![]() It reads CSV files and converts them into a Graph. Invoice is delivered if it is required.Graph visualization of the "Cosmic Web" dataset, study of the network of galaxies List of graph visualization librariesĪfterGlow : a script written in Perl that assists with the visualization of log data. Total cost for the 8 session of 19 to 22 hours (24 hours altogether). Practical in R/Bioconductor and Cytoscape.Immunoprecipitation of chromatin followed by sequencing: ChIP-seq.Search in silico and microRNAs and transcription factor binding sites in vivo.Statistical analysis of results differential post-expression part III. Transcriptomic will be calculated from data: networks of co-expression of interaction protein-protein. Statistical analysis of biological networks.Statistical analysis of results differential post-expression part II. Practical in Cytoscape: introduction to Cytoscape.Practical in R/Bioconductor: analysis of enrichment of biological processes, molecular function, cellular components (GO) and biological pathways (KEGG).Use of functional annotation databases: Gene Ontology (GO), KEGG.Statistical analysis of biological pathways.Statistical analysis of results differential post-expression part I. Practical in R/Bioconductor: statistical analysis of differential expression of RNA-seq and microarray data.Analysis of differential expression: comparison of frequentist and Bayesian statistical methods. ![]() Transcriptomics: Microarrays, RNA-seq Bulk and single-cell RNA-seq.Practical in R/Bioconductor: workflow using public FASTQ.Workflow: Experimental design, trimming and filtering of files FASTQ, mapping species, phylogenetic tree construction, statistical analysis of alpha diversity, beta, range and composition of communities.Operational Taxonomic Unit (OTU) versus Amplicon Sequence Variant (ASV).Applications of molecular markers: 16/18S using Illumina.Analysis of Microbiota by amplification of 16/18S. Supervised methods L/hdc, KNN, SVM, CART and Random Forest. Unsupervised methods PCA, t-SNE, NMDS, PCoA, Clustering. Massive sequencing and single molecule technologies.Massive sequencing and Machine Learning in Bioinformatics. Management of FASTA files, FASTQ, BAM, BED, csv, txt.Control flow and conditionals, loops, functions, family apply.Types of variables: vectors, matrices, factors, data frames, ready.Introduction to programming in R and use RStudio.Bayesian statistics: a posteriori probability and decision theory.Correction for multiple comparisons: adjusted p-values.Classical statistics: null hypothesis, alternative hypothesis, p-values. ![]() Fundamentals of statistics for bioinformatics. This will be achieved through understanding fully of the workflow involved in each technology, to finally get results whose biological interpretation is charged with meaning and allows correct decision making. "The overall objective of this workshop is to empower his assistants in the understanding of omics technologies and different methods of analysis that represents this type of data, using free software such as"R/Bioconductor"alternatives and" CYTOSCAPE". ![]()
0 Comments
Leave a Reply. |