Ramachandran, P. | Search bioRxiv

bioRxiv2018

Tyrosinase mediated humic substances synthesis by Bacillus aryabhattaiTFG5

The present investigation aims at understanding the mechanism of Humic Substances (HS) formation and enhancement through tyrosinase produced by Bacillus aryabhattai TFG5. A bacterium isolated from termite mound produced tyrosinase (1.34 U.ml-1) and laccase (2.1 U.ml-1) at 48 and 60 h of fermentation respectively. The protein from B. aryabhattai TFG5 was designated as TyrB and it had a predicted molecular weight of 35.23 kDa. Swiss modelling of protein revealed a bi copper protein with its conserved residues required for activity. Interestingly, TyrB efficiently transformed and polymerized standard phenols besides transforming free phenols of Coir pith Wash Water (CWW). In addition, spectroscopic evidences suggest that TyrB enhanced the HS production from coir pith biomass. Furthermore, degradative products and changes in biomass structure by TyrB analysed through FT-IR suggests that TyrB might follow the polyphenol theory of HS synthesis.

microbiology↗

bioRxiv2017

Uncovering Robust Patterns of MicroRNA Co-Expression across Cancers using Bayesian Relevance Networks

Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing--with its unique statistical properties--became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca/Software.html.

bioinformatics↗