Alignment, Clustering and Extraction of Structured Motifs in DNA Promoter Sequences

A simple motif is a short DNA sequence found in the promoter region and believed to act as a binding site for a transcription factor protein. A structured motif is a sequence of simple motifs (boxes) separated by short sequences (gaps). Biologists theorize that the presence of these motifs play a key role in gene expression regulation. Discovering these patterns is an important step towards understanding protein-gene and gene-gene interaction thus facilitates the building of accurate gene regulatory network models. DNA sequence motif extraction is an important problem in bioinformatics. Many studies have proposed algorithms to solve the problem instance of simple motif extraction. Only in the past decade has the more complex structured motif extraction problem been examined by researchers. The problem is inherently challenging as structured motif patterns are segmented into several boxes separated by variable size gaps for each instance. These boxes may not be exact copies, but may have multiple mismatched positions. The challenge is extenuated by the lack of resources for real datasets covering a wide range of possible cases. Also, incomplete annotation of real data leads to the discovery of unknown motifs that may be regarded as false positives. Furthermore, current algorithms demand unreasonable amount of prior knowledge to successfully extract the target pattern. The contributions of this research are four new algorithms. First, SMGenerate generates simulated datasets of implanted motifs that covers a wide range of biologically possible cases. Second, SMAlign aligns a pair of structured motifs optimally and efficiently given their gap constraints. Third, SMCluster produces multiple alignment of structured motifs through hierarchical clustering using SMAlign\u27s affinity score. Finally, SMExtract extracts structured motifs from a set of sequences by using SMCluster to construct the target pattern from the top reported two-box patterns (fragments), extracted using an existing algorithm (Exmotif) and a two-box template. The main advantage of SMExtract is its efficiency to extract longer degenerate patterns while requiring less prior knowledge, about the pattern to be extracted, than current algorithms

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population