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joss/paper.md

@@ -46,7 +46,7 @@ Omics studies look at large amounts of biological data to understand changes tha
 
 # Statement of Need
 
-Omics is used extensively in biological research today. However, the development of omics technologies has vastly outpaced the expertise of researchers in its analysis, and the resulting “data deluge” now overwhelms the capacity of human cognition [@RN16; @RN20; @RN19]. Analysis of omics data is therefore the major bottleneck in most research projects today and its use in precision medicine remains limited [@RN26; @RN63]. Pathway analysis has since become ubiquitous to help interpret omics data and elucidate mechanisms of biological phenomena under study [@RN6]. Despite the last decade bringing a host of different computational tools to perform pathway analysis, they each generally result in lists of results too long to manually inspect and extract relevant targets for downstream wet lab validation without introducing biases [@RN5; @RN81]. Interpretation of results is accordingly the greatest expense in any omics project [@RN21]. With the total volume of omics data continuing to grow, novel ways of data management are needed [@RN22]. FAIR (Findable, Accessible, Interoperable, Reusable) scientific data principles necessitate automated interpretation of omics results [@RN25].
+Omics is used extensively in biological research today. However, the development of omics technologies has vastly outpaced the expertise of researchers in its analysis, and the resulting “data deluge” now overwhelms the capacity of human cognition [@RN16; @RN20; @RN19]. Analysis of omics data is therefore the major bottleneck in most research projects today and its use in precision medicine remains limited [@RN26; @RN63]. Pathway analysis has since become ubiquitous to help interpret omics data and elucidate mechanisms of biological phenomena under study [@RN6]. Despite the last decade bringing a host of different computational tools to perform pathway analysis, they each generally result in lists of results too long to manually inspect and extract relevant targets for downstream wet lab validation without introducing biases [@RN5; @RN81]. Interpretation of results is therefore the greatest expense in any omics project [@RN21]. With the total volume of omics data continuing to grow, novel ways of data management are needed [@RN22]. FAIR (Findable, Accessible, Interoperable, Reusable) scientific data principles necessitate automated interpretation of omics results [@RN25].
 
 # Overview