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Table 1. A comparison between the performance of our tag selection program and that of PepNovo at different tag lengths
"... In PAGE 5: ... We then compared the generated tags with the sequencing results by SEQUEST and obtained the percentages of correct tags at different lengths for both of our program and PepNovo. Table1 lists the results of our experiments on the two datasets and the comparison with PepNovo at different tag lengths. Our approach achieves comparable performance to PepNovo and is more computationally efficient (over 10 times faster than PepNovo at all different tag lengths).... ..."
Table 1. Existing (quasi) de novo classifiers, in chronological order, for distinguishing novel pre-miRs from genomic pseudo hairpins.
"... In PAGE 2: ..., 2006). ( Table1 ) To surmount the technical shortfalls of comparative works for distinguishing species-specific and non-conserved pre-miRs, several state-of-the-art de novo (or ab initio) predictive approaches have been extensively developed. The inaugural and definitive work by Sewer et al.... In PAGE 4: ...,836 pseudo hairpins) by specificity and sensitivity; details at Table S1. Dash lines denote overall performances. For clarity, only specie names are as- signed in left-bottom quarter. D) Performance comparison with existing (quasi) de novo classifiers ( Table1 ).... In PAGE 5: ...o the host (i.e., human) pre-miRs that miPred was trained on. 3.3 Performance comparison with existing predictors (Figure 1D) By evaluating the published results of existing (quasi) de novo classifiers ( Table1 ), both RNAmicro (Hertel and Stadler 2006) and miPred are the highest-scoring predictors in identifying putative pre-miRs from a genomic pool of candidate hairpins. RNAmicro dis- plays comparable %Fm (F-measure) and %MCC (Matthew apos;s Correla- tion Coefficient) of 98.... In PAGE 5: ... Since ncRNAs and mRNAs were not included in the initial training, it will be very instructive to as- sess how well miPred can discriminate them as non pre-miRs without relying on their specific dinucleotide sequence, structural, and topo- logical characteristics. Moreover, such assessment was lacking or not available from existing (quasi) de novo predictors ( Table1 ). (Figure 1E and Table S3) Evaluating miPred and 3SVM (Xue et al.... In PAGE 5: ...cross 155 types) and 0.00% (0/31 mRNAs) for SP (figure not shown). Upon scrutiny, its quot;better quot; performances are attained at the expense of excluding 9,983 ncRNAs spanning 302 types (IE-NC) and 31 mRNAs (IE-M) that fold into complex structures containing multiple loops. This structural exclusion is a major limitation experienced commonly by most of the existing (quasi) de novo classifiers ( Table1 ) that extract modularized features from predefined RNA sub-structures. The com- parison with 3SVM clearly demonstrates that miPred trained solely on human pre-miRs and pseudo hairpins, can provide reasonable generali- zation in identifying unambiguously at least two-thirds of all the sam- ples in IE-NC and IE-M as bona fide negatives.... In PAGE 6: ... Despite the importance, only 3SVM (Xue et al., 2005) among the existing (quasi) de novo clas- sifiers ( Table1 ) has conducted an analysis (less detailed than ours) on its feature selection. (Figure 1F and Table S5) We evaluate the F-scores F1 and F2 (see supplementary quot;Materials and Methods quot; for definitions) on the class- conditional distributions, which measure the discriminative power of the miPred apos;s 29 attributes.... ..."
Table 1. Output summary of the novoSNP, PolyPhred, and PolyBayes SNP analysis on the SCN1A mutation and MAPT SNP data sets analyzed under different quality cutoff values
"... In PAGE 4: ...Claes et al. 2003; Rademakers et al. 2004). All three programs provide a quality score for each detected variation. Depending on the quality score cutoff used, several SNPs are detected for each program ( Table1 ). At the lowest- quality cutoff score, novoSNP detected all 38 variations in the SCN1A data set that were previously observed by visual inspec- tion, including five INDELs, and missed only 10 out of 452 known SNPs (2.... In PAGE 4: ... At the lowest- quality cutoff score, novoSNP detected all 38 variations in the SCN1A data set that were previously observed by visual inspec- tion, including five INDELs, and missed only 10 out of 452 known SNPs (2.2%) and five out of 36 INDELs in the MAPT data set ( Table1 ). PolyPhred found all but three of the SNPs in the SCN1A data set at the lowest cutoff, but missed all five INDELs (Table 1A) while listing more false-positive INDELs (23) than novoSNP (nine).... In PAGE 4: ...2%) and five out of 36 INDELs in the MAPT data set (Table 1). PolyPhred found all but three of the SNPs in the SCN1A data set at the lowest cutoff, but missed all five INDELs ( Table1 A) while listing more false-positive INDELs (23) than novoSNP (nine). PolyPhred analysis of the MAPT data set showed that a large number of SNPs (172, or 38.... In PAGE 4: ... PolyPhred analysis of the MAPT data set showed that a large number of SNPs (172, or 38.1%) were not detected ( Table1 B) and also that only two of 36 INDELs were correctly identified, while the number of false-positive INDELs (101) was again higher compared to novoSNP (63). PolyBayes was included in this comparative analysis as it is often used for SNP discovery.... In PAGE 4: ... Because of these limitations, PolyBayes identified only a small percentage of the SNPs in the SCN1A data set (54.5%) and the MAPT data set (31%) ( Table1 ). An overall comparison of the true SNPs and false positives (FP) detected by the three programs is represented as a Venn diagram in Figure 2.... In PAGE 4: ... Somewhat surprisingly, most of the false positives were not shared between the different programs but were program-specific. The use of low-quality cutoff values resulted in a large num- ber of false positives for all three programs ( Table1 ). Using higher-quality cutoffs, at the expense of detecting less true varia- tions, diminished the number of false positives.... In PAGE 5: ...Table1 ). Even at a quality cutoff of 15, novoSNP detected con- siderably more SNPs compared to the lowest-quality cutoff for PolyPhred, with a lower false-positive rate than PolyPhred at the highest possible quality (Table 1).... In PAGE 5: ... Furthermore, novoSNP is not only able to efficiently detect INDELs but also provides the user with the cor- rect sequence of the INDEL. A high false-positive rate was observed for all three programs used in this study ( Table1 ; Fig. 2).... In PAGE 5: ... Another way is by relying on the quality scores assigned to the SNP. Indeed, the results presented here showed that the quality score given by novoSNP is a reliable measure of the cor- rectness of the SNP ( Table1 ). Using a relatively low cutoff score of 10, 97.... ..."
Table 1 - De novo gene prediction performance for human Sensitivity (Sn) and specificity (Sp) were evaluated at the gene, exon and nucleotide levels and reported as percentages. Also shown are the average number of genes and exons predicted for each cross-validation fold. The column headings indicate the predictor and informants used.
2007
"... In PAGE 5: ... All evaluations were performed using the Eval package [26]. Table1 shows the accuracy of CONTRAST using all 11 informants, its accuracy using mouse alone and the accuracy of N-SCAN. As CONTRAST only predicts the protein-coding portions of a gene, we ignored untranslated regions when measuring performance.... ..."
Table 5 - De novo gene prediction performance for Drosophila melanogaster Sensitivity (Sn) and specificity (Sp) were evaluated at the gene, exon and nucleotide levels and reported as percentages. Also shown are the average number of genes and exons predicted for each cross-validation fold. The column headings indicate the predictor and informants used.
2007
"... In PAGE 9: ... We used a four-fold cross-validation procedure as in the previous experiments. Table5 shows the accuracy of CONTRAST using all 13 informants, its accuracy using the best single informant (Drosophila ananassae) and the accuracy of N-SCAN using Drosophila yakuba, Drosophila pseudoobscura and A. gambiae as informants.... ..."
Table 12. The performance of preprocess and anti-symmetric model on PepNovo. The accuracies in cells are represented in a (specificity/sensitivity) format. Dataset No. of spectrum PepNovo PepNovo with preprocess PepNovo*
2007
"... In PAGE 12: ... Table12 . The performance of preprocess and anti-symmetric model on PepNovo.... In PAGE 72: ... Since each of the preprocessed results is still a set of peaks, the application of these algorithms is easy. Results ( Table12 ) show that by removing noisy peaks, preprocess can also increase the sequencing accuracies for these algorithms. We have then performed analysis of new anti-symmetric model (restricted anti- symmetric).... ..."
Table 5 - De novo gene prediction performance for Drosophila melanogaster
in CONTRAST: A Discriminative, Phylogeny-free Approach to Multiple Informant De Novo Gene Prediction
Table 1: De-novo identification of active motifs in the galactose system. For each detected active site we list both the initial ASAP score and the global TP model score gain derived by comparing models score with and without each TF. Negative scores are possible due to rounding errors or sub-optimality of the solution. Note that TFs that had high activity scores (e.g., U2) may be completely redundant and that this was discovered due to the additional structure imposed on the model.
2003
"... In PAGE 8: ... We then screened all 6-mers with one gap of size 0-12 bases and optimized all hits above the noise level. The active motifs are listed in Table1 . We were able to accurately re- discover de-novo the known GAL4, GCN4 and ADR1/MIG1 motifs.... In PAGE 8: ... We tested the score contribution of each of the six putative motifs by comparing for each ones the global model score with and without the motif. Results show (see Table1 ) that the known motifs are scored higher than the putative ones, and that at least one of the putative motifs does not contribute to the model score. We conclude that two of the putative motifs (U1,U3) may be promising targets for further experimentation.... ..."
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TABLE II Homologous contig sequences from the venom dataset The segments identical to the de novo reconstructions are shown underlined. On the de novo sequences, parentheses indicate sequences where the order of the amino acids was not determined; square brackets indicate indistinguishable amino acid masses (on ion trap spectra). A homologous sequence is confirmed (H12011) if it matches the peptides obtained by independent traditional database search of the assembled MS/MS spectra. This confirmation step turned out positive whenever the homologous peptide was present in the database (albeit on a protein from a different snake species); assembled spectra in the remaining homologous contig sequences had no significant match to the database and were thus neither confirmed nor refuted. All C. atrox homologies were either matched to a different snake species or can be explained by single nucleotide polymorphisms of the original sequences, which were also detected in our sample. The complete list of all putative homolog peptides can be found in our supplemental materials as well as annotated MS/MS spectra for all novel homologies.
Table 4.2: The balls of stimulation of the 10 test antigens all overlapped each other; thus, many of the clones within a ball of stimulation were generated by the lazy algorithm oper- ating on prior overlapping antigens. The proportions generated by each antigen are shown in this table. For example, for the fourth antigen, on average, 0.33 of the clones in its ball of stimulation were already generated by the first antigen, 0.06 by the second antigen, 0.17 by the third antigen, and 0.45 were generated de novo by the lazy algorithm on injection of the fourth antigen. The data were calculated by metering the lazy algorithm to record which balls of stimulation a newly generated clone fell within. The varying proportions suggest that the 10 antigens were a reasonable test of the lazy algorithm.
1997
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