Notes

Quicker Growth and development of COVID-19 Vaccines: Engineering Programs, Benefits, along with Associated Dangers.
Cultivated soybean (Glycine max) is an important source for protein and oil. Many elite cultivars with different traits have been developed for different conditions. Each soybean strain has its own genetic diversity, and the availability of more high-quality soybean genomes can enhance comparative genomic analysis for identifying genetic underpinnings for its unique traits. In this study, we constructed a high-quality de novo assembly of an elite soybean cultivar Jidou 17 (JD17) with chromosome contiguity and high accuracy. We annotated 52,840 gene models and reconstructed 74,054 high-quality full-length transcripts. We performed a genome-wide comparative analysis based on the reference genome of JD17 with 3 published soybeans (WM82, ZH13, and W05), which identified 5 large inversions and 2 large translocations specific to JD17, 20,984-46,912 presence-absence variations spanning 13.1-46.9 Mb in size. A total of 1,695,741-3,664,629 SNPs and 446,689-800,489 Indels were identified and annotated between JD17 and them. Symbiotic nitrogen fixation genes were identified and the effects from these variants were further evaluated. It was found that the coding sequences of 9 nitrogen fixation-related genes were greatly affected. The high-quality genome assembly of JD17 can serve as a valuable reference for soybean functional genomics research.Neurofibromatosis type 1 is a rare neurogenetic syndrome, characterized by pigmentary abnormalities, learning and social deficits, and a predisposition for benign and malignant tumor formation caused by germline mutations in the NF1 gene. With the cloning of the NF1 gene and the recognition that the encoded protein, neurofibromin, largely functions as a negative regulator of RAS activity, attention has mainly focused on RAS and canonical RAS effector pathway signaling relevant to disease pathogenesis and treatment. However, as neurofibromin is a large cytoplasmic protein the RAS regulatory domain of which occupies only 10% of its entire coding sequence, both canonical and non-canonical RAS pathway modulation, as well as the existence of potential non-RAS functions, are becoming apparent. In this Special article, we discuss our current understanding of neurofibromin function.
ASES is a versatile tool for assessing the impact of alternative splicing, initiation and termination of transcription on protein diversity in evolution. It identifies exon and transcript orthogroups from a set of input genes/species for comparative transcriptomics analyses. It computes an evolutionary splicing graph, where the nodes are exon orthogroups, allowing for a direct evaluation of alternative splicing conservation. It also reconstructs a transcripts' phylogenetic forest to date the appearance of specific transcripts and explore the events that have shaped them. ASES web server features a highly interactive interface enabling the synchronous selection of events, exons or transcripts in the different outputs, and the visualisation and retrieval of the corresponding amino acid sequences, for subsequent 3D structure prediction.

http//www.lcqb.upmc.fr/Ases.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above.

In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data.

The inferelator software is available on GitHub (https//github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https//inferelator.readthedocs.io/).

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Myocarditis in systemic sclerosis (SSc) is associated with a poor prognosis. Cardiac Magnetic Resonance (CMR) is the non-invasive diagnostic modality of choice for SSc-myocarditis. Our study investigates the performance of the mapping techniques, included in the revised Lake Louise Criteria (LLC), for the identification of SSc-myocarditis.

CMR data (right and left ventricular function and morphology, early and late gadolinium enhancement [LGE], T2 ratio, and T1 mapping, extra-cellular volume [ECV] and T2 mapping) of SSc patients diagnosed with myocarditis were reviewed. Myocarditis was defined by the presence of symptoms of SSc-heart involvement with increased high-sensitive troponin T(hs-TnT) and/or NT-proBNP and at least an abnormality at 24 h-ECG-Holter and/or echocardiography and/or CMR. A p-value < 0.05 was considered as statistically significant.

19 patients (median age 54 [46-70] years; females 78.9%; diffuse SSc 52.6%; anti-Scl70 + 52.6%) were identified 11(57.9%) had echocardiographic, and 8heart involvement. The evaluation of T2 mapping increases diagnostic accuracy for the recognition of myocardial inflammation in SSc.
Single-cell RNA sequencing (scRNA-seq) technology provides the possibility to study cell heterogeneity and cell development on the resolution of individual cells. Arguably, three of the most important computational targets on scRNA-seq data analysis are data visualization, cell clustering and trajectory inference. Although a substantial number of algorithms have been developed, most of them do not treat the three targets in a systematic or consistent manner.

In this paper, we propose an efficient scRNA-seq analysis framework, which accomplishes the three targets consistently by non-uniform ε - neighborhood network (NEN). Firstly, a network is generated by our NEN method, which combines the advantages of both k-nearest neighbors (KNN) and ε - neighborhood (EN) to represent the manifold that data points reside in gene space. Then from such a network, we use its layout, its community and further its shortest path to achieve the purpose of scRNA-seq data visualization, clustering and trajectory inference. The results on both synthetic and real datasets indicate that our NEN method not only can visually provide the global topological structure of a dataset accurately compared to t-SNE and UMAP, but also has superior performances on clustering and pseudotime ordering of cells over the existing approaches.

This analysis method has been made into a python package called ccnet and is freely available at https//github.com/Just-Jia/ccNet.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
To evaluate response rates at week 16 with ixekizumab in patients with radiographic axial spondyloarthritis (r-axSpA) and elevated or normal/low baseline inflammation, measured by serum C-reactive protein (CRP) or spinal MRI, using data from 2 randomized, double-blind, placebo-controlled phase III trials.

Biologic-naive (COAST-V) or tumor necrosis factor inhibitor-experienced (COAST-W) adults with active r-axSpA received 80 mg ixekizumab every 2 weeks (IXEQ2W) or 4 weeks (IXEQ4W) or placebo (PBO); or active reference (40 mg adalimumab Q2W; ADA) in COAST-V. At week 16, patients receiving ixekizumab continued as assigned; patients receiving PBO or ADA were re-randomized 11 to IXEQ2W or IXEQ4W through week 52. ASAS40 response rates were examined by baseline CRP (≤5 or > 5 mg/l) and SPARCC MRI spine inflammation score (<2 or ≥ 2).

In the COAST-V/W integrated dataset (N = 567), significantly more patients treated with ixekizumab achieved ASAS40 response at week 16 by CRP ≤5 mg/l (27% IXEQ4W p<0.05, 35% IXEQ2W p<0.01 vs 12% PBO), CRP >5 mg/l (39% IXEQ4W p<0.001, 43% IXEQ2W p<0.001 vs 17% PBO), SPARCC MRI spine score <2 (40% IXEQ4W p<0.01, 52% IXEQ2W p<0.001 vs 16% PBO), and SPARCC MRI spine score ≥2 (44% IXEQ4W p<0.001, 47% IXEQ2W p<0.001 vs 19% PBO). ASAS40 response was observed with CRP ≤5 mg/l and SPARCC MRI spine score <2 with IXEQ4W (29%) and was significant with IXEQ2W (48%, p<0.05) vs PBO (13%).

Ixekizumab demonstrated efficacy in the treatment of ankylosing spondylitis/r-axSpA in patients with and without elevated CRP or evidence of spinal inflammation on MRI.

ClinicalTrials.gov, https//clinicaltrials.gov NCT02696785, NCT02696798.
ClinicalTrials.gov, https//clinicaltrials.gov NCT02696785, NCT02696798.
Genome annotation pipelines traditionally exclude Open Reading Frames shorter than 100 codons to avoid false identifications. However, studies have been showing that these may encode functional microproteins with meaningful biological roles. We developed µProteInS, a proteogenomics pipeline that combines genomics, transcriptomics, and proteomics to identify novel microproteins in bacteria. Our pipeline employs a model to filter out low confidence spectra, to avoid the need for manually inspecting Mass Spectrometry data. It also overcomes the shortcomings of traditional approaches that usually exclude overlapping genes, leaderless transcripts, and non-conserved sequences, characteristics that are common among smORFs and hamper their identification.

µProteInS is implemented in Python 3.8 within an Ubuntu 20.04 environment. It is an open-source software distributed under the GNU General Public License v3, available as a command-line tool. It can be downloaded at https//github.com/Eduardo-vsouza/uproteins and either installed from source or executed as a Docker image.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
microRNAs are important post-transcriptional regulators of gene expression, but the identification of functionally relevant targets is still challenging. 7ACC2 Recent research has shown improved prediction of microRNA-mediated repression using a biochemical model combined with empirically-derived k-mer affinity predictions, however these findings are not easily applicable.

We translate this approach into a flexible and user-friendly bioconductor package, scanMiR, also available through a web interface. Using lightweight linear models, scanMiR efficiently scans for binding sites, estimates their affinity, and predicts aggregated transcript repression. Moreover, flexible 3'-supplementary alignment enables the prediction of unconventional interactions, such as bindings potentially leading to target-directed microRNA degradation or slicing. We showcase scanMiR through a systematic scan for such unconventional sites on neuronal transcripts, including lncRNAs and circRNAs. Finally, in addition to the main bioconductor package implementing these functions, we provide a user-friendly web application enabling the scanning of sequences, the visualization of predicted bindings, and the browsing of predicted target repression.
Website: https://www.selleckchem.com/products/7acc2.html