Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions

Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from k -mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.

Study of Essential Sequence Analysis Tools in Bioinformatics : A Brief Overview

Sequence analysis program is outlined that analyzes and investigates homology between various nucleic acid or protein sequence. The dot matrix technique compares the sequences and the consensus sequence is obtained by superimposing on each other all the dot matrices. Local Alignment and Global Alignment both sequence from start to end is the best possible alignment over the entire duration between the two sequences. This method is more important to align with two closely related sequences roughly the same length. This method may not able to generate optimal results for divergent sequences and variable length sequence because between the two sequences it does not recognize very similar local region.

Stability of SARS-CoV-2 Phylogenies

The SARS-CoV-2 pandemic has led to unprecedented, nearly real-time genetic tracing due to the rapid community sequencing response. Researchers immediately leveraged these data to infer the evolutionary relationships among viral samples and to study key biological questions, including whether host viral genome editing and recombination are features of SARS-CoV-2 evolution. This global sequencing effort is inherently decentralized and must rely on data collected by many labs using a wide variety of molecular and bioinformatic techniques. There is thus a strong possibility that systematic errors associated with lab-specific practices affect some sequences in the repositories. We find that some recurrent mutations in reported SARS-CoV-2 genome sequences have been observed predominantly or exclusively by single labs, co-localize with commonly used primer binding sites and are more likely to affect the protein coding sequences than other similarly recurrent mutations. We show that their inclusion can affect phylogenetic inference on scales relevant to local lineage tracing, and make it appear as though there has been an excess of recurrent mutation and/or recombination among viral lineages. We suggest how samples can be screened and problematic mutations removed. We also develop tools for comparing and visualizing differences among phylogenies and we show that consistent clade- and tree-based comparisons can be made between phylogenies produced by different groups. These will facilitate evolutionary inferences and comparisons among phylogenies produced for a wide array of purposes. Building on the SARS-CoV-2 Genome Browser at UCSC, we present a toolkit to compare, analyze and combine SARS-CoV-2 phylogenies, find and remove potential sequencing errors and establish a widely shared, stable clade structure for a more accurate scientific inference and discourse.ForewordWe wish to thank all groups that responded rapidly by producing these invaluable and essential sequence data. Their contributions have enabled an unprecedented, lightning-fast process of scientific discovery---truly an incredible benefit for humanity and for the scientific community. We emphasize that most lab groups with whom we associate specific suspicious alleles are also those who have produced the most sequence data at a time when it was urgently needed. We commend their efforts. We have already contacted each group and many have updated their sequences. Our goal with this work is not to highlight potential errors, but to understand the impacts of these and other kinds of highly recurrent mutations so as to identify commonalities among the suspicious examples that can improve sequence quality and analysis going forward.

A novel gene expression system for Ralstonia eutropha based on the T7 promoter

Background: Ralstonia eutropha (syn. Cupriavidus necator) is a model microorganism for the metabolism of polyhydroxyalkanoates (PHAs) and a potential chassis for protein expression. Although current plasmid systems for R. eutropha provide a basic platform for gene expression, the performance of the induction systems is still limited. In addition, the sizes of the cloned genes is limited due to the large sizes of the plasmid backbones.Results: In this study, an R. eutropha T7 expression system was established by integrating a T7 RNA polymerase gene driven by the PBAD promoter into genome of R. eutropha, and cloning the T7 promoter into a pBBR1-derived plasmid for gene expression. In addition, the essential sequence necessary for pBBR1 plasmid replication was identified, and the redundant parts were deleted, reducing the expression plasmid size to 3392 bp, and the electroporation efficiency was improved 4 times. As a result, the highest expression level of Rfp was enhanced slightly, and the L-arabinose concentration necessary for induction was decreased 20 times. Conclusions: R. eutropha with the T7 expression system provides an efficient platform for protein expression and synthetic biology applications.