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repgenHMM: a dynamic programming tool to infer the rules of immune receptor generation from sequence data.

Elhanati Y, Marcou Q, Mora T, Walczak AM - Bioinformatics (2016)

Bottom Line: To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences.The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire.We estimate this diversity to be [Formula: see text] for human T cells.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de physique théorique, CNRS, UPMC and Ecole normale supérieure, Paris, France.

No MeSH data available.


Performance of the algorithm on synthetic data. Sequences generated using a known model were given as an input to the inference algorithm. The results of the inference are compared to the true model used for generation, for (a) the distribution of the number of insertions (inset: usage of inserted nucleotides) and (b) V, J gene usage. The error bars, which correspond to sample noise, are smaller than symbol size for (a). In (a) we also report the distribution of insertions obtained using MiXCR
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btw112-F4: Performance of the algorithm on synthetic data. Sequences generated using a known model were given as an input to the inference algorithm. The results of the inference are compared to the true model used for generation, for (a) the distribution of the number of insertions (inset: usage of inserted nucleotides) and (b) V, J gene usage. The error bars, which correspond to sample noise, are smaller than symbol size for (a). In (a) we also report the distribution of insertions obtained using MiXCR

Mentions: In order to check the validity of the algorithm, we ran it on sequences that were produced according to a known model. We generated 100 000 synthetic sequences according to the model learned in the previous section, and relearned a model from these sequences using our algorithm. In Figure 4 we compare the parameters of the model used for generation to those of the inferred model. Sampling was repeated 5 times to estimate sample noise, which was found to be very small for all parameters, except for gene usage (error bars in Fig. 4b).Fig. 4.


repgenHMM: a dynamic programming tool to infer the rules of immune receptor generation from sequence data.

Elhanati Y, Marcou Q, Mora T, Walczak AM - Bioinformatics (2016)

Performance of the algorithm on synthetic data. Sequences generated using a known model were given as an input to the inference algorithm. The results of the inference are compared to the true model used for generation, for (a) the distribution of the number of insertions (inset: usage of inserted nucleotides) and (b) V, J gene usage. The error bars, which correspond to sample noise, are smaller than symbol size for (a). In (a) we also report the distribution of insertions obtained using MiXCR
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920122&req=5

btw112-F4: Performance of the algorithm on synthetic data. Sequences generated using a known model were given as an input to the inference algorithm. The results of the inference are compared to the true model used for generation, for (a) the distribution of the number of insertions (inset: usage of inserted nucleotides) and (b) V, J gene usage. The error bars, which correspond to sample noise, are smaller than symbol size for (a). In (a) we also report the distribution of insertions obtained using MiXCR
Mentions: In order to check the validity of the algorithm, we ran it on sequences that were produced according to a known model. We generated 100 000 synthetic sequences according to the model learned in the previous section, and relearned a model from these sequences using our algorithm. In Figure 4 we compare the parameters of the model used for generation to those of the inferred model. Sampling was repeated 5 times to estimate sample noise, which was found to be very small for all parameters, except for gene usage (error bars in Fig. 4b).Fig. 4.

Bottom Line: To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences.The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire.We estimate this diversity to be [Formula: see text] for human T cells.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de physique théorique, CNRS, UPMC and Ecole normale supérieure, Paris, France.

No MeSH data available.