Long non-coding RNAs (lncRNAs) play an important role in genome regulation. Specifically, many lncRNAs interact with chromatin, recruit epigenetic complexes and in this way affect large-scale gene expression programs. However, the experimental data about lncRNA-chromatin interactions is still limited. The majority of experimental protocols do not provide any insight into the mechanics of lncRNA-based genome-wide epigenetic regulation. Here we present the HiMoRNA (Histone-Modifying RNA) database, a resource containing correlated lncRNA–epigenetic changes in specific genomic locations genome-wide. HiMoRNA integrates a large amount of multi-omics data to characterize the effects of lncRNA on epigenetic modifications and gene expression. The current release of HiMoRNA includes more than five million associations in humans for ten histone modifications in multiple genomic loci and 4145 lncRNAs. HiMoRNA provides a user-friendly interface to facilitate browsing, searching and retrieving of lncRNAs associated with epigenetic profiles of various chromatin loci. Analysis of the HiMoRNA data suggests that several lncRNA including JPX might be involved not only in regulation of XIST locus but also in direct establishment or maintenance of X-chromosome inactivation. We believe that HiMoRNA is a convenient and valuable resource that can provide valuable biological insights and greatly facilitate functional annotation of lncRNAs.
A Molecular Features Set (MFS), is a result of a vast diversity of bioinformatics pipelines. The lack of a “gold standard” for most experimental data modalities makes it difficult to provide valid estimation for a particular MFS's quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrices (DM) and their power to distinguish between phenotypes. The quality of a DM can be assessed by summarizing its power to quantify the differences of inner-phenotype and outer-phenotype distances. This estimation of the DM quality can be construed as a measure of the MFS's quality. Here we propose Hobotnica, an approach to estimate MFSs quality by their ability to stratify data, and assign them significance scores, that allow for collating various signatures and comparing their quality for contrasting groups.
A Molecular Features Set (MFS), is a result of a vast diversity of bioinformatics pipelines. The lack of a “gold standard” for most experimental data modalities makes it difficult to provide valid estimation for a particular MFS's quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrices (DM) and their power to distinguish between phenotypes. The quality of a DM can be assessed by summarizing its power to quantify the differences of inner-phenotype and outer-phenotype distances. This estimation of the DM quality can be construed as a measure of the MFS's quality. Here we propose Hobotnica, an approach to estimate MFSs quality by their ability to stratify data, and assign them significance scores, that allow for collating various signatures and comparing their quality for contrasting groups.
Cell fate engineering technologies are critically important for basic and applied science, yet many protocols for direct cell conversions are still unstable, have a low yield and require improvement. There is an increasing need for a data aggregator containing a structured collection of protocols - preprocessed, verified, and represented in a standardized manner to facilitate their comparison, and providing a platform for the researchers to evaluate and improve the protocols. We developed CFM (cell fate mastering), a database of experimentally validated protocols for chemical compound-based direct reprogramming and direct cell conversion. The current version of CFM contains 169 distinct protocols, 113 types of cell conversions, and 158 small molecules capable of inducing cell conversion. CFM allows stem cell biologists to compare and choose the best protocol with high efficiency and reliability for their needs. The protocol representation contains PubChem CIDs and Mechanisms Of Action (MOA) for chemicals, protocol duration, media , and yield with a comment on a measurement strategy. Ratings of the protocols and feedback from the community will help to promote high-quality and reproducible protocols. We are committed to a long-term database maintenance strategy. The database is currently available at https://cfm.mipt.ru}{cfm.mipt.ru
A Molecular Features Set (MFS), is a result of vast diversity of bioinformatics pipelines. In case when MFS is used for further analysis to distinguish between phenotypes, it is often referred to as a signature. Lack of the "gold standard" for most experimental data modalities makes it hard to provide valid estimation for a particular MFS's quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrix (DM) and their power to distinguish between phenotypes. The quality of a DM can be assessed by summarizing its power to quantify the differences of inner-phenotype and outer-phenotype distances. This estimation of the DM quality can be construed as a measure of the MFS's quality. Here we propose Hobotnica, an approach to estimate MFS's quality by their ability to stratify data, and assign them significance scores, that allows for collating various signatures and comparing their quality for contrasting groups.
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