A Comparative Analysis of Feature Selection Methods for Biomarker Discovery in Study of Toxicant-Treated Atlantic Cod (Gadus Morhua) Liver

Univariate and multivariate feature selection methods can be used for biomarker discovery in analysis of toxicant exposure. Among the univariate methods, differential expression analysis (DEA) is often applied for its simplicity and interpretability. A characteristic of methods for DEA is that they treat genes individually, disregarding the correlation that exists between them. On the other hand, some multivariate feature selection methods are proposed for biomarker discovery. Provided with various biomarker discovery methods, how to choose the most suitable method for a specific dataset becomes a problem. In this paper, we present a framework for comparison of potential biomarker discovery methods: three methods that stem from different theories are compared by how stable they are and how well they can improve the classification accuracy. The three methods we have considered are: Significance Analysis of Microarrays (SAM) which identifies the differentially expressed genes; minimum Redundancy Maximum Relevance (mRMR) based on information theory; and Characteristic Direction (GeoDE) inspired by a graphical perspective. Tested on the gene expression data from two experiments exposing the cod fish to two different toxicants (MeHg and PCB 153), different methods stand out in different cases, so a decision upon the most suitable method should be made based on the dataset under study and the research interest

A Comparative Analysis of Feature Selection Methods for Biomarker Discovery in Study of Toxicant-Treated Atlantic Cod (Gadus Morhua) Liver

Univariate and multivariate feature selection methods can be used for biomarker discovery in analysis of toxicant exposure. Among the univariate methods, differential expression analysis (DEA) is often applied for its simplicity and interpretability. A characteristic of methods for DEA is that they treat genes individually, disregarding the correlation that exists between them. On the other hand, some multivariate feature selection methods are proposed for biomarker discovery. Provided with various biomarker discovery methods, how to choose the most suitable method for a specific dataset becomes a problem. In this paper, we present a framework for comparison of potential biomarker discovery methods: three methods that stem from different theories are compared by how stable they are and how well they can improve the classification accuracy. The three methods we have considered are: Significance Analysis of Microarrays (SAM) which identifies the differentially expressed genes; minimum Redundancy Maximum Relevance (mRMR) based on information theory; and Characteristic Direction (GeoDE) inspired by a graphical perspective. Tested on the gene expression data from two experiments exposing the cod fish to two different toxicants (MeHg and PCB 153), different methods stand out in different cases, so a decision upon the most suitable method should be made based on the dataset under study and the research interest

Histone H2A.Z cooperates with RNAi and heterochromatin factors to suppress antisense RNAs

Eukaryotic transcriptomes are characterized by widespread transcription of non-coding and antisense RNAs1–3, which is linked to key chromosomal processes, such as chromatin remodeling, gene regulation, and heterochromatin assembly4–7. However, these transcripts can be deleterious, and their accumulation is suppressed by several mechanisms including degradation by the nuclear exosome8,9. The mechanisms by which cells differentiate coding RNAs from transcripts targeted for degradation are not clear. Here we show that the variant histone H2A.Z, which is loaded preferentially at the 5' ends of genes by the Swr1 complex containing a JmjC domain protein, mediates suppression of antisense transcripts in the fission yeast Schizosaccharomyces pombe genome. H2A.Z is partially redundant in this regard with the Clr4/Suv39h-containing heterochromatin silencing complex that is also distributed at euchromatic loci, and with RNAi component Argonaute (Ago1). Loss of Clr4 or Ago1 alone has little effect on antisense transcript levels, but cells lacking either of these factors and H2A.Z show markedly increased levels of antisense RNAs that are normally degraded by the exosome. These analyses suggest that in addition to performing other functions, H2A.Z is a component of a genome indexing mechanism that cooperates with heterochromatin and RNAi factors to suppress read-through antisense transcripts