GLORI-tools v1.0

GLORI-tools currently works, but is still being optimized for a better user experience.

*🔥NEW🔥 Hi, many friends have encountered various issues while building the STAR index. Please make sure you run this step with enough memory. Additionally, you can use chromosomes 1-22 + X, Y, Z in hg38 as your genome. Please ensure that your genome sequence’s name begins with “chr” as we have only tested GLORI on human and mouse genomes.

News

As of June 15th, 2023, We have updated the format of the .totalCR.txt file to make it more user-friendly and easier to read. Please see Section 5.3 for details.
As of June 23th, 2023, We have added an ciatiaon information for GLORI-tools.
As of June 23th, 2023, an additional run parameter has been added to obtain a comprehensive list of all annotated A citations. Please refer to Section 4.5 for details

Table of content

Background
Pre-processing the raw sequencing data
Installation and Requirement
Example and Usage
Maintainers and Contributing
License

Background

GLORI

We developed an absolute m6A quantification method (“GLORI”) that is conceptually similar bisulfite sequencing-based quantification of DNA 5-methylcytosine. GLORI relies on glyoxal and nitrite-mediated deamination of unmethylated adenosines while keeping m6A intact, thereby achieving specific and efficient m6A detection.

Pre-processing the raw sequencing data

Annotations

GLORI-tools exclusively accepts single-end sequencing reads. Prior to use, it is critical to ensure that the input reads are A-to-G converted reads. Therefore, it is important to carefully verify the orientation of the reads before processing them.
Before inputting data into GLORI-tools, data cleaning is necessary. This involves removing sequencing adapters, low-quality bases, PCR duplicates based on unique molecular identifiers (UMIs), and finally, removing the UMIs themselves.
Installation
trim_galore
seqkit
FASTX-Toolkit (including fastx_trimmer)
Example code
trim_galore -q 20 --stringency 1 -e 0.3 --length {length(5’UMI) +25nt} --path_to_cutadapt {cutadapter} --dont_gzip -o {output_dir1} {inputfile}
seqkit rmdup -j {Thread} -s -D {output_dupname} {filtered_file} > {filter_file2}
fastx_trimmer -Q 33 -f {length(5′UMI) +1nt} -i {filter_file2} -o {filter_file3}

GLORI-tools

GLORI-tools is a bioinformatics pipeline tailored for the analysis of high-throughput sequencing data generated by GLORI. GLORI pipeline

Installation and Requirement

Installation

GLORI-tools is written in Python3 and is executed from the command line. To install GLORI-tools simply download the code and extract the files into a GLORI-tools installation folder.

GLORI-tools needs the following tools to be installed and ideally available in the PATH environment:

STAR ≥ v2.7.5c
bowtie (bowtie1) version ≥ v1.3.0
samtools ≥ v1.10
python ≥ v3.8.3

GLORI-tools needs the following python package to be installed:

pysam,pandas,argparse,time,collections,os,sys,re,subprocess,multiprocessing,copy,numpy,scipy,math,sqlite3,Bio,statsmodels,itertools,heapq,glob,signal

Example and Usage:

1. Generate annotation files (required)

1.1 download files for annotation (required, using hg38 as example):

wget https://ftp.ncbi.nlm.nih.gov/refseq/H_sapiens/annotation/annotation_releases/109.20190905/GCF_000001405.39_GRCh38.p13/GCF_000001405.39_GRCh38.p13_assembly_report.txt
wget https://ftp.ncbi.nlm.nih.gov/refseq/H_sapiens/annotation/annotation_releases/109.20190905/GCF_000001405.39_GRCh38.p13/GCF_000001405.39_GRCh38.p13_genomic.gtf.gz

1.2 download reference genome and transcriptome

wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
wget https://ftp.ncbi.nlm.nih.gov/refseq/H_sapiens/annotation/annotation_releases/109.20190905/GCF_000001405.39_GRCh38.p13/GCF_000001405.39_GRCh38.p13_rna.fna.gz

1.3 Unify chromosome naming in GTF file and genome file:

python ./get_anno/change_UCSCgtf.py -i GCF_000001405.39_GRCh38.p13_genomic.gtf -j GCF_000001405.39_GRCh38.p13_assembly_report.txt -o GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens

2. get reference for reads alignment (required)

2.1 build genome index using STAR

python ./pipelines/build_genome_index.py -f $genome_fastafile -p 20 -pre hg38

you will get:

$ hg38.rvsCom.fa
$ hg38.AG_conversion.fa
the corresponding index from STAR

2.2 build transcriptome index using bowtie

2.2.1 get the longest transcript for genes (required)

Get the required annotation table files:

python ./get_anno/gtf2anno.py -i GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens -o GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl
awk '$3!~/_/&&$3!="na"' GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl | sed '/unknown_transcript/d' > GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl2

Get the longest transcript:

python ./get_anno/selected_longest_transcrpts_fa.py -anno GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl2 -fafile GCF_000001405.39_GRCh38.p13_rna.fna --outname_prx GCF_000001405.39_GRCh38.p13_rna2.fa

2.2.2 build reference with bowtie

python ./pipelines/build_transcriptome_index.py -f GCF_000001405.39_GRCh38.p13_rna2.fa -pre GCF_000001405.39_GRCh38.p13_rna2.fa

you will get:

$ GCF_000001405.39_GRCh38.p13_rna2.fa.AG_conversion.fa
the corresponding index from bowtie

3. get_base annotation (optional)

3.1 get annotation at single-base resolution

python ./get_anno/anno_to_base.py -i GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl2 -threads 20 -o GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl2.baseanno

3.2 get required annotation file for further removal of duplicated loci

python ./get_anno/gtf2genelist.py -i GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens -f GCF_000001405.39_GRCh38.p13_rna.fa -o GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.genelist > output2
awk '$6!~/_/&&$6!="na"' GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.genelist > GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.genelist2

3.3 Removal of duplicated loci in the annotation file

python ./get_anno/anno_to_base_remove_redundance_v1.0.py -i GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl2.baseanno -o GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl2.noredundance.base -g GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.genelist2

Finally, you will get annotation files:

GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl2.noredundance.base

4. alignment and call sites (required)

GLORI-tools takes cleaned reads as input and finally reports files for the conversion rate (A-to-G) of GLORI for each gene and m6A sites at single-base resolution with corresponding A rate representative for modification level.

4.1 Example shell scripts

Used files
Thread=1
genomdir=your_dir
genome=${genomdir}/hg38.AG_conversion.fa
genome2=${genomdir}/hg38.fa
rvsgenome=${genomdir}/hg38.AG_conversion.fa
TfGenome=${genomdir}/GCF_000001405.39_GRCh38.p13_rna2.fa.AG_conversion.fa
annodir=your_dir
baseanno=${annodir}/GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl2.noredundance.base
anno=${annodir}/GCF_000001405.39_GRCh38.p13_genomic.gtf_change2Ens.tbl2
outputdir=your_dir
tooldir=/tool_dir/GLORI -tools
prx=your_prefix
file=your_cleaned_reads

4.2 Call m6A sites annotated the with genes

python ${tooldir}/run_GLORI.py -i $tooldir -q ${file} -T $Thread -f ${genome} -f2 ${genome2} -rvs ${rvsgenome} -Tf ${TfGenome} -a $anno -b $baseanno -pre ${prx} -o $outputdir --combine --rvs_fac

4.3 Call m6A sites without annotated genes.

python ${tooldir}/run_GLORI.py -i $tooldir -q ${file} -T $Thread -f ${genome} -f2 ${genome2} -rvs ${rvsgenome} -Tf ${TfGenome} -a $anno -pre ${prx} -o $outputdir --combine --rvs_fac
In this situation, the background for each m6A sites is the overall conversion rate.
The site list obtained by the above two methods is basically the similar, and there may be a few differential sites in the list.

4.4 mapping with samples without GLORI treatment

python ${tooldir}/run_GLORI.py -i $tooldir -q ${file} -T $Thread -f ${genome} -Tf ${TfGenome} -a $anno -pre ${prx} -o $outputdir --combine --untreated

4.5 call all the annotated A cites

with annotation:

python ${tooldir}/run_GLORI.py -i $tooldir -q ${file} -T $Thread -f ${genome} -f2 ${genome2} -rvs ${rvsgenome} -Tf ${TfGenome} -a $anno -b $baseanno -pre ${prx} -o $outputdir --combine --rvs_fac -c 1 -C 0 -r 0 -p 1.1 -adp 1.1 -s 0

or without annotation

python ${tooldir}/run_GLORI.py -i $tooldir -q ${file} -T $Thread -f ${genome} -f2 ${genome2} -rvs ${rvsgenome} -Tf ${TfGenome} -a $anno -pre ${prx} -o $outputdir --combine --rvs_fac -c 1 -C 0 -r 0 -p 1.1 -adp 1.1 -s 0

5 Resultes:

5.1 Output files of GLORI

Output files	Interpretation
${your_prefix}_merged.sorted.bam	The overall mapping of reads in .bam format
${your_prefix}_referbase.mpi	The text pileup output from .bam files
${your_prefix}.totalCR.txt	The text file containing the median value of the overall A-to-G conversion rate for each transcriptome and gene
${your_prefix}.totalm6A.FDR.csv	The final list of m6A sites obtained, with the A rate serving as the m6A level

5.2 GLORI sites files

Columns	Interpretation
Chr	chromosome
Sites	genomic loci
Strand	strand
Gene	annotated gene
CR	conversion rate for genes
AGcov	reads coverage with A and G
Acov	reads coverage with A
Genecov	mean coverage for the whole gene
Ratio	A rate for the sites/ or methylation level for the sites
Pvalue	test for A rate based on the background
P_adjust	FDR ajusted P value

5.3 GLORI conversion rate (.totalCR) files

Columns	Interpretation
SA	chromosomes or genes
A-to-G_ratio	A-to-G conversion rate for each chromosome and gene

Maintainers and Contributing

GLORI-tools is developed and maintained by Cong Liu (liucong-1112@pku.edu.cn).
The development of GLORI-tools is inseparable from the open source software RNA-m5C (https://github.com/SYSU-zhanglab/RNA-m5C).

Licences

Released under MIT license

Citation

please cite: Liu C., Sun H., Yi Y., Shen W., Li K., Xiao Y., et al. (2022). Absolute quantification of single-base m6A methylation in the mammalian transcriptome using GLORI. Nat. Biotechnol. (https://www.nature.com/articles/s41587-022-01487-9)

GLORI-tools v1.0

News

Table of content

Background

GLORI

Pre-processing the raw sequencing data

Annotations

Installation

Example code

GLORI-tools

Installation and Requirement

Installation

GLORI-tools needs the following tools to be installed and ideally available in the PATH environment:

GLORI-tools needs the following python package to be installed:

Example and Usage:

1. Generate annotation files (required)

1.1 download files for annotation (required, using hg38 as example):

1.2 download reference genome and transcriptome

1.3 Unify chromosome naming in GTF file and genome file:

2. get reference for reads alignment (required)

2.1 build genome index using STAR

2.2 build transcriptome index using bowtie

3. get_base annotation (optional)

3.1 get annotation at single-base resolution

3.2 get required annotation file for further removal of duplicated loci

3.3 Removal of duplicated loci in the annotation file

4. alignment and call sites (required)

4.1 Example shell scripts

4.2 Call m6A sites annotated the with genes

4.3 Call m6A sites without annotated genes.

4.4 mapping with samples without GLORI treatment

4.5 call all the annotated A cites

5 Resultes:

5.1 Output files of GLORI

5.2 GLORI sites files

5.3 GLORI conversion rate (.totalCR) files

Maintainers and Contributing

Licences

Citation