Development of multiple genome-wide proteome microarrays comprised wafer substrate-based chip and its scanner: An advanced high-throughput and sensitivity for molecular interactions studies.

Proteome microarray technology enables high-throughput analysis of protein interactions with all kinds of molecules. Wafer (6-inch) substrates offer a promising alternative to conventional glass (2.6 × 7.

Complete genomic profiles of 1496 Taiwanese reveal curated medical insights.

Introduction: The population of Taiwan has a long history of ethno-cultural evolution. The Taiwanese population was isolated from other large populations such as the European, Han Chinese, and Japanese population. The Taiwan Biobank (TWB) project has built a nationwide database, particularly for personal whole-genome sequence (WGS) to facilitate basic and clinical collaboration nationally and internationally, making it one of the most valuable public datasets of the East Asian population.

SigUNet: signal peptide recognition based on semantic segmentation.

Background: Signal peptides play an important role in protein sorting, which is the mechanism whereby proteins are transported to their destination. Recognition of signal peptides is an important first step in determining the active locations and functions of proteins. Many computational methods have been proposed to facilitate signal peptide recognition.

An automatic method to calculate heart rate from zebrafish larval cardiac videos.

Background: Zebrafish is a widely used model organism for studying heart development and cardiac-related pathogenesis. With the ability of surviving without a functional circulation at larval stages, strong genetic similarity between zebrafish and mammals, prolific reproduction and optically transparent embryos, zebrafish is powerful in modeling mammalian cardiac physiology and pathology as well as in large-scale high throughput screening. However, an economical and convenient tool for rapid evaluation of fish cardiac function is still in need.

CoopTFD: a repository for predicted yeast cooperative transcription factor pairs.

In eukaryotic cells, transcriptional regulation of gene expression is usually accomplished by cooperative Transcription Factors (TFs). Therefore, knowing cooperative TFs is helpful for uncovering the mechanisms of transcriptional regulation. In yeast, many cooperative TF pairs have been predicted by various algorithms in the literature.

CCProf: exploring conformational change profile of proteins.

In many biological processes, proteins have important interactions with various molecules such as proteins, ions or ligands. Many proteins undergo conformational changes upon these interactions, where regions with large conformational changes are critical to the interactions. This work presents the CCProf platform, which provides conformational changes of entire proteins, named conformational change profile (CCP) in the context.

Light-RCV: a lightweight read coverage viewer for next generation sequencing data.

Background: Next-generation sequencing (NGS) technologies has brought an unprecedented amount of genomic data for analysis. Unlike array-based profiling technologies, NGS can reveal the expression profile across a transcript at the base level. Such a base-level read coverage provides further insights for alternative mRNA splicing, single-nucleotide polymorphism (SNP), novel transcript discovery, etc.

A regulatory similarity measure using the location information of transcription factor binding sites in Saccharomyces cerevisiae.

Background: Defining a measure for regulatory similarity (RS) of two genes is an important step toward identifying co-regulated genes. To date, transcription factor binding sites (TFBSs) have been widely used to measure the RS of two genes because transcription factors (TFs) binding to TFBSs in promoters is the most crucial and well understood step in gene regulation. However, existing TFBS-based RS measures consider the relation of a TFBS to a gene as a Boolean (either 'presence' or 'absence') without utilizing the information of TFBS locations in promoters.

Combining phylogenetic profiling-based and machine learning-based techniques to predict functional related proteins.

Annotating protein functions and linking proteins with similar functions are important in systems biology. The rapid growth rate of newly sequenced genomes calls for the development of computational methods to help experimental techniques. Phylogenetic profiling (PP) is a method that exploits the evolutionary co-occurrence pattern to identify functional related proteins.

PRASA: an integrated web server that analyzes protein interaction types.

This work presents the Protein Association Analyzer (PRASA) (http://zoro.ee.ncku.

YGA: identifying distinct biological features between yeast gene sets.

The advance of high-throughput experimental technologies generates many gene sets with different biological meanings, where many important insights can only be extracted by identifying the biological (regulatory/functional) features that are distinct between different gene sets (e.g. essential vs.

AutoBind: automatic extraction of protein-ligand-binding affinity data from biological literature.

Motivation: Determination of the binding affinity of a protein-ligand complex is important to quantitatively specify whether a particular small molecule will bind to the target protein. Besides, collection of comprehensive datasets for protein-ligand complexes and their corresponding binding affinities is crucial in developing accurate scoring functions for the prediction of the binding affinities of previously unknown protein-ligand complexes. In the past decades, several databases of protein-ligand-binding affinities have been created via visual extraction from literature.

DBD2BS: connecting a DNA-binding protein with its binding sites.

By binding to short and highly conserved DNA sequences in genomes, DNA-binding proteins initiate, enhance or repress biological processes. Accurately identifying such binding sites, often represented by position weight matrices (PWMs), is an important step in understanding the control mechanisms of cells. When given coordinates of a DNA-binding domain (DBD) bound with DNA, a potential function can be used to estimate the change of binding affinity after base substitutions, where the changes can be summarized as a PWM.

A study on promoter characteristics of head-to-head genes in Saccharomyces cerevisiae.

Background: Head-to-head (h2h) genes are prone to have association in expression and in functionality and have been shown conserved in evolution. Currently there are many studies on such h2h gene pairs. We found that the previous studies extremely focused on human genome.

Predicting target DNA sequences of DNA-binding proteins based on unbound structures.

DNA-binding proteins such as transcription factors use DNA-binding domains (DBDs) to bind to specific sequences in the genome to initiate many important biological functions. Accurate prediction of such target sequences, often represented by position weight matrices (PWMs), is an important step to understand many biological processes. Recent studies have shown that knowledge-based potential functions can be applied on protein-DNA co-crystallized structures to generate PWMs that are considerably consistent with experimental data.

AH-DB: collecting protein structure pairs before and after binding.

This work presents the Apo-Holo DataBase (AH-DB, http://ahdb.ee.ncku.

A study on the flexibility of enzyme active sites.

Background: A common assumption about enzyme active sites is that their structures are highly conserved to specifically distinguish between closely similar compounds. However, with the discovery of distinct enzymes with similar reaction chemistries, more and more studies discussing the structural flexibility of the active site have been conducted.

Results: Most of the existing works on the flexibility of active sites focuses on a set of pre-selected active sites that were already known to be flexible.

YPA: an integrated repository of promoter features in Saccharomyces cerevisiae.

This study presents the Yeast Promoter Atlas (YPA, http://ypa.ee.ncku.

Predicting protein-protein interactions in unbalanced data using the primary structure of proteins.

Background: Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs.

Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm.

Background: MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, ab initio approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches.

Predicting the protein-protein interactions using primary structures with predicted protein surface.

Background: Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information.

seeMotif: exploring and visualizing sequence motifs in 3D structures.

Sequence motifs are important in the study of molecular biology. Motif discovery tools efficiently deliver many function related signatures of proteins and largely facilitate sequence annotation. As increasing numbers of motifs are detected experimentally or predicted computationally, characterizing the functional roles of motifs and identifying the potential synergetic relationships between them are important next steps.

Using a kernel density estimation based classifier to predict species-specific microRNA precursors.

Background: MicroRNAs (miRNAs) are short non-coding RNA molecules participating in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, ab initio approaches obtain more attention because that they can discover species-specific pre-miRNAs.

Real value prediction of protein solvent accessibility using enhanced PSSM features.

Background: Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures.