Rosenberg Langhoff (birdjewel2)

com/weibozheng/ADFinder. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.Biliverdin reductase B (BLVRB) family members are general flavin reductases critical in maintaining cellular redox with recent findings revealing that BLVRB alone can dictate cellular fate. However, as opposed to most enzymes, the BLVRB family remains enigmatic with an evolutionarily changing active site and unknown structural and functional consequences. Here, we applied a multi-faceted approach that combines X-ray crystallography, NMR, and kinetics methods to elucidate the structural and functional basis of the evolutionarily changing BLVRB active site. Using a panel of three BLVRB isoforms (human, lemur, and hyrax) and multiple human BLVRB mutants, our studies reveal a novel evolutionary mechanism where coenzyme "clamps" formed by arginine side chains at two co-evolving positions within the active site serve to slow coenzyme-release (position 14 and 78). We find that coenzyme-release is further slowed by the weaker binding substrate, resulting in relatively slow turnover numbers. However, different BLVRB active sites imposed by either evolution or mutagenesis exhibit a surprising inverse relationship between coenzyme-release and substrate turnover that is independent of the faster chemical step of hydride transfer also measured here. Collectively, our studies have elucidated the role of the evolutionarily changing BLVRB active site that serves to modulate coenzyme-release and has revealed that coenzyme-release is coupled to substrate turnover. © The Author(s) 2020. Published by Oxford University Press on behalf of the Japanese Biochemical Society. All rights reserved.MOTIVATION Computing the uniqueness of k-mers for each position of a genome while allowing for up to e mismatches is computationally challenging. However, it is crucial for many biological applications such as the design of guide RNA for CRISPR experiments. More formally, the uniqueness or (k, e)-mappability can be described for every position as the reciprocal value of how often this k-mer occurs approximately in the genome, i.e., with up to e mismatches. RESULTS We present a fast method GenMap to compute the (k, e)-mappability. We extend the mappability algorithm, such that it can also be computed across multiple genomes where a k-mer occurrence is only counted once per genome. This allows for the computation of marker sequences or finding candidates for probe design by identifying approximate k-mers that are unique to a genome or that are present in all genomes. GenMap supports different formats such as binary output, wig and bed files as well as csv files to export the location of all approximate k-mers for each genomic position. AVAILABILITY GenMap can be installed via bioconda. Binaries and C ++ source code are available on https//github.com/cpockrandt/genmap. © The Author(s) 2020. Published by Oxford University Press.MOTIVATION Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual-SNP analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple testing correction burden. On the other hand, multiple-SNP analyses (e.g., gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype-phenotype association. RESULTS In this paper, we