Rapid and accurate nucleobase detection using FnCas9 and its application in COVID-19 diagnosis - Azhar, Mohd; Phutela, Rhythm; Kumar, Manoj; Ansari, Asgar Hussain; Rauthan, Riya; Gulati, Sneha; Sharma, Namrata; Sinha, Dipanjali; Sharma, Saumya; Singh, Sunaina; Acharya, Sundaram; Sarkar, Sajal; et al. (Biosensors & Bioelectronics, 2021)
Rapid detection of DNA/RNA pathogenic sequences or variants through point-of-care diagnostics is valuable for accelerated clinical prognosis, as witnessed during the recent COVID-19 outbreak. Traditional methods relying on qPCR or sequencing are tough to implement with limited resources, necessitating the development of accurate and robust alternative strategies. Here, we report FnCas9 Editor Linked Uniform Detection Assay (FELUDA) that utilizes a direct Cas9 based enzymatic readout for detecting nucleobase and nucleotide sequences without transcleavage of reporter molecules. We also demonstrate that FELUDA is 100% accurate in detecting single nucleotide variants (SNVs), including heterozygous carriers, and present a simple web-tool JATAYU to aid end-users. FELUDA is semi-quantitative, can adapt to multiple signal detection platforms, and deploy for versatile applications such as molecular diagnosis during infectious disease outbreaks like COVID-19. Employing a lateral flow readout, FELUDA shows 100% sensitivity and 97% specificity across all ranges of viral loads in clinical samples within 1hr. In combination with RT-RPA and a smartphone application True Outcome Predicted via Strip Evaluation (TOPSE), we present a prototype for FELUDA for CoV-2 detection closer to home.
Keywords: CRISPRDx; FELUDA; FnCas9; LFA; SARS-CoV2; SNV detection

Development of an in silico platform (TRIPinRNA) for the identification of novel RNA intramolecular triple helices and their validation using biophysical techniques - Rakheja, Isha; Bharti, Vishal; Sahana, S.; Das, Prosad Kumar; Ranjan, Gyan; Kumar, Ajit; Jain, Niyati; Maiti, Souvik (Biochemistry, 2024)
There are surprisingly few RNA intramolecular triple helices known in the human transcriptome. The structure has been most well-studied as a stability-element at the 3 ` end of lncRNAs such as MALAT1 and NEAT1, but the intrigue remains whether it is indeed as rare as it is understood to be or just waiting for a closer look from a new vantage point. TRIPinRNA, our Python-based in silico platform, allows for a comprehensive sequence-pattern search for potential triplex formation in the human transcriptome-noncoding as well as coding. Using this tool, we report the putative occurrence of homopyrimidine type (canonical) triple helices as well as heteropurine-pyrimidine strand type (noncanonical) triple helices in the human transcriptome and validate the formation of both types of triplexes using biophysical approaches. We find that the occurrence of triplex structures has a strong correlation with local GC content, which might be influencing their formation. By employing a search that encompasses both canonical and noncanonical triplex structures across the human transcriptome, this study enriches the understanding of RNA biology. Lastly, TRIPinRNA can be utilized in finding triplex structures for any organism with an annotated transcriptome.