Background: Protein-protein, protein-DNA and protein-RNA interactions are of central importance in biological systems. Quadrapole Time-of-flight (Q-TOF) mass spectrometry is a sensitive, promising tool for studying these interactions. Combining this technique with chemical crosslinking, it is possible to identify the sites of interactions within these complexes. Due to the complexities of the mass spectrometric data of crosslinked proteins, new software is required to analyze the resulting products of these studies.

miRNAs are non-coding RNAs that play a regulatory role in expression of genes and are associated with diseases. Quantitatively measuring expression levels of miRNAs can help in understanding the mechanisms of human diseases and discovering new drug targets. There are three major methods that have been used to measure the expression levels of miRNAs: real-time reverse transcription PCR (qRT-PCR), microarray, and the newly introduced next-generation sequencing (NGS). NGS is not only suitable for profiling of known miRNAs as qRT-PCR and microarray can do too but it also is able to detect unknown miRNAs which the other two methods are incapable of doing. Profiling of miRNAs by NGS has progressed rapidly and is a promising field for applications in drug development. This paper reviews the technical advancement of NGS for profiling miRNAs, including comparative analyses between different platforms and software packages for analyzing NGS data. Examples and future perspectives of applications of NGS profiling miRNAs in drug development will be discussed.

Background: Structure matching plays an important part in understanding the functional role of biological structures. Bioinformatics assists in this effort by reformulating this process into a problem of finding a maximum common subgraph between graphical representations of these structures. Among the many different variants of the maximum common subgraph problem, the maximum common induced subgraph of two graphs is of special interest.

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Currently there are definitions from many agencies and research societies defining “bioinformatics” as deriving knowledge from computational analysis of large volumes of biological and biomedical data. Should this be the bioinformatics research focus? We will discuss this issue in this review article. We would like to promote the idea of supporting human-infrastructure (HI) with no-boundary thinking (NT) in bioinformatics (HINT).