It　is　well　known　that　microRNAs　(miRNAs　or　miRs)　are　small　(18-25　nt)　yet　highly　potent　non-coding　RNA-derived　RNAs　(ndRNAs),　originating　from　pre-miRNA　fragmentation,　that　have　been　shown　to　alter　the　post-transcriptional　functionality　of　many　messenger　RNAs　(mRNAs).　Biologically,　the　identification　and　study　of　miRNAs　is　very　critical　due　to　their　increasing　significance　as　biomarkers　for　many　types　of　cancers　and　other　genetic　diseases.　While　empirical　evidence　supporting　the　existence　of　several　novel　ndRNAs　excised　from　other　longer　non　coding　RNAs　(ncRNAs)　is　growing,　recent　evidence　suggests　the　full　extent　of　their　prevalence　is　likely　underappreciated.　Although　some　computational　methods　have　been　designed　to　help　domain　experts　identify　and　understand　miRNAs　by　analyzing　Next　Generation　Sequencing　(NGS)　datasets,　there　are　some　crucial　challenges,　such　as　efficiency,　effectiveness,　and　generalizability,　in　the　state-of-the-art　in-silico　methods.　To　address　such　problems,　our　group　proposed　a　new　algorithm　to　mine　ndRNAs　by　applying　wavelet-based　signal　processing　techniques　as　opposed　to　the　current　string-based　NGS　sequence　alignment/analysis.　However,　due　to　novelty　of　the　approach,　our　initial　version　of　the　algorithm　was　focused　specifically　on　mining　miRNAs,　snoRNA-derived　RNAs　(sdRNAs)　and　transfer　RNA　(tRNA)　fragments　(tRFs)　because　of　their　importance　in　the　literature　plus　the　availability　of　experimentally　validated　databases　to　confirm　our　findings.　Despite　the　computational　issues,　we　still　lack　a　basic　understanding　of　the　existence　and　the　range　of　ndRNA　functionalities　from　a)　ndRNAs　other　than　miRs,　sdRNAs　tRFs　in　humans,　and　b)　all　ndRNAs　in　millions　of　organisms　other　than　humans.　Hence,　there　is　an　urgent　requirement　to　automate　the　extraction　and　experimentation　of　ndRNAs,　especially　considering　the　rate　at　which　NGS　data　is　being　produced.　Therefore,　in　the　current　article,　we　extended　our　algorithm　to　be　applicable　to　500　organisms　-　including　eukaryotes,　plants,　bacteria,　fungi,　and　protists　-　along　with　all　their　ncRNAs　available　in　the　current　NCBI　annotation.　We　also　constructed　a　real-time　user-friendly　platform,　SURFR,　available　at　salts.soc.southalabama.edu/surfr,　to　aid　domain　experts　and　the　aspiring　biomedical　scientists　to　perform　RNA-Seq　experiments　to　study　ndRNAs.　Not　only　our　platform　is　extremely　efficient,　but　we　are　also　capable　of　allowing　the　users　to　identify,　analyze,　visualize,　and　compare　ndRNAs　from　up　to　30　NGS　files　to　perform　rigorous　experimentation.　Moreover,　access　to　NGS　files　from　public　databases　like　SRA,　and　ndRNAs　from　private　databases　like　TCGA　are　made　readily　available　to　the　users　to　further　validate　their　novel　findings.　Finally,　we　provide　theoretical　validation　to　examine　our　platform＇s　effectiveness.　©　2021　IEEE.