m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information

N6-methyladenosine (m6A) is one of the most prevalent RNA post-transcriptional modifications and is involved in various vital biological processes such as mRNA splicing, exporting, stability, and so on. Identifying m6A sites contributes to understanding the functional mechanism and biological significance of m6A. The existing biological experimental methods for identifying m6A sites are time-consuming and costly. Thus, developing a high confidence computational method is significant to explore m6A intrinsic characters. In this study, we propose a predictor called m6AGE which utilizes sequence-derived and graph embedding features. To the best of our knowledge, our predictor is the first to combine sequence-derived features and graph embeddings for m6A site prediction. Comparison results show that our proposed predictor achieved the best performance compared with other predictors on four public datasets across three species. On the A101 dataset, our predictor outperformed 1.34% (accuracy), 0.0227 (Matthew’s correlation coefficient), 5.63% (specificity), and 0.0081 (AUC) than comparing predictors, which indicates that m6AGE is a useful tool for m6A site prediction. The source code of m6AGE is available at https://github.com/bokunoBike/m6AGE.

Getting SMART About Social Skills Interventions for Students With ASD in Inclusive Classrooms

Children with autism demonstrate considerable heterogeneity in their social skills and, therefore, their school intervention needs. No single intervention is expected to address the needs of all children with autism. In addition, not all evidence-based school interventions can be provided to all children with autism at all times. Thus, there is a need to understand how best to combine, sequence, and individualize social skills interventions to meet the heterogeneous needs of these children. Adaptive interventions (AIs) are prespecified sequences of decision rules used to guide schools in how best to combine, sequence, and individualize social skills interventions. However, there are currently no empirically derived AIs shown to improve social skills in schoolchildren with autism; moreover, there is a dearth of literature on the acceptability and feasibility of schoolwide, multilevel AIs that combine both environmental-level and individual-level interventions. The purpose of this study is to understand the acceptability and feasibility of four AIs in a SMART (sequential multiple-assignment randomized trial) implemented by educators and parents. The AIs include environmental (Remaking Recess, classroom supports) and individual interventions (parent assisted, peer mediated). Thirty-three elementary-age students with autism (male = 76%, Hispanic = 73%) were educated in 21 classrooms across seven schools by 25 teachers and 24 teaching assistants. Treatment expectations, acceptability, feasibility, and implementation data were collected over 18 weeks. Results indicated respondents were agreeable to treatment changes, but perceived feasibility was average and implementation was moderate. A number of lessons learned and proposed changes for scaling up are discussed.

RNA-Dependent RNA Polymerase From SARS-CoV-2. Mechanism Of Reaction And Inhibition By Remdesivir

We combine sequence analysis, molecular dynamics and hybrid quantum mechanics/molecular mechanics simulations to obtain the first description of the mechanism of reaction of SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) and of the inhibition of the enzyme by Remdesivir. Despite its evolutionary youth, the enzyme is highly optimized to have good fidelity in nucleotide incorporation and a good catalytic efficiency. Our simulations strongly suggest that Remdesivir triphosphate (the active form of drug) is incorporated into the nascent RNA replacing ATP, leading to a duplex RNA which is structurally very similar to an unmodified one. We did not detect any reason to explain the inhibitory activity of Remdesivir at the active site. Displacement of the nascent Remdesivir-containing RNA duplex along the exit channel of the enzyme can occur without evident steric clashes which would justify delayed inhibition. However, after the incorporation of three more nucleotides we found a hydrated Serine which is placed in a perfect arrangement to react through a Pinner’s reaction with the nitrile group of Remdesivir. Kinetic barriers for crosslinking and polymerization are similar suggesting a competition between polymerization and inhibition. Analysis of SARS-CoV-2 mutational landscape and structural analysis of polymerases across different species support the proposed mechanism and suggest that virus has not explored yet resistance to Remdesivir inhibition.

TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks

Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge PPI networks for many model species, the computational methods revealed a great requirement to elucidate protein function based on many strategies. In this respect, most computational approaches integrate diverse kinds of functional interactions to unveil protein functions by transferring annotations across different species by relying on a similar sequence, structure 2D/3D, amino acid patterns of phylogenetic profiles. Results: In this work, we introduce a new approach, called TANA, for inferring protein functions. The main originality of the introduced approach stands on the function prediction for the unannotated protein by transferring annotation via a network alignment as well as from the direct interaction neighborhood within their PPI networks. In doing so, we are able to discover the functions of proteins that could not be easily described by sequence homology. We assess the performance of our approach using the standard metrics established by the CAFA challenge and highlight a sharp significant improvement over other competitive methods, in particular for predicting molecular functions and cellular components. Conclusions: This research is one of the first attempts that combine sequence and networks-multiple-alignment-based function prediction approaches. We have been able to assess the accuracy of the prediction using pairwise and multiple alignment of the PPI networks for the compared species. Therefore, we recommend using different strategies (i.e. pairwise, multiple, with/without neighborhood networks) especially in situations where the functions of the protein are not known beforehand

TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks

Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge PPIs networks for many model species, the computational methods revealed a great requirement to elucidate protein function based on many strategies. In this respect, most computational approaches integrate diverse kinds of functional interactions to unveil protein functions by transferring annotations across different species by relying on similar sequence, structure 2D/3D, amino acid patterns or phylogenetic profiles. Results: In this work, we introduce a new approach, called TANA, for inferring protein functions. The main originality of the introduced approach stands on the function prediction for the unannotated protein by transferring annotation via a network alignment as well as from the direct interaction neighborhood within their PPI networks. Doing so, we are able to discover the functions of proteins that could not to be easily described by sequence homology. We assess the performance of our approach using the standard metrics established by the CAFA challenge and highlight a sharp significant improvement over other competitive methods, in particular for predicting molecular functions. Conclusions: This research is one of the first attempts that combine sequence and networks-multiple-alignment-based function prediction approaches. We have been able to assess the accuracy of the prediction using pairwise and multiple alignment of the PPI networks for the compared species. Therefore, we recommend using different strategies (i.e pairwise, multiple, with/without neighborhood networks) especially in situations where the functions of the protein are not known beforehand.

Early Family Life Course Standardization in Sweden: The Role of Compositional Change

The diversity of early family life courses is thought to have increased, although empirical evidence is mixed. Less standardized family formation is attributed to compositional changes in educational attainment, labour market participation, and childhood living conditions. I investigate whether and why family trajectories have become more or less standardized across birth cohorts in Sweden. I combine sequence metrics with Oaxaca–Blinder decompositions to assess the compositional shifts that drive changes in family formation standardization. Family trajectories of individuals born in 1952, 1962, and 1972 from age 18 to 35 are reconstructed using Swedish register data. My results demonstrate that early family formation has become more standardized across birth cohorts. Further, compositional differences between birth cohorts partially account for this standardization, especially for women. For example, higher levels of educational attainment are associated with family formation standardization. This substantiates arguments that family formation may re-standardize following the second demographic transition.

Improved bacteria population structure analysis on thousands of genomes using unsupervised methods

Over ten thousand genomes ofEscherichia coliare now available, and this number will continue to grow for this and other important microbial species. The first approach often used to better understand microbes is phylogenetic group analysis followed by pan-genome analysis of highly related genomes. Here, we combine sequence-based features with unsupervised clustering on up to 2,231E. coligenomes and a total of 1,367Clostridium difficilegenomes. We show that Non-negative Matrix Factorization (NMF) can identify “mixed”/cryptic genomes, and can better determine inter-related genome groups and their distinguishing features (genes) relative to prior methods.

Combining NCBI and BOLD databases for OTU assignment in metabarcoding and metagenomic data: The BOLD_NCBI _Merger

Metabarcoding and metagenomic approaches are becoming routine techniques in biodiversity assessment and ecological studies. The assignment of taxonomic information to sequences is challenging, as many reference libraries are lacking information on certain taxonomic groups and can contain erroneous sequences. Combining different reference databases is therefore a promising approach for maximizing taxonomic coverage and reliability of results. This tutorial shows how to use the “BOLD_NCBI_Merger” script to combine sequence data obtained from the National Center for Biotechnology Information (NCBI) GenBank and the Barcode of Life Database (BOLD) and prepare it for taxonomic assignment with the software MEGAN.

Combining NCBI and BOLD databases for OTU assignment in metabarcoding and metagenomic data: The BOLD_NCBI _Merger

Metabarcoding and metagenomic approaches are becoming routine techniques in biodiversity assessment and ecological studies. The assignment of taxonomic information to sequences is challenging, as many reference libraries are lacking information on certain taxonomic groups and can contain erroneous sequences. Combining different reference databases is therefore a promising approach for maximizing taxonomic coverage and reliability of results. This tutorial shows how to use the “BOLD_NCBI_Merger” script to combine sequence data obtained from the National Center for Biotechnology Information (NCBI) GenBank and the Barcode of Life Database (BOLD) and prepare it for taxonomic assignment with the software MEGAN.

30 YEARS OF THE MINERALOCORTICOID RECEPTOR: Evolution of the mineralocorticoid receptor: sequence, structure and function

The mineralocorticoid receptor (MR) is descended from a corticoid receptor (CR), which has descendants in lamprey and hagfish, cyclostomes (jawless fish), a taxon that evolved at the base of the vertebrate line. A distinct MR and GR first appear in cartilaginous fishes (Chondrichthyes), such as sharks, skates, rays and chimeras. Skate MR has a strong response to corticosteroids that are mineralocorticoids and glucocorticoids in humans. The half-maximal responses (EC50s) for skate MR for the mineralocorticoids aldosterone and 11-deoxycorticosterone are 0.07 nM and 0.03 nM, respectively. EC50s for the glucocorticoids cortisol and corticosterone are 1 nM and 0.09 nM, respectively. The physiological mineralocorticoid in ray-finned fish, which do not synthesize aldosterone, is not fully understood because several 3-ketosteroids, including cortisol, 11-deoxycortisol, corticosterone, 11-deoxycorticosterone and progesterone are transcriptional activators of fish MR. Further divergence of the MR and GR in terrestrial vertebrates, which synthesize aldosterone, led to emergence of aldosterone as a selective ligand for the MR. Here, we combine sequence analysis of the CR and vertebrate MRs and GRs, analysis of crystal structures of human MR and GR and data on transcriptional activation by 3-ketosteroids of wild-type and mutant MRs and GRs to investigate the evolution of selectivity for 3-ketosteroids by the MR in terrestrial vertebrates and ray-finned fish, as well as the basis for binding of some glucocorticoids by human MR and other vertebrate MRs.