Sequence clustering is a basic bioinformatics task that is attracting renewed attention with
the development of metagenomics and microbiomics. The latest sequencing techniques …

Co-expression network analysis is one of the most powerful approaches for interpretation of
large transcriptomic datasets. It enables characterization of modules of co-expressed genes …

Background RNA-seq is widely used for transcriptomic profiling, but the bioinformatics
analysis of resultant data can be time-consuming and challenging, especially for biologists …

Motivation As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) is
recently shown to play crucial roles in restriction–modification systems. For better …

Biclustering is a powerful data mining technique that allows clustering of rows and columns,
simultaneously, in a matrix-format data set. It was first applied to gene expression data in …

Motivation The biclustering of large-scale gene expression data holds promising potential
for detecting condition-specific functional gene modules (ie biclusters). However, existing …

Cell wall recalcitrance is the major challenge to improving saccharification efficiency in
converting lignocellulose into biofuels. However, information regarding the transcriptional …

Metagenomic and metatranscriptomic sequencing approaches are more frequently being
used to link microbiota to important diseases and ecological changes. Many analyses have …

A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene
expression states regulated by different transcriptional regulatory inputs across individual …

Background Despite their lack of protein-coding potential, long noncoding RNAs (lncRNAs)
and circular RNAs (circRNAs) have emerged as key determinants in gene regulation, acting …