meRanTK 1.3.0 methylated RNA analysis Tool Kit © Dietmar Rieder, 2022 AUTHOR/SUPPORT: Dietmar Rieder, dietmar . rieder (at) i-med . ac .at MANUAL: http://icbi.at/software/meRanTK/downloads/1.3.0/meRanTK_userGuide.pdf DIRECTORIES CONTENTS: ./src: all source files of meRanTK ./doc: documentation ./extutils: pre-compiled platform specific reuired external programs Linux x86_64 only (MAC OS planned) ./util: Some useful Perl scripts INSTALL: AS PRECOMPILED STANDALONE LINUX 64Bit EXECUTABLE (the easy way) Once you have downloaded meRanTK extract the contents of the ZIP file in the system folder where you want to install meRanTK. You should now be ready to run the meRan tools! In case you do not want to use the provided versions of the required third party programs (STAR, bowtie2, hisat2, see also meRanTK-userGuide.pdf in docs), please make sure that these programs are installed on your system and can be found in your systems PATH ($PATH). If your system has these tools installed, you should either rename or delete the “./extutils” folder in the meRanTK main folder, this way the third party tools from your system will be used. Note: In order to be able to create m-bias plots (see manual) with meRanT/G you will need to install the libgd2 on your system. If it is not installed you’ll see an error message like the following: “Can't locate object method "new" via package "GD::Graph::lines" at script/meRanGh.pl line xxxx” INSTALLING/RUNNING FROM SOURCE: Please see the meRanTK-userGuide.pdf from the doc directory for detailed documentation. HARDWARE REQUIREMENTS: x86-64 compatible processors 64 bit Linux or Mac OS X - meRanGs: 60GB of RAM for human genome - meRanGh/meRanT: ~15GB of RAM for human genome SOFTWARE REQUIREMENTS: - Perl >5.18 Only needed if running from source - Bowtie2 http://bowtie-bio.sourceforge.net/bowtie2/index.shtml 2.2.9, 2.4.5 (pre compiled binary for 64bit Linux is included in meRanTK) - STAR https://github.com/alexdobin/STAR/releases any of: 2.4.0k, 2.2.4a, 2.5.0c, 2.5.2a, 2.5.2b, 2.6.1d, 2.7.10a, (pre compiled binary for 64bit Linux is included in meRanTK) - HiSat2 http://ccb.jhu.edu/software/hisat2/index.shtml 2.0.4, 2.2.1 (pre compiled binary for 64bit Linux is included in meRanTK) NOTE: This release was tested with the default parameters on Linux x86_64 for mouse genomes. INSTALLING/RUNNING FROM CONDA: - use the merantk.yml to create a conda env with all required dependencies e.g. conda config --add channels defaults conda config --add channels bioconda conda config --add channels conda-forge conda env create --name merantk –file=merantk.yml mv extutil extutil_back conda activate merantk cp the .pl files from the src directory to the install directory and run them after setting the executable flag whith chmod +x Quick start: ------------ Some testing examples for each of the meRanTK tools are listed below. All the required testdata should be included in the meRanTK distribution and can be found in the ./testdata directory. Please note: For keeping the size of the distribution small and test run times short, we included only very small datasets and reference sequences, so please do NOT expect high mapping efficienies or many m5C calls in these testing examples. If you want to test meRanTK on full datasets please take a look at the "Mus musculus test data" from the "Download" section at the meRanTK website: http://icbi.at/meRanTK/ For a detailed description of the options used and for all available options, please see the meRanTK_userGuide.pdf in the doc directory or use the "-h" option, i.e.: meRanT -h meRanT mkbsidx -h meRanT align -h meRanGs -h meRanGs mkbsidx -h meRanGs align -h meRanGh -h meRanGh mkbsidx -h meRanGh align -h meRanCall -h meRanCompare -h meRanAnnotate -h meRanGs - align RNA-BSseq reads to a reference genome (STAR, fast, precise, memory intensive) ============================================================================================= 1. Example for generating a bisulfite index for meRanGs using the included test data: ./meRanGs mkbsidx \ -t 2 \ -fa ./testdata/mm10/chr19.fa \ -GTF ./testdata/mm10/ref_GRCm38.p2_top_level_no_prediction_chr19_sort.gff3 \ -GTFtagEPT Parent \ -GTFtagEPG gene \ -id ./testdata/mm10/meRanGsIDX Note this testing example is only using mm10 chromosome 19! The index is optimized for 100bp long reads, which is the default (-sjO 49 would set it 49). ATTENTION: the sequence ids (chromosome names) in GTF/GFF file have to match those in the genome fasta file! 2. Example for aligning RNA-BSseq reads to the test genome using the meRanGs index created in before. ./meRanGs align \ -t 2 \ -f ./testdata/fastq/clean_KHOD_400k_test.fastq \ -id ./testdata/mm10/meRanGsIDX \ -bg \ -o ./testdata/results \ -S meRanGs_test.sam \ -MM \ -un \ --star_genomeLoad NoSharedMemory 3. Example for calling m5Cs from meRanGs aligned RNA-BSseq reads. ./meRanCall \ -p 2 \ -s ./testdata/results/meRanGs_test_sorted.bam \ -f ./testdata/mm10/chr19.fa \ -gref \ -o ./testdata/results/meRanGs_meRanCall_m5Cs.txt \ -md 50 \ -cr 0.99 \ -mBQ 30 \ -bed63 \ -sc 5 meRanGh - align RNA-BSseq reads to a reference genome (HiSat2, fast, memory saving) =================================================================================== 1. Example for generating a bisulfite index for meRanGh using the included test data: ./meRanGh mkbsidx \ -t 2 \ -fa ./testdata/mm10/chr19.fa \ -id ./testdata/mm10/meRanGhIDX Note this testing example is only using mm10 chromosome 19! 2. Example for aligning RNA-BSseq reads to the test genome using the meRanGh index created before. ./meRanGh align \ -t 2 \ -f ./testdata/fastq/clean_KHOD_400k_test.fastq \ -id ./testdata/mm10/meRanGhIDX \ -GTF ./testdata/mm10/ucsc_mm10_chr19.gtf \ -bg \ -o ./testdata/results \ -S meRanGh_test.sam \ -MM \ -un 3. Example for calling m5Cs from meRanGh aligned RNA-BSseq reads. ./meRanCall \ -p 2 \ -s ./testdata/results/meRanGh_test_sorted.bam \ -f ./testdata/mm10/chr19.fa \ -gref \ -o ./testdata/results/meRanGh_meRanCall_m5Cs.txt \ -md 50 \ -cr 0.99 \ -mBQ 30 \ -bed63 \ -sc 5 meRanT - align RNA-BSseq reads to a set of reference transcripts =================================================================================== ---------- refSeq transcripts ----------- 1. Example for generating a refSeq transcript to genen name map file: ./util/mkRefSeq2GeneMap.pl \ -f ./testdata/refSeq/mm10.refSeqRNA-noPRED.500.fa \ -m ./testdata/refSeq/mm10.refSeqRNA-noPRED.500.t2g.map 2. Example for generating a refSeq bisulfite index for meRanT using the included test data: ./meRanT mkbsidx \ -t 2 \ -fa ./testdata/refSeq/mm10.refSeqRNA-noPRED.500.fa \ -id ./testdata/mm10/meRanTIDX/ Note: this testing example is only using 500 transcripts of the mm10 refSeq database! 3. Example for aligning RNA-BSseq reads to the test transcriptome using the meRanT index created before. ./meRanT align \ -t 2 \ -f ./testdata/fastq/clean_KHOD_400k_test.fastq \ -i2g ./testdata/refSeq/mm10.refSeqRNA-noPRED.500.t2g.map \ -o ./testdata/results \ -S meRanT_test.sam \ -x ./testdata/mm10/meRanTIDX/mm10.refSeqRNA-noPRED.500_C2T 4. Example for calling m5Cs from meRanT aligned RNA-BSseq reads (refSeq transcriptome 500 transcripts). ./meRanCall \ -p 2 \ -s ./testdata/results/meRanT_test.sam \ -f ./testdata/refSeq/mm10.refSeqRNA-noPRED.500.fa \ -tref \ -o ./testdata/results/meRanT_meRanCall_m5Ct.txt \ -md 50 \ -cr 0.99 \ -mBQ 30 \ -sc 5 Note: you want get any m5Cs called, since we just used a very small subsets of reads and transcripts from the refernce databases. ---------- tRNAs mm10 ----------- 1. Example for generating a _tRNA_ bisulfite index for meRanT using the included test data: ./meRanT mkbsidx \ -t 2 \ -fa ./testdata/tRNAs/mm10.tRNAs.20140204.fa \ -id ./testdata/mm10/meRanT_tRNA_IDX Note: this testing example is for mm10 tRNAs! 2. Example for aligning RNA-BSseq reads to the mm10 tRNAs using the meRanT index created before. ./meRanT align \ -t 2 \ -f ./testdata/fastq/clean_KHOD_400k_test.fastq \ -i2g ./testdata/tRNAs/mm10.tRNA.20140204.map \ -o ./testdata/results \ -S meRanT_tRNAs_test.sam \ -x ./testdata/mm10/meRanT_tRNA_IDX/mm10.tRNAs.20140204_C2T 3. Example for calling m5Cs from meRanT aligned RNA-BSseq reads (mm10 tRNAs). ./meRanCall \ -p 2 \ -s ./testdata/results/meRanT_tRNAs_test.sam \ -f ./testdata/tRNAs/mm10.tRNAs.20140204.fa \ -tref \ -o ./testdata/results/meRanT_tRNAs_meRanCall_m5Ct.txt \ -md 50 \ -cr 0.99 \ -mBQ 30 \ -sc 5 With tRNAs you should get quite some m5Cs called, since the RNA-BSseq many reads from Khoddami & Cairns used in the example map to tRNAs.