Paean-Reproduction-Scripts

This repository is dedicated to the reproduction of the manuscript titled "Paean: A unified and efficient transcriptome quantification system using heterogeneous computing."

The official source code is at the following link

System Requirements and Reproducibility

The current reproduced environment and the minimal reproduced requirements are

Resource	Current	Minimal
System	Ubuntu 20.04	Ubuntu 20.04
CPU	AMD Ryzen 9 5900X 12-Core Processor(24 cores)	A CPU with 24 cores
Memory	125GB	64GB
GPU	NVIDIA RTX 3090	NVIDIA GTX 1080ti
CUDA	12.4	9.2

Download Data

We use Sequencing Quality Control (SEQC) dataset for gene expression quantification and Human Verified Sample (HVS) dataset for alternative splicing. All files of each dataset (12 files for SEQC and 6 files for HVS) can be downloaded from NCBI by the following Run IDs.

Dataset	Run ID
SEQC	SRR896663 SRR896679 SRR896695 SRR896743 SRR896759 SRR896775 SRR896823 SRR896839 SRR896855 SRR896903 SRR896919 SRR896935
HVS	SRR536342 SRR536344 SRR536346 SRR536348 SRR536350 SRR536352

Install software

Install Paean

To use Paean, you should build it from source. For complete details please follows Paean'repository.

First we should install some dependent packages. You should prepare an environment in which Ubuntu >= 20.04 and CUDA >= 9.2.

apt update
# zlib
apt install -y zlib1g-dev autoconf libcurl4-openssl-dev
# libdeflate
git clone --branch v1.21 https://github.com/ebiggers/libdeflate.git
cd libdeflate
cmake -B build && cmake --build build
cd build && make install
# htslib, download htslib-1.21.tar.bz2 from https://sourceforge.net/projects/samtools/files/samtools/1.21/
tar -xvf htslib-1.21.tar.bz2
cd htslib-1.21/
autoreconf -i
./configure --with-libdeflate
make -j && make install

Then build Paean.

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
git clone https://github.com/Bio-Acc/Paean.git
cd Paean/Paean-GPU
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j && make install

For other compared software, install them via conda.

Install miniconda

mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm ~/miniconda3/miniconda.sh

Install softwares

Since these softwares have incompatible dependencies, we need to install them using multiple conda envs.

# set conda env
source ~/miniconda3/bin/activate
conda create -n test -y python=3.8
conda activate test
conda install -y -c conda-forge -c bioconda samtools=1.21 \
                                            hisat2=2.2.1 \
                                            stringtie=2.2.3 \
                                            salmon=1.9.0
# the following softwares are incompatible with other softwares
# rsem
conda create -n rsem -y python=3.7
conda activate rsem
conda install -y -c conda-forge -c bioconda rsem=1.3.3
# rmats
conda create -n rmats -y python=3.6
conda activate rmats
conda install -y -c conda-forge -c bioconda rmats=4.1.0
# misopy
conda create -n misopy -y python=2.7
conda activate misopy
conda install -y -c conda-forge -c bioconda misopy=0.5.4 pybedtools gffutils=0.8.7.1

For kallisto because there exists a bug, we build kallisto from source.

git clone --branch v0.51.0 https://github.com/pachterlab/kallisto.git
cd kallisto
mkdir build && cd build
cmake .. -DENABLE_AVX2=OFF -DCOMPILATION_ARCH=OFF
# cannot parallelly compile
make && make install

Download reference files

You can download hg38 reference fasta and gff3/gtf files from gencode website

wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_36/GRCh38.primary_assembly.genome.fa.gz
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_36/gencode.v36.annotation.gff3.gz
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_36/gencode.v36.transcripts.fa.gz
gunzip GRCh38.primary_assembly.genome.fa.gz
gunzip gencode.v36.annotation.gff3.gz
gunzip gencode.v36.transcripts.fa.gz

For other files, you can find in Paean'repository.

Prepare data

Build reference index

# hisat2 index
conda activate test
mkdir hisat2_index
hisat2-build -p 20 GRCh38.primary_assembly.genome.fa hisat2_index/hg38
# star index
conda activate rmats
mkdir star_index
STAR --runThreadN 20 --runMode genomeGenerate --genomeDir star_index/hg38 --genomeFastaFiles GRCh38.primary_assembly.genome.fa --sjdbGTFfile gencode.v36.annotation.gtf --sjdbOverhang 125

Mapping fastq to bam

For RNA-seq softwares which require bam data, we use hisat2 to do mapping.

hisat2 -x hisat2_index/hg38 -t -p 20 -dta -1 /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX_1.fastq -2 /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX_2.fastq -S /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX.sam
samtools view -h /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX.sam -o /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX.bam

Run

Paean

cd Paean/Paean-GPU
time paean -g input/gene.annotation.gff3 -l input/length_table.csv -r /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX.bam -x SE,A3SS -y input/csv/SE.annotation.csv,input/csv/A3SS.annotation.csv -o paean_result -t 20 -m 2

Rsem

# build index
conda activate rsem
mkdir resem_index
rsem-prepare-reference --gtf gencode.v36.annotation.gtf --star --star-path /home/lguanjiawen/miniconda3/envs/rmats/bin -p 20 --star-sjdboverhang 125 GRCh38.primary_assembly.genome.fa rsem_index/hg38
# run
time rsem-calculate-expression --paired-end --star --star-path /home/lguanjiawen/miniconda3/envs/rmats/bin -p 20 /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX_1.fastq /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX_2.fastq rsem_index/hg38 rsem_result

stringtie

conda activate test
time samtools sort -@20 /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX.bam -o /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX.sort.bam
time stringtie -p 20 -o stringtie.out.gtf -G gencode.v36.annotation.gff3 /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX.sort.bam

Kallisto

# build index
kallisto index -i kallisto_index/hg38 GRCh38.primary_assembly.genome.fa
# run
time kallisto quant -i kallisto_index/hg38 -t 20 -o kallisto_result /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX_1.fastq /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX_2.fastq

Salmon

conda activate test
time salmon quant -t gencode.v36.transcripts.fa -l IU -p 20 -a /path/to/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX.bam -o salmon_result/SEQC_ILM_BGI_A_1_L01_ATCACG_AC0AYTACXX

rMats

# make two group file
echo "/path/to/SRR536342.bam,/path/to/SRR536344.bam,/path/to/SRR536346.bam" > b1.txt
echo "/path/to/SRR536348.bam,/path/to/SRR536350.bam,/path/to/SRR536352.bam" > b2.txt
# run
conda activate rmats
time rmats.py --b1 b1.txt --b2 b2.txt --gtf gencode.v36.annotation.gtf --bi start_index/hg38 -t paired --readLength 101 --nthread 20 --od rmats_result --tmp /tmp/tmp_output

misopy

Prepare

# separete alternative splicing event from annotation
# download UCSC tables
wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes/hg38.ensGene.gtf.gz
gunzip hg38.ensGene.gtf.gz
# install converter tool
conda activate test
conda install -y -c conda-forge -c bioconda ucsc-gtftogenepred
gtfToGenePred -genePredExt -ignoreGroupsWithoutExons hg38.ensGene.gtf ensGene.txt
conda activate misopy
git clone https://github.com/yarden/rnaseqlib.git
# fix bug
sed -i 's/\["bin",/\[/g' /home/lguanjiawen/miniconda3/envs/misopy/lib/python2.7/site-packages/rnaseqlib/tables.py
# generate gff3 files of multiple alternative splicing event
python rnaseqlib/rnaseqlib/gff/gff_make_annotation.py ./ ./gff --flanking-rule commonshortest --genome-label hg38

after generateing event gff3 files, we get the gff/commonshortest directory includes

SE.hg38.gff3
RI.hg38.gff3
A3SS.hg38.gff3
A5SS.hg38.gff3
MXE.hg38.gff3

Sort bam

conda activate test
time samtools sort -@20 /path/to/SRR536342.bam -o /path/to/SRR536342.sort.bam
time samtools index /path/to/SRR536342.sort.bam

Then for each event, we run misopy

conda activate misopy
index_gff --index gff/commonshortest/SE.hg38.gff3 indexed/
# run
time miso --run indexed/ /path/to/SRR536342.sort.bam --output-dir miso_result --read-len 101 --paired-end 250 30 -p 20 --event-type SE