) analysis enables researchers to address a wide range of biological questions related to cellular heterogeneity, gene expression dynamics, and cell state transitions. Single-cell RNASeq analysis can be used to (i) identify and classify distinct cell types within a heterogeneous tissue or organism; (ii) infer cellular lineages and developmental trajectories, providing insights into cell fate decisions and differentiation processes; (iii) reveal transitions between different cellular states, such as quiescent to activated states or stem cell to differentiated states; (iv) identify co-expressed gene modules and the inference of regulatory networks within specific cell types or states; (v) elucidate the molecular basis of diseases by identifying dysregulated genes, cell types, or signaling pathways associated with specific conditions; (vi) study immune cell populations and their responses in various contexts, including infection, cancer, and autoimmune diseases; and (vii) analyze gene expression patterns within intact tissue samples by combining scRNA-seq with spatial transcriptomics techniques.

Methods

The workflow is designed to analyze single-cell RNASeq data. Read files are generated by demultiplexing sequence run files with blc2fastq [1]. Sequence reads are mapped and genes are counted using CellRanger. Expression profiles are used to annotate and cluster cells using Seurat [2], SingleCellTK [3], SingleR [4] and CellDX [4]. SingleCellTK is an R package that integrates several existing tools and workflows for single-cell analysis including (i) data loading and preprocessing such as cell filtering, normalization, and log-transformation; (ii) quality control and outlier detection; (iii) dimensionality reduction and clustering with methods like PCA, t-SNE, and uniform manifold approximation and projection (UMAP) for dimensionality reduction and hierarchical clustering or density-based spatial clustering of applications with noise (DBSCAN) for clustering; and (iv) visualization with scatter plots, heatmaps, and gene expression trajectories. Annotations are only available for Human or Mouse samples using the Human Protein Cell Atlas [5] or a mouse RNASeq dataset [6].

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