Data instructions:

The following instructions may be useful to make sense of the usage of the functional modules, "Pri-miR", "MiR—Tar", and "Self-reg", which were constructed based on various high-throughput sequencing (HTS) data.

1. Features of HTS data:

• (1) MPSS (massively parallel signature sequencing) data:
  All the MPSS short reads were derived from poly(A)-tailed transcripts, according to the previous report (the position of the MPSS tag for a given gene or transcript should coincide with the first Sau3A site 5' of the polyadenylation site) (Ref. 14). For the question "what is MPSS", please refer to Ref. 22, or "FAQs & Help" at http://mpss.udel.edu/at/mpss_index.php.
• (2) PARE (parallel analysis of RNA ends) data:
  The PARE method was developed by combining the HTS technologies with modified 5' RACE (rapid amplification of cDNA ends). It is one type of plant degradome sequencing technologies. Considering its high-throughput feature, it has been widely employed for plant microRNA (miRNA) target validation (Ref. 15-18). The PARE short reads are derived from plant degradomes, which include the miRNA-mediated cleavage remnants and other 3' cleavage remnants. Hence, both the miRNA target sites and the signal intensities of miRNA-mediated cleavages can be inferred from the PARE data.

2. Data usages:

• (1) "Pri-miR":
  Most miRNA genes are transcribed by RNA polymerase II (Ref. 2 and Ref. 23). Hence, the primary transcripts of miRNA genes, i.e. pri-miRNAs (primary microRNAs), possess 3' poly(A) tails. Considering this feature, theoretically, most pri-miRNAs can be cloned and sequenced by MPSS method. In other words, the transcriptional contexts of pri-miRNAs can be partially unraveled by MPSS sequencing. Because it is more difficult to determine the sequence ranges of pri-miRNAs than those of pre-miRNAs (precursor microRNAs), no comprehensive collection of the sequence information of pri-miRNAs has been available in current databases for both plants and animals. Here, the "Pri-miR" module enables us to gain a global view of the transcriptional scene of a specific miRNA gene in a tissue-specific manner. Therefore, the data presented by the "Pri-miR" module is a valuable resource for the study of plant miRNA transcription. Specifically, it can facilitate primer or probe designing for pri-miRNA detection. Besides, the level of a specific pri-miRNA will not only indicate the extent of miRNA transcription, but also the stability of the pri-miRNA in vivo. Thus, "Pri-miR" will also provide valuable insights into the efficiency of pri-miRNA processing in a specific tissue.
• (2) "MiR—Tar":
  Considering the feature of PARE data, whole-transcriptome identification of miRNA—target pairs were carried out in both Arabidopsis and rice. The cleavage sites can be exactly identified by mapping these PARE short reads to the predicted miRNA target mRNAs, and the cleavage signal intensities in specific tissues can be inferred from the normalized short read counts (in RPM, reads per million). For researchers who intend to validate miRNA—target pairs of interest, the "MiR—Tar" module can provide the information of cleavage signal intensities. Thus, the priority of each miRNA—target pair to be validated could be determined before modified 5' RACE assays. A sizable portion of 3' cleavage remnants, especially those at trace levels, are difficult to be detected by the traditional RACE method. Instead, they could be reflected by PARE sequencing, considering the in-depth feature of HTS technology. Together, the "MiR—Tar" module provides a comprehensive set of miRNA—target pair candidates with user-friendly outputs, including novel pairs with subtle regulatory relationships.
• (3) "Self-reg":
  In plants, pri-miRNAs are processed into pre-miRNAs, then into miRNA/miRNA* duplexes by DCL1 (Dicer-like 1)-mediated two-step cleavages (Ref. 2). The first-step cleavages generate two types of 3' remnants from both the left arms (generate longer remnants) and the right arms (shorter ones) of pri-miRNAs. Theoretically, these 3' remnants can be cloned and sequenced by the PARE method. Hence, the processing of pri-miRNAs can be investigated by using the "Self-reg" module, which was established based on PARE datasets. On the other hand, miRNAs or miRNA*s can recognize their host precursors as the targets based on the complementary sequences present in the stem regions of pri- or pre-miRNAs. This type of miRNA- or miRNA*-mediated self-regulation of miRNA genes can be also represented by plant degradome PARE sequencing data. The users can learn more about the degradome sequencing data-based analyses of miRNA precursor metabolism from our recently published opinion paper (Ref. 24).

Instruction for dynamic outputs of "Pri-miR":

It is necessary to explain the graphic presentation of "Pri-miR" here. The scale bar on the top of each resulting figure measures the sequence range surrounding a specific pre-miRNA (～5 kb on both sides) approximately. The positions with detected transcriptional signals reflected by MPSS data are marked by blue vertical bars. The green movable window can be used to select for a specific region within the ～10 kb region surrounding the pre-miRNA for deep view. Once selected, the detailed transcriptional scene of this short region can be visited in a tissue-specific manner, and the transcriptional levels are indicated by the red color intensities. In summary, the top row of each figure provides a global view of the transcriptional context of the ～10 kb region, whereas the rows below indicated by various libraries prepared from different tissues indicate the detailed transcriptional scene of a selected region by using the movable window. From this point of view, we think that the design of the presentations of the results of "Pri-miR", "MiR—Tar", and "Self-reg", which were implemented by SVG (scalable vector graphics), is user-friendly and easy to use.