1. Computational analyses of high-throughput sequencing dataset

1.1 rRNA sequences used to find rRFs

We downloaded five 45S precursor rRNA sequences (RNA45SN1 to RNA45SN5) from NCBI RefSeq and a 45S gene sequence (first 13,314 bp of U13369) from GenBank (Figure 1A). The longest RNA45SN5 was chosen as a representative isoform to identify rRFs. For other isoforms of 45S, the isoform numbers were appended to the end of each region for naming the rRFs generated from it. For example, 18S, 5.8S, 28S rRNAs originated from RNA45SN1 were named as 18S-001, 5_8S-001 and 28S-001, respectively. External transcribed spacer (ETS) and internal transcribed spacer (ITS) were considered as precursors of rRFs, for example, the two ETS regions of U13369 were named as ETS1-U13369 and ETS2-U13369. In addition to all 5S rRNAs from RefSeq, we incorporated transcripts annotated as 5S rRNAs from the latest Ensembl database (version 110). After collapsing identical sequences, we obtained 25 unique sequences for rRNAs. RNA5S6 was used as a representative 5S sequence and the other 24 sequences were named as 5S-002 to 5S-025. RNR1 and RNR2 were used as 12S and 16S, respectively.

1.2 Analysis of CLASH data

CLASH data for Ago1 in HEK293 cells (Helwak et al., 2013) were from the SRA database (SRR959751 to SRR959759). We used fastx_toolkit 0.0.13 (http://hannonlab.cshl.edu/fastx_toolkit/) to remove barcode and adapter sequences and collapse identical reads. We followed our pipeline previously utilized with hg38 genome(Guan, et al. 2021) assembly but this time we utilized the latest haplotype-resolved telomere-to-telomere (T2T, which has additional 4.5Gbp) reference genome to annotate target RNAs. The rationale in using the T2T genome is to use the resolved sequence in repeat regions (including rDNA) and to find potential targets among novel T2T-specific genes. After preprocessing the CLASH dataset, we used an additional filtering step to remove chimeric reads that completely aligned with the T2T genome to reduce artifacts and obtain high-confidence rRF-target interactions. We used the complete T2T transcriptome and genome sequences to identify the rRF targets and used the Ensembl T2T (https://rapid.ensembl.org/Homo_sapiens_GCA_009914755.4/Info/Index).

1.3 Analysis of PAR-CLIP data

We downloaded PAR-CLIP datasets for Ago1 to Ago4 in HEK293 cells (SRR048973 to SRR048979) (Hafner et al., 2010) from SRA database. We used Fastx_toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) to remove adaptors and Bowtie 1.3.1 (Langmead et al., 2009) to align the reads to rRNA references in end-to-end mode, allowing one T to C mismatch and giving preference to perfect matches, as in the earlier rRF analysis. rRFs shorter than 16nt were excluded and their abundance were normalized to reads per million mapped to the genome (RPM). T>C conversion spots were firstly aligned to rRNAs and then mapped to CLASH rRFs.

2. Naming convention of rRFs

We gave every rRF isoform informative and extensible names with the format Mature_rRNA-(Isoform_Num)-Start-End.  The Isoform_Number field was optional for rRFs that are mapped to the representative rRNA sequences. For example, 28S-47-68 represents a rRF of 22 nts in length, cleaved at position 47 and 68 of 28S rRNA. Since this rRF is also mapped to the conserved region of other rRNA isoforms, alternative IDs such as 28S-004-55-76 were also annotated to this rRF. Alternative IDs including the coordinates on precursor rRNA are also assigned, e.g., 45S-7971-7992 is same as 28S-47-68. We provided one representative ID on the result page and all alternative IDs can be found on the details page.

3. Other terms

3.1 Unique hybrids

A unique hybrid represents a hybrid pair between a rRF isoform and a target sequence. Pairs of which the rRF isoforms are different or the target sequences varied by at least 1nt are considered as different unique hybrids. A target gene can therefore have multiple unique hybrids with the same rRF.

3.2 Read count

All sequenced reads in CLASH are used for calculating read count.

3.2 Motif

We firstly combined rRF isoforms of the same rRF type for every rRNA isoform. Their target sequences were combined and we kept only the longest sequence per target gene. rRFs with less than 5 target genes were ignored. We used MEME (Bailey, et al. 2009) to search for enriched motifs in targets (-mod zoops -minw 5 -minsites 5 -evt 0.05 -maxw 12, e-value < 0.01) and used FIMO (Grant, 2011) to match it back to rRF sequences (p-value < 0.001).

3.3 Direction of pairs

If the rRF covers the 5' end of the CLASH read followed by the target sequence on the 3' end, it is called a "forward pair". If the rRF ends at the last nucleotide of the reads and the target sequence is on the 5' end, it is called a "reverse pair".

4. Search

We provide multiple filters to fulfill different requirements to query rRFs and their targets.

4.1 Search by rRFs

The rRFs can be searched using the two secondary structures available in the website by selecting the position. The large subunit (LSU) secondary structure can be used for searching rRFs from 28S, 5.8S, and 5S. The small subunit (SSU) secondary structure can be used for searching rRFs from 18S. The genome of the enconded rRNA gene is indicated as Nuclear (N) for 45S isoforms and Mitochondria (M) for 12S and 16S. If "Exact S/E" is checked, rRFs with exact start and end positions on the rRNA genes are returned. Otherwise, all rRFs that are included in the range of Start to End are shown. "rRF ID" in defined in Section 2 and partial input of the ID is allowed. If you search rRFs by sequences, tarDB will report all hits that cover the entire input sequence without mismatches.

4.2 Search by targets

These filters can be used together with the filters for rRFs. When "Exact name" is checked, interactions with exact input gene name are returned, otherwise, partial input for the gene name is allowed. Same as searching rRFs by sequences, mismatches are not allowed in the input for the target sequence. If you are not 100% sure about the target sequence, please input a shorter and partial sequence and tarDB will look for all target sequences that cover the input sequence. 

4.3 Other filters

Additional criteria can be specified in the query. Range of the MFE (minimum free energy) of the interaction of rRF and target sequence could be specified, input needs to be negative values. Direction of a rRF and target pair is defined in 3.3 which could be either "Forward" or "Reverse". When "Both" is seleted, tarDB will report interactions of rRFs and targets which are found in both forward and reverse pairs. Minimum number of CLASH reads supporting every unique hybrid could be specified. One can also look for genes targeted in multiple regions by giving high unique hybrids. By default, rRFs with motifs are returned and this can be disabled by unselecting the "Motif". 

4.4 Default filters

Based on the data distribution that we observed and in order to show high-confidence interactions, default filters (target type=mRNA, MFE<=-15, read counts>=5 and motif=True) are selected on the search page and they can be removed by clicking on the "Clear" button. Please note that if tarDB is accessed from direct url link, these default filters will be automatically applied and shown on the page.

5. Database Interoperability

We provide a mechanism by which one can simply link a rRF or a rRF-gene pair to its rRFtargetDB page if the rRF sequence and gene name are known. Variables in the URL are highlighted. All target genes and other details of a given rRF can be found at: https://grigoriev-lab.camden.rutgers.edu/tardb/rrf_isoform.php?guide_seq=rRF_Sequence. 
All target sites of a rRF on a given gene can be found at: https://grigoriev-lab.camden.rutgers.edu/tardb/rrf_gene.php?guide_seq=rRF_Sequence&gene_name=Target_Gene_Name. 
Default filters will be selected for such links if no filter is specified in the url. These filter settings may be changed after landing on the rRFtargetDB page.