A non-coding RNA (ncRNA) is a functional RNA molecule that is not translated into a protein. There are abundant and functionally important types of ncRNAs, and they are the long ncRNAs, microRNAs, piRNAs，circRNAs, snoRNAs, snRNAs, exRNAs, scaRNAs and so on.

P4PC is a manually curated portal about bioinformatics resources of piRNA and circRNA. In order to provide guidelines and contribute to ncRNA research and will be regularly updated.

Anyone – P4PC is user-friendly and open-access service. No registration is needed to exploit its full functionality.

In order to provide guidelines and contribute to ncRNA research, P4PC provides computational tools, databases and papers. All entries in P4PC are classified in the five categories. They are piRNA databases, piRNA computational tools, circRNA databases, circRNA computational tools, and papers. In the first page of each part, the list of resources summary is provided, and users can search or filter them according their research proposes. Users are also select concerning resource and view its detail information and comments.

Yes – to submit new tool or database, please go to the “Submit” section from main menu and fill in the form (required fields are minimum). After data validation, we will incorporate submitted it into P4PC.

Yes. For information, comments and/or suggestions, please feel free to leave your message here or email us at liuyajun@xaut.edu.cn.

To assist researchers, we subdivided the above each group according to their functionality and labeled as subgroup type.

The computational tools of ncRNAs were categorized into the detail subgroups according to their function, such as ncRNA identification, function subset identification, function sites identification, subcellular localization, target identification, association prediction, functional analysis and so on. For special properties, the particular subgroup types were set for corresponding ncRNA. For example, piRNA are distributed in clusters, so tools for piRNA cluster identification was adopt.

The databases of ncRNAs were categorized into six general subgroups (RNA comprehensive database with specific ncRNA annotate, ncRNA-specific database, species-specific database, function database, target database, disease-related database and other). Due to the particular characters of each ncRNAs types, special subgroups settings are also used, such as piRNA cluster database.

The essential data about resources of each general groups are clearly listed in page. With the purpose of efficiency, filter panels were set to removing redundant items. There are dozens of attributes and we consider three aspect as following:

1. Species: The well-studied model species are listed such as Homo sapiens, M. musculus, D. melanogaster and so on. Meanwhile, researches about livestock and poultry has attracted attention, so relevant species are considered here.

2. Subgroup type: According to the addressed research question, researchers can select the subgroup types which is mentioned in section 2.4 to focus on the interested resources.

3. Availability: User may use resources and this setting ensured that the available resources are timely chosen.

To help users to focus on the resources in subgroups, our platform provides six ranking options and descripted as following:

1. Publication year: It is the publication year of the resource. The longer it public, the more mature but older it is.

2. Latest update year: It means the latest updated year of the resource. The more active resource is likely to have the newer update.

3. Number of references: It implies the number of the papers about the resource. The series of articles represents the continuous maintenance of resources.

4. Citation count: Citation counts is used as a main science indicator to measure the performance of publications. Therefore, we sum the citations of the papers about each resource from google scholar.

5. Average vote: Users can vote and review the resource in our platform and the average score of the all user votes is shown in the page.

6. Type Normalized Citation Impact (TNCI) score: An impact factor is designed for eliminating the differences in the citations of bioinformatics resources in different subgroup types and publication years.

Citation count is an important indicator for measuring academic influence. However, there are significant differences in citation counts about papers in different subgroup types, and the publication year could extremely affect the citation count. For example, the citations of papers about comprehensive annotation databases is significantly higher than specialized databases, and the citations of papers published earlier are generally higher than those recently published. Therefore, inspired from an effective bibliometric factor called CNCI (Category Normalized Citation Impact), we present a new measure TNCI. The TNCI of a resource is obtained by dividing its citation frequency by the average citation frequency of papers about this resource in the same subgroup type. The baseline value of the average citation frequency in a subgroup type is calculated by means of the average value and the TNCI of a paper equals to the citation frequency of the paper divided by the corresponding baseline value. The TNCI of a resource is the average value of the TNCI of the group of papers about this resource.

The navigation of P4PC is shown in figure 1(a) and includes 9 sections. They are home, piRNA databases, piRNA tools, circRNA databases, circRNA tools, papers, submit, help/FAQ and contact. Users can additionally filter, rank and search available resources according to their research needs, capabilities and preferences as represented in figure 1(b). Beside basic summary seen at first glance, the user can get detailed information concerning each tool as shown in figure 1(c).

Fig.1. The webpages of P4PC.

To illustrate how P4PC can facilitate researchers to investigate the suitable resource, we presented a case studies where P4PC is applicable. If researchers inquire a piRNA comprehensive annotate database, they could conveniently get the results through three steps with P4PC.

1. Click on the “piRNA databases” hyperlink at the navigation area of the website (Figure 2.a). The “Home” page will be switched to the “piRNA databases” page.

2. Tick “piRNA comprehensive annotate” the option of filter panel and click the “Filter” button (Figure 2.b). Researchers can focus on the interested resources of piRNA comprehensive annotate databases.

3. By simply clicking the hyperlink in the six ranking options mentioned in section 2.6 from table header of query results, a ranked list of piRNA comprehensive annotate database would be shown (Figure 2.c).

Fig.2. The case study of P4PC.