DOI: 10.5281/zenodo.7457659
title: Genome indexes for Mus musculus (mm39)

description: <strong>BUILDING HISAT2 INDEXES IN CSC</strong><br> Here is the case for house mouse genome (mm39). The genome indexing step requires big memory and it might not be possible to carry out it on a laptop. Genome indexes for Mus musculus (mm39) were created using HISAT2 v2.2.1 on CSC (IT Center for Science), thanks to CSC-Puhti. <strong>1. Create conda environment folder file to install the required packages, install and add the bin directory to the path.</strong><br> mkdir STRTN-env<br> conda-containerize new --prefix STRTN-env STRTN-env.yml<br> export PATH="&lt;install_dir&gt;/STRTN-env/bin:$PATH" <strong>2. Load the required module.</strong><br> module load tykky<br> export PATH="&lt;install_dir&gt;/STRTN-env/bin:$PATH"<br> module load r-env<br> if test -f ~/.Renviron; then<br> sed -i '/TMPDIR/d' ~/.Renviron<br> fi<br> echo "TMPDIR=${WorkingDir_PATH}" &gt;&gt; ~/.Renviron<br> <br> <strong>3. Obtain the genome sequences of reference and ERCC spike-ins.</strong> <strong><em>You may add the ribosomal DNA repetitive unit for human (U13369) and mouse (BK000964).</em></strong><br> wget https://hgdownload.soe.ucsc.edu/goldenPath/mm39/bigZips/mm39.fa.gz<br> unpigz -c mm39.fa.gz | ruby -ne '$ok = $_ !~ /^&gt;chrUn_/ if $_ =~ /^&gt;/; puts $_ if $ok' &gt; mouse_reference.fasta<br> wget https://tsapps.nist.gov/srmext/certificates/documents/SRM2374_putative_T7_products_NoPolyA_v2.FASTA<br> cat SRM2374_putative_T7_products_NoPolyA_v2.FASTA &gt;&gt; mouse_reference.fasta <strong>4. Extract splice sites and exons from a GTF file.<em> Here we used wgEncodeGencodeBasicVM30 as the annotation file. You may additionally perform `hisat2_extract_snps_haplotypes_UCSC.py` to extract SNPs and haplotypes from a dbSNP file for human and mouse.</em></strong><br> wget https://hgdownload.soe.ucsc.edu/goldenPath/mm39/database/wgEncodeGencodeBasicVM30.txt.gz<br> unpigz -c wgEncodeGencodeBasicVM30.txt.gz | hisat2_extract_splice_sites.py - | grep -v ^chrUn &gt; splice_sites.txt<br> unpigz -c wgEncodeGencodeBasicVM30.txt.gz | hisat2_extract_exons.py - | grep -v ^chrUn &gt; exons.txt<br> <br> <strong>5. Build the HISAT2 index<em>. This outputs a set of files with suffixes. Here, `mouse_reference.1.ht2`, `mouse_reference.2.ht2`, ..., `mouse_reference.8.ht2` are generated.&lt;br&gt;In this case, `mouse_reference` is the basename used for `-i, --index`.</em></strong><br> hisat2-build mouse_reference.fasta --ss splice_sites.txt --exon exons.txt mouse_index/mouse_reference <strong>6. Create the sequence dictionary for the reference and Spike-in sequences.<em> This is required for the Picard</em></strong> MergeBamAlignment program. Note that the original FASTA file (`mouse_reference.fasta` here) is also required.<br> picard CreateSequenceDictionary R=mouse_reference.fasta O=mouse_reference.dict<br> <br> <strong>7. Put the genome indexes, genome fasta file, sequence dictionary to same folder.</strong><br> mv mouse_reference.dict mouse_reference<br> mv mouse_reference.fasta mouse_reference
description: {"references": ["Kim D., Paggi J.M., Park C., Bennett C., and Salzberg S.L. (2019). Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. Aug;37(8):907-915."]}