chosenobih / hamrlinc

High-throughput pipeline for modified mRNA annotation and long intergenic non-coding RNA annotation

HAMRLNC: High-throughput Annotation of Modified Ribonucleotides and Long Non-Coding RNAs

Overview

• HAMRLNC is a multipurpose toolbox that expedites the analysis pipeline for HAMR and Evolinc. The former was developed by Paul Ryvkin et al, and the latter by Andrew D.L. Nelson et al. HAMRLNC aims to make the original methods more accessible by automating the tedious pre-processing steps and expanding on their functionalities with its built-in post-processing steps, allowing users to visualize epitranscriptomic analysis with experimental condition contexts.
• HAMRLNC is high-throughput and performs RNA-modification annotation and long intergenic non-coding RNAs(lincRNA) annotation at a bioproject scale. HAMRLNC performs constitutive trimming of acquired reads using Trim-Galore, and makes use of STAR as the default aligning tool; mapped reads are pre-processed using selected methods from GATK, gffread, CPC2, infernal, samtools, etc. Users can also opt to quantify transcripts alongside these steps.
• HAMRLNC is optimized for partial parallel processing and modularization. Specifying a larger thread count where hardware permits will greatly increase the speed of a single run. If only partial functionality is needed (e.g. only analyzing modified ribonucleotides), users can implement flags to activate the function modules desired. See below for more details.

Command Line Arguments and Description

Read the Wiki for detailed descriptions on selected flags.

Command	Description
Required
-o	<pipeline output directory> name of the directory where you would like your hamrlnc run to be
-c	<filenames for each fastq.csv> a csv file that corresponds each srr code (or name of fastq file) to your desired nomenclature for each read
-g	<reference genome.fa> a fasta file of the genome of the model organism
-i	<reference genome annotation.gff3> a gff3 file of the genome of the model organism, note we require gff3 instead of gtf
Optional
-l	<minimum average read length> default: auto-detect
-n	[number of threads] default=4
-r	[perform fastqc] default=false
-d	[raw fastq folder] default=NA
-t	[trim raw fastq] default=false
-D	[raw bam folder] default=NA
-b	[sort raw bam] default=false
-I	[STAR genome index folder] default=NA
-k	[activate modification annotation workflow] default=false
-p	[activate lncRNA annotation workflow] default=false
-u	[activate featurecount workflow] default=false
-f	[HAMR filter] default=filter_SAM_number_hits.pl
-m	[HAMR model] default=euk_trna_mods.Rdata
-Q	[HAMR minimum quality score: 0-40] default=30
-C	[HAMR minimum coverage: 0-∞] default=10
-E	[HAMR sequencing error: 0-1] default=0.01
-P	[HAMR maximum p-value: 0-1] default=1
-F	[HAMR maximum FDR: 0-1] default=0.05
-O	[Panther organism taxon ID] default="3702"
-A	[Panther annotation dataset] default="GO:0008150"
-Y	[Panther test type: FISHER or BINOMIAL] default="FISHER"
-R	[Panther correction type: FDR, BONFERRONI, or NONE] default="FDR"
-y	[keep intermediate bam files] default=false
-z	[keep raw fastq files downloaded from SRA] default=false
-q	[halt program upon completion of checkpoint 2] default=false
-G	[attribute used for featurecount] default="gene_id"
-x	[max intron length for lncRNA-annotation-unique STAR mapping] default=NA
-H	[SERVER alt path for panther]
-U	[SERVER alt path for HAMRLNC]
-W	[SERVER alt path for GATK]
-S	[SERVER alt path for HAMR]
-J	[SERVER alt path for CPC2]
-M	[SERVER alt path for Rfam]
-h	[help message]

Running HAMRLNC

Required dependencies

1. Linux-based computer, server, or cluster.
2. Docker
3. Minimum memory of 32 GB and minimum disk space of 120 GB, could require higher specs for organisms with larger genomes like human.

# pull HAMRLNC docker image:  
docker pull chosenobih/hamrlnc:v0.01

# clone HAMRLNC repo
git clone https://github.com/harrlol/HAMRLNC.git
cd HAMRLNC

# download the genome file for Arabidopsis thaliana from ENSEMBL
wget https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-59/fasta/arabidopsis_thaliana/dna/Arabidopsis_thaliana.TAIR10.dna.toplevel.fa.gz
gunzip Arabidopsis_thaliana.TAIR10.dna.toplevel.fa.gz

# download the annotation file for Arabidopsis thaliana from ENSEMBL
wget https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-59/gff3/arabidopsis_thaliana/Arabidopsis_thaliana.TAIR10.59.gff3.gz
gunzip Arabidopsis_thaliana.TAIR10.59.gff3.gz

# make sure your fa and gff3 files are in your working directory, and enter that directory
cd /your/working/directory
# run HAMRLNC with SRA IDs with all three arms activated
docker run \
  --rm -v $(pwd):/working-dir \
  -w /working-dir chosenobih/hamrlnc:v0.01 \
  -o test_run \
  -c /demo/demo_filenames.csv \
  -g Arabidopsis_thaliana.TAIR10.dna.toplevel.fa \
  -i Arabidopsis_thaliana.TAIR10.59.gff3 \
  -l 50 -n 8 -k -p -u

Running HAMRLNC as an application on CyVerse's Discovery Environment

HAMRLNC has been integrated as an app on CyVerse's Discovery Environment (DE), and it is available for use by researchers. Search for “HAMRLINC" and then select the 1.0.0 version. A short tutorial on how to run the app is available at this CyVerse wiki. CyVerse's DE provides an easy-to-use graphic user interphase for running several Life Sciences computational pipelines.

Step-by-step walkthrough

For more detailed documentation and step-by-step tutorial for running HAMRLNC using docker, please visit the Wiki.

Issues

If you encounter any issues while running HAMRLNC, please open an issue on this GitHub repo, and we'll attend to it as soon as possible.

Copyright

Copyright (c) 2024 HAMRLNC Team

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