The world is now struggling with a pandemic known as COVID-19 which is caused by a novel coronavirus that was first identified in December 2019 in a local sea food market in Wuhan, China [2] . Due to the similarity of its genomic sequence to that of Severe Acute Respiratory Syndrome (SARS-CoV), which is a member of the subgenus of Sarbecovirus, the aforementioned novel coronavirus was named SARS-CoV-2. Phylogenetic studies have found a bat origin for this virus [3, 4] . As of April 29 th , 2020, this virus has infected more than 3,150,000 people in 185 countries, caused more than 227,000 cases of death, and has become a global health concern leading to massive lock downs and quarantine all around the world.
Since the emergence of SARS-CoV in China in 2002, which infected around 8,000 people world-wide, multiple research efforts have tried to understand that virus and to suggest potential treatments. Despite the fact that no vaccines or antivirals have been approved to date for any of coronaviruses, improvements on reducing the severity of the disease and mortality rate have been reported. Because of the similarity of the recent fast-spreading coronavirus SARS-CoV-2, which shares more than 79% of its genomic sequence with SARS-CoV, one plausible way to understand how it works and suggest possible treatments would be to port what has been found for SARS-CoV previously to SARS-CoV-2.
Non-coding RNAs (ncRNAs) are, as the name suggests, RNAs that do not translate to proteins. Although it is likely the case that some of them do not play a major role in the cell [5, 6] , some have crucial functions, such as transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), micro RNAs (miRNAs), etc. Some of the ncRNAs, such as miRNAs, play a role in post-transcriptional regulation of gene expression. They, through a procedure called gene silencing, bind with the complementary parts of the target RNAs, and prevent the translation of those RNAs through cleavage of their strand, shortening their poly-A tail, or downgrading the efficiency of their translation by making some nucleotides unavailable to the ribosomes [7, 8] .
First viral ncRNA was identified by Reich et al. [9] . Since then a plethora of viral-associated ncRNAs have been identified and this has been accelerated by the advances in technology [10] . Especially, deep sequencing has facilitated the detection of small virus-associated RNAs [11, 12] . Some of these ncRNAs are known to be responsible in counteracting the antiviral defense mechinism that are present in the host cells, mostly through inhibition of protein kinase R (PKR) [13] . Therefore, they aid in the life cycle of the virus [14, 15] , such as svRNAs in influenza A virus that are involved in the mechanisms this virus uses for switching between transcription and replication [12] .
It had been well-known that nuclear and DNA viruses encode miRNAs [16] that play a role in persistence [17] of the virus as well as changing the transcriptopme in the host cell [18] . Using the deep sequencing technologies, it had been revealed that cytoplasmic RNA viruses also express ncRNAs [11, 12, 1] and most of them induce various cytoplasmic pathways to express their ncRNAs [12] . Flaviviruses can be mentioned as examples of cytoplasmic RNA viruses, which are very sensitive to interferons and have evolved a variety of mechanisms to avoid their action [19] . It has been shown that ncRNAs in flaviviral RNA bounds to genes responsible for regulation of antiviral state of the host cell and affects the interferon response agains the virus [20] .
A recent research has reported three small viral RNAs (svRNAs) that are derived from the genomic regions of SARS-CoV [1] . Morales et al. have have shown the presence of these positive sense svRNAs, which are "mapped to nsp3 at the 5 end of the Replicase gene and the N gene (svRNA-N) at the 3 end of the genomic RNA (gRNA)" [1] , by using specific small RNA RT-qPCR assays. Their experiments on a mouse model of the infection [21, 22] show that these svRNAs contribute to SARS-CoV pathogenesis, and also suggest a potential antiviral treatment using antagomir-mediated inhibition of these svRNAs.
Small non-coding RNAs that play an improtant roll have been shown to be highly conserved among genuses and families. Given that SARS-CoV-2 is in the the same subgenus as SARS-CoV and their genomic sequence has more than 79% similarity, if orthologs of the svRNAs found in SARS-CoV be present and found in SARS-CoV-2, then (antagomir-mediated) inhibition of those svRNA orthologs is expected to lead to reduction of SARS-CoV-2 titers and help decrease severity of COVID-19 in the majority of patients, as was shown to be the case for SARS-CoV [1] . What is needed is the sequence of the three aforementioned svRNAs in SARS-CoV-2 genome, from which the corresponding antagomirs are simply designed through base pair complementarity.
To target SARS-CoV-2 svRNAs, we first characterize the sequence of three svRNAs in SARS-CoV-2. We achieve that goal by aligning the sequences of SARS-CoV svRNAs to the SARS-CoV-2 genome sequences. After investigating many of the available sequenced genomes of SARS-CoV-2 that have been reported in various locations around the globe, we discovered the presence of three svRNAs that were highly conserved orthologs of the svRNAs that played a role in pathogenesis of SARS-CoV. This in silico discovery still needs to be confirmed using in vitro and in vivo experiments, but our findings reported in the following sections suggest strong likelihood of our hypothesis.
In order to find the svRNAs in SARS-Cov-2 that are orthologs of those in SARS-CoV, we selected all of the complete genomic sequences of SARS-CoV-2, that were made available on the NCBI portal as of March 27 th (173 complete sequences) as well as 27 more randomly chosen ones among the more recent uploads on the NCBI portal. These genomic sequences are from different states and countries, including but not limited to: New York, Washington, California, Illinois, Utah, China, Japan, South Korea, Italy, India, Brazil, Germany, Australia, Turkey, and Greece.
We used a variant of the algorithm introduced by Smith and Waterman, which is known in the community as the fit alignment [23] , as the core of investigation for the svRNAs of interest. The algorithm devised by Smith and Waterman is itself a variant of the global alignment algorithm known as Needleman-Wunsch algorithm [24] , and can be used to find the regions of two genomic sequences that are similar. Fit alignment is a variant of this algorithm that searches a reference genome for a subsection that is highly similar to another shorter sequence.
We used our own in-house alignment tool that implements a faster variant of local and fit alignment algorithms, based on the idea of limiting the search to considering only a subset of the alignment search space that represents high similarity and pruning the regions that fall below the desired threshold. Also, when aligning two sequences, there might be multiple alignments with the same score as the highest score. Therefore, our tool keeps track of all the alignments with a score equal to the highest score. For this specific problem, we also added the feature of choosing the best alignment that exists in all the other reference sequences. However, we should note that, for all the sequences considered (the ones in Table 1 ) the loci reported for the exact match to our suggested svRNAs have also the highest score of alignment with the svRNAs reported by Morales et al. in their respective sequence.
Figures 1, 2, and 3 show the sequences that we have found for three svRNAs in SARS-CoV-2 that are orthologs of the three aforementioned svRNAs in SARS-CoV. To further test our hypothesis, we searched for all these svRNAs in 200 different complete reference sequences of the virus. Our three svRNAs are wholly present, without any mutation, in all the reference sequences. Table 1 shows the NCBI ID of each of these sequences, as well as the string loci of each of the svRNAs. As you can see 199 out of 200 tested sequences are present in the table and contain the exact match of the proposed RNAs. The missing entry of the table is LR757997 which showed the presence of the third svRNA at loci 28604 but does not contain the other two because there is a gap in the sequence from loci 3001 to 3235 (filled with Ns), and this is the region where the first two svRNAs reside in according to the loci values in the table for these two columns.
We identify a non-detrimental mismatch by : in Figures 1, 2 , and 3. RNA-RNA binding energies is mainly governed by Watson-Crick base pairing, namely A-U, G-U, and C-G. Particularly, U can pair with both A and G. Hence, an A vs. G mismatch (substitution) in an ortholog RNA is non-detrimental for the binding to a target RNA. Similarly, G can pair with both U and C. Hence, an U/T vs. C mismatch (substitution) in an ortholog RNA is non-detrimental for the binding to a target RNA. Figure 1 : Alignment of the first svRNA in SARS-CoV and its identified ortholog in SARS-CoV-2. Top: nsp3.1 svRNA sequence GAGGAAGAAGAGGACGAT in SARS-CoV according to [1] . Bottom: Ortholog of nsp3.1 svRNA sequence GATGAAGAAGAAGGTGAT in SARS-CoV-2. | represents a match, − represents a gap (indel), empty represents a mismatch, and : represents a non-detrimental mismatch. Figure 2 : Alignment of the second svRNA in SARS-CoV and its identified ortholog in SARS-CoV-2. Top: nsp3.2 svRNA sequence GAGGAAGAAGAAGAGGAAGACT in SARS-CoV according to [1] . Bottom: Ortholog of nsp3.2 svRNA sequence GAAGAAGAGCAAGAAGAAGATT in SARS-CoV-2. | represents a match, − represents a gap (indel), empty represents a mismatch, and : represents a non-detrimental mismatch. Figure 3 : Alignment of the third svRNA in SARS-CoV and its identified ortholog in SARS-CoV-2. Top: N svRNA sequence AGGAACTGGCCCAGAAGCTTC in SARS-CoV according to [1] . Bottom: Ortholog of N svRNA sequence AGGAACTGGGCCAGAAGCTGGAC in SARS-CoV-2. | represents a match, − represents a gap (indel), empty represents a mismatch, and dotted line represents a potential omission. It is possible that some or all of the last four (4) 2999  3125  28558  163  MT253701  2999  3125  28558  164  MT253702  2999  3125  28558  165  MT253703  2999  3125  28558  166  MT253704  2999  3125  28558  167  MT253705  2999  3125  28558  168  MT253706  2999  3125  28558  169  MT253707  2999  3125  28558  170  MT253708  2999  3125  28558  171  MT253709  2999  3125  28558  172  MT253710  2999  3125  28558  173  MT327745  3049  3175  28608  174  MT328032  3053  3179  28612  175  MT334563  3052  3178  28611  176  MT345880  3006  3132  28565  177  MT350251  3037  3163  28596  178  MT350266  3053  3179  28612  179  MT350282  3053  3179  28612  180  MT359866  3043  3169  28602  181  MT370944  3013  3139  28572  182  MT370968  2989  3115  28548  183  MT370975  2998  3124  28557  184   MT371019  2998  3124  28557  185  MT371024  2981  3107  28540  186  MT371034  3006  3132  28565  187  MT371035  2998  3124  28557  188  MT371036  2998  3124  28557  189  MT371037  2989  3115  28548  190  MT371048  3053  3179  28612  191  MT371568  2929  3055  28488  192  MT371572  2945  3071  28504  193  MT372481  3048  3174 
We also analyzed the potential target genes of these svRNAs in human body. To this end, we considered more than 32,000 known RNAs that are transcribed in human body. We processed the sequence of each of these RNAs and removed the intron regions of each. Finally, we aligned the reverse complement of our svRNAs to all the exon sequences. A highly similar region to the reverse complement of an svRNA is highly complementary to the svRNA and hence suggests a high chance of interacting with that svRNA. Tables showing the results of that alignment for the regions that had an alignment score higher than 0.7 with these three svRNAs are available in the Appendix Tables 2, 3, and 4. To compute the score of the alignment, we used a reward of one (1) for a pair of matching nucleotides and a penalty of negative one (-1) for substitutions and insertion/deletions (indels). In the end, the total score was divided by the length of each svRNA to normalize the scores.
Our proposed svRNAs for SARS-CoV-2 are highly conserved versions of the svRNAs of SARS-CoV. Presence of these svRNAs in all the 200 reference sequences that we used to test our hypothesis increases the possibility of correctness of our claim. Also, our proposed svRNAs occur at very similar loci in different reference sequences, and these loci are almost the same as the ones for the original svRNAs reported for SARS-CoV. The fact that the two viruses are in the same subgenus makes our hypothesis more plausible. However, still this hypothesis has to be verified experimentally.
As mentioned earlier, the complete tables showing the possible target RNAs of our proposed svRNAs are available in the supplementary document. However, it is worth mentioning some of them. The second highest match to the reverse complement of the first svRNA is HIF3A transcript which is a transcriptional regulator in adaptive response to low oxygen levels. Silencing this gene affects the reaction of the body in response to hypoxia. The second best match with the reverse complement of the second svRNA is MEX3B transcript which is a member of MEX3 translational regulators. MEX3 are RNA-binding proteins that are evolutionarily conserved and their in vivo functions is yet to be fully characterized.
In this paper, we reported three potential svRNAs, which are orthologs of SARS-CoV svRNAs, in the SARS-CoV-2 genome. To validate our results, we confirmed the discovered orthologs are fully conserved in all the publicly available genomes of various strains of SARS-CoV-2; the loci at which the SARS-CoV-2 orthologs occur are close to the loci at which SARS-CoV svRNAs occur. Furthermore, our proposed svRNAs occur at very similar loci in different reference sequences, and these loci are almost the same as the ones for the original svRNAs reported in SARS-CoV.
We also reported potential targets for these svRNAs. We hypothesize that the discovered orthologs play a role in pathogenesis of SARS-CoV-2, and therefore, antagomir-mediated inhibition of these SARS-CoV-2 svRNAs inhibits COVID-19. This in silico discovery still needs to be confirmed using in vitro and in vivo experiments. Tables 2, 3, and 4 show the matches with a score of at least 70% between more than 32,000 RNAs transcribed in the human body and reverse-complement of our propossed svRNAs for Sars-CoV-2. The ones with a high score are likely to be target genes of the corresponding svRNA. The entries of each table are sorted based on the normalized score of the alignment.
Tables 5, 6, and 7 show the matches with a score of at least 80% between our proposed svRNAs and human reference genome. Patch release 13 of build 38 was used for as the reference genome. NC000003.12 chr3 0.84 AGTGAAGCATGGGCCAGAAGCT 47360544 AG-GAA-C-TGGGCCAGAAGCT Table 7 : Potential target regions in the human genome for svRNA3 (truncated version).