The new development of RNAInter:

1. Expand data sources and coverage of species.

2. Provide a confidence score for each interaction.

3. Redesigned database based on the Django Model-View-Controller (MVC) framework.

4. Add GeneID, circBaseID or PubChemID for 15153 interactors without external ID link, covering mRNA, circRNA, piRNA and compound.

5. Add the annotation with disease association from MNDR v3.0 and lncRNADiseasev2.0, and tissue specific expression from GTEx database.

6. Add filter tool in result page of search and browse.

The homepage is displayed in the following:

1. Main functions of the database are provided in menu bar form (boxed in light blue).

2. Other databases contributed by our group.

3. Cite information.

In previous version of RNAInter, we developed confidence score based on different detect method. To a certain extent, it can guide users to filter the RNA-associated interactions of interest. However, such a method was too arbitrary. In this version, the new confidence score was defined by integrating the trust of the scientific community(S), the trust of experimental evidence(E) and types of tissues/cells(T), which increases the scoring reliability.

1. The trust of the scientific community(S):

The trust of the scientific community can be reflected by the number of citations and publication years derived Google Scholar. A higher number of citations corresponds to a higher confidence score. This metrics S can be calculated as follow:

where i is the number of publications or prediction tools which can support the interaction, ri represents the citations of the i-th publication or prediction tool, and yi stands for the publication year of the i-th publication or prediction tool.

2. The trust of experimental evidence(E):

For the trust of experimental evidence, since a small-scale experiment is more reliable than a large-scale screening, publications describing few interactions contributed more than those describing many interactions. This metrics E can be calculated as follow:

where i is the number of publications or prediction tools which can support the interaction, ni represents the interaction number described or predicted by the i-th publication or prediction tool.

3. The types of tissues/cells(T):

Additionally, the more tissues/cells the interaction was detected in, the higher confidence score the interaction has. This metrics T can be calculated as follow:

The three metrics were scaled in the range of [0, 1], separately. Then the Euclidean distance of them was calculated as the confidence score.

The final confidence scores were log2-transformed and scaled to [0, 1]. Accordingly, interactions reported by highly cited papers and detected in more tissues/cells will obtain a higher confidence score.

We evaluated new confidence scoring system based on the three different levels of supporting evidence. The interactions can be divided into three levels according to the different sources of evidence from which they were derived. Interactions with strong evidence were supported by strong experiments, while those with weak evidence by weak experiments and those with predicted evidence were only supported by predicted methods. The greatest enrichment score intervals of interactions with experimental evidence (blue and green bar) were from 0.2-0.3, while those of interactions with predicted evidence (red bar) were from 0.1-0.2. The mean scores of interactions with strong and weak evidences were 0.2886 and 0.2767, which was obviously higher than 0.1814, mean of interactions with predicted evidence.

This tutorial is as follows.

1. Firstly, we have to choose the type of keyword. There are three keyword types in our search as the picture shows. In this example, we choose RNA/Protein/DNA Symbol as the keyword type.

2. Next, we enter the keyword according to the keyword type selected in the previous step. In this example, we choose 'MEG3' as the keyword.

3. Then select the category for the keyword you entered. In this example, we choose 'lncRNA' as the category of the keyword 'MEG3'.

4. The next step is to choose the type of interaction. If you want to search for interaction of proteins with a particular RNA, you can choose 'RNA-Protein interaction'. In this example, we choose 'RNA-Protein interaction'. Under this condition, we can get RNA('MEG3')-protein interactions.

5. Then select the species for the keyword you entered. In this example, we choose 'Homo sapiens' as the species of the keyword 'MEG3'.

6. You can also choose the type of method that detects interaction as the filter. In this example, we want query the interaction detected by strong experimental evidence, so we choose 'Strong Experimental Evidence'.

7. We provide a score for each interaction. The greater the value, the higher the credibility. To filter low-confidence interactions, in this example, we choose the 0.2 as the minimum score and 1.0 as the maximum score.

After several seconds, the result will occur.

1. Your search conditions are in the head of the web page.

2. You can use the filter option to further screen search results by interactor, interactor category and species.

3. All the interactions are represented in th table format.

Firstly, you can get general information including RNAInter ID, confidence score, interaction type and predicted binding sites in the detail page.

Secondly, you can also get the basic information, homology interaction, target region information, evidence support and references of each entry.

Thridly, the annotation of RNA editing, localization, modification, structure and assication with disease also been provided.

Fourthly, the interaction network, tissue specific expression from GTEx database and the dynamic expression of interactors in spermatogenesis and haematopoietic stem cell of each entry has been represented in RNAInter.

1. Click Entrez ID/miRBase Accession/PubChem CID to see its basic description in NCBI Gene/miRBase/NCBI PubChem Compound database.

2. Category, UniProt, aliases, other ids and description of each interactor symbol.

RNA editing information from Lncediting, RADAR and DARNED is provided.

RNA localization information from RNALocate is provided, include symbol, subcellular localization, tissue or cell line, PMID. Click each subcellular localization can jump to RNALocate database.

RNA modification information from RMBase is provided, include modification positions, modification types and genomic contexts for each RNA symbol.Click each modification position can get more detail information in RMBase detail page.

RNA secondary structure by the prediction tool of RNA structure is provided. Select any transcript accession to see its secondary structure.

The annotation with disease association from MNDR v3.0 is shown.

The evidence support includes strong evidence, weak evidence, computaional prediction.

The reference includes pubmed ID, the source of database, tissue or cell line and description.

Interaction network for each interactor were provided.(only show the top 100 interactions of each interactor ranked by confidence score in our database ). Click each edge will redirect to corresponding detail page of interaction data.

Tissue specific expression from GTEx database and dynamic expression of interactors in spermatogenesis and haematopoietic stem cell of interactor(s) are provided.

Pearson correlation coefficients of each stage of spermatogenesis/HSC lineage commitment were provided.

Integration of source databases which use different interactors naming conventions is challenging. To ensure maximal connectivity of data, we transform each interactor name found in the input sources to the appropriate naming convention.

1. For miRNA, we use miRBase ID and miRBase Accession.

2. For compound, we use NCBI PubChem Compound symbol and CID.

3. For histone modification, we use ChIPBase symbol.

4. For others, we use official Gene Symbol and Entrez ID.

5. For species, we normalized organism names according to NCBI Taxonomy Database.

IntaRNA is a program for the fast and accurate prediction of interactions between two RNA molecules is provided.

1. Input RNA sequences or upload files with FASTA format.

2. Select and check the parameters.

The search results include target, query, position and energy.

PRIdictor is a tool for predicting protein-RNA interaction. You should input RNA and(or) protein sequence(s) as follow:

The search results include sequence, confidence and prediction sites.

DeepBind is a tool for predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. You should input target sequence(s) or upload a file with FASTA format, and select query protein in left box. as follow:

The search results include target symbol, protein and its predict score. The results can be download.

Abbreviation Full name
ac4C N4-acetylcytidine
acp3U 3-(3-amino-3-carboxypropyl)uridine
Am 2'-O-methylguanosine
Ar(p) 2-O-ribosyladenosine (phosphate)
Cm 2'O-methylcytidine
cmnm5s2U 5-carboxymethylaminomethyl-2-thiouridine
cmnm5U 5-carboxymethylaminomethyluridine
D dihydrouridine
f5C 5-formylcytidine
galQtRNA galactosyl-queuosine
Gm 2'-O-methylguanosine
I inosine
i6A N6-isopentenyladenosine
m1A 1-methyladenosine
m1G 1-methylguanosine
m1I 1-methylinosine
m1Y 1-methylpseudouridine
m2,2G N2,N2-dimethylguanosine
m2G N2-methylguanosine
m3C 3-methylcytidine
m3U 3-methyluridine
m5C 5-methylcytidine
m5C 5-methylcytidine
m5U 5-methyluridine
m5Um 5,2'-O-dimethyluridine
m5Um 5,2'-O-dimethyluridine
m62A N6,N6-dimethyladenosine
m6A N6-methyladenosine
m7G 7-methylguanosine
mcm5s2U 5-methoxycarbonylmethyl-2-thiouridine
mcm5U 5-methoxycarbonylmethyluridine
ncm5U 5-carbamoylmethyluridine
o2yW peroxywybutosine
QtRNA queuosine
t6A N6-threonylcarbamoyladenosine
Tm 2'-O-methylguanosine
tm5s2U 5-taurinomethyl-2-thiouridine
tm5U 5-taurinomethyluridine
Um 2'-O-methylguanosine
xA unknown modified adenosine
xG unknown modified guanosine
xU unknown modified uridine
Y pseudouridine
Ym 2'-O-methylpseudouridine
yW wybutosine

The RNA-associated interactions are collected from different types of resources under one common framework, including experimental literature mining and computational prediction evidence. The experimental methods were divided into strong detection methods and weak detection methods by a manual assignment, depending on the nature and qualitative annotation of the experiment method.

Strong detection methods include:

3D-FISH3RACE3UTR indicator assay
3UTR reporter assay4C5RACE
Affinity technologyAgo2-IPAGO-CLIP
AGO-IPAllele-specific ChIPASO assay
ATPase assayBeta-galactosidase activity assayBiFC
Bio-plex assayBiotin pull-down assayBrdU incorporation assay
BSP assayBS-PCRBulge-loop miRNA RT-PCR
CHARTCHART-MSChemosensitivity assay
ChRIPChromatin accessibility assayChromatography technology
circRIPCleavage assayCLIP
CopurificationCross-linking assayDNA-FISH
DNase I footprintingDot-Blot assayDrug assay
Drug efflux assayDual fluorescent reporter assayDual luciferase reporter assay
ELISAEMSAEnzyme assay
EPRFilter binding assayFilter trap assay
FISHFISH-immunoFluorescence reporter assay
FootprintingFRAPGel electrophoresis
Gel zymographyHi-CHPLC
HRR assayHuR-IPHybrid-PCR
ImmunoassayImmunoblotIndicator assay
Inhibition analysisIPISH
ITCLabel transfer techniqueLC/MS
LC-MS/MSLuciferase reporter assayMass spectrometry
miR-Mask assaymiRNA assaymiRNA qPCR
miRNA RT-PCRmRNA decay assayMS2-RIP
MSPMTS assayMTT assay
Mutation analysisNMRNorthern blot
Northern hybridizationPAGEPCR
Primer extension assayProbe interaction assayProximity ligation assay
pSILACPull-down assayqChIP
RAP-MSREMSAReporter assay
Rescue assayRFLPRIP
RNA chromatographyRNA Co-IPRNA footprinting assay
RNA pull-down assayRNA TRAPRNA-ChIP
RNA-FISHRNA-protein pulldown assayRNAscope
SILACsmFISHSouthern blot
Stem-loop qPCRStem-loop qRT-PCRStem-loop RT-PCR
Strand specific RT-PCRSYBR green PCRSYBR green qPCR
TaqMan microRNA assayTaqMan miRNA assayTaqMan miRNA RT-PCR
TaqMan qPCRTaqMan qRT-PCRTaqMan RT-PCR
TLDAToeprinting assayTRAP
Triplex capture assayTwo hybridU.V.-Crosslinking
Viral infectivity assayWestern blotWST-1 assay
WST-8 assayRISC-trap assayUV-RIP
X-ray crystallographyYeast two-hybrid analysisYeast three-hybrid analysis
WSTImmunostainingMRM analysis
Transwell assayTP-PCRTaqMan assay
SPR assaysqRT-PCRSolid-phase assay
In situ hybridizationRNase-resistant assayRNase protection assay
RNA-RNA pull-down assayRNA-RNA in vitro interaction assayRNA-RNA binding assay
RNA-protein filter binding assayRNA-protein binding assayRNA binding assay
RNA interferenceRNA hybridRIP binding assay
Immunofluorescence assayRNase H cleavage assayReverse transcription qPCR
Reverse RIP assayReciprocal IPReal-time qPCR
Real-time RT-PCRProtein precipitation assayPAGE-Northern blot
MS2-TRAPmiRNA pull-down assayIn vitro binding assay
GST pull-down assayEGFP reporter assayddPCR
DNA pull-down assayCRISPR-Cas9CISH
Biotin-coupled miRNA captureAgo2-RIPAgo2 pull-down assay

Weak detection methods include:

454 sequencingArrayATAC-seq
Bisulfite genomic-sequencingBS-seqCHART-seq
Deep sequencingDegradome-seqdiMARGI
Genome-wide transcriptome sequencingGMUCTGRO-seq
High-throughput sequencingHiSeqHITS-CLIP
MeDIP-seqMicroarraymiRNA array
RISC-seqRNA CaptureSeqRNA ChIP-on-chip
Small RNA Ultrahigh throughput sequencingsmRNA-seqSolexa sequencing
SPLASHsRNA-seqTaqMan array
TaqMan microRNA arrayTaqMan miRNA arrayTiling array
uvCLAPmiRMA PCR arrayHuprotTM protoarray
Protein microarrayhigh-throughput protein-RNA interaction analysiscircRNA profiling analysis
circRNA microarray4C-sequencing

Prediction methods include:

Bayesian target prediction algorithmcatRAPIDcRep