#Biology chapter 8 answers how to#

Clustering and differential expression analysis, for example, typically generate lists of вЂsignificantlyвЂ™ clustered and Differentially Expressed Genes (DEGs), respectively.

Most of the microarray and RNA-Seq data analysis methods are designed to determine the lists of genes or gene products that show distinct patterns and/or significant differences. One of the challenges in DNA microarray and RNA-Seq data analysis is to extract biological meanings from the massive amounts of transcriptome expression data.

#Biology chapter 8 answers how to#

How to organize and visualize a massive and redundant annotation list of genes or gene sets into a unified framework of biological understanding.

Compare in terms of semantic interpretation the functional annotation analysis methods for co-expressed genes as in clustering and for pre-defined gene sets as in GSEA.

How to identify gene groups that are differentially expressed or differentially co-expressed between normal and disease samples.

What are the limitations of individual gene-based analysis for determining differentially expressed genes (even with multiple hypothesis correction).

Which data resources are useful for interpreting large-scale gene expression profiles.

How to characterize biological properties of a particular list of genes.

How to find biological pathways and/or biomedical ontology terms for the interpretation of particular gene groups associated with a particular disease.

How to find genes associated with a particular disease (or condition) from microarray or RNA-Seq data.

This array of methods for biological knowledge assembly and interpretation has been developed during the past decade and clearly improved our biological understanding of large-scale genomic data from the high-throughput technologies. A number of biomedical annotation and external repositories including clinical resources can be systematically integrated by biological semantics within the framework of concept lattice analysis. Outcomes in DNA microarray and RNA-Seq data can be transformed into the graphical structure that represents biological semantics. Differential co-expression analysis determines the degree of co-expression difference of paired gene sets across different conditions. Unlike previous approaches, Gene Set Enrichment Analysis takes quite the reverse approach by using pre-defined gene sets. The groups of genes are assigned to the associated biological annotations using Gene Ontology terms or biological pathways and then tested if they are significantly enriched with the corresponding annotations. Biomedical ontology and pathway-based functional enrichment analysis is widely used to interpret the functional role of tightly correlated or differentially expressed genes. The most challenging and rate-liming step, however, is to determine what the resulting lists of genes and/or transcripts biologically mean. Most methods for large-scale gene expression microarray and RNA-Seq data analysis are designed to determine the lists of genes or gene products that show distinct patterns and/or significant differences.