Barker Sutherland (spotbear3)

MOTIVATION Motif-HMM (mHMM) scanning has been shown to possess unique advantages over standardly used sequence-profile search methods (e.g. HMMER, PSI-BLAST) since it is particularly well suited to discriminate proteins with variations inside conserved motifs (e.g. family subtypes) or motifs lacking essential residues (false positives, e.g. pseudoenzymes). RESULTS In order to make mHMM widely accessible to a broader scientific community we developed Leitmotif, a mHMM web application with many parametrization options easily accessible through intuitive interface. Substantial improvement of performance (ROC scores) was obtained by using two novel parameters. click here To the best of our knowledge Leitmotif is the only available mHMM application. AVAILABILITY Leitmotif is freely available at https//leitmotif.irb.hr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.MOTIVATION One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the disease's process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous data sets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards (PH), which is unlikely to hold for each feature. When applied to genomic features exhibiting some form of non-proportional hazards (NPH), these methods could lead to an under- or over-estimation of the effects. We propose a broad array of marginal screening techniques that aid in feature ranking and selection by accommodating various forms of NPH. First, weble at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.SUMMARY B- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full-length variable region immune receptor sequences by tuning the following immune receptor features (i) species and chain type (BCR, TCR, single, paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation, and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis such as germline gene annotation, diversity, and overlap estimation, sequence similarity, network architecture, clustering analysis, and machine learning methods for motif detection. AVAILABILITY The package is available via https//github.com/GreiffLab/immuneSIM and on CRAN at https//cran.r-project.org/web/packages/immuneSIM. The documentation is hosted at https//immuneSIM.readthedocs.io. SUPPLEMENTARY INFORMATION Supplementary data will be available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions