All predictions are available in an online database, which instantaneously returns the top correlated genes for any PFAM, TIGRFAM, or KEGG query. In total, PhyloCorrelate detected 29,762 high confidence associations between bacterial gene/protein pairs, and generated functional predictions for 834 DUFs and proteins of unknown function.

PhyloCorrelate is available as a web-server at phylocorrelate.uwaterloo.ca as well as an R package for analysis of custom datasets. We anticipate that PhyloCorrelate will be broadly useful as a tool for predicting function and interactions for gene families.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Despite the improvement in variant detection algorithms, visual inspection of the read-level data remains an essential step for accurate identification of variants in genome analysis. We developed BamSnap, an efficient BAM file viewer utilizing a graphics library and BAM indexing. In contrast to existing viewers, BamSnap can generate high-quality snapshots rapidly, with customized tracks and layout. As an example, we produced read-level images at 1000 genomic loci for >2500 whole-genomes.

BamSnap is freely available at https//github.com/parklab/bamsnap.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

We developed Diamond, a Nextflow-based, containerized, multi-modal data-independent acquisition (DIA) mass spectrometry (MS) data processing pipeline for peptide identification and quantification. Diamond integrated two mainstream workflows for DIA data analysis, namely, spectrum-centric scoring (SCS) and peptide-centric scoring (PCS), for use cases both with and without assay libraries. This multi-modal pipeline serves as a versatile, easy-to-use, and easily extendable toolbox for large-scale DIA data processing.

The Docker image is available at https//hub.docker.com/r/zeroli/diamond and the source codes are freely accessible at https//github.com/xmuyulab/Diamond.

The Docker image is available at https//hub.docker.com/r/zeroli/diamond and the source codes are freely accessible at https//github.com/xmuyulab/Diamond.

Electron tomography (ET) has become an indispensable tool for structural biology studies. In ET, the tilt series alignment and the projection parameter calibration are the key steps towards high-resolution ultrastructure analysis. Usually, fiducial markers are embedded in the sample to aid the alignment. Despite the advances in developing algorithms to find correspondence of fiducial markers from different tilted micrographs, the error rate of the existing methods is still high such that manual correction has to be conducted. In addition, existing algorithms do not work well when the number of fiducial markers is high.

In this paper, we try to completely solve the fiducial marker correspondence problem. We propose to divide the workflow of fiducial marker correspondence into two stages (i) initial transformation determination, and (ii) local correspondence refinement. In the first stage, we model the transform estimation as a correspondence pair inquiry and verification problem. The local geometric constr/6adtk4.

Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively, and speedily identify biologically-relevant molecules from large databases. selleck kinase inhibitor So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular-input line-entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring ∼103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based onf-Action). For results, reproducibility and hyperparameters, refer to Supplementary Notes.

KNIT is a web application that provides a hierarchical, directed graph on how a set of genes is connected to a particular gene of interest. Its primary aim is to aid researchers in discerning direct from indirect effects that a gene might have on the expression of other genes and molecular pathways, a very common problem in omics analysis. As such, KNIT provides deep contextual information for experiments where gene or protein expression might be changed, such as gene knock-out and overexpression experiments.

KNIT is publicly available at http//knit.ims.bio. It is implemented with Django and Nuxtjs, with all major browsers supported.

Supplementary information Supplementary data are available at Bioinformatics online.

Supplementary information Supplementary data are available at Bioinformatics online.

Poor protein solubility hinders the production of many therapeutic and industrially useful proteins. Experimental efforts to increase solubility are plagued by low success rates and often reduce biological activity. Computational prediction of protein expressibility and solubility in Escherichia coli using only sequence information could reduce the cost of experimental studies by enabling prioritisation of highly soluble proteins.

A new tool for sequence-based prediction of soluble protein expression in Escherichia coli, SoluProt, was created using the gradient boosting machine technique with the TargetTrack database as a training set. When evaluated against a balanced independent test set derived from the NESG database, SoluProt’s accuracy of 58.5% and AUC of 0.62 exceeded those of a suite of alternative solubility prediction tools. There is also evidence that it could significantly increase the success rate of experimental protein studies. SoluProt is freely available as a standalone program and a user-friendly webserver at https//loschmidt.