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Caution is needed in interpreting repertoire data due to potential contamination with mis-sequence reads.
They also reveal sequence features that may be used to purge erroneous sequences, and thereby enhance confidence in repertoire data.
The profile of errors that accumulate in TCR repertoire data acquired by next generation sequencing has not been fully explored.
However, the statistical research for diversity estimators of large-scale immune repertoire data is still in its infancy and needs further development.
Our results have a number of implications both in regards to the extent to which sequencing errors may permeate repertoire data and methods through which these can be refined.
We coupled diversity profiles with unsupervised (hierarchical clustering) and supervised (support vector machine and feature selection) machine learning approaches in order to correlate patients' immunological statuses with their B- and T-cell repertoire data.
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Therefore, we set out to answer the following questions: (i) To what extent do diversity indices robustly capture the immunological information inherent in high-throughput immune repertoire sequencing data?
Here, we review the current literature on bioinformatic and statistical analysis of immune repertoire HTS data and discuss underlying assumptions, applicability, and scope.
The guidelines presented here highlight the major steps involved in the analysis of B-cell repertoire sequencing data, along with recommendations on how to avoid common pitfalls.
Currently, hUbiquitome has in its repertoire curated data comprising 1 enzymeyme, 12 enzymesmes, 138 E3 ligases or complexes, 279 different substrate proteins and 17 deubiquitination enzyme terms.
Although many studies have attributed infection in CBT patients to low T cell numbers and a restricted TCR repertoire, our data show that T cell numbers are below the normal physiological range and that the TCR repertoire is restricted in nearly all HSCT patients as long as one year post-transplant.
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