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Abstract
Salvia miltiorrhiza has been comprehensively studied as a medicinal model plant. However, research progress on this species is significantly hindered by its unavailable genome sequences and limited number of expressed sequence tags in the National Center for Biotechnology Information database. Thus, a transcript database must be developed to assist researchers to browse, search, and align sequences for gene cloning and functional analysis in S. miltiorrhiza. In this study, the Danshen Transcriptional Resource Database (DsTRD) was built using 76,531 transcribed sequences assembled from 12 RNA-Seq transcriptomes. Among these 12 RNA-seq data, ten were downloaded from NCBI database. The remaining two were enced on the Hiseq2000 platform using the stem and hairy-root of S. miltiorrhiza. The transcripts were annotated as protein-coding RNAs, long non-coding RNAs, microRNA precursors, and phased secondary small-interfering RNA genes through several bioinformatics methods. The tissue expression levels for each transcript were also calculated and presented in terms of RNA-Seq data. Overall, DsTRD facilitates browsing and searching for sequences and functional annotations of S. miltiorrhiza. DsTRD is freely available at http://bi.sky.zstu.edu.cn/DsTRD/home.php.
Citation: Shao Y, Wei J, Wu F, Zhang H, Yang D, Liang Z, et al. (2016) DsTRD: Danshen Transcriptional Resource Database. PLoS ONE 11(2): e0149747. https://doi.org/10.1371/journal.pone.0149747
Editor: Yu Xue, Huazhong University of Science and Technology, CHINA
Received: October 5, 2015; Accepted: February 4, 2016; Published: February 24, 2016
Copyright: © 2016 Shao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data are available from the supplemental files and NCBI database. The NCBI accession numbers are: SRR1043998, SRR1045051, SRR1020591, SRR946948, SRR946949, SRR946950, SRR946951, SRR037804, SRR1005880, SRR924322, SRR1557860, SRR1557861, SRR1557862 and SRR1557863.
Funding: This work was supported by the 521 Talent Foundation of Zhejiang Sci-Tech University, the website is http://www.zstu.edu.cn/. This work was supported by the Doctoral Scientific Research Foundation of Zhejiang Sci-Tech University, the website is http://www.zstu.edu.cn/. This work was supported by the grants of the Natural Science Foundation of China (grant No. 81373908 and 81403033), and the website is http://isisn.nsfc.gov.cn/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Salvia miltiorrhiza Bunge is a perennial plant that belongs to the Lamiaceae family. The dried root or rhizome of this plant is called “Danshen” in traditional Chinese medicine. S. miltiorrhiza is comprehensively studied as a medicinal model plant because of its small genome, short life cycle, and stable genetic transformation system. Elucidation and regulation of biosynthesis pathways involving the active ingredients of S. miltiorrhiza are considered major research topics. However, the unavailable genome sequence and limited nucleotide sequences (only 1,048 nucleotides and 10,288 expressed sequence tags, ESTs) of S. miltiorrhiza in the National Center for Biotechnology Information (NCBI) database significantly hinder research progress on this species, specifically in terms of molecular growth mechanisms, developmental and stress responses, and biosynthesis of active ingredients.
Numerous transcriptomes of S. miltiorrhiza have been extensively recovered through high-throughput sequencing and deposited into the NCBI Sequence Read Archive (SRA) database. Nevertheless, researchers should continuously analyze large amounts of raw sequencing reads. Despite the currently available routine processes and methods, considerable time and effort are expended to retrieve sequences and features and install diverse software packages. Thus, a transcript resource, which can assemble and annotate raw reads and can be easily searched by humans and computers, must be developed. In this regard, we developed the Danshen Transcriptional Resource Database (DsTRD), which is a simple but comprehensive transcript resource for S. miltiorrhizae.
Materials and Methods
RNA-Seq data information
Ten RNA-seq data including 7 pair-end sequencing data based on Hiseq2000 platform and three single-end sequencing data based on 454 platform were downloaded from NCBI database with Sequence Read Archive (SRA) formation (S1 Table).
In addition, two RNA-seq data (RNAseq_1 and RNAseq_2) were added for transcripts assambly and tissue expression analysis by using the stem and hairy-root of S. miltiorrhiza and sequencing on the Hiseq2000 platform (S1 Table). The stems of S. miltiorrhiza were collected from a Salvia planting base in shangluo, China. And the hairy-root of S. miltiorrhiza were cultured by our lab.
Transcriptional assembly annotation and quantification
The assembly flowchart of S. miltiorrhiza RNA-Seq data is shown in Fig 1. The adapters and poor quality sequences were firstly removed from RNA-seq data using FASTX toolkit downloaded from http://hannonlab.cshl.edu/fastx_toolkit/download.html. Clean sequencing data were assembled into transcripts by using Trinity software with the parameters “—min_kmer_cov 3—min_glue 3” [1]. All assembled transcripts from the total samples were clustered by tgicl v2.1 with default parameters [2] (S2 Table). These RNAs were annotated and classified by the Blast2GO software (http://www.blast2go.com/) with the NR database and e-values < 1 e−5. Unknown transcripts were classified as unknown coding RNA or noncoding RNA (ncRNA) based on the length of the longest open reading frame (> 100 amino acids = unknown coding RNA, < 100 amino acids = ncRNA). Finally, ncRNAs were considered as long ncRNAs (lncRNAs) if their length is > 200 nt. The remaining ncRNAs were considered as other ncRNAs. To profile the tissue expression features of the assembled transcripts, we derived the count and expression levels (fragments per kilobase per million mapped reads) from RNA-seq data by using the RSEM pipeline with default parameters [3]. RNA-seq datasets were selected from the 12 RNA-seq datasets (S1 Table). The selected sets were produced from the same sequencing platform (Hiseq2000) with pair-end sequencing, high-depth sequencing, and five different tissue samples (SRR1043998, SRR1045051, SRR1020591, RNAseq_1, and RNAseq_2) (S3 Table).
Annotation and quantification of miRNAs in S. miltiorrhiza
Four sRNA-Seq data were downloaded from the NCBI database with SRA formation (S3 Table). The other sRNA-Seq data (sRNAseq_1) were added for miRNA identification and tissue expression analysis; these data were sequenced on the Hiseq2000 platform by using the hairy root of S. miltiorrhiza (S3 Table).
In DsTRD, miRNAs were classified into three types. The first type is named as “miRxxx-like,” with a sequence similar to the known “miRxxx” in other plants (published in miRBase with the version 21.0). The precursor with stem-loop structure of this miRNA could not be retrieved from the assembled transcripts. The second type is named as “Smi-miRxxx,” with a sequence also similar to the known “miRxxx” in other plants (published in miRBase with the version 21.0). But the precursor with stem-loop structure of this miRNA could be retrieved from the assembled transcripts. The third is novel miRNA identified in a plant and satisfied the criteria established by Meyers et al. [4] and Thakur et al [5]. Among novel miRNAs, those supported with the reads of miRNA* were annotated as real novel miRNAs, whereas the unsupported reads were annotated as novel miRNA candidates. To profile the tissue expression features of miRNAs, we derived the counts and expression levels (reads per million, RPM) from sRNA-seq data by using an in-house Perl script.
Annotation and quantification of the phased secondary small interfering RNAs (phasiRNA) in S. miltiorrhiza
Phased, secondary small-interfering RNAs (phasiRNAs) are important in post-transcriptional regulatory networks in plants [6]. Well-characterized trans-acting siRNAs (tasiRNAs), as a special subgroup of phasiRNAs, are initiated by miRNA-mediated cleavage and converted to dsRNA to yield siRNAs in a 21-nt phase [6–8]. All sRNA-Seq data applied in the miRNA annotation were used for phasiRNA identification and quantification through the PhaseTank pipeline with default parameters [9]. The identified phasiRNA genes triggered by miRNAs were also considered as tasiRNAs and named “TASxxx,” whereas the remaining phasiRNA genes were named “PASxxx” in the DsTRD. We also derived the counts and RPM expression levels to profile the tissue expression features of phasiRNAs by using an in-house Perl script.
Database design and implementation
The web pages of DsTRD were constructed using a Hypertext Preprocessor (PHP) language and run on a Linux system (Centos 6.4). All data were stored in a MySQL (5.1.66) database (http://www.mysql.com), whereas the assembled transcripts, miRNAs, and phasiRNAs for download were stored as files in FASTA format.
Results and Discussion
Web interface of DsTRD
We created a freely accessible web interface for DsTRD. The interface comprises six core-page sections: “Home,” “Browse,” “Search,” “Pathway,” and “Download” (Fig 2).
Home page
Users can access DsTRD at http://bi.sky.zstu.edu.cn/DsTRD/home.php. The home page contains an introduction to DsTRD and a search engineer (Fig 2). This section provides users an outline of DsTRD and can be conveniently used to search gene information.
Browse
Browse facilitates users to browse the details of various transcripts, including coding RNAs, lncRNAs, miRNAs, and phasiRNAs (Fig 3A). Users can click the coding RNA, lncRNA, miRNA, and phasiRNA to browse the summaries (Seq id, annotation, and the length of the transcripts) of the corresponding RNAs. Moreover, data source can be selected in the search results to download the sequences and information in FASTA format (Fig 3B). The details of each transcript is displayed by clicking on “Seq id.” The tissue expression features for each transcript is also shown in the information page (Fig 3C).
(A) Users can browse the summaries of four kinds of RNAs. (B) Users can search and view the features of coding RNA. (C) Information page of RNA.
Search
Search functionalities are designed to facilitate users to retrieve useful information. In the search frame (Fig 4), users can search for information regarding id, gene name, gene annotation, gene ontology (GO) number, and GO classification by encoding one or more key words. As DsTRD maintained the nucleotide-sequence data of various RNAs, a sequence alignment tool, namely, the Basic Local Alignment Search Tool (BLAST), was integrated into DsTRD. Users can compare a query sequence with a library of coding RNA, lncRNA, miRNA, and phasiRNA sequences by using BLAST search and identify the sequences that closely resemble the query sequence. BLAST will meet the users’ requirements in finding homologous transcripts of interest. In the BLAST frame (Fig 4), users can supply one or more query sequences by uploading or directly pasting these sequences to search against the available databases using BLAST default parameters. Users can also specify additional parameters for BLAST search to control search sensitivity and result format as desired. The BLAST results are displayed in another page in a pairwise format by default.
Pathway
A hot research field is included to reveal and regulate biosynthesis pathways involving the active ingredients of S. miltiorrhiza. The biosynthesis pathways for salvianolic acid B and tanshinone, which are active ingredient representatives of the hydrophilic and lipophilic components, respectively, were comprehensively studied. By searching DsTRD, we found that 46 transcripts participate in salvianolic acid B synthesis, and 60 transcripts are involved in tanshinone synthesis (Fig 5). In the pathway page, users can view the outlines of these two pathways. Users can also view detailed information on each RNA of the enzyme by clicking the enzyme name (showed in blue font) on the pathways.
Downloads
DsTRD data can be downloaded to perform local analysis. In the download page, users can download coding RNAs, lncRNAs, miRNAs and their precursors, and phasiRNA and their precursors in FASTA format.
Conclusion
S. miltiorrhiza is an extensively investigated medicinal model plant because of its small genome, short life cycle, and stable genetic transformation system. However, the lack of available genome sequences and the limited EST sequences stored in NCBI for S. miltiorrhiza considerably restrict study progress on the molecular mechanisms of this species. In this study, we aim to produce large numbers of transcript sequences with corresponding annotation information and make these data freely accessible to users. As such, we developed DsTRD, which exhibits simplicity of use for researchers and contains 76531 transcribed sequences assembled from the RNA-seq data. This database provides information regarding the sequences and functional annotations of the transcripts, including protein-coding RNAs, lncRNAs, other ncRNA, miRNAs, and phasiRNAs. The database also includes the tissue expression feature for each transcript, which was calculated and shown based on RNA-seq data. As an efficient bioinformatics tool, DsTRD is important in studying various kinds of molecular processes in S. miltiorrhiza.
Availability and Requirements
DsTRD is publicly available at http://bi.sky.zstu.edu.cn/DsTRD/home.php. DsTRD supports all the latest major web browsers, preferably Mozilla Firefox, Google Chrome, or Apple Safari, for visualization and performance purposes.
Supporting Information
S1 Table. Transcriptome data downloaded from the NCBI database according to the accession numbers.
https://doi.org/10.1371/journal.pone.0149747.s001
(DOC)
S2 Table. All assembled transcripts in fasta format.
https://doi.org/10.1371/journal.pone.0149747.s002
(ZIP)
S3 Table. sRNA-Seq data downloaded from the NCBI database according to the accession numbers.
https://doi.org/10.1371/journal.pone.0149747.s003
(DOC)
Author Contributions
Conceived and designed the experiments: ZL WJ. Performed the experiments: YS JW. Analyzed the data: YS. Contributed reagents/materials/analysis tools: HZ DY. Wrote the paper: FW WJ.
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