The new Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a zoonotic pathogen that has rapidly mutated and become transmissible to humans. There is little existing data on the mutations in SARS-CoV-2 and the impact of these polymorphisms on its transmission and viral load. In this study, the SARS-CoV-2 genomic sequence was analyzed to identify variants within the 3’UTR region of its cis-regulatory RNA elements. A 43-nucleotide genetic element with a highly conserved stem-loop II-like motif (S2M), was discovered. The research revealed 32 G>U and 16 G>U/A mutations located within the S2M sequence in human SARS-CoV-2 models. These polymorphisms appear to make the S2M secondary and tertiary structures in human SARS-CoV-2 models less stable when compared to the S2M structures of bat/pangolin models. This grants the RNA structures more flexibility, which could be one of its escape mechanisms from host defenses or facilitate its entry into host proteins and enzymes. While this S2M sequence may not be omnipresent across all human SARS-CoV-2 models, when present, its sequence is always highly conserved. It may be used as a potential target for the development of vaccines and therapeutic agents.

It is extremely important to identify transcription factor binding sites (TFBSs). Some TFBSs are proposed to be bipartite motifs known as two-block motifs separated by gap sequences with variable lengths. While position weight matrix (PWM) is commonly used for the representation and prediction of TFBSs, dinucleotide weight matrix (DWM) enables expression of the interdependencies of neighboring bases. By incorporating DWM into the detection of bipartite motifs, we have developed a novel tool for ab initio motif detection, DIpartite (bi partite motif detection tool based on di nucleotide weight matrix) using a Gibbs sampling strategy and the minimization of Shannon’s entropy. DIpartite predicts the bipartite motifs by considering the interdependencies of neighboring positions, that is, DWM. We compared DIpartite with other available alternatives by using test datasets, namely, of CRP in E . coli , sigma factors in B . subtilis , and promoter sequences in humans. We have developed DIpartite for the detection of TFBSs, particularly bipartite motifs. DIpartite enables ab initio prediction of conserved motifs based on not only PWM, but also DWM. We evaluated the performance of DIpartite by comparing it with freely available tools, such as MEME, BioProspector, BiPad, and AMD. Taken the obtained findings together, DIpartite performs equivalently to or better than these other tools, especially for detecting bipartite motifs with variable gaps. DIpartite requires users to specify the motif lengths, gap length, and PWM or DWM. DIpartite is available for use at https://github.com/Mohammad-Vahed/DIpartite .

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The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.