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Getting Started

About

tRNA Analysis of eXpression (tRAX) is a software package built for in-depth analyses of tRNA-derived small RNAs (tDRs), mature tRNAs, and inference of RNA modifications from high-throughput small RNA sequencing data. While the tRAX workflow adopts popular RNA sequencing data analysis methods, which includes adapter trimming of raw sequencing data, read alignment to the reference, transcript abundance estimation, and differential expression analysis among samples, it specifically consists of features designed to support special characteristics of tRNAs and tDRs. To ensure alignment of sequencing reads to tRNA transcripts, tRAX uses a custom-built reference database that not only includes the reference genome but also mature tRNA transcripts with the addition of 3′ CCA tail not encoded genomically. Unlike popular read counting tools that only consider or recommend for uniquely mapped reads for RNA sequencing, tRAX allows reads to be mapped to multiple transcripts and gene loci, which is necessary for conserved tRNA isodecoders (tRNAs with the same anticodon but different sequences in the gene body) and identical tRNA genes that are commonly found in eukayotes. Read coverage for each tRNA transcript is reported in four categories – transcript-specific, isodecoder-specific, isotype-specific, and non-specific – that provide precise results on the level of uniqueness of the aligned reads. Moreover, tRAX computes separate read counts of tRNA fragments that align at the 5′ end, 3′ end, and the middle region of tRNA transcripts to distinguish the abundance of different fragment types. Differential expression comparison across samples is performed using read counts for tRNA transcripts and tRNA fragments to provide better understanding of possible distinction in different tRNA isotypes or isodecoders and fragment types. In addition, tRAX measures the base frequency at each position aligned to the tRNA transcripts for estimating the mis-incorporations that may represent RNA modifications essential for function, stability, and regulation.

To enable researchers to study the analysis results at multiple levels, tRAX presents over 50 types of visual images and data files such as read distribution summarizations, read coverage per tRNA isodecoder, volcano plots of differential expression comparison between samples, and misincorporation location charts. More details are available at Understanding Output Results and the Reference Manual.

tRAX workflow

Installation

System requirements

tRAX requires to be run on a Linux/Unix system with at least 8 cores and 16 GB memory. Due to the large size of sequencing data, we do not recommend using tRAX on a regular desktop or laptop.

Using Docker Image

To eliminate the need of installing dependencies, you can download the Docker image from our DockerHub repository using the command 

docker pull ucsclowelab/trax

Using Conda Environment

For those who prefer to use conda, you can create the environment using the command 

conda env create -f trax_env.yaml

Getting source code

The source code can be downloaded from GitHub at https://github.com/UCSC-LoweLab/tRAX. tRAX was developed with Python and does not require compilation or installation.

To run tRAX from source code, dependencies listed below are required to be installed.

Dependencies

  • Python 2.7 or higher
  • pysam Python library (latest verion - older versions have a memory leak)
  • Bowtie2
  • Samtools 1.9 or higher
  • R
  • DESeq2 bioconductor library 1.26 or higher (DESeq a possible substitution)
  • getopt R library
  • ggplot2 R library
  • ggrepel R library
  • Infernal 1.1.2 or higher
  • Cutadapt 1.18 (required for running trimadapters.py)
  • SeqPrep (required for running trimadapters.py)
  • NCBI SRA toolkit (required for test run)

Tutorial

Test run

To try out tRAX with a small public data set, we provide a script TestRun.bash. It includes downloading small RNA sequencing data from NCBI SRA, trimming sequencing adapters, building the reference database using the human tRNA annotations from GtRNAdb and the GRCh37/hg19 reference genome from UCSC Genome Browser, and analyzing the data with results generation. The script should be run in the directory where outputs are desired to be stored.

Additionally, ARM-Seq and DM-tRNA-seq data described in the following publications can be downloaded at http://trna.ucsc.edu/tRAX/data/ARMseq and http://trna.ucsc.edu/tRAX/data/DMtRNAseq respectively. Sample meta data files are also provided. Please refer to the included README for description and usage.

Cite

Cozen AE, Quartley E, Holmes AD, et al. (2015) ARM-seq: AlkB-facilitated RNA methylation sequencing reveals a complex landscape of modified tRNA fragments. Nature Methods 12:879–884.

Zheng G, Qin Y, Clark WC, et al. (2015) Efficient and quantitative high-throughput tRNA sequencing. Nature Methods 12:835–837.

Components

tRAX contains three main tools:

Data needed as inputs

  • Raw sequencing data from Illumina platform in FASTQ file format
  • tRNAscan-SE outputs of targeted genome downloaded from GtRNAdb
  • Genome sequence FASTA file (can be downloaded from UCSC Genome Browser)
  • Non-tRNA gene annotations in Ensembl GTF file format

Note

The chromosome names must be the same across all the input files. For example, if chr1 is used as chromosome 1 in tRNA annotations, the same chromosome name must be used in the genome sequence FASTA file and the non-tRNA gene annotation GTF file.

Info

If the GTF file is downloaded from Ensembl, the following command can be used as an example for converting the chromosome names to UCSC Genome Browser style and obtaining the non-coding RNA annotations.

wget -q -O - ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/Homo_sapiens.GRCh37.75.gtf.gz | gzip -cd | grep -v '^#' | awk '{print "chr" $0;}' | grep -e Mt_rRNA -e miRNA -e misc_RNA -e rRNA -e snRNA -e snoRNA -e ribozyme -e sRNA -e scaRNA > hg19-genes.gtf

How to Run

Step 1: Build custom reference database

Before analyzing the sequencing data, a custom reference database has to be built. If tRAX is used with human GRCh37/hg19, human GRCh38/hg38, mouse GRCm38/mm10, or Saccharomyces cerevisiae S288c, you can use the provided script quickdb.bash that automatically downloads the required input files and builds the database. The command is:

quickdb.bash <genome> <path (optional)>
where <genome> = hg19 for human GRCh37/hg19
or hg38 for human GRCh38/hg38
or mm10 for mouse GRCm38/mm10
or sacCer3 for Saccharomyces cerevisiae S288c

For mitochondrial tRNAs, this command downloads the mito tRNA database without the need of using maketrnadb.py as described below. (The current version of maketrnadb.py does not work properly for degenerative mitochondrial tRNAs.)

or hg19mito for human GRCh37/hg19 mito tRNAs
or hg38mito for human GRCh38/hg38 mito tRNAs
or mm10mito for mouse GRCm38/mm10 mito tRNAs

By default the reference database will be created in the same folder the command is run unless an optional path is specified as a secondary argument.

If using a docker container the command, quickstart.bash build <genome> can be used to generate a docker volume containg the database that is mounted at /rnadb of the docker container. /rnadb will be the container directory for mounting volume when running docker container.

If another directory is desired, it can be supplied as the second argument when running quickdb.bash.

The reference database can also be built using maketrnadb.py with the pre-downloaded input files.

maketrnadb.py --databasename=dbname --genomefile=genome.fa 
--trnascanfile=trnascan.txt --namemap=db-tRNAs_name_map.txt
  • dbname is the name that will be used for the reference database
  • genome.fa contains a FASTA file of the reference genome
  • trnascan.txt is the output file generated by tRNAscan-SE and can be downloaded from GtRNAdb
  • db-tRNAs_name_map.txt is the map file that converts the tRNAscan-SE IDs to GtRNAdb gene symbols. It is also included in the GtRNAdb downloaded tarball.

If maketrnadb.py is run within the tRAX source directory, a file path where the reference database will be created should be included as part of the dbname, for example, /db_path/hg38.

Step 2: Remove sequencing adapters

Raw sequencing data has to be preprocessed before analysis. This includes removing sequencing adapters and merging paired end reads into single end reads.

A tab or whitespace delimited file with sample information has to be prepared as input for trimadapters.py. The file contains two or three columns depending on single or paired end reads.

  • column 1: sample name
  • column 2: single-end FASTQ file or read 1 FASTQ file for paired end reads
  • column 3: read 2 FASTQ file for paired end reads

Example

For single end reads:

sample1  samplefile1.fq.gz  
sample2  samplefile2.fq.gz  
sample3  samplefile3.fq.gz

for paired end reads: 

sample1  samplefile1_1.fq.gz  samplefile1_2.fq.gz  
sample2  samplefile2_1.fq.gz  samplefile2_2.fq.gz 
sample3  samplefile3_1.fq.gz  samplefile3_2.fq.gz

To preprocess the single end sequencing data, run 

trimadapters.py --runname runname --runfile runfile.txt --singleend
or paired end sequencing data, run 
trimadapters.py --runname runname --runfile runfile.txt

  • runname is the name to be used in output files
  • runfile.txt is the sample information file described above

The tool should be run in the directory where the output files will be generated. The output FASTQ file with trimmed sequencing reads will be named as *_trimmed.fastq.gz for single end reads or *_merge.fastq.gz for paired end reads.

Step 3: Analyze sequencing data for gene expression

Two meta data files have to be created as inputs for analysis. The first one is a tab or whitespace delimited sample file with three columns:

  • column 1: unique sample replicate name
  • column 2: sample or group name common for replicates
  • column 3: preprocessed FASTQ file name

Example

WildType1  WildType  samplefile1_merged.fq.gz  
WildType2  WildType  samplefile2_merged.fq.gz 
WildType2  WildType  samplefile3_merged.fq.gz 
Heterozygous1  Heterozygous  samplefile4_merged.fq.gz  
Heterozygous2  Heterozygous  samplefile5_merged.fq.gz 
Heterozygous3  Heterozygous  samplefile6_merged.fq.gz
Homozygous1  Homozygous  samplefile7_merged.fq.gz  
Homozygous2  Homozygous  samplefile8_merged.fq.gz 
Homozygous3  Homozygous  samplefile9_merged.fq.gz

The second meta data file contains the pairs of samples for differential expression comparison. The sample names must match with the sample/group names (column 2) of the above sample file.

Example

WildType  Heterozygous 
WildType  Homozygous

To start the analysis for mature tRNAs, run the following command:

processsamples.py --experimentname=expname --databasename=dbname 
--ensemblgtf=genes.gtf.gz --samplefile=samplefile.txt 
--exppairs=samplespairs.txt --nofrag

For tRNA-derived small RNAs, omit the --nofrag option. If desired to generate UCSC Genome Browser read coverage tracks for visualization, include the --makehub option.

  • expname is a name that will be used for results as well as the folder that contains the results
  • dbname is the directory and name of the reference database generated by maketrnadb.py
  • genes.gtf.gz is the non-tRNA gene annotation GTF file
  • samplefile.txt is the sample file described above
  • samplespairs.txt is the sample pair file described above

The BAM files containing the read alignments will be saved in the current working directory while the other output results will be located in the sub-directory named as expname.

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