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Comparative Study
. 2017 Feb 21;7:41184.
doi: 10.1038/srep41184.

MINTmap: Fast and Exhaustive Profiling of Nuclear and Mitochondrial tRNA Fragments From Short RNA-seq Data

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Free PMC article
Comparative Study

MINTmap: Fast and Exhaustive Profiling of Nuclear and Mitochondrial tRNA Fragments From Short RNA-seq Data

Phillipe Loher et al. Sci Rep. .
Free PMC article

Abstract

Transfer RNA fragments (tRFs) are an established class of constitutive regulatory molecules that arise from precursor and mature tRNAs. RNA deep sequencing (RNA-seq) has greatly facilitated the study of tRFs. However, the repeat nature of the tRNA templates and the idiosyncrasies of tRNA sequences necessitate the development and use of methodologies that differ markedly from those used to analyze RNA-seq data when studying microRNAs (miRNAs) or messenger RNAs (mRNAs). Here we present MINTmap (for MItochondrial and Nuclear TRF mapping), a method and a software package that was developed specifically for the quick, deterministic and exhaustive identification of tRFs in short RNA-seq datasets. In addition to identifying them, MINTmap is able to unambiguously calculate and report both raw and normalized abundances for the discovered tRFs. Furthermore, to ensure specificity, MINTmap identifies the subset of discovered tRFs that could be originating outside of tRNA space and flags them as candidate false positives. Our comparative analysis shows that MINTmap exhibits superior sensitivity and specificity to other available methods while also being exceptionally fast. The MINTmap codes are available through https://github.com/TJU-CMC-Org/MINTmap/ under an open source GNU GPL v3.0 license.

Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. The five structural categories of tRFs.
This is a schematic showing examples of tRFs aligned to the characteristic secondary structure of a tRNA molecule. Typically, each of the five categories of tRFs will comprise many more molecules than are shown here.
Figure 2
Figure 2
(A) A schematic of the tRNA hierarchy. The amino acids are at the top level. At the bottom level one finds individual tRNA genes (isodecoders for a given anticodon). (B) Alignment of several isodecoders for the same anticodon (tRNAAspGTC). Some of the segments that are shared by various subsets of the listed isodecoders are shown shaded in different colors. (C) Alignment of isodecoders from different anticodons (such as tRNAAlaAGC and tRNACysGCA). As in (B), sequence segments shared by the listed isodecoders are shown shaded in different colors.
Figure 3
Figure 3. An example of a Summary Record from MINTbase (http://cm.jefferson.edu/MINTbase/).
For each reported tRF, or candidate false positive tRF, the HTML files generated by MINTmap contain links to MINTbase “report cards” that summarize what is currently known for the corresponding tRF across public datasets.
Figure 4
Figure 4. An example of an incomplete mature tRNA sequence that can be found in a genomic region outside of tRNA space.
The sequence shown in magenta is present on chromosome 7 and matches the first exon of several distinct isodecoders of the intron-containing tRNAIleTAT. However, the second exon of tRNAIleTAT is not present in the immediate vicinity of the shown sequence from chromosome 7. There are hundreds of such incomplete tRNA sequences in the human genome that need to be taken into account during tRF mapping and profiling.
Figure 5
Figure 5. Comparison of the sensitivity and specificity attributes of MINTmap, tDRmapper, and tRFdb.
The three methods were evaluated using nine public short RNA-seq datasets from human cell lines–see also text. Left: overlap of the output of each approach when focusing only on bona fide tRFs from mature tRNAs. Right: overlap of the output of tDRmapper and tRFdb when focusing only on reported sequences that exist identically inside as well as outside tRNA space.
Figure 6
Figure 6. Flowchart of MINTmap.
Genomic sequences of the tRNA reference set (A) are processed to simulate exon splicing (B), and then get modified to admit the non-templated CCA addition (C) and the “−1” nucleotide of tRNAHis (D). The resulting sequences are fragmented computationally into (overlapping) segments of variable lengths and entered into a lookup table: sequences that are not exclusive to tRNA space are flagged at this point using metadata added to the table. The lookup table is then used to process a (quality-filtered and adapter-trimmed) short RNA-seq dataset (E) to generate a tRF expression profile table (F). Red: introns. Magenta: CCA tail. Orange: nucleotide at -1 position. Asterisk: tRF not exclusive to tRNA space (possible false positive tRF).
Figure 7
Figure 7. For each tRF, we determine whether it is exclusive to tRNA space.
The exonic mask file (step 7 of the scheme) should contain a value of 1 (shown in blue) for positions representing tRNA exons, a value of 2 (shown in green) for positions representing tRNA −1/CCA post-transcriptional modifications, or a value of 0 (shown in yellow) for all other positions.

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