rPredictorDB

• 0. Terms of Use
• 1. rPredictorDB User Documentation
  • 1. rWeb Tutorial
  • 2. Data
  • 3. Toolkit
  • 4. Toolkit reference
  • 5. Database record information (Sequence detail)
  • 6. Exporting results
  • 7. Similarity search
  • 8. CP-predict: Custom rRNA prediction algorithm
  • 9. Structural features
  • 10. FAQ
  • 11. Glossary
  • 12. Biological background
• 2. rPredictorDB Technical Documentation
  • 1. rPredictorDB architecture
  • 2. Setup
  • 3. rPredictorDB data & database
  • 4. rETL: getting data
  • 5. rPredictorDB website
  • 6. Creating your own tool
  • 7. Creating your own analytical model
  • 8. Database queries using DbTool

Introduction

Get started right away with the rWeb Tutorial !

rPredictorDB is a project dedicated to ribosomal RNA (rRNA) secondary structure research. The two main contributions of rPredictorDB are:

• The rPredictorDB infrastructure: a website, database and toolkit for search and prediction
• CP-predict: a new secondary structure prediction algorithm specifically for ribosomal RNA

The project was partially supported by grant no. 550214 of the Charles University Grant Agency (GAUK).

Goals of rPredictorDB

The aim of rPredictorDB is twofold. We develop and deploy a technique of predicting ribosomal RNA secondary structure and make the resulting structural information readily available. At the same time, the rPredictorDB database contains rich annotations of rRNA structures and the underlying sequences, providing an unified interface for rRNA research for both academics and professionals outside academia.

A secondary goal of rPredictorDB is extensibility: it should be easy to integrate third-party tools.

Motivation

Gene translation is the process of implementation of genetic information, which forms a living organism. The unit central to translation is the ribosome.

The “scaffold” (and major part) of the ribosome consists of ribosomal ribonucleic acids (rRNA), which are critical for its function. Because the function of biological molecules is mostly determined by their spatial structure, understanding the role of rRNA in translation depends on understanding rRNA structures. While rRNA nucleotide sequences can be obtained relatively easily, determining their three-dimensional structure is very demanding: sequences are known for hundreds of eucaryotic organisms while spatial structures only for about 5.

Secondary structures are an intermediate step between sequences and three-dimensional structures. Understanding secondary structures enables at least partial study of rRNA behavior, and secondary structures can be predicted from sequences (to a much greater extent than the spatial structures). However, ribosomal RNA are notoriously hard problems for secondary structure prediction.

In recent years, improved imaging methods have led to detailed measurement of spatial structures of the ribosome in a few organisms (and a secondary structure can be derived from these measurements). The availability of these structures, together with a high degree of conservation in the ribosome, should make it possible to derive secondary structures for other ribosomal RNA as well.

While the importance of ribosomes and ribosomal RNA has been recognized for several decades, support for bioinformatical work over the available ribosomal data is fragmented and unsatisfactory. Various sites dedicated to rRNA exist: most notably the SILVA database and the Comparative RNA Website (CRW). However, none of them are satisfactory: the CRW site is hard to navigate, fails to provide crucial information (e.g. origin of provided secondary structure) and does not support mass retrieval; SILVA has no support for structural bioinformatics. The rPredictorDB infrastructure aims to overcome the shortcomings of these sites and make working with rRNA as easy as possible.

rPredictorDB components

The rPredictorDB infrastructure has several key components:

• a database of rRNA sequences and secondary structures (rData) and Extraction-Transformation-Load mechanisms to build rData (rETL),
• a set of tools that perform standard tasks on the data like similarity search or secondary structure prediction (rTools),
• a new algorithm dedicated to rRNA secondary structure prediction (CP-predict),
• an internet portal and back-end that makes the rData and rTools components accessible to the research community and general public (rWeb),
• documentation, including relevant scientific literature (rDoc)

The rPredictorDB infrastructure is built and maintained by the rPredictorDB student team from the Computer Science branch of the Faculty of Mathematics and Physics, Charles University, Prague. (Contact us here.)

What now?

To start using the rPredictorDB website right away, go search or predict.

If you wish to read more about rPredictorDB:

• For a tutorial on how to make the most of what rPredictorDB can offer, go read the rWeb Tutorial.
• To find out what tools are available through rPredictorDB and how to use them, browse the The rPredictorDB Toolkit.
• To learn about the rPredictorDB database and data sources, read rPredictorDB data and database (or The Data of rPredictorDB for a more technical description).
• To find out how the rPredict algorithm works, see CP-predict: a two-phase algorithm for rRNA structure prediction.
• To learn more about the biological background of rPredictorDB, go Biological background.
• For the technical documentation of rPredictorDB, go to rPredictorDB Technical Documentation.
• The API reference documentation of rPredictorDB is here