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Webinar on 'An integrated RNA and DNA approach to unravel genetic regulation in cancer'

Abstract

Whole exome DNA sequencing (WES) or whole genome DNA sequencing (WGS) allows detection of mutations and polymorphisms in all exonic and genomic regions, respectively, while messenger RNA sequencing (RNA-Seq) enables quantitative analysis of gene expression. Mutations in the genome result in diverse transcriptional aberrations that can be missed in a stand-alone WES/WGS analysis. An integration of DNA variant analysis and RNA-Seq analysis enables one to investigate the consequences of genomic changes in the RNA transcripts including germline and somatic changes, imprinting, RNA editing and allele specific expression (ASE). In this webinar, we will demonstrate this integrated approach using Strand NGS to identify high confidence mutations, RNA editing events and ASE in cancer.

Webinar Details

Register here: http://www.strand-ngs.com/webinar_registration

About Speaker:

Dr. Veena Hedatale, has a PhD in Plant Genetics from The Radboud University, Netherlands focused on meiosis and recombination. Her prior academic experience at Cornell University was on genetic mapping and gene transformation in Rice. She has worked with Monsanto, and contributed to data mining, database development as well as gene/promoter/pathway discovery for traits related to yield and stress in crop species. At Strand, Veena has worked on Pharmacogenomic analysis of targets and Gene family analysis projects. Currently, she is part of the Strand NGS Application Science team and is involved in the analysis of next generation sequencing data.

Please feel free to contact us 24/5, for availing free online training or if you have any questions.

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Address of the bookmark: https://amp.pharm.mssm.edu/biojupies/

Address of the bookmark: http://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html

Introductory Articles/ppt/sources:

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375

http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf

http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html

http://davetang.org/muse/tag/pathway/

https://www.biostars.org/p/42219/

http://bioinformatics.ca//files/public/Pathways_2014_Module4_v2.pdf

http://bioinformatics.ca//files/public/Pathways_2014_Module2.pdf

Impotant Database and Tools:

GeneMANIA, Cytoscape, IPA and Metacore (Commerical ), Pathway Commons, Reactome ,Panther, BioCyc, WikiPathways, Pathvisio, KEGG, NCI, Stringdb, Amigo, WebGestalt ,ConsensusPathDB ,GSEA,Blast2go

Popular R based tools:

Reactome.db, ReactomePA, ClusterProfiler, Gage, SPIA, topGO, Pathview,DOSE,GOStat

More:

http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+

by Dr. Pandurang Kolekar, Bioinformatics Engineer, Strand Life Sciences

Abstract:

Unique Molecular Identifiers (UMIs) are short random nucleotide sequences that are increasingly being used in high-throughput sequencing experiments. In this webinar, we will highlight the UMI-friendly features of Strand NGS v3.1 including support for handling well known and customised UMI libraries, QC metrics, consensus alignment, UMI-based family size filters for read list, genome browser enabled with UMI-specific features and filters, UMI-aware variant calling parameters, and exporting UMI-tagged aligned samples. These all features together empower users to harness the potential of UMI-tagged NGS data for deeper insights. A case study demonstrating application of these UMI-based features in Strand NGS for low frequency variant calling in cfDNA sample will be presented.

UMI-tagged NGS libraries allow, ultra-sensitive detection of low frequency variants from liquid biopsy samples using DNA-Seq and accurate quantification of transcript-level expression using RNA-Seq. The recent release of Strand NGS v3.1, is equipped with the necessary features to efficiently analyse UMI-tagged NGS data helping researchers and labs involved in rare variant calling like in cfDNA based cancer diagnostics, and accurate transcript quantification with RNA-Seq.

Webinar Details:

Session 1: 13 Dec 2017, 2:30 PM IST
Session 2: 13 Dec 2017, 9:30 PM IST

Register here: http://www.strand-ngs.com/webinar_registration

What is RNA-Seq?

RNA-Seq is a next-generation sequencing (NGS) technology used to study the transcriptome—the complete set of RNA molecules in a cell. It quantifies gene expression, detects novel transcripts, and captures alternative splicing events with high sensitivity and resolution.

Workflow for RNA-Seq Analysis

RNA-Seq analysis involves several stages, each requiring computational tools and expertise.

1. Experimental Design and Data Acquisition

Before diving into analysis, bioinformaticians should consider:

• Biological Replicates: Ensure statistical power to detect meaningful differences.
• Sequencing Depth: Align sequencing depth to study objectives (e.g., higher depth for low-abundance transcripts).
• Paired-End vs. Single-End: Paired-end sequencing provides more detailed information on transcript structure.

Once sequencing is complete, raw data is provided in FASTQ format, containing sequence reads and quality scores.

2. Quality Control and Preprocessing

Quality control (QC) ensures data integrity. Tools such as FastQC evaluate metrics like base quality, GC content, and adapter contamination.

Preprocessing Steps:

• Trimming: Tools like Trimmomatic or Cutadapt remove low-quality bases and adapter sequences.
• Filtering: Discard reads below a certain quality threshold or length.

3. Read Alignment

Reads are mapped to a reference genome or transcriptome to determine their origin. Alignment tools include:

• HISAT2: Handles large genomes efficiently and supports spliced alignments.
• STAR: High-speed aligner optimized for RNA-Seq.
• Bowtie2: Suitable for short-read alignment.

Output: A SAM/BAM file containing aligned reads.

4. Transcript Assembly and Quantification

This step involves identifying transcripts and quantifying their expression levels. Tools used include:

• StringTie: Assembles and quantifies transcripts from aligned reads.
• Salmon/Kallisto: Perform pseudo-alignment for rapid and accurate quantification.

Expression levels are typically measured as TPM (transcripts per million) or FPKM (fragments per kilobase of transcript per million mapped reads).

5. Differential Expression Analysis

To identify genes with altered expression between conditions, bioinformaticians use tools such as:

• DESeq2: Accounts for data normalization and variability.
• edgeR: Handles overdispersed count data efficiently.
• Limma-voom: Combines linear modeling with RNA-Seq count data.

The output includes a list of differentially expressed genes (DEGs) with statistical significance and fold-change values.

6. Functional Annotation and Pathway Analysis

Understanding the biological significance of DEGs involves:

• Gene Ontology (GO) Analysis: Tools like DAVID or clusterProfiler categorize genes based on their biological functions.
• Pathway Enrichment Analysis: Identifies pathways enriched in DEGs using tools like KEGG, Reactome, or GSEA.

7. Visualization

Visualizing results enhances interpretability. Common visualizations include:

• Heatmaps: Show expression patterns across samples (e.g., pheatmap).
• Volcano Plots: Highlight significant DEGs (e.g., ggplot2).
• PCA/UMAP: Assess sample clustering and variability (e.g., Seurat).

Challenges in RNA-Seq Analysis

1. Batch Effects: Technical variability can confound biological signals. Combat this with normalization techniques or batch-correction tools like ComBat.
2. Low-Quality Samples: Poor-quality RNA impacts downstream analyses.
3. Computational Complexity: RNA-Seq generates massive datasets, requiring robust computing resources and optimized pipelines.

Key Tools and Resources

• Bioconductor: A treasure trove of R packages for RNA-Seq analysis.
• Galaxy: A web-based platform for running RNA-Seq workflows.
• Nextflow/Snakemake: Workflow management tools to streamline analyses.

Applications of RNA-Seq

RNA-Seq is used in diverse research areas, including:

• Cancer Transcriptomics: Identifying tumor-specific expression profiles.
• Developmental Biology: Studying dynamic transcriptome changes.
• Drug Discovery: Screening genes modulated by therapeutic compounds.

Conclusion

RNA-Seq analysis is a cornerstone of modern transcriptomics, offering bioinformaticians a versatile toolkit for unraveling gene expression and regulation. Mastering RNA-Seq workflows and tools empowers researchers to transform raw sequencing data into biological discoveries.

Whether you’re investigating disease mechanisms, exploring cellular pathways, or developing new therapeutics, RNA-Seq is a powerful ally in your bioinformatics arsenal.

Address of the bookmark: https://bioconductor.org/packages/3.7/bioc/vignettes/dupRadar/inst/doc/dupRadar.html

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