Comparative Transcriptomics Analysis

  title={Comparative Transcriptomics Analysis},
  author={Y-h. Taguchi},
  booktitle={Encyclopedia of Bioinformatics and Computational Biology},

RNA-Seq data analysis for Planarian with tensor decomposition-based unsupervised feature extraction

A recently proposed tensor decomposition (TD)-based unsupervised feature extraction (FE) is applied to the RNA-seq data obtained for a non-model organism, Planarian and successfully obtained a limited number of transcripts whose expression was altered between normal and defective samples as well as during time development.

Applications of PCA Based Unsupervised FE to Bioinformatics

  • Y-h. Taguchi
  • Biology
    Unsupervised and Semi-Supervised Learning
  • 2019
This chapter will apply PCA based unsupervised FE to various bioinformatics problems, which ranges from biomarker identification and identification of disease causing genes to in silico drug discovery.

Genetic Diversity and Population Assessment of

These CDDP functional gene-based markers were informative and very efficient in resolving GD, and population indices among the banana and plantain accessions of different genomes.

Honeysuckle (Lonicera japonica) and Huangqi (Astragalus membranaceus) Suppress SARS-CoV-2 Entry and COVID-19 Related Cytokine Storm in Vitro

It is demonstrated that honeysuckle and Huangqi have the potential to be used as an inhibitor of SARS-CoV-2 virus entry that warrants further in vivo analysis and functional assessment of miRNAs to confirm their clinical importance.

Tensor decomposition- and principal component analysis-based unsupervised feature extraction to select more reasonable differentially expressed genes: Optimization of standard deviation versus state-of-art methods

Tensor decomposition- and principal component analysis-based unsupervised feature extraction are perhaps better than state-of-art methods in regard to predicting differentially expression genes because they achieve the desired property that the less expressed differentially expressed genes should be less likely selected or even associated with the same amount of logarithmic fold change.



Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

It is demonstrated how the GSEA method yields insights into several cancer-related data sets, including leukemia and lung cancer, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer.

limma powers differential expression analyses for RNA-sequencing and microarray studies

The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package

It is demonstrated that the non-parametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods.

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

Orchestrating high-throughput genomic analysis with Bioconductor

An overview of Bioconductor, an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology, which comprises 934 interoperable packages contributed by a large, diverse community of scientists.

Challenges and emerging directions in single-cell analysis

The state of the field and recent advances are discussed, and significant challenges remain in the analysis, integration, and interpretation of single-cell omics data.

Significance analysis of microarrays applied to the ionizing radiation response

A method that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements is described, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.

Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods

It is showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.

g:Profiler—a web server for functional interpretation of gene lists (2016 update)

The 2016 update of g:Profiler introduces several novel features, including transcription factor binding site predictions, Mendelian disease annotations, information about protein expression and complexes and gene mappings of human genetic polymorphisms.