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Week 3 - Demultiplexing

  1. Introduction
  2. Learning Objectives
  3. Review Material
  4. Lecture Material
  5. Assignment
  6. Project

Introduction

This week we will begin our command line analysis training.

Due to the design of most NGS systems each flowcell is single-use and produces a constant number of sequence reads. Multiplexing is a common technique in NGS sequencing practice to get the most out of each run. If you don't need all 5M reads for a single sample, you can multiplex multiple samples into the same sequencing run.

This is accomplished by adding a short & unique barcode to each sample during the library preparation stage. Then, each sample is pooled in equimolar ratios and sequenced on the same flowcell. After sequencing, one can examine the barcode and attribute each read to the specific sample. This process is called demultiplexing.

I have created a multiplexed fastq from the samples in the Snowflake Yeast Datset. We will use a tool called seqkit to split the samples and explore the results.

Learning Objectives

  • Relate the reason for multiplexing multiple samples in a single run.
  • Describe how multiplexing is commonly accomplished in NGS.
  • Recognize and describe fasta & fastq files.
  • Practice basic terminal commands like mkdir, mv, rm, less, head.
  • Employ seqkit grep to find reads matching a pattern.
  • Employ seqkit stats to describe the contents of sequencing files.
  • Interpret seqkit stats across demultiplexing results.
  • Exporting data from the SRA using sra-tools.

Reading Material

  • Video - Multiplexing and molecular barcodes (indexes) in NGS (Next Gen Sequencing). the bumbling biochemist

Project

For your project.

  • Download sequencing data from the short read archive using fasterq-dump. Due to the bandwidth and computational requirements of this process, we may need to schedule this asynchronously.
  • Use seqkit stats to generate a report.
  • Write a markdown file that describes your dataset. Include that seqkit stats table as a markdown table.
  • Describe the number of reads per sample in your study. Does it tell you anything interesting?