BrAPI-an application programming interface for plant breeding applications

Motivation: Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. Results: To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases

Khmer Spell Checker

Khmer is the official language of Cambodia. It is a complex language. Similar to Chinese, Japanese and Thai, Khmer words are written without spaces or other word delimiters. This is a major challenge in spell checking Khmer since there is no simple way to determine word boundaries. However, it is feasible to spell check Khmer. The process of spell checking Khmer is different from the spell checking process in other languages that have word delimiters like English. In Khmer, words are constructed from root words that are made up of consonantal clusters, which can be misspelled. In order to do the spell checking, first we need to find the approximate clusters of each input clusters. We then give the possible sequences of the consonantal clusters to a hidden Markov model. The model will give the score of every sequence of consonantal clusters. Based the possible sequences and their scores, we know the word boundaries, whether or not a word is correctly spelled and some alternative words if it is misspelled. i