About TENOR

"TENOR (Transcriptome ENcyclopedia Of Rice)" is a database to privide transcriptional activity on the rice genome at the nucleotide level based on the RNA-Seq data under 140 environmental stresses and plant hormone treated conditions. As well as expression profiles, information of cis-regulatory elements in promoter regions and co-expressed transcript are provided for each transcript.

About samples and mRNA-Seq data

Treatment conditions

The rice (Oryza sativa L. ssp. japonica cv. Nipponbare) seedlings were used for all experiments. All samples were hydroponically grown at 28ÂșC temperatures, 16h-light/8h-dark cycle with the light period from 6:00 A.M. to 10:00 P.M., and 70-80% relative humidity. 0h (control) samples were harvested at 9:00 A.M.

Treatments Tissue Time points Developmental stage of 0h samples Medium
0h 1h 3h 6h 12h 1d 3d 4d 5d 10d 10d+1d rec.4
High salinity (150mM NaCl)1 Shoot/Root O O 7 days after germination distilled water
High phosphate (3 mM KH2PO4)2 Shoot/Root O O O O O (only for root) 14 days after germination Yoshida's nutrient medium
Low phosphate (0 mM NaH2PO4)2 Shoot/Root O O O O O (only for root) 14 days after germination Yoshida's nutrient medium
High cadmium (50 µM CdSO4)3 Shoot/Root O O O O O 10 days after germination Yoshida's nutrient medium
Low cadmium (1 µM CdSO4) Shoot/Root O O O O 10 days after germination Yoshida's nutrient medium
Very low cadmium (0.2 µM CdSO4) Shoot/Root O O O O 10 days after germination Yoshida's nutrient medium
Drought ( grown without medium) Shoot/Root O O O O O O 10 days after germination Yoshida's nutrient medium
Flood (completely submerged in medium) Shoot/Root O O O O O O O 10 days after germination Yoshida's nutrient medium
Cold (4ºC) Shoot/Root O O O O O O 10 days after germination Yoshida's nutrient medium
Osmotic (0.6 M Mannitol) Shoot/Root O O O O O 10 days after germination Yoshida's nutrient medium
ABA (100 µM) Shoot/Root O O O O O O 10 days after germination Yoshida's nutrient medium
JA (100 µM) Shoot/Root O O O O O O 10 days after germination Yoshida's nutrient medium
Developmental time (no treatment) Shoot/Root O O O O O O O O O O 10 days after germination Yoshida's nutrient medium

mRNA-Seq data

Sample Instrument Read type DRA Accession
High salinity Illumina, GAIIx SE, 36bp DRA000159
High phosphate Illumina, GAIIx SE, 51bp DRA000314
Low phosphate Illumina, GAIIx SE, 51bp DRA000314
High cadmium Illumina, GAIIx SE, 76bp DRA001092
Low cadmium Illumina, GAIIx SE, 76bp DRA000959
Very low cadmium Illumina, GAIIx SE, 76bp DRA000959
Drought Illumina, GAIIx SE, 76bp DRA000959
Flood Illumina, GAIIx SE, 76bp DRA000959
Cold Illumina, GAIIx SE, 76bp DRA000959
Osmotic Illumina, GAIIx SE, 76bp DRA000959
ABA Illumina, GAIIx SE, 76bp DRA000959
JA Illumina, GAIIx SE, 76bp DRA000959
Developmental time Illumina, GAIIx SE, 76bp DRA000959

Database Contents and Functions

Entrances to TENOR

The following three entrances are available to access expression data in TENOR.

ThreeEntrances

(A) Keyword search

This function will return the list of transcript IDs that contain "search words" in their ID or description fields. Responsive expression patterns (fold-change and FDR) for each condition are displayed as well as transcript IDs and descriptions. Each transcript has four links to the following external information.

KeywordSearchResult

(B) Genome Browser

GBrowse

(C) Responsive Gene Search

Users can search transcripts by their responsive expression patterns and thresholds. A list of transcripts whose expression pattern is consistent with specified responsive expression pattern will be shown in the bottom frame as in the keyword search.

ResponsiveGeneSearch

Gene prediction program PARPNTE

PARPNTE can predict both protein-coding and non-coding transcript structures simultaneously with coding sequence (CDS) information based on the alignment of mRNA-Seq reads on the genome and the hidden Markov model (HMM) trained with RAP-DB representative transcripts with FLcDNA evidence.

Performance comparison with Cufflinks2

Sensitivity (Sn) and Specificity (Sp) were compared betwee PARPNTE and Cufflinks2 using cuffcompare.
mRNA-Seq data in TENOR and representative transcripts in RAP-DB were used for the gene prediction by two programs.

	PARPNTE:
	#     Query mRNAs :   48366 in   38228 loci  (30293 multi-exon transcripts)
	#            (5183 multi-transcript loci, ~1.3 transcripts per locus)
	# Reference mRNAs :   43466 in   37870 loci  (30911 multi-exon)
	
	#--------------------|   Sn   |  Sp   |  fSn |  fSp
	        Base level:      51.2    64.1     -       -
	        Exon level:      52.4    58.9    54.6    61.3
	      Intron level:      70.1    80.6    71.8    82.6
	Intron chain level:      32.5    33.1    57.3    58.5
	  Transcript level:       0.0     0.0     0.1     0.1
	       Locus level:      26.2    25.9    34.1    33.2
	
	     Matching intron chains:   10035
	              Matching loci:    9907
	
	          Missed exons:   47549/173117  ( 27.5%)
	           Novel exons:   25775/154037  ( 16.7%)
	        Missed introns:   33323/125962  ( 26.5%)
	         Novel introns:   14557/109525  ( 13.3%)
	           Missed loci:   15352/37870   ( 40.5%)
	            Novel loci:   12423/38228   ( 32.5%)
	Cufflinks:
	#     Query mRNAs :   45415 in   30617 loci  (30249 multi-exon transcripts)
	#            (9625 multi-transcript loci, ~1.5 transcripts per locus)
	# Reference mRNAs :   43466 in   37870 loci  (30911 multi-exon)
	
	#--------------------|   Sn   |  Sp   |  fSn |  fSp
	        Base level:      59.0    50.8     -       -
	        Exon level:      49.1    54.2    51.4    56.7
	      Intron level:      69.3    79.5    71.4    81.9
	Intron chain level:      31.3    32.0    55.3    56.5
	  Transcript level:       0.0     0.0     0.0     0.0
	       Locus level:      25.1    31.0    34.4    40.6
	
	     Matching intron chains:    9681
	              Matching loci:    9513
	
	          Missed exons:   44641/173117  ( 25.8%)
	           Novel exons:   20563/156764  ( 13.1%)
	        Missed introns:   34431/125962  ( 27.3%)
	         Novel introns:   15408/109802  ( 14.0%)
	           Missed loci:   13968/37870   ( 36.9%)
	            Novel loci:    5892/30617   ( 19.2%)
		

Download PARPNTE

Please download from "Data Downloads" page

How to use PARPNTE

PARPNTE predicts gene structures and protein-coding regions based on mapping data of mRNA-Seq reads and genome sequence data.
The prediction depends on parameters generated from genome sequnece, splicing junction and depth of mRNA-Seq reads and reference gene annotation data.

Step 1) Building HMM

1. Prepare input files for training
- Reference genome sequence (FASTA)
- splicing junction data (BED format file reported by TopHat)
- read depth file (output file of SAMtools "depth" command with accepted_hits.bam file reported by TopHat)
- reference annotation (GFF)
2. Run building HMM
$ java -jar parpnte.jar -ms -g -q -d -j -sb
- socrefiles_basename_path : Set the name of the directory and prefix in which PARPNTE will write all of its output.
If you set /home/bio/parpnte/rice, all output files (e.g., rice.depth_score, rice.base_score, rice.trans_score) will be generated under the "/home/bio/parpnte" direcotry.
3. Output files
- transcripts.gff : result of gene prediction
- amino_acid_sequences.fasta : amino acid sequneces of predicted genes
- cds_sequences.fasta : nucleotide sequences of protein coding regions
- mrna_sequences.fasta : nucleotide sequences of transcribed regions
- junctions_modified.bed : splicing junction data (BED) after removing duplicated and noise data
- junctions_removed.bed : splicing junction data (BED) removed
- run.log : running log file for gene prediction
- error.log : error log file for gene prediction

Step 2) Gene prediction

1. Prepare input files for gene prediction
- Reference genome sequence (FASTA)
- splicing junction data (BED format file reported by TopHat)
- read depth file (output file of SAMtools "depth" command with accepted_hits.bam file reported by TopHat)
- basename for scoring matrix generated in the Building HMM step (e.g., /home/bio/parpnte/rice)
2. Run gene prediction
$ java -jar parpnte.jar -g -d -j -sb -o -chr
3. Output files
- transcripts.gff : result of gene prediction
- amino_acid_sequences.fasta : amino acid sequneces of predicted genes
- cds_sequences.fasta : nucleotide sequences of protein coding regions
- mrna_sequences.fasta : nucleotide sequences of transcribed regions
- junctions_modified.bed : splicing junction data (BED) after removing duplicated and noise data
- junctions_removed.bed : splicing junction data (BED) removed
- run.log : running log file for gene prediction
- error.log : error log file for gene prediction

Licence

This software is released under the MIT License.

The MIT License (MIT)

Copyright © 2015 National Institute of Agrobiological Sciences

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.