Robertsonrobertson7428

Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes.

Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae.

The source code is freely available on GitHub (https//github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

The importance of a data management strategy is increasingly necessary for demonstrating value and driving performance within pharmacy departments. Data analytics capabilities often do not match the pace of data accumulation. At our organization, the establishment of an embedded pharmacy analytics and outcomes (PAO) team has been instrumental to pharmacy services in generating and demonstrating value and proactively supporting a business intelligence strategy grounded in a data-driven culture.

The PAO team was established to support the operational and strategic needs of clinical, financial, and operational pharmacy services. The team is charged with implementing the vision of extending medication-use influence and data insight to drive value-based patient care outcomes while decreasing waste, optimizing therapeutic decisions, and achieving medication management standardization across the continuum of healthcare. The PAO team is composed of 3 pharmacist full-time equivalents (FTEs), 5 business analyst FTEhe analytics process where medication data are involved. This structure has led to measurable improvements in patient outcomes, operational efficiency, and financial performance.

B cell receptor (BCR) and T cell receptor (TCR) repertoires are generated through somatic DNA rearrangements and are responsible for the molecular basis of antigen recognition in the immune system. Next-generation sequencing (NGS) of DNA and the falling cost of sequencing due to continued development of these technologies have made sequencing assays an affordable way to characterize the repertoire of adaptive immune receptors (sometimes termed the 'immunome'). Many new workflows have been developed to take advantage of NGS and have placed the resulting immunome datasets in the public domain. The scale of these NGS datasets has made it challenging to search through the Complementarity-determining region 3 (CDR3), which is responsible for imparting specific antibody-antigen interactions. Thus, there is an increasing demand for sequence analysis tools capable of searching through CDR3s from immunome data collections containing millions of sequences. To address this need, we created a software package called ClonoMatch that facilitates rapid searches in bulk immunome data for BCR or TCR sequences based on their CDR3 sequence or V3J clonotype.

Documentation, software support and the codebase are all available at https//github.com/crowelab/clonomatch. This software is distributed under the GPL v3 license.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Accurately estimating protein model quality in the absence of experimental structure is not only important for model evaluation and selection, but also useful for model refinement. Progress has been steadily made by introducing new features and algorithms (especially deep neural networks), but the accuracy of quality assessment (QA) is still not very satisfactory, especially local QA on hard protein targets.

We propose a new single-model-based QA method ResNetQA for both local and global quality assessment. U0126 supplier Our method predicts model quality by integrating sequential and pairwise features using a deep neural network composed of both 1 D and 2 D convolutional residual neural networks (ResNet). The 2 D ResNet module extracts useful information from pairwise features such as model-derived distance maps, co-evolution information, and predicted distance potential from sequences. The 1 D ResNet is used to predict local (global) model quality from sequential features and pooled pairwise information generated by 2 D ResNet. Tested on the CASP12 and CASP13 datasets, our experimental results show that our method greatly outperforms existing state-of-the-art methods. Our ablation studies indicate that the 2 D ResNet module and pairwise features play an important role in improving model quality assessment.

https//github.com/AndersJing/ResNetQA.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

aCLImatise is a utility for automatically generating tool definitions compatible with bioinformatics workflow languages, by parsing command-line help output. aCLImatise also has an associated database called the aCLImatise Base Camp, which provides thousands of pre-computed tool definitions.

The latest aCLImatise source code is available within a GitHub organisation, under the GPL-3.0 license https//github.com/aCLImatise. In particular, documentation for the aCLImatise Python package is available at https//aclimatise.github.io/CliHelpParser/, and the aCLImatise Base Camp is available at https//aclimatise.github.io/BaseCamp/.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.Using electric fields to control crystallization processes shows a strong potential for improving pharmaceuticals, but these field effects are not yet fully explored nor understood. This study investigates how the application of alternating high electric fields can influence the crystallization kinetics as well as the final crystal product, with a focus on the possible difference between alternating (ac) and static (dc) type fields applied to vinyl ethylene carbonate (VEC), a molecular system with field-induced polymorphism. Relative to ac fields, static electric fields lead to more severe accumulation of impurity ions near the electrodes, possibly affecting the crystallization behavior. By tuning the amplitude and frequency of the electric field, the crystallization rate can be modified, and the crystallization outcome can be guided to form one or the other polymorph with high purity, analogous to the findings derived from dc field experiments. Additionally, it is found that low-frequency ac fields reduce the induction time, promote nucleation near Tg, and affect crystallization rates as in the dc case.