Annotation of Human Exome Gene Variants with Consensus Pathogenicity.

A novel approach is developed to address the challenge of annotating with phenotypic effects those exome variants for which relevant empirical data are lacking or minimal. The predictive annotation method is implemented as a stacked ensemble of supervised base-learners, including distributed random forest and gradient boosting machines. Ensemble models were trained and cross-validated on evidence-based categorical variant effect annotations from the ClinVar database, and were applied to 84 million non-synonymous single nucleotide variants (SNVs).

Expitope 2.0: a tool to assess immunotherapeutic antigens for their potential cross-reactivity against naturally expressed proteins in human tissues.

Background: Adoptive immunotherapy offers great potential for treating many types of cancer but its clinical application is hampered by cross-reactive T cell responses in healthy human tissues, representing serious safety risks for patients. We previously developed a computational tool called Expitope for assessing cross-reactivity (CR) of antigens based on tissue-specific gene expression. However, transcript abundance only indirectly indicates protein expression.

Assessment of cancer and virus antigens for cross-reactivity in human tissues.

Motivation: Cross-reactivity (CR) or invocation of autoimmune side effects in various tissues has important safety implications in adoptive immunotherapy directed against selected antigens. The ability to predict CR (on-target and off-target toxicities) may help in the early selection of safer therapeutically relevant target antigens.

Results: We developed a methodology for the calculation of quantitative CR for any defined peptide epitope.

Peak picking NMR spectral data using non-negative matrix factorization.

Background: Simple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional NMR spectra, but often produce wrong intensities and frequencies for overlapping peak clusters. For example, NOESY-type spectra have considerable overlaps leading to significant peak-picking intensity errors, which can result in erroneous structural restraints. Precise frequencies are critical for unambiguous resonance assignments.

Effects of NMR spectral resolution on protein structure calculation.

Adequate digital resolution and signal sensitivity are two critical factors for protein structure determinations by solution NMR spectroscopy. The prime objective for obtaining high digital resolution is to resolve peak overlap, especially in NOESY spectra with thousands of signals where the signal analysis needs to be performed on a large scale. Achieving maximum digital resolution is usually limited by the practically available measurement time.