As a base for human transcriptome and functional genomics, we created the "full-length long Japan" (FLJ) collection of sequenced human cDNAs. We determined the entire sequence of 21,243 selected clones and found that 14,490 cDNAs (10,897 clusters) were unique to the FLJ collection. About half of them (5,416) seemed to be protein-coding. Of those, 1,999 clusters had not been predicted by computational methods. The distribution of GC content of nonpredicted cDNAs had a peak at ∼58% compared with a peak at ∼42%for predicted cDNAs. Thus, there seems to be a slight bias against GC-rich transcripts in current gene prediction procedures. The rest of the cDNAs unique to the FLJ collection (5,481) contained no obvious open reading frames (ORFs) and thus are candidate noncoding RNAs. About one-fourth of them (1,378) showed a clear pattern of splicing. The distribution of GC content of noncoding cDNAs was narrow and had a peak at ∼42%, relatively low compared with that of protein-coding cDNAs.

A whole-genome radiation hybrid (RH) panel was used to construct a high-resolution map of the rat genome based on microsatellite and gene markers. These include 3,019 new microsatellite markers described here for the first time and 1,714 microsatellite markers with known genetic locations, allowing comparison and integration of maps from different sources. A robust RH framework map containing 1,030 positions ordered with odds of at least 1,000:1 has been defined as a tool for mapping these markers, and for future RH mapping in the rat. More than 500 genes which have been mapped in mouse and/or human were localized with respect to the rat RH framework, allowing the construction of detailed rat-mouse and rat-human comparative maps and illustrating the power of the RH approach for comparative mapping.

The program is available on request from the authors.

Functional prediction of open reading frames coded in the genome is one of the most important tasks in yeast genomics. Among a number of large-scale experiments for assigning certain functional classes to proteins, experiments determining protein-protein interaction are especially important because interacting proteins usually have the same function. Thus, it seems possible to predict the function of a protein when the function of its interacting partner is known. However, in vitro experiments often suffer from artifacts and a protein can often have multiple binding partners with different functions. We developed an objective prediction method that can systematically include the information of indirect interaction. Our method can predict the subcellular localization, the cellular role and the biochemical function of yeast proteins with accuracies of 72.7%, 63.6% and 52.7%, respectively. The prediction accuracy rises for proteins with more than three binding partners and thus we present the open prediction results for 16 such proteins.

Functional prediction of open reading frames coded in the genome is one of the most important tasks in yeast genomics. Among a number of large-scale experiments for assigning certain functional classes to proteins, experiments determining protein-protein interaction are especially important because interacting proteins usually have the same function. Thus, it seems possible to predict the function of a protein when the function of its interacting partner is known. However, in vitro experiments often suffer from artifacts and a protein can often have multiple binding partners with different functions. We developed an objective prediction method that can systematically include the information of indirect interaction. Our method can predict the subcellular localization, the cellular role and the biochemical function of yeast proteins with accuracies of 72.7%, 63.6% and 52.7%, respectively. The prediction accuracy rises for proteins with more than three binding partners and thus we present the open prediction results for 16 such proteins.