Next Generation Sequencing (NGS)/Expression quantification

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RNA-seq expression quantification

Overview[edit | edit source]

  • Objective: to estimate the relative abundance of transcripts from an rna-seq sample.
  • Biological questions?
  • Inputs:
  • Reference:
  • Annotated Genome (fasta/gff)
  • Transcriptome (fasta)
  • RNA-seq raw reads (fastq, Ilumina).
  • Outputs:
  • Relative abundance per transcript.

Experimental Design[edit | edit source]

See (link) RNA sequencing

Typical Steps in the Method: (table or workflow or paragraph?)

  • Data generation | Get some data. | Seq (link) Sequencing RNA technologies,
  • Get a reference | You need a reference with all the involved transcripts for which you want relative abundance |
  • RNA-seq read QC | Check the quality of the sequencing itself. |
  • Adaptor trimming / quality filtering / contamination filtering | Reads will contain sequence that is not from the transcript but from the technology. |
  • Mapping | Align your reads to the reference |

- Quantification | Get transcript abundance |

Workflows[edit | edit source]

Galaxy Workflows[edit | edit source]

Example workflow[edit | edit source]

Real life workflows[edit | edit source]

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Command line scripts[edit | edit source]

Example workflow:
Real life workflows:
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Some tools and resources[edit | edit source]

  • Get a reference: MetaDB (link)genome assembly and annotation (link) transcriptome denovo assembly ....
  • QC: SeqWiki: FastQC, KAT
  • Trimming: SeqWiki; Trimmomatic, Sickle, Cutadapt....
  • Mapping: Seqwiki: tools...... ... ... ... ... ... ... .. ....
  • Quant: Seqwiki: tools

Discussion and current best practices on specific steps on the method[edit | edit source]

Top X Biostar questions on this topic/method/whatever[edit | edit source]