miRNAs are small non coding RNAs of length ~21 nt which are found to be expressed in both human and plants, playing multiple important roles in regulating gene expression and biological pathways. Over the years the major focus and understanding has been biased towards such small RNAs like miRNAs which followed the canonical miRNA biogenesis pathway and exhibited typical features such as presence of terminal loop, hairpin structured duplex generated mature miRNAs, a particular thermodynamic stability and energy, sequence conservation and 3' overhang etc. However, in recent years, especially after the advent of Next Generation Sequencing, a burst in miRNA reporting has occured which also reveals the need to revise the concept of regulatory sRNA world which appears to go beyond miRNAs. The classic approach towards defining miRNAs does not appear to be sufficient as high degree of deviation of the characteristic features and parameters of typical canonical miRNAs is evident. It appears that the historical events in understanding the s/miRNA biology have greately influenced the way we conceptualized the regulatory sRNAs, the way we approached their problems, the way we practised certain protocols, technologies and discarded some ignorantly. For example, though despite of continuous appearance of reportings that the repetitive elements are genomic goldmines which could be involved in regulation, most of the regulatory sRNA/miRNA discovery studies plainly discard the reads occuring multiple times across the genome or mask the genome for repeats while searching for miRNAs. Simiarly, the initial and pioneer work with small interferring RNAs found duplex originated miRNAs coming from typical hairpin-loop precursors. Based on homology more such precursors were identified beyond C. elegans. Later from such precursors models for miRNAs were developed and parameters were established which were taken as de facto properties of being the miRNAs. And rest were discarded, making the appraoches, algorithms and techniques limited to a certain clan of regulatory sRNAs. In light of advances in next generation technology, it is possible to detect even those sRNAs expressing at small levels, it has become possible to capture them interacting with methods like CLASH and AGO HITS CLIP cross linking studies. The low cost nature of these experiments has facilitated to carry out replicated studies and across large number of samples, making it easy to discriminate between what is random or degradation product and what is not. The present portal is a resultant of a study carried out to look for regulatory sRNAs beyond the typical concepts of hair-pin loop bred regulatory sRNAs, utilizing data from 4,997 individuals, 25 pairs of cancer and normal states, AGO cross linking from 14 experimental conditions, proteome data for 4776 individuals, expression data for 3013 experimental conditions and large amount of CLASH and DGCR8 knockout evidences. Analysis over such huge dimensions has ensured that what we report here as regulatory sRNAs were certainly seen non-randomly existent across multiple conditions, individuals and platforms with very high degree of confidence for interactions with their targets. In this portal we introduce 11, 234 high confidence regulatory sRNAs, their experimental evidences, evidences for their target interactions and how these sRNAs are impacting the cell systems deeply. A lot rich visualization and representation has been given to extract the details about all these sRNAs. The world of regulatory sRNAs appears as "A legion much larger than the microRNA microcosm"!

We have performed a comprehensive study and identified 11,234 novel small regulatory RNAs (rsRNAs) which regulate about 17,000 unique genes. The role of these 11,234 novel rsRNAs were studied with respect to 25 different cancerous conditions. Data for these cancerous conditions was downloaded from The Cancer Genome Atlus (TCGA) and Gene Expression Omnibus (GEO) . Data from 4,997 individuals were considered for the analysis which resulted into 260 Gb of processed data. Total raw data for the complete analysis was about 20 Tb .
The rsRNA:target gene interactions were identified primarily using TAREF and Targetscan, which were validated using experimental data from Argonaute CLIP-sequencing (14 experimental conditions), CLASH-sequencing(1 experiment), anti-coexpression for expression using protein abundance data (4776 individuals) and RNA expression data from s/RNA-seq and microarrays studies (3013 conditions). From these analyses we have identified several important genes involved in the regulation of cancer, pathways related to cancer, growth and development, cell cycle and apoptosis are highly represented. The analyses performed in this study suggest that these rsRNAs originate from multiple loci including repetitive elements and intronic regions. Also these novel rsRNAs share highly conserved seed region with known mature miRNAs of humans reported in miRBase version 21. Furthermore, these rsRNAs were searched acrossed the sRNAs expressed specifically in the presence of DGCR8 and which were absent in the DGCR8 knockout in order to identify rsRNAs processed by DGCR8 microprocessor complex. A total of 2,999 rsRNAs were identified in this study which exhibited exclusive expression in the presence of DGCR8 ecomplex. Most of them displayed the presence of typical hairpin loop precursor structure.

After the introduction of NGS-sequencing technologies many new miRNAs were reported in miRBase which do not follow the above criteria such as presence of terminal loop region (hsa-miR-181d, hsa-miR-141) absence of pairing mature miRNAs (in about 45% of total mature miRNAs reported (hsa-miR-944, hsa-miR-378d-2), mature miRNAs coming from loop regions instead of stem region (hsa-miR-451a, hsa-miR-7111), rsRNA reads mapping on other region instead of reported known miRNAs (hsa-miR-5680, hsa-miR-5697), mature miRNAs with no overhangs (hsa-miR-7108). These miRNAs suggest that following only the canonical or traditional pathway for miRNA biogenesis could become a bottleneck in addressing a larger question or regulatory sRNAs, creating bias and report mainly those miRNAs which follow the similar characteristics patterns as the already reported ones. The data here provides the list of miRNAs which follow atypical look and deviate from the standard concepts of miRNAs. They are reported in miRBase version 21. Similarly, a huge number of iso-miRs, phased miRNAs and miRNA offset RNAs (mORs) have been reported recently, suggesting that from a single precursor itself a large number of regulatory sRNAs arise in actual, grossly disobeying typical miRNA properties defined so far. Majority of such a typical miRNAs/rsRNAs display lower abundance or highly spatio-temporal expression, a reason why most of the standard discovery protocols missed them so far or confused them with random degradation product. Being tagged as random product, most of them have been discarded. Therefore, in the way of discovery of such regulatory sRNAs, a sensible big objective has been to first establish them as non-random elements. To address this all collectively, nclduing the funcational and regulatory importance of such sRNAs, the present study worked with three fundamental approaches:
1) Recurrence of any given sRNA across large number of experimental samples/individuals provides strong evidence for non-random existence of any given sRNA, 2) For the given samples, if the RNA-seq data or whole transcriptome expression data is available, it is possible to estimate the anti-correlation for expression between a sRNA and its putative targets, multiple times in re-affirmative manner. Use of proteome abundance data in measuring anti-correlation with the target gene immensely supports study of the regulatory impacts of sRNAs on its targets. 3) Support of data from different technologies like AGO-HITS CLIP sequencing and CLASH provide very high confidence experimental proof of interactions between the sRNA and targets.
This many layers of cross validation using experimental data from different sources provide a high confidence support to call a sRNA's existence as non-random element having regulatory impact.

The present study has been carried out over 25 cancer conditions. Considering cancer for this study has several reasons which includes the following:
1) Cancer is once of the most dreaded health condtions spreading very fast and yet unanswered.
2) A huge amount of experimental data is available which fulfull the above mentioned three prime requirements to carry out this study. Presence of publicly available data for large number of replicates, individuals and experimental conditions in portals like GEO and The Cancer Genomics Atlas (TCGA) make it possible to successfully carry a study of this magnitude, providing multilayered evidences.
The Cancer Genomics Atlas (TCGA) in the recent times has emerged as the prime repository for all cancer related genomics data. TCGA hosts an enormous amount of data for approximately 20 various cancer types, freely available for researchers worldwide. The data includes RNA-seq based digital gene expression, sRNA reads, protein based expression, Microarray based gene expression, DNA methylation data etc. The given image illustrates the volume of data and information contributed by TCGA while conducting the present study. The TCGA data used in the present study were taken after TCGA's permission, and the present study abides by their condtions for use.

Unique small RNA reads were selected from multiple cancerous tissues and their corresponding normal states. These unique reads were mapped to human genome build HG19 assembly and known mature miRNA sequences, downloaded from miRBase version 21, using BOWTIE with maximum of two mismatches. To filter out small RNA reads which could be a degradation or random product. Two different criteria were applied: 1) sRNAs whose copy abundance was more than five in any given experiment, and 2) The sRNA was found reported in at least two different experimental conditions. All such reads were subjected to target identification using TAREF and Targetscan. Further, validation of identified sRNA:targets interactions was done by searching the interactions across Argonaute CLIP-seq and CLASH sequencing data. Using expression data of sRNAs, genes and proteins, Pearson correlation coefficient (PCC) was calculated to further verify functional relevance of these sRNAs. Argonaute CLIP-seq data was available for four Argonaute proteins namely AGO1, AGO2, AGO3 and AGO4 while, CLASH sequencing data was available for AGO1. An analysis of these sRNAs against their presence the various experimental conditions and AGO cross linking data suggested that the number of sRNAs falls steeply from its presence in single experimental condition to presence in at least two experimental conditions. Thereafter, the number of sRNAs decreases monotonously linear with the number of experimental conditions, while the degree of interaction support increases linearly as shown in the figure.
After target predictions, small RNAs were found to target 17,612 unqiue genes. For further studies, from the sRNA:target interactions data only those sRNAs were considered which were expressed two fold or above in any state. After applying these filters, a total of 11,234 potential novel regulatory sRNAs were identified. Out of 11,234 regulatory sRNAs, 9,860 regulatory small RNAs displayed higher abundance in cancerous conditions, whereas 564 rsRNAs showed higher abundance in normal conditions. The remaining 810 rsRNAs did not show any preference for cancerous or normal states as they were overexpressed in some cancerous as well as normal states. To identify the possible DGCR8-DROSHA mediated rsRNAs, rsRNAs were scanned across the DGCR8 expressed data and compared against the DGCR8 knockout sRNA sequencing data. The analysis revealed that 2,999 (26.69%) of the novel rsRNAs were identified as those processed by DGCR8.

To identify if these DGCR8 specific rsRNAs exhibited pre-miRNA like typical hairpin loop structure, their genomic sequences with 200 bp flanking regions were scanned using MirEval and RNAfold. It was found that 2,204 rsRNAs precursors were having hair-pin loop precursor like structure. 1,125 such sRNAs were present almost perfectly within the stem region.

Validation of rsRNA:target inetaractions was done by searching the inetractions in the Argonaute sequencing data downloaded from starBase version 2. A total of 149,344, 150,049, 145,561 and 148,251 novel putative small regulatory RNA:target interactions were identified for 8,036, 6,247, 8,342 and 7,196 unique rsRNAs and 14,265, 12,448, 14,438 and 13,674 unique targeted genes, for AGO1, AGO2, AGO3 and, AGO4-CLIP sequencing data, respectively.
CLASH-seq data was also used for the identification of rsRNA: target interactions. As compared to CLIP-seq, CLASH-seq data gives more specific information about the interactions as the interactions are precisely arrested through ligation of the target site and targeting small RNA. Using CLASH-seq data, 16,371 unique genes were found being targeted by 10,048 putative rsRNAs, comprising 474,770 unique target interactions. The list of common and unique genes identified in AGO based HITS-CLIP data and CLASH data.

Further to validate the interactions, expression anti-correlation based validation study was performed using sRNA expression (RPM) and expression of target genes (RPKM) obtained from TCGA and GEO, a total of 3,013 cancerous and normal tissue based experimental conditions were considered. For those cancerous conditon in which RPKM data was not available, microarray absed expression date from TCGA (for 137 patients and normal conditions) were used. A high level of agreement was observed between different methods of validation for the identified rsRNA:target interactions.

In order to find the common targets identified reported by the avarious above mentioned validation approaches i.e. (AGO1-4, CLASH-seq, Protein abundance support, RPKM based, Microarray based) a venn representation was done. As apparent here, for most of the interactions two or more methods agreed, strongly suggesting the regulatory existence of these sRNAs.

These novel sRNAs were found regulating many genes including some important genes involved in cacer like BRCA2, p53, Rb, Myc, 14-3-3 epsilon, CycD and CycE, and in general pathways related to cancer.

*Red = overexpressed in cancer ; Green = overexpressed in normal.
From the mapped data, it was found that these small regulatory RNAs have multiple biogenesis loci mainly belonging to repetitive elements (48.22%), Intronic region (36.02%) and ncRNA (9.31%) regions. Many of the known miRNAs have been reported from intronic region, in-fact 46.03% (866 out of 1881) miRNAs, in current version of miRBase are from the intronic regions.

Several rsRNAs were found originating from the Alu elements. While observing the distribution profile of these sRNAs across the length of Alu consensus, it was observed that, these rsRNAs follow a conserved pattern of biogenesis for a number of different experimental conditions. A Multiple chart visualization of Alu derived sRNA expression profile variations for the different cancer states and normal tissues has been made available to showcase this behavior of Alu derived sRNAs . The sRNAs originating from Alu displayed high conservation of profiles across the individuals, which was also different between the normal and cancer samples. A series of t-tests between cancer v/s normal conditions gave consistently significant p-values (p<0.05) for such observation, suggesting the sRNA profile originating from Alu differ significantly between cancer and normal states

Percentage of reads distribution on rsRNAs originating from repeats were also calculated. This analysis was performed to normalize the rsRNA reads as a given rsRNA from repeats could map across multiple loci. It was found that rsRNAs from ERVL and Alu families were distributed non-randomly across the genome followed by other repeat families.

The pathway enrichment analysis of rsRNAs significantly up-regulated in cancer and originating from Alu regions showed that these rsRNAs target genes were involved in regulation of apoptosis, colorectal cancer, renal cell carcinoma, melanoma, small cell lung cancer, pancreatic cancer and chronic myeloid leukemia pathways. Whereas rsRNAs significantly up-regulated in normal conditions target genes involved in apoptosis, neuroactive ligand receptor interaction, colorectal cancer, renal cell carcinoma, fatty acid biosynthesis, tight junction, glycosaminoglycan degradation, cell cycle.

One such novel small regulatory RNA derived from Alu, identified as "rsRNA-6458-n", targets some important genes like ADAR, ATM, BCL2L1, BIRC2, ERBB3, ITGA2, NRAS, RPS6, SRC, STAT3, SYK, VHL and YWHAB. One of its important target, SRC is an important component in signal transduction which is usually found over-expressed in cancer conditions. rsRNA-6458-n was found exhibiting higher expressions (two fold and above) in the normal tissue for READ, KIRC, LUAD, BRCA, HNSC, STAD, PRAD, LUSC, UCEC, OV and BLCA types of cancer states. Consequently, the expression of its target gene, SRC, exhibited over expression in these cancerous states in terms of RNA abundance (RPKM) as well as in terms of protein abundance.

These identified rsRNAs were clustered using four different methods, namely 1) seed region similarity ( comapred with known miRNA seeds) , 2) sRNA length and Argonatue association, 3) All smaller sRNAs covered by a single long sRNA (longest coordinates bound based) and, 4) Expression based clustering. These clusterings were done to identify the rsRNAs sharing similar properties. rsRNAs in a cluster were found targeting same/similar gene, regulating similar pathways and similar functions. Expression based clustering resulted into 362 distinct clusters which includes 7,292 rsRNAs. These rsRNAs shared high co-expression with each other ("r" > 0.5) calculated for 4,997 experimental conditions.

Clusters identified through the given approaches were analyzed for overlap measure among them. High commonalities between the clusters generated based upon different properties suggest a degree of similarity and relationship between the two properties. A 4X4 matrix was generated for similarity scoring between the clusters of above mentioned four types. It was found that expression based clusters shared ~82% similarity with the clusters generated using coordinates bound based clustering, 22% with seed clusters and 0.23% with length based clustering. Coordinates bound based clusters displayed 77% overlap with seed based cluster and 0.09% clusters overlap with length based clustering. This analysis suggested that the three clustering methods based on seed region, coordinates bound and expression similarity agreed with each other, where the co-expressed sRNAs were also similar to each other in terms of genomic coordinates, which also shared a good amount of common targets.

Several novel regulatory small RNAs were found significantly differentially expressed between cancerous and normal conditions. The differential expression was evaluated across large number of individual samples, followed by t-test for significance between normal sample sets and cancer sample sets, for every cancer condition.

Cancer TissuersRNA ID Fold change in Cancer P-value rsRNA ID Fold change in Normal P-value
BLCArsRNA-7062-n 140.36 0.02907 rsRNA-7208-n 4.39 0.01157
BLCArsRNA-9580-n 150.21 2.69E-013 rsRNA-6454-n 4.46 0.02114
BLCArsRNA-1754-n 154.26 5.41E-008 rsRNA-4562-n 4.49 0.02677
BLCArsRNA-3397-n 158.76 0.0001006 rsRNA-9520-n 4.87 0.001836
BLCArsRNA-11206-n 162.45 2.89E-013 rsRNA-10291-n 5 0.0241
BLCArsRNA-1365-n 166.76 1.48E-010 rsRNA-3722-n 5.1 0.04994
BLCArsRNA-10684-n 173.21 1.83E-008 rsRNA-10119-n 6.59 0.01585
BLCArsRNA-5987-n 173.43 3.17E-012 rsRNA-9620-n 6.73 0.001029
BLCArsRNA-813-n 197.63 2.20E-016 rsRNA-6728-n 6.97 0.01342
BLCArsRNA-1558-n 273.63 1.69E-011 rsRNA-7952-n 7.15 0.002217
BRCArsRNA-11080-n 50.33 2.20E-016 rsRNA-7343-n 4.13 0.002289
BRCArsRNA-1017-n 51.78 2.82E-013 rsRNA-7089-n 4.16 0.002339
BRCArsRNA-4331-n 52.93 2.20E-016 rsRNA-7002-n 4.29 0.009076
BRCArsRNA-2973-n 55.6 2.20E-016 rsRNA-1335-n 4.65 0.03145
BRCArsRNA-3808-n 66.72 2.20E-016 rsRNA-1432-n 6.4 1.05E-011
BRCArsRNA-2432-n 86.48 3.22E-008 rsRNA-4362-n 6.51 1.12E-011
BRCArsRNA-6663-n 99.52 2.73E-014 rsRNA-9417-n 6.69 0.01212
BRCArsRNA-7481-n 111.78 2.20E-016 rsRNA-10829-n 9.12 2.15E-006
BRCArsRNA-7063-n 149.74 4.63E-016 rsRNA-7392-n 13.43 0.006228
BRCArsRNA-10204-n 194.9 2.24E-013 rsRNA-10410-n 18.73 0.03788
COADrsRNA-4606-n 57.01 2.20E-016 rsRNA-7553-n 16.02 0.01836
COADrsRNA-7906-n 58.99 0.02553 rsRNA-5491-n 16.26 0.007327
COADrsRNA-3808-n 62.24 1.85E-012 rsRNA-8900-n 17.16 0.003794
COADrsRNA-2973-n 67.37 2.81E-008 rsRNA-8708-n 18.47 0.006546
COADrsRNA-11212-n 78.37 2.28E-006 rsRNA-7914-n 18.5 0.01926
COADrsRNA-11080-n 80.38 2.93E-012 rsRNA-11171-n 18.88 0.02686
COADrsRNA-7063-n 81.23 1.47E-011 rsRNA-7392-n 19.95 0.01256
COADrsRNA-3599-n 94 6.73E-008 rsRNA-219-n 27.05 0.01528
COADrsRNA-5004-n 124.33 5.54E-005 rsRNA-4125-n 27.41 0.04796
COADrsRNA-4146-n 542.44 4.43E-006 rsRNA-359-n 90.28 0.00621
HNSCrsRNA-3940-n 23.76 2.20E-016 rsRNA-4260-n 4.38 1.63E-005
HNSCrsRNA-7039-n 23.92 0.008792 rsRNA-7076-n 4.5 0.0009993
HNSCrsRNA-11210-n 24.59 1.82E-014 rsRNA-6973-n 4.54 2.20E-016
HNSCrsRNA-2088-n 24.94 4.13E-013 rsRNA-2227-n 5.38 0.01776
HNSCrsRNA-1597-n 25.73 3.58E-011 rsRNA-9083-n 5.78 0.02459
HNSCrsRNA-6870-n 27.15 0.003742 rsRNA-4387-n 7.82 0.0001261
HNSCrsRNA-5403-n 28.49 1.67E-005 rsRNA-1828-n 8.88 1.99E-009
HNSCrsRNA-9553-n 32.22 1.05E-008 rsRNA-2634-n 11.22 0.04651
HNSCrsRNA-9270-n 46.36 1.11E-009 rsRNA-8264-n 12.56 0.03923
HNSCrsRNA-5975-n 47.33 3.04E-008 rsRNA-5483-n 14.93 0.0009894
KIRCrsRNA-7481-n 30.42 0.01488 rsRNA-9299-n 3.58 3.70E-005
KIRCrsRNA-3599-n 32.5 0.0003393 rsRNA-5530-n 3.63 0.01643
KIRCrsRNA-6093-n 35.82 0.0001098 rsRNA-7697-n 4.11 5.56E-005
KIRCrsRNA-2025-n 41.17 1.59E-014 rsRNA-3539-n 4.2 1.06E-012
KIRCrsRNA-6503-n 41.53 2.40E-011 rsRNA-7730-n 4.21 5.97E-006
KIRCrsRNA-9173-n 42.48 0.01697 rsRNA-2277-n 4.23 2.93E-010
KIRCrsRNA-10131-n 44.01 1.51E-008 rsRNA-8251-n 4.26 3.97E-006
KIRCrsRNA-1194-n 50.23 0.004899 rsRNA-4976-n 4.32 3.91E-014
KIRCrsRNA-4146-n 75.19 4.05E-011 rsRNA-3441-n 4.5 0.0002262
KIRCrsRNA-8879-n 98.17 2.20E-016 rsRNA-2373-n 6.28 0.005737
KIRPrsRNA-10016-n 17.98 0.008155 rsRNA-7908-n 10.95 0.004548
KIRPrsRNA-9563-n 22.79 0.01748 rsRNA-1936-n 12.13 0.02918
KIRPrsRNA-9290-n 24.16 0.01314 rsRNA-8859-n 13.14 0.04315
KIRPrsRNA-2973-n 25.62 0.005962 rsRNA-1498-n 13.31 0.003261
KIRPrsRNA-7295-n 27.75 0.02244 rsRNA-4772-n 16.93 0.03947
KIRPrsRNA-9338-n 28.62 0.02839 rsRNA-10677-n 19.57 0.04369
KIRPrsRNA-1314-n 31.86 0.0001441 rsRNA-7156-n 19.57 0.01189
KIRPrsRNA-7442-n 34.45 0.02777 rsRNA-2393-n 31.8 0.03626
KIRPrsRNA-9173-n 43.58 0.03868 rsRNA-8092-n 36.95 0.002172
KIRPrsRNA-4950-n 46.67 0.01188 rsRNA-3441-n 106.05 0.03241
LUADrsRNA-8879-n 32.16 0.0001573 rsRNA-9213-n 8.56 0.0002941
LUADrsRNA-8074-n 32.36 2.52E-006 rsRNA-9520-n 9.31 1.87E-008
LUADrsRNA-2973-n 32.36 0.01857 rsRNA-10829-n 9.31 0.0001279
LUADrsRNA-7658-n 32.49 8.86E-015 rsRNA-9808-n 10.4 0.006902
LUADrsRNA-5004-n 32.66 5.86E-005 rsRNA-5929-n 12.77 1.11E-007
LUADrsRNA-6911-n 34.43 0.001641 rsRNA-7952-n 13.02 0.02594
LUADrsRNA-4982-n 38.54 4.39E-014 rsRNA-465-n 16.07 0.01844
LUADrsRNA-6636-n 52.89 2.29E-005 rsRNA-9221-n 18.06 0.01585
LUADrsRNA-10576-n 92.43 0.00468 rsRNA-7392-n 23.46 0.0138
LUADrsRNA-11080-n 111.01 3.84E-005 rsRNA-10410-n 24.26 0.000788
LUSCrsRNA-3599-n 56.15 2.86E-007 rsRNA-4582-n 7.07 0.01295
LUSCrsRNA-6496-n 58.43 0.001655 rsRNA-10667-n 7.55 3.69E-005
LUSCrsRNA-9256-n 63.42 0.003116 rsRNA-6091-n 7.59 0.0001526
LUSCrsRNA-1933-n 65.21 0.001441 rsRNA-5420-n 7.59 7.85E-005
LUSCrsRNA-2567-n 66.18 2.10E-011 rsRNA-7460-n 8.2 0.002286
LUSCrsRNA-2248-n 82.53 1.62E-010 rsRNA-6820-n 8.63 1.37E-005
LUSCrsRNA-3257-n 90.3 2.20E-016 rsRNA-931-n 10.05 1.12E-006
LUSCrsRNA-4436-n 101.56 1.04E-008 rsRNA-6167-n 12.82 0.007293
LUSCrsRNA-9881-n 128.07 2.20E-016 rsRNA-5929-n 13.12 0.0003293
LUSCrsRNA-4146-n 428.84 8.12E-010 rsRNA-10410-n 27.75 0.02072
OVrsRNA-680-n 39.2 2.20E-016 rsRNA-6136-n 14.55 2.20E-016
OVrsRNA-3032-n 39.7 4.80E-007 rsRNA-10363-n 16.2 7.21E-012
OVrsRNA-5577-n 41.6 2.20E-016 rsRNA-1828-n 16.99 2.20E-016
OVrsRNA-10199-n 44.79 2.20E-016 rsRNA-10791-n 17.63 2.30E-009
OVrsRNA-2242-n 44.87 1.27E-010 rsRNA-6488-n 18.07 0.01842
OVrsRNA-7462-n 48.34 2.20E-016 rsRNA-9620-n 23.16 0.02536
OVrsRNA-10402-n 52.23 0.0001216 rsRNA-11000-n 27 1.79E-008
OVrsRNA-2049-n 54.18 4.82E-007 rsRNA-3411-n 39.54 6.12E-010
OVrsRNA-1986-n 56.05 0.0003903 rsRNA-4441-n 71.63 2.20E-016
OVrsRNA-4673-n 165.65 2.47E-012 rsRNA-7460-n 114.02 2.20E-016
PRADrsRNA-1271-n 35.13 2.07E-010 rsRNA-7776-n 4.54 0.04095
PRADrsRNA-8942-n 35.44 0.0004146 rsRNA-2606-n 4.55 0.02798
PRADrsRNA-5993-n 43.06 0.0001654 rsRNA-1213-n 4.75 7.61E-005
PRADrsRNA-5049-n 44.13 1.38E-009 rsRNA-10955-n 4.82 0.003896
PRADrsRNA-9923-n 45.92 6.54E-009 rsRNA-8775-n 4.99 9.98E-007
PRADrsRNA-6355-n 47.87 7.34E-007 rsRNA-5339-n 5.01 3.11E-005
PRADrsRNA-1472-n 54.62 0.001062 rsRNA-10785-n 5.15 0.001983
PRADrsRNA-4860-n 59.62 0.000138 rsRNA-1241-n 5.57 0.01212
PRADrsRNA-4312-n 61.95 0.00755 rsRNA-7734-n 6.05 4.84E-005
PRADrsRNA-4994-n 67.97 6.08E-007 rsRNA-10981-n 8.71 0.004934
READrsRNA-11037-n 169.78 0.03641 rsRNA-4287-n 2.14 0.007182
READrsRNA-5568-n 171.62 0.03825 rsRNA-10044-n 2.2 0.03419
READrsRNA-2875-n 185.68 0.01036 rsRNA-6100-n 2.45 0.03117
READrsRNA-1547-n 186.54 8.86E-010 rsRNA-8297-n 2.46 0.03083
READrsRNA-2218-n 186.87 1.66E-007 rsRNA-5667-n 2.6 0.01226
READrsRNA-5026-n 234.79 0.0001037 rsRNA-10803-n 3.09 2.20E-016
READrsRNA-8208-n 521.29 3.00E-005 rsRNA-5763-n 3.26 0.02535
READrsRNA-7063-n 1413.02 1.87E-005 rsRNA-10160-n 3.41 0.02195
READrsRNA-3599-n 1596.56 1.46E-008 rsRNA-2425-n 5.47 0.02266
READrsRNA-5004-n 6239.69 9.57E-006 rsRNA-2373-n 7.45 0.0003594
STADrsRNA-5005-n 39.81 1.43E-008 rsRNA-10119-n 3.69 0.00414
STADrsRNA-8074-n 42.81 7.24E-011 rsRNA-10046-n 3.79 1.00E-005
STADrsRNA-2894-n 43.48 1.52E-014 rsRNA-11185-n 3.97 0.003068
STADrsRNA-11065-n 43.78 0.01523 rsRNA-9097-n 3.99 0.006557
STADrsRNA-4365-n 47.92 0.002176 rsRNA-9933-n 4.22 2.92E-005
STADrsRNA-1933-n 47.92 2.55E-009 rsRNA-10141-n 4.84 0.03617
STADrsRNA-7799-n 54.25 5.03E-014 rsRNA-8378-n 4.86 0.01982
STADrsRNA-5024-n 72.39 6.55E-008 rsRNA-8395-n 5.61 0.01321
STADrsRNA-2992-n 74.17 0.025 rsRNA-6220-n 6.29 0.02675
STADrsRNA-7223-n 126.48 1.73E-008 rsRNA-7220-n 10.71 0.03923
THCArsRNA-3441-n 4.43 3.97E-005 rsRNA-7940-n 15.22 1.56E-008
THCArsRNA-7906-n 4.68 2.20E-016 rsRNA-4301-n 15.63 0.0009558
THCArsRNA-16-n 5.56 0.0006849 rsRNA-4853-n 15.86 1.04E-005
THCArsRNA-3257-n 5.69 3.28E-015 rsRNA-8870-n 18.27 9.59E-005
THCArsRNA-8260-n 5.82 0.003016 rsRNA-8771-n 19.17 1.81E-007
THCArsRNA-7439-n 5.86 0.005565 rsRNA-8345-n 19.19 0.0006329
THCArsRNA-9128-n 5.91 2.20E-016 rsRNA-2791-n 19.44 0.0004284
THCArsRNA-6636-n 6.05 5.93E-010 rsRNA-6997-n 21.21 0.004805
THCArsRNA-4686-n 6.82 0.0002256 rsRNA-10793-n 23.07 2.49E-005
THCArsRNA-6554-n 7.12 2.20E-016 rsRNA-1832-n 24.17 3.53E-007
UCECrsRNA-3808-n 116.17 2.20E-016 rsRNA-9097-n 6.03 0.001132
UCECrsRNA-4146-n 117.51 5.48E-007 rsRNA-5387-n 6.29 0.0002645
UCECrsRNA-7063-n 124.16 2.20E-016 rsRNA-10667-n 6.53 0.0394
UCECrsRNA-5915-n 127.42 4.49E-006 rsRNA-1643-n 6.81 0.0006566
UCECrsRNA-3599-n 130.03 2.20E-016 rsRNA-10762-n 7.54 0.001034
UCECrsRNA-2338-n 131.17 6.81E-009 rsRNA-4582-n 8.62 0.009029
UCECrsRNA-10336-n 142.71 2.20E-016 rsRNA-11156-n 8.93 2.29E-005
UCECrsRNA-2973-n 154.87 2.20E-016 rsRNA-8492-n 8.97 0.00245
UCECrsRNA-5080-n 160.3 0.006781 rsRNA-9409-n 10.02 0.02379
UCECrsRNA-11080-n 217.15 2.81E-005 rsRNA-6820-n 12.74 0.001836
ACCrsRNA-8649-n 11.84 3.46E-005 rsRNA-7634-n 3.19 0.01726
ACCrsRNA-6573-n 11.98 0.004794 rsRNA-7899-n 3.21 0.007634
ACCrsRNA-10083-n 12.4 5.39E-006 rsRNA-7260-n 3.39 0.03738
ACCrsRNA-5594-n 12.77 0.0002168 rsRNA-3001-n 3.44 0.03262
ACCrsRNA-358-n 13.37 2.26E-005 rsRNA-10644-n 3.45 0.0155
ACCrsRNA-6964-n 13.73 0.0002076 rsRNA-5314-n 3.47 0.01572
ACCrsRNA-6931-n 13.89 1.81E-006 rsRNA-5489-n 3.61 0.01631
ACCrsRNA-9130-n 27.48 0.0005026 rsRNA-4189-n 4.38 0.02726
ACCrsRNA-7124-n 31.06 3.15E-005 rsRNA-9215-n 4.68 0.01465
ACCrsRNA-8849-n 53.78 2.20E-005 rsRNA-11083-n 8.73 0.01585
DCISrsRNA-4485-n 9.84 0.01172 rsRNA-8332-n 14.8 0.01278
DCISrsRNA-1888-n 9.97 0.03678 rsRNA-5670-n 16.45 0.005579
DCISrsRNA-529-n 10.09 0.02255 rsRNA-4691-n 21.07 0.003245
DCISrsRNA-6922-n 10.09 0.04589 rsRNA-7660-n 21.1 0.02702
DCISrsRNA-6557-n 15.14 0.002119 rsRNA-1648-n 27.91 0.04295
DCISrsRNA-4216-n 20.65 0.01928 rsRNA-5075-n 29.09 0.0005523
DCISrsRNA-548-n 33.69 0.0006843 rsRNA-10570-n 29.22 0.0007474
DCISrsRNA-2959-n 40.1 0.04197 rsRNA-11092-n 45.05 0.03561
DCISrsRNA-8027-n 73.68 0.01164 rsRNA-8294-n 45.76 0.0002412
DCISrsRNA-10368-n 110.24 0.009909 rsRNA-5909-n 46.29 0.003855
IDCrsRNA-361-n 10.36 0.01004 rsRNA-8294-n 5.4 0.0009205
IDCrsRNA-1834-n 10.62 0.03809 rsRNA-4691-n 5.46 0.008105
IDCrsRNA-221-n 10.87 0.02676 rsRNA-5452-n 5.48 0.009395
IDCrsRNA-402-n 11.34 0.001479 rsRNA-1339-n 5.67 0.04641
IDCrsRNA-10130-n 12.23 0.00935 rsRNA-161-n 6.17 0.03443
IDCrsRNA-1314-n 13.07 0.01647 rsRNA-8332-n 8.63 0.0164
IDCrsRNA-2957-n 13.67 0.04502 rsRNA-6812-n 9.39 0.02839
IDCrsRNA-2959-n 21.67 0.002465 rsRNA-10570-n 14.99 0.0009916
IDCrsRNA-2035-n 22.27 0.002806 rsRNA-5475-n 21.62 0.001151
IDCrsRNA-10368-n 55.96 0.01004 rsRNA-7660-n 45.84 0.02369
KIDNEYrsRNA-11006-n 5.01 0.04456 rsRNA-2277-n 4.21 0.03087
KIDNEYrsRNA-7688-n 5.02 0.0005859 rsRNA-10624-n 4.42 0.04426
KIDNEYrsRNA-3443-n 5.14 0.03747 rsRNA-7076-n 4.69 0.008871
KIDNEYrsRNA-8324-n 5.33 0.03102 rsRNA-9379-n 5.07 0.01436
KIDNEYrsRNA-5074-n 5.49 0.04186 rsRNA-10323-n 5.2 0.0452
KIDNEYrsRNA-9061-n 5.7 0.02896 rsRNA-6454-n 6.68 0.04664
KIDNEYrsRNA-7984-n 6.44 0.01275 rsRNA-4463-n 7.12 0.007212
KIDNEYrsRNA-5790-n 7.46 0.03852 rsRNA-7782-n 9.64 0.02606
KIDNEYrsRNA-11232-n 11.81 0.01847 rsRNA-4009-n 10.42 0.01599
KIDNEYrsRNA-10243-n 12.38 0.004851 rsRNA-6084-n 16.11 0.03772

Pathway enrichment analysis of the rsRNAs up-regulated in cancer shows that the rsRNAs target genes were enriched for apoptosis, renal cell carcinoma, role of brca1 brca2 and atr in cancer susceptibility, caspase cascade in apoptosis and EGFR1 Signaling Pathway(Mus musculus). Whereas, rsRNAs up-regulated in normal tissues were found targeting genes involved in induction of apoptosis through dr3 and dr4/5 death receptors, vegf hypoxia and angiogenesis, hedgehog signaling pathway, jak stat signaling pathway and Signaling Pathways in Glioblastoma(Homo sapiens).

An interesting case is rsRNA 9881 . This small regulatory RNA was found overexpressed in almost all cancer states studied here, suggesting about some central points being affected by this regulatory small RNA. There were 20 different target genes which were found strongly negatively correlated to its expression. A closer analysis revealed that the target genes were enriched for pathways critical for cell development and cancer, at the interfaces of diverse pathways (apoptosis, cell death, p53 signaling, hiv-1 nef, caspase cascade, TLR, TNFR-1 signaling and FAS pathway), reasoning why the regulatory small RNA 9881 was found abundant in most of the studied cancer conditions. The figure below shows how its target genes are interlinked and positioned, making it critical for cancer conditions. Adiopogenesis was the common factor found most affected.

These novel rsRNAs (11,234 in this study) were found to regulate many important biological pathways. Related to cell growth and development and pathways which decide the fate of the cell converting from normal to tumor cell. Some of the pathways include pathways in cancer (hsa05200); Apoptosis (hsa04210) ; Cell cycle (hsa04110); p53 signaling pathway (hsa04115) and many more important pathways.

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List of small RNAs which was not supported by interaction sequencing data.
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Studio of Computational Biology & Bioinformatics,
Biotech Division,
CSIR-Institute of Himalayan Bioresource Technology,
Palampur 176061 (Himachal Pradesh), India
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