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Gene Expression

This primarily refers to dealing with transcriptome wide analysis (eg RNA-seq, tag-seq). Please see also Roberts and Gavery (2018) Opportunities in Functional Genomics: A Primer on Lab and Computational Aspects There are several workflows out there, but here we outline common workflows in our lab.

QC

A normal starting point would be having raw sequence data provided by a core facility. When downloading said data you need to make sure you check the integrity by making sure the hash at the source is the same once you get it to where you want to analyse it.

To begin with, you should run fastqc to assess quality. This might vary based on the details of your project but generally you can ID outliers and those samples with poor read quality. Presence of adapters can also be visualized.

trimming

It is debatable how necessary how necessary trimming reads is, though if done correctly there is likely no reason it is detrimental.

reference choice

The next step is to take the sequence reads, align, and compare counts. Alignent be done using either a transcriptome or genome. Distinct software that is genome-aware will be needed for the latter.

alignment: kallisto (pseudo-alignment)

See the official documentation.

User Guides

Use cases from our lab

Alignment: HiSat2

See the official documentation (linked above).

Benefits to using HISAT2 for alignments:

  • Fast.

  • Can detect exon/intron junctions (i.e. alternative isoform splice sites).

For RNA-Seq, HISAT2 alignments are frequently followed up using StringTie for transcript assembly and quantitation of splice variants.

General usage:

  1. Build a HISAT2 reference sequence index:

    # Create Hisat2 exons tab file
"${programs_array[hisat2_exons]}" \
"${transcripts_gtf}" \
> "${exons}"

# Create Hisat2 splice sites tab file
"${programs_array[hisat2_splice_sites]}" \
"${transcripts_gtf}" \
> "${splice_sites}"

# Build Hisat2 reference index using splice sites and exons
"${programs_array[hisat2_build]}" \
"${genome_fasta}" \
"${genome_index_name}" \
--exon "${exons}" \
--ss "${splice_sites}" \
-p "${threads}" \
2> hisat2-build_stats.txt

  2. Perform alignment(s):

    # Hisat2 alignments
"${programs_array[hisat2]}" \
-x "${genome_index_name}" \
-1 "${fastq_list_R1}" \
-2 "${fastq_list_R2}" \
-S "${sample_name}".sam \
--threads "${threads}" \
2> "${sample_name}"-hisat2_stats.txt

# Sort SAM files, convert to BAM, and index
${programs_array[samtools_view]} \
-@ "${threads}" \
-Su "${sample_name}".sam \
| ${programs_array[samtools_sort]} - \
-@ "${threads}" \
-o "${sample_name}".sorted.bam
${programs_array[samtools_index]} "${sample_name}".sorted.bam


# Delete unneccessary index files
rm "${genome_index_name}"*.ht2

# Delete unneeded SAM files
rm ./*.sam

See links in the "use cases" section below for full-fledged scripts and advanced usage (e.g. assigning read groups to alignment files (SAM) for improved downstream handling/accessiblity).

Use cases from our lab

DESeq2

See also the official documentation.

User Guides - Analyzing RNA-seq data with DESeq2

Use cases from our lab