Run RNA-seq pipeline using the Telomere-2-Telomere (T2T) latest human genome
Since its initial release in 2000, the human reference genome has covered only the euchromatic fraction of the genome, leaving important heterochromatic regions unfinished. Addressing the remaining 8% of the genome, the Telomere-to-Telomere (T2T) Consortium presents a complete 3.055 billion–base pair sequence of a human genome, T2T-CHM13, that includes gapless assemblies for all chromosomes except Y, corrects errors in the prior references, and introduces nearly 200 million base pairs of sequence containing 1956 gene predictions, 99 of which are predicted to be protein coding. The completed regions include all centromeric satellite arrays, recent segmental duplications, and the short arms of all five acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies (Nurk et al., Science, 2022 https://www.science.org/doi/10.1126/science.abj6987).
T2T genome
The latest T2T human genome and annotation has been downloaded from NCBI:
https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/009/914/755/GCF_009914755.1_T2T-CHM13v2.0/
You can access this genome at: /work/training/references/ncbi/T2T
Check available files:
ls -l /work/training/references/ncbi/T2T/
GCF_009914755.1_T2T-CHM13v2.0_assembly_report.txt GCF_009914755.1_T2T-CHM13v2.0_genomic.fna GCF_009914755.1_T2T-CHM13v2.0_genomic.gff GCF_009914755.1_T2T-CHM13v2.0_genomic.gtf GCF_009914755.1_T2T-CHM13v2.0_protein.faa GCF_009914755.1_T2T-CHM13v2.0_rna.fna
Run RNAseq pipeline using a custom genome
We can use the nf-core/rnaseq pipeline to profile the expression of genes in a custom genome (e.g., T2T or any animal or plant genome) of your interest, as long as there is a reference genome (FASTA file) and genome annotation (GTF or GFF3).
To use your own genome assembly - you need 1) FASTA genome sequence and 2) GFF/GTF genome annotation file
--fasta my_custom_genome.fasta # de novo assembled genome or genome not available as an igenomes reference --gtf my_custom_genome.gtf # genome annotatio showing the location of genes
Copy and paste the code below to the terminal:
cp $HOME/workshop/2024-2/session4_RNAseq/data/samplesheet.csv $HOME/workshop/2024-2/session4_RNAseq/runs/run4_RNAseq_T2T cp $HOME/workshop/2024-2/session4_RNAseq/scripts/launch_nf-core_RNAseq_pipeline_T2T.pbs $HOME/workshop/2024-2/session4_RNAseq/runs/run4_RNAseq_T2T cd $HOME/workshop/2024-2/session4_RNAseq/runs/run4_RNAseq/T2T
Line 1: Copy the samplesheet.csv file to the working directory
Line 2: Copy the launch scrip to run expression profiling using the T2T genome
Print the content of the “launch_nf_core_RNAseq_T2T.pbs” script:
cat launch_nf_core_RNAseq_T2T.pbs
#!/bin/bash -l #PBS -N nfRNAseq_T2T #PBS -l select=1:ncpus=2:mem=4gb #PBS -l walltime=48:00:00 #work on current directory cd $PBS_O_WORKDIR #load java and set up memory settings to run nextflow module load java export NXF_OPTS='-Xms1g -Xmx4g' #run the RNAseq pipeline nextflow run nf-core/rnaseq --input samplesheet.csv \ --outdir results \ -r 3.14.0 \ --fasta /work/training/references/ncbi/T2T/GCF_009914755.1_T2T-CHM13v2.0_genomic.fna \ --gtf /work/training/references/ncbi/T2T/GCF_009914755.1_T2T-CHM13v2.0_genomic.gtf \ --remove_ribo_rna \ -profile singularity \ --aligner star_salmon \ --extra_trimgalore_args "--quality 30 --clip_r1 10 --clip_r2 10 --three_prime_clip_r1 1 --three_prime_clip_r2 1 " \ -resume
NOTE:
Do not specify the -genome parameter for pipeline version 3.14.0, previous version required to define either -genome null or -genome custom, but not with the latest version
Submit the job to the cluster
qsub launch_nf_core_RNAseq_T2T.pbs
Tip: Read the help information for Nextflow pipelines
Information on how to run a nextflow pipeline and additional available parameters can be provided on the pipeline website (i.e., https://nf-co.re/rnaseq/3.12.0/docs/usage/ ). You can also run the following command to get help information:
nextflow run nf-core/rnaseq --help
Some pipelines may need file names, and others may want a CSV file with file names, the path to raw data files, and other information.