Unique small RNA reads selected from multiple cancerous tissues, these unique reads were mapped to human genome build HG19 assembly and known mature miRNAs downloaded from miRBase using BOWTIE with maximum of two mismatches. To filter out small RNA reads which can be a degradation or random product, two different criteria were applied: 1) reads whose abundance is more than five in any given experiment and 2) the read is reported in at least for two different experimental conditions. All such reads were subjected to target identification using TAREF and Targetscan. Curter, validation of identified sRNA:targets interactions was done by searching the interaction across Argonaute CLIP-seq data and CLASH sequencing data. Using expression data of sRNA, 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.

After target predictions, small RNAs were found to target 17,612 genes with maximum of two mismatches between target and small RNAs interaction. From the sRNA:target data only those sRNAs were considered which were over-expressed (two fold or above) either in the cancerous state or in the normal condition. After applying these filters, a total of 11,234 potential novel regulatory sRNAs were identified. Out of 11,234 regulatory sRNAs displayed (rsRNAs), 7,996 rsRNAs show high abundance in cancerous conditions, whereas remaining 3,238 regulatory sRNAs showed higher expression in normal conditions. To identify the possible DGCR8-DROSHA mediated rsRNAs, rsRNAs were searched in the DGCR8 over expressed data. The analysis revealed that 2,999 (26.69%) of the novel rsRNAs were identified as those processed by DGCR8. Figure 3 summarizes the findings made in this section.


Validation of rsRNA:target inetaractionswas 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 precise information about the interactions as the interactions are precisely arrested through ligation of the target 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.


Furter to validate targets, 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.


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.


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 video compilation 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 .

These identified rsRNAs were clustered using four different methods namely 1) seed region similarity, 2) sRNA length and Argonatue association, 3) all smaller sRNAs are covered by a single long sRNA and, 4) Expression based clustering. These clustering was done to identify rsRNAs sharing similar properties. rsRNAs in a cluster were found targeting same/similar gene, regulating similar pathways and similar functions. Expression based clustering results in 362 distinct clusters which includes 7,292 rsRNAs these rsRNAs share high co-expression with each other ("r" > 0.5) calculated for 4,997 experimental conditions.

For clusters identified form a given approach overlapping clusters were identifed. High commonalities between the clusters generated based upon different properties suggest a degree of similarity and relationship between the two properties. This way 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 in cancerous conditions and in a normal condition. The differential expression was evaluated across large number of individual samples, followed by t-test for significance between normal sample sets and cancer patients sample sets, for every cancer condition.

A small regulatory RNA 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.