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Furthermore, the occurrences of the out-of-frame Tcra transcript—bearing cells in the peak of the primary response appeared to be clonal Figure 2 C. Successful and unsuccessful cases of the CONS ensemble method are discussed.

Receive exclusive offers and updates from Oxford Academic. This technique may ngi broad applications in mouse models of human disease. CAFA2, for which an evaluation meeting was held as a special interest group meeting at the Intelligent Systems in Molecular Biology ISMB conference in Boston, a total oftarget protein sequences from 27 species were provided.

We used 2 approaches to identify the productive Tcra transcript in out-of-frame cells.


Out of the remaining sequences, those with a Smith-Waterman alignment better than the threshold are used. GigaScienceVolume 4, Issue 1, 1 Decembers, https: The number of sequences in Swiss-Prot-SeqDB is expanded in the new database to more than double the size 2.

The reason for the small gain in coverage can probably be attributed to the fact that GO annotations in Swiss-Prot have been far better developed since then, and annotations in different databases are now better shared between databases.

The six individual methods used in the ensemble methods may not be the best choice, since their performances were imbalanced, i. The TRBV usage frequencies were then determined by flow cytometry.

The consensus confidence score for a GO term is essentially the weighted sum of scores of the GO term from individual methods. However, to support the biological community, it is equally important to maintain and update previously developed software tools so that users can continue using them.


These dual—in-frame CTLs have the potential to recognize 2 distinct and non—cross-reactive pMHCI epitopes, making them of considerable interest in light of the much-discussed possibility that virus infections may trigger autoimmunity 24 However, unless we use lymphocytes from TCR-transgenic mice, our capacity to follow the fate and persistence of defined clonotypes is very limited.

Then, the top 1, most-frequent GO terms in Swiss-Prot were added to the set of predicted GO terms and sorted by the normalized score.

Therefore, the TRBV-specific forward oligonucleotide primers both external and internal were designed to amplify individual V regions, except in the case of multisequence families like TRBV12 and TRBV13 where a single oligonucleotide was designed to amplify all family members Supplemental Table 4. For CAFA2, we updated the annotation databases used by our methods, protein function prediction PFP and extended similarity group ESGand benchmarked their function prediction performances using the original older and updated databases.

Thus, combining prediction methods could not increase the scores of the correct terms, and rather, introduced over incorrect terms. Among these UniRef50 clusters, we selected one representative protein from each of the clusters that satisfied the following two criteria: L, bp ladder lane. Additional file 1 also contains additional text and Figures S1-S5.

Probabilistic protein function prediction from heterogeneous genome-wide data. For a prediction method, it is important that the prediction accuracy be improved over time so that it can keep pace with other existing methods of the same type.

de Havilland

The y -axis reports the average F max score See Methods Section over all benchmark targets. The usefulness of this study in generating large numbers of distinct antigen-specific T cell receptors is immediately apparent, as similar technology is now used for specific Ig expression In Table 2we show the effects of combining multiple annotation resources from which annotations are transferred for the updated PFPDB in terms of the sequence coverage and the GO coverage.

PFPDB is discussed in detail later in this section. Later, Tao et al. In this study, we also wanted to examine the improved methods for predicting the current GO annotations of protein sequences by using the updated databases.


The latter database is referred to as the annotation database. FPM selects combinations of GO terms that are computed from the predictions of multiple methods with a sufficiently high score see Methods.

Here big used the flavor of the latter technique to construct an ensemble protein function prediction method from the underlying six individual AFP methods. In the above toy example, for a weight cut-off of 0.

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For each target in the benchmark data, the FPM algorithm runs once and generates the most frequently predicted GO terms for that target. The sequence coverage is the percentage of sequences in Swiss-Prot that have at least one GO term annotation.

Performance with prior GO term distribution. When the bhi dataset has multiple target proteins, method weights can be different for each target. Iterations continue until no new GO set can be generated. Among the six individual methods, ESG with the updated database performed the 5090. Structure- and sequence-based function prediction for non-homologous proteins. Several approaches have been used to estimate the extent of TCR diversity and to track clonally expanded T cell populations throughout the course of antigen-specific CTL responses 4but none has given the complete picture.

For all the prediction methods, adding prior GO distribution did not improve the accuracy, which can be seen from the plots and the Fmax values shown in the symbol legends. More than meets the eye. For the PCR cloning studies see Figure 3 Bcells with nonproductive transcripts were identified from the sequence analysis by their well number. These features are spread among 14 feature types.