The volume of nucleic acid sequence data has exploded recently, amplifying the challenge of transforming data into meaningful information. Processing data can require an increasingly complex ecosystem of customized tools, which increases difficulty in communicating analyses in an understandable way yet is of sufficient detail to enable informed decisions or repeats. This can be of particular interest to institutions and companies communicating computations in a regulatory environment. BioCompute Objects (BCOs; an instance of pipeline documentation that conforms to the IEEE 2791-2020 standard) were developed as a standardized mechanism for analysis reporting. A suite of BCOs is presented, representing interconnected elements of a computation modeled after those that might be found in a regulatory submission but are shared publicly - in this case a pipeline designed to identify viral contaminants in biological manufacturing, such as for vaccines.

• MeSH
• Workflow MeSH
• Vaccines * MeSH
• Computational Biology * MeSH
• High-Throughput Nucleotide Sequencing MeSH
• Publication type
• Journal Article MeSH

Metformin, the first drug chosen to be tested in a clinical trial aimed to target the biology of aging per se, has been clinically exploited for decades in the absence of a complete understanding of its therapeutic targets or chemical determinants. We here outline a systematic chemoinformatics approach to computationally predict biomolecular targets of metformin. Using several structure- and ligand-based software tools and reference databases containing 1,300,000 chemical compounds and more than 9,000 binding sites protein cavities, we identified 41 putative metformin targets including several epigenetic modifiers such as the member of the H3K27me3-specific demethylase subfamily, KDM6A/UTX. AlphaScreen and AlphaLISA assays confirmed the ability of metformin to inhibit the demethylation activity of purified KDM6A/UTX enzyme. Structural studies revealed that metformin might occupy the same set of residues involved in H3K27me3 binding and demethylation within the catalytic pocket of KDM6A/UTX. Millimolar metformin augmented global levels of H3K27me3 in cultured cells, including reversion of global loss of H3K27me3 occurring in premature aging syndromes, irrespective of mitochondrial complex I or AMPK. Pharmacological doses of metformin in drinking water or intraperitoneal injection significantly elevated the global levels of H3K27me3 in the hepatic tissue of low-density lipoprotein receptor-deficient mice and in the tumor tissues of highly aggressive breast cancer xenograft-bearing mice. Moreover, nondiabetic breast cancer patients receiving oral metformin in addition to standard therapy presented an elevated level of circulating H3K27me3. Our biocomputational approach coupled to experimental validation reveals that metformin might directly regulate the biological machinery of aging by targeting core chromatin modifiers of the epigenome.

• MeSH
• Biocatalysis MeSH
• Neoplasms, Experimental drug therapy   metabolism   MeSH
• Histone Demethylases antagonists & inhibitors   metabolism   MeSH
• Enzyme Inhibitors chemistry   pharmacology   MeSH
• Nuclear Proteins antagonists & inhibitors   metabolism   MeSH
• Humans MeSH
• Ligands MeSH
• Metformin chemistry   pharmacology   MeSH
• Models, Molecular MeSH
• Molecular Structure MeSH
• Mice, Knockout MeSH
• Mice MeSH
• Dose-Response Relationship, Drug MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Mice MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Non-coding RNAs (ncRNAs) are regulatory molecules encoded in the intergenic or intragenic regions of the genome. In prokaryotes, biocomputational identification of homologs of known ncRNAs in other species often fails due to weakly evolutionarily conserved sequences, structures, synteny and genome localization, except in the case of evolutionarily closely related species. To eliminate results from weak conservation, we focused on RNA structure, which is the most conserved ncRNA property. Analysis of the structure of one of the few well-studied bacterial ncRNAs, 6S RNA, demonstrated that unlike optimal and consensus structures, suboptimal structures are capable of capturing RNA homology even in divergent bacterial species. A computational procedure for the identification of homologous ncRNAs using suboptimal structures was created. The suggested procedure was applied to strongly divergent bacterial species and was capable of identifying homologous ncRNAs.

• MeSH
• RNA, Bacterial chemistry   MeSH
• Nucleic Acid Conformation MeSH
• Molecular Sequence Data MeSH
• Mycobacterium genetics   MeSH
• RNA, Untranslated chemistry   MeSH
• Base Sequence MeSH
• Sequence Homology, Nucleic Acid MeSH
• Streptomyces genetics   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

BACKGROUND: The first systematic study of small non-coding RNAs (sRNA, ncRNA) in Streptomyces is presented. Except for a few exceptions, the Streptomyces sRNAs, as well as the sRNAs in other genera of the Actinomyces group, have remained unstudied. This study was based on sequence conservation in intergenic regions of Streptomyces, localization of transcription termination factors, and genomic arrangement of genes flanking the predicted sRNAs. RESULTS: Thirty-two potential sRNAs in Streptomyces were predicted. Of these, expression of 20 was detected by microarrays and RT-PCR. The prediction was validated by a structure based computational approach. Two predicted sRNAs were found to be terminated by transcription termination factors different from the Rho-independent terminators. One predicted sRNA was identified computationally with high probability as a Streptomyces 6S RNA. Out of the 32 predicted sRNAs, 24 were found to be structurally dissimilar from known sRNAs. CONCLUSION: Streptomyces is the largest genus of Actinomyces, whose sRNAs have not been studied. The Actinomyces is a group of bacterial species with unique genomes and phenotypes. Therefore, in Actinomyces, new unique bacterial sRNAs may be identified. The sequence and structural dissimilarity of the predicted Streptomyces sRNAs demonstrated by this study serve as the first evidence of the uniqueness of Actinomyces sRNAs.

• MeSH
• Algorithms MeSH
• RNA, Bacterial genetics   chemistry   MeSH
• Species Specificity MeSH
• Financing, Organized MeSH
• Genome, Bacterial MeSH
• DNA, Intergenic MeSH
• Nucleic Acid Conformation MeSH
• Models, Molecular MeSH
• RNA, Untranslated genetics   chemistry   MeSH
• Reverse Transcriptase Polymerase Chain Reaction MeSH
• Base Sequence MeSH
• Oligonucleotide Array Sequence Analysis MeSH
• Streptomyces coelicolor genetics   MeSH
• Streptomyces genetics   MeSH
• Terminator Regions, Genetic MeSH
• Computational Biology MeSH