mim-tRNAseq

Original paper - https://doi.org/10.1016/j.molcel.2021.01.028

Modification-induced misincorporation tRNA sequencing.

This package is an automated analysis pipeline for the quantitation and analysis of tRNA expression and modifications. Given trimmed sequencing reads in fastq format, this pipeline will:

Cluster tRNAs, index modifications, and perform SNP-tolerant read alignment with GSNAP
Deconvolve cluster aligned reads back into unique tRNA transcript-level reads
Calculate coverage information and plots (useful for QC)
Quantify expression
Calculate tRNA differential expression with DESeq2.
Analyze functional tRNA pools and tRNA completeness via 3'-CCA analysis
Comprehensive modification quantification and misincorporation signature analysis

Method strategy

Detailed methodology is shown in the image below, and described in Behrens et al. (2020)

Installation and usage

Please see the full documentation for explanations of dependencies, inputs formatting, and outputs.

To use mim-tRNAseq, it is recommended to install the package using conda, preferably in its own environment. Significant time improvements can be made to installing mimseq using mamba which we will use within the mimseq environment:

	conda create -n mimseq python=3.7
	conda activate mimseq
	conda config --add channels conda-forge
	conda install -c conda-forge mamba
	mamba install -c bioconda mimseq

usearch needs to be acquired and installed. Please do the following:

	wget https://drive5.com/downloads/usearch10.0.240_i86linux32.gz
	gunzip usearch10.0.240_i86linux32.gz
	chmod +x usearch10.0.240_i86linux32
	mv usearch10.0.240_i86linux32 usearch
	cp usearch /usr/local/bin

For this last cp command, root access is required. However, if this is not possible, please add the path to the usearch binary to your PATH (replace full/path/to/usearch with location of your usearch binary from above:

export PATH=$PATH:full/path/to/usearch

Alternatively, mim-tRNAseq can be installed with pip, in which case all additional non-python package dependencies (see documentation) will also need to be installed.

	pip install mimseq

Once installed, mim-tRNAseq should be executable and help displayed, by running

	mimseq --help

An example command to run mim-tRNAseq may look as follows. This will run an analysis between HEK293T and K562 cells on an example dataset included in the package:

	mimseq --species Hsap --cluster-id 0.97 --threads 15 --min-cov 0.0005 --max-mismatches 0.075 --control-condition HEK293T -n hg38_test --out-dir hg38_HEK239vsK562 --max-multi 4 --remap --remap-mismatches 0.05 sampleData_HEKvsK562.txt

The run should take around 15 minutes on a server using 15 processors (--threads 15: please update according to your server capabilities).

Contact

Please log all issues/suggestions on the mim-tRNAseq GitHub page: https://github.com/nedialkova-lab/mim-tRNAseq/issues

Drew Behrens: abehrens@biochem.mpg.de

Danny Nedialkova: nedialkova@biochem.mpg.de

Nedialkova laboratory: https://www.biochem.mpg.de/nedialkova

Cite

Behrens et al., 2021, High-resolution quantitative profiling of tRNA abundance and modification status in eukaryotes by mim-tRNaseq. Molecular Cell 81, 1–14 (https://doi.org/10.1016/j.molcel.2021.01.028)

shenqislx / mim-tRNAseq