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tRNAscan-SE: An improved tool for transfer RNA detection

Patricia Chan, Brian Lin, and Todd Lowe

School of Engineering, University of California, Santa Cruz, CA
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Current release: 2.0.4 (September 2019)

tRNAscan-SE was written in the PERL (version 5) script language.
Input consists of DNA or RNA sequences in FASTA format. tRNA
predictions are output in standard tabular or ACeDB format.
tRNAscan-SE does no tRNA detection itself, but instead combines the
strengths of three independent tRNA prediction programs by negotiating
the flow of information between them, performing a limited amount of
post-processing, and outputting the results in one of several
formats.

tRNAscan-SE pioneers the large-scale use of covariance models to
annotate tRNA genes in genomes. A covariance model is an implementation of
a stochastic context-free grammar, able to integrate both primary
sequence and secondary structure information, and is trained on an
aligned, structurally annotated set of RNAs. Any given sequence can be
searched for tRNAs by alignment to a tRNA covariance model. tRNAscan-SE 2.0
combines the use of the latest Infernal v1.1 (1) as the covariance model
search engine and covariance models specifically trained and built
using tRNA sequences from available genomes in the three domains of life
for gene prediction. The method replaces the original use of COVE (2)
with two prefilters - tRNAscan 1.3 (3) and an implementation of an
algorithm described by Pavesi and colleagues (4) for searching eukaryotic
pol III tRNA promoters (our implementation referred to as EufindtRNA),
which is still available as a backward compatible option. Predicted
tRNA genes will then be assessed using a set of isotype-specific covariance
models. Comparative analysis among these models enables better annotation,
particularly of atypical tRNAs, some of which may produce �recoding�
events due to mutations in the anticodon. The new tRNAscan-SE also enables
better recognition of tRNA-derived SINEs that are abundant in many
eukaryotic genomes by using a post quality filter.

This distribution includes the PERL script tRNAscan-SE, the convariance
models, all the files necessary to compile and run the complete COVE
package (version 2.4.4), all the files necessary to compile and run the modified
version of tRNAscan (version 1.4), and all the files needed to compile
and run eufindtRNA 1.0 (the cove programs, tRNAscan 1.4, and
eufindtRNA are included for use with the tRNAscan-SE program, but may
also be run as stand-alone programs). Installation of the PERL
(Practical Extraction and Report Language, Larry Wall) interpreter
package version 5.0 or later is required to run the tRNAscan-SE PERL
script. Users also need to download and install Infernal before installing
and using tRNAscan-SE. The Infernal source package can be obtained at
http://eddylab.org/infernal/.

You can obtain a copy of this software from
http://lowelab.ucsc.edu/software/tRNAscan-SE.tar.gz

If you use this software, please cite the Nucleic Acids Research paper
describing the program & its analysis of several genomes (4).

If you have any questions, bug reports, or suggestions, please e-mail

   Todd Lowe
   lowe@soe.ucsc.edu

   Department of Biomolecular Engineering
   University of California
   1156 High Street
   Santa Cruz, ZA 95064


References

1. Nawrocki, E.P. and Eddy, S.R. (2013) "Infernal 1.1:
   100-fold Faster RNA Homology Searches", Bioinformatics, 29, 2933-2935.

2. Eddy, S.R. and Durbin, R. (1994) "RNA sequence analysis using
   covariance models", Nucl. Acids Res., 22, 2079-2088.

3. Fichant, G.A. and Burks, C. (1991) "Identifying potential tRNA
   genes in genomic DNA sequences", J. Mol. Biol., 220, 659-671.

4. Pavesi, A., Conterio, F., Bolchi, A., Dieci, G., Ottonello,
   S. (1994) "Identification of new eukaryotic tRNA genes in genomic DNA
   databases by a multistep weight matrix analysis of transcriptional
   control regions", Nucl. Acids Res., 22, 1247-1256.

5. Lowe, T.M. & Eddy, S.R. (1997) "tRNAscan-SE: A program
   for improved detection of transfer RNA genes in genomic
   sequence", Nucl. Acids Res., 25, 955-964.