Francomonroe7298

In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool early development of D. melanogaster. Fly T-WEoN together with its step-by-step guide is available at https//weon.readthedocs.io.Breast cancer is a complex, heterogeneous disease at the phenotypic and molecular level. In particular, the transcriptional regulatory programs are known to be significantly affected and such transcriptional alterations are able to capture some of the heterogeneity of the disease, leading to the emergence of breast cancer molecular subtypes. Recently, it has been found that network biology approaches to decipher such abnormal gene regulation programs, for instance by means of gene co-expression networks, have been able to recapitulate the differences between breast cancer subtypes providing elements to further understand their functional origins and consequences. Network biology approaches may be extended to include other co-expression patterns, like those found between genes and non-coding transcripts such as microRNAs (miRs). As is known, miRs play relevant roles in the establishment of normal and anomalous transcription processes. Etrasimod clinical trial Commodore miRs (cdre-miRs) have been defined as miRs that, based on their connectivity and redundancy in co-expression networks, are potential control elements of biological functions. In this work, we reconstructed miR-gene co-expression networks for each breast cancer molecular subtype, from high throughput data in 424 samples from the Cancer Genome Atlas consortium. We identified cdre-miRs in three out of four molecular subtypes. We found that in each subtype, each cdre-miR was linked to a different set of associated genes, as well as a different set of associated biological functions. We used a systematic literature validation strategy, and identified that the associated biological functions to these cdre-miRs are hallmarks of cancer such as angiogenesis, cell adhesion, cell cycle and regulation of apoptosis. The relevance of such cdre-miRs as actionable molecular targets in breast cancer is still to be determined from functional studies.Glioblastoma (GBM) is the most aggressive and common brain cancer in adults with the lowest life expectancy. The current neuro-oncology practice has incorporated genes involved in key molecular events that drive GBM tumorigenesis as biomarkers to guide diagnosis and design treatment. This study summarizes findings describing the significant heterogeneity of GBM at the transcriptional and genomic levels, emphasizing 18 driver genes with clinical relevance. A pattern was identified fitting the stem cell model for GBM ontogenesis, with an upregulation profile for MGMT and downregulation for ATRX, H3F3A, TP53 and EGFR in the mesenchymal subtype. We also detected overexpression of EGFR, NES, VIM and TP53 in the classical subtype and of MKi67 and OLIG2 genes in the proneural subtype. Furthermore, we found a combination of the four biomarkers EGFR, NES, OLIG2 and VIM with a remarkable differential expression pattern which confers them a strong potential to determine the GBM molecular subtype. A unique distribution of somatic mutations was found for the young and adult population, particularly for genes related to DNA repair and chromatin remodelling, highlighting ATRX, MGMT and IDH1. Our results also revealed that highly lesioned genes undergo differential regulation with particular biological pathways for young patients. This multi-omic analysis will help delineate future strategies related to the use of these molecular markers for clinical decision-making in the medical routine.Alignments of discrete objects can be constructed in a very general setting as super-objects from which the constituent objects are recovered by means of projections. Here, we focus on contact maps, i.e. undirected graphs with an ordered set of vertices. These serve as natural discretizations of RNA and protein structures. In the general case, the alignment problem for vertex-ordered graphs is NP-complete. In the special case of RNA secondary structures, i.e. crossing-free matchings, however, the alignments have a recursive structure. The alignment problem then can be solved by a variant of the Sankoff algorithm in polynomial time. Moreover, the tree or forest alignments of RNA secondary structure can be understood as the alignments of ordered edge sets.The study of long non-coding RNAs (lncRNAs), greater than 200 nucleotides, is central to understanding the development and progression of many complex diseases. Unlike proteins, the functionality of lncRNAs is only subtly encoded in their primary sequence. Current in-silico lncRNA annotation methods mostly rely on annotations inferred from interaction networks. But extensive experimental studies are required to build these networks. In this work, we present a graph-based machine learning method called FGGA-lnc for the automatic gene ontology (GO) annotation of lncRNAs across the three GO subdomains. We build upon FGGA (factor graph GO annotation), a computational method originally developed to annotate protein sequences from non-model organisms. In the FGGA-lnc version, a coding-based approach is introduced to fuse primary sequence and secondary structure information of lncRNA molecules. As a result, lncRNA sequences become sequences of a higher-order alphabet allowing supervised learning methods to assess individual GO-term annotations. Raw GO annotations obtained in this way are unaware of the GO structure and therefore likely to be inconsistent with it. The message-passing algorithm embodied by factor graph models overcomes this problem. Evaluations of the FGGA-lnc method on lncRNA data, from model and non-model organisms, showed promising results suggesting it as a candidate to satisfy the huge demand for functional annotations arising from high-throughput sequencing technologies.The live attenuated yellow fever (YF) vaccine was developed in the 1930s. Currently, the 17D and 17DD attenuated substrains are used for vaccine production. The 17D strain is used for vaccine production by several countries, while the 17DD strain is used exclusively in Brazil. The cell passages carried out through the seed-lot system of vaccine production influence the presence of quasispecies causing changes in the stability and immunogenicity of attenuated genotypes by increasing attenuation or virulence. Using next-generation sequencing, we carried out genomic characterization and genetic diversity analysis between vaccine lots of the Brazilian YF vaccine, produced by BioManguinhos-Fiocruz, and used during 11 years of vaccination in Brazil. We present 20 assembled and annotated genomes from the Brazilian 17DD vaccine strain, eight single nucleotide polymorphisms and the quasispecies spectrum reconstruction for the 17DD vaccine, through a pipeline here introduced. The V2IDA pipeline provided a relationship between low genetic diversity, maintained through the seed lot system, and the confirmation of genetic stability of lots of the Brazilian vaccine against YF.