Dr. Bin Ma has made significant contributions in bioinformatics, an important interdisciplinary subject where computer scientists develop new models, algorithms and software for solving biological problems. Dr. Ma’s main contributions span two important areas of bioinformatics: computational mass spectrometry and sequence homology search. His contributions in both areas have had major impact in the field, providing innovative approaches for computer scientists to confront biological problems and equipping biologists with novel tools to analyze their data in hundreds of research labs worldwide.

Computational mass spectrometry is a relatively new, but hugely influential area in bioinformatics. The computational methods developed in this area helped establish modern proteomics, a subject that studies all the proteins/peptides existing in a biological tissue at a given moment. The study of proteomics has already led to the development of new drugs and treatments for major human diseases such as cancer. Dr. Ma has made a major contribution to this area, by developing the industrial standard “de novo sequencing” software (PEAKS) for identifying novel peptides from mass spectrometry data. Based on an innovative algorithm, his program was the first “de novo sequencing” software widely used in mass spectrometry labs. It is now the most widely used “de novo sequencing” software today, and is regarded as the best by the users’ community.

After the wide adoption of his de novo sequencing tool, Dr. Ma has also been working on other analytical software tools to assist biochemists and health researchers in quantifying and characterizing proteins and post-translational modifications with mass spectrometry. Since abnormal proteins and modifications are directly related to disease, such tools are of great importance in finding protein biomarkers and in understanding some important disease pathways.

In the sequence homology search area, scientists want to find all DNA sequences in a large database that are similar to a given sequence, since those similar sequences are likely to have similar functions to the given one. This is a central problem in bioinformatics has applications in almost every other bioinformatics research area. Dr. Ma proposed the “spaced seed method” for more efficient filtration in the similarity search. This method rapidly replaced the consecutive seed method that had been used in the area for two decades, and has now been established as the standard method for all newly developed homology search software. Recently, the spaced seed method has also been widely used in analyzing the next generation of DNA sequencing data.

Overall, Dr. Ma’s research in bioinformatics has resulted in significantly improved practical solutions. The outcomes are widely used in practice, generating significant impacts in both biological and health studies.