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The CRISPR–Cas9 system is a widely used gene-editing tool, offering unprecedented opportunities for treating various diseases. Controlling Cas9/dCas9 activity at specific location and time to avoid undesirable effects is very important. Here, we report a conditionally active CRISPR–Cas9 system that regulates target gene expression upon sensing cellular environmental change. We conjugated the oxygen-sensing transcription activation domain (TAD) of hypoxia-inducing factor (HIF-1α) with the Cas9/dCas9 protein. The Cas9-TAD conjugate significantly increased endogenous target gene cleavage under hypoxic conditions compared with that under normoxic conditions, whereas the dCas9-TAD conjugate upregulated endogenous gene transcription. Furthermore, the conjugate system effectively downregulated the expression of SNAIL, an essential gene in cancer metastasis, and upregulated the expression of the tumour-related genes HNF4 and NEUROD1 under hypoxic conditions. Since hypoxia is closely associated with cancer, the hypoxia-dependent Cas9/dCas9 system is a novel addition to the molecular tool kit that functions in response to cellular signals and has potential application for gene therapeutics. [ABSTRACT FROM AUTHOR]

Motivation: RNAs fold into complex structures that are integral to the diverse mechanisms underlying RNA regulation of gene expression. Recent development of transcriptome-wide RNA structure profiling through the application of structure-probing enzymes or chemicals combined with high-throughput sequencing has opened a new field that greatly expands the amount of in vitro and in vivo RNA structural information available. The resultant datasets provide the opportunity to investigate RNA structural information on a global scale. However, the analysis of high-throughput RNA structure profiling data requires considerable computational effort and expertise. Results: We present a new platform, StructureFold, that provides an integrated computational solution designed specifically for large-scale RNA structure mapping and reconstruction across any transcriptome. StructureFold automates the processing and analysis of raw high-throughput RNA structure profiling data, allowing the seamless incorporation of wet-bench structural information from chemical probes and/or ribonucleases to restrain RNA secondary structure prediction via the RNAstructure and ViennaRNA package algorithms. StructureFold performs reads mapping and alignment, normalization and reactivity derivation, and RNA structure prediction in a single userfriendly web interface or via local installation. The variation in transcript abundance and length that prevails in living cells and consequently causes variation in the counts of structure-probing events between transcripts is accounted for. Accordingly, StructureFold is applicable to RNA structural profiling data obtained in vivo as well as to in vitro or in silico datasets. StructureFold is deployed via the Galaxy platform. [ABSTRACT FROM AUTHOR]

Motivation: DNA methylation plays critical roles in gene regulation and cellular specification without altering DNA sequences. The wide application of reduced representation bisulfite sequencing (RRBS) and whole genome bisulfite sequencing (bis-seq) opens the door to study DNA methylation at single CpG site resolution. One challenging question is how best to test for significant methylation differences between groups of biological samples in order to minimize false positive findings.Results: We present a statistical analysis package, methylSig, to analyse genome-wide methylation differences between samples from different treatments or disease groups. MethylSig takes into account both read coverage and biological variation by utilizing a beta-binomial approach across biological samples for a CpG site or region, and identifies relevant differences in CpG methylation. It can also incorporate local information to improve group methylation level and/or variance estimation for experiments with small sample size. A permutation study based on data from enhanced RRBS samples shows that methylSig maintains a well-calibrated type-I error when the number of samples is three or more per group. Our simulations show that methylSig has higher sensitivity compared with several alternative methods. The use of methylSig is illustrated with a comparison of different subtypes of acute leukemia and normal bone marrow samples.Availability: methylSig is available as an R package at http://sartorlab.ccmb.med.umich.edu/software.Contact: sartorma@umich.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The article focuses on deciphering gene regulatory networks by in silico approaches which is is a crucial step in the study of the molecular perturbations that occur in diseases. It is mentioned that the the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. It idiscusses the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions.

Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimization and design problems by building solutions that are 'more fit' relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve previously unimaginable or intractable computational and biological problems. [ABSTRACT FROM AUTHOR]

Summary: Exploring microRNA (miRNA) regulations and protein–protein interactions could reveal the molecular mechanisms responsible for complex biological processes. Mirin is a web-based application suitable for identifying functional modules from protein–protein interaction networks regulated by aberrant miRNAs under user-defined biological conditions such as cancers. The analysis involves combining miRNA regulations, protein–protein interactions between target genes, as well as mRNA and miRNA expression profiles provided by users. Mirin has successfully uncovered oncomirs and their regulatory networks in various cancers, such as gastric and breast cancer.Availability and implementation: Mirin is freely available at http://mirin.ym.edu.tw/.Contact: hsuancheng@ym.edu.tw or yukijuan@ntu.edu.twSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]