Classification Of Family Research Articles

Evolution and Classification of Musaceae Based on Male Floral Morphology.

Classification of the banana family (Musaceae) into three genera, Musa, Ensete and Musella, and infrageneric ranking are still ambiguous. Within the genus Musa, five formerly separated sections were recently merged into sections Musa and Callimusa based on seed morphology, molecular data and chromosome numbers. Nevertheless, other key morphological characters of the genera, sections, and species have not been clearly defined. This research aims to investigate male floral morphology, classify members of the banana family based on overall similarity of morphological traits using 59 banana accessions of 21 taxa and make inferences of the evolutionary relationships of 57 taxa based on ITS, trnL-F, rps16 and atpB-rbcL sequences from 67 Genbank and 10 newly collected banana accessions. Fifteen quantitative characters were examined using principal component analysis and canonical discriminant analysis and 22 qualitative characters were analyzed by the Unweighted Pair Group Method with an Arithmetic Mean (UPGMA). The results showed that fused tepal morphology, median inner tepal shape and length of style supported the three clades of Musa, Ensete and Musella, while shapes of median inner tepal and stigma classified the two Musa sections. In conclusion, a combination of morphological characters of male flowers and molecular phylogenetics well support the taxonomic arrangement within the banana family and the Musa genus and assist in selection of characters to construct an identification key of Musaceae.

Abelian varieties and Riemann surfaces with generalized quaternion group action

We consider abelian varieties with a group of automorphisms isomorphic to a generalized quaternion group. We provide isogeny decompositions, compute dimensions of the corresponding factors and give conditions under which this decomposition is nontrivial. We then specialize our results to Jacobians and relate them to the so-called genus-zero actions on Riemann surfaces. A complete classification of uniparametric families of Riemann surfaces with a generalized quaternion group action is given, extending known results for the quasiplatonic case. Finally, we construct explicit families of abelian varieties with a quaternion group action and derive a period matrix for the Jacobian of the surface with full automorphism group of second largest order among the hyperelliptic surfaces of genus four.

ImageDroid: Using Deep Learning to Efficiently Detect Android Malware and Automatically Mark Malicious Features

The popularity of the Android platform has led to an explosion in malware. The current research on Android malware mainly focuses on malware detection or malware family classification. These studies need to extract a large number of features, which consumes a lot of manpower and material resources. Moreover, some malware use obfuscation to evade decompiler tools extracting features. To address these problems, we propose ImageDroid, a method based on the image format of Android applications that can not only detect and classify malware without prior knowledge but also detect the obfuscated malware. Furthermore, we utilize the Grad-CAM interpretable mechanism of the deep learning model to automatically label the image that play a key role in determining maliciousness in a visual way. We evaluate ImageDroid over 10,000 Android applications. Experimental results show that the accuracy of malicious detection and multifamily classification achieve 97.2% and 95.1%, respectively, and the detection accuracy of obfuscated malware achieves 94.6%.

Sex-Determined Growth of Yearling Pacific Oysters (Crassostrea gigas)

The sexes of sequentially hermaphroditic cupped oysters are both environmentally and genetically determined. Sex is also associated with growth or body size, females being larger than males of the same age on average. Whether sex determines size or size determines sex has not been resolved. To examine the relationship between growth and sex, 1,360 tagged individuals from six F2 families of Pacific oyster Crassostrea gigas were reared through their second summer in Thorndyke Bay, Washington; individual live weights were recorded for 5 consecutive months, starting in June. At the last weighing, microscopic examination of gonadal tissue revealed four “sexes” in the cohort, females (43% of cohort), males (29%), hermaphrodites (2%), and sex-undetermined individuals with no gametes (26%). Live weights were fit to various sigmoidal growth curves (logistic, Gompertz, and Richards) with fixed and random parameters to account for individual variation. The best fitting curves suggested that growth of the cohort was exponential until late August, when temperatures declined and more than 70% of final weight had been gained. Individual live weights, growth-curve parameters, and growth measures were fit to general linear models with family and sex classification variables. Family-by-sex interaction was significant in many such analyses. Females were larger than males on average and appeared to attain their size advantage from superior growth characteristics rather than from initial size. Males were strikingly either like females or like the smaller sex-undetermined oysters with respect to growth comparisons within family. Sex-undetermined oysters may have been “true” males that were too small to mature as yearlings, though a size-threshold for maturation was not apparent. Genetically determined sex and family genetics appeared to control growth.

ENCYCLOBREPHUS BREVIVITELLUS N. SP. (DIGENEA: PLAGIORCHIOIDEA, INCERTAE SEDIS) INFECTING THE MEKONG SNAIL-EATING TURTLE, MALAYEMYS SUBTRIJUGA, IN THE MEKONG RIVER, VIETNAM, WITH PHYLOGENETIC ANALYSES BASED ON RDNA.

A new digenean species belonging in EncyclobrephusSinha, 1949 is described, and the generic diagnosis is amended to accommodate variation in several features of the new species. Worms were collected from the intestines of 2 specimens of the Mekong snail-eating turtle, Malayemys subtrijuga (Schlegel and Müller, 1845). Permanent whole-mounted worms were studied using light microscopy, and ribosomal DNA (rDNA) sequences were generated from 3 worms. We investigated the phylogenetic relationship of the new species among some digenea using separate Bayesian inference analyses, 1 based on the 28S rDNA gene and rooted using a species from the Monorchioidea Odhner, 1911, and a second based on the internal transcribed spacer 1 region rooted by a species in the Microphalloidea Ward, 1901. Prior to the analyses, Encyclobrephus was classified in the Encyclometridae Mehra, 1931. Previous studies using rDNA from the type species for the family, Encyclometra colubrimurorum (Rudolphi, 1819) Baylis and Cannon, 1924, have demonstrated that En. colubrimurorum is closely related to species of Polylekithum Arnold, 1934 in the Gorgoderoidea Looss, 1901. Nevertheless, phylograms from both analyses indicated that the new species of Encyclobrephus belongs in the Plagiorchioidea Lühe, 1901, related to species in the families Cephalogonimidae Looss, 1899, Plagiorchiidae Lühe, 1901, Reniferidae Pratt, 1902, and Telorchiidae Looss, 1899. The present results suggest that Encyclobrephus is not closely related to En. colubrimurorum. Familial classification of Encyclobrephus is contingent on molecular data availability for the type species but it should be removed from the Encyclometridae and classified as incertae sedis within the Plagiorchioidea. Encyclometridae belongs in the Gorgoderoidea, not the Plagiorchioidea.

Distributed dynamic strain sensing of very long period and long period events on telecom fiber-optic cables at Vulcano, Italy

Volcano-seismic signals can help for volcanic hazard estimation and eruption forecasting. However, the underlying mechanism for their low frequency components is still a matter of debate. Here, we show signatures of dynamic strain records from Distributed Acoustic Sensing in the low frequencies of volcanic signals at Vulcano Island, Italy. Signs of unrest have been observed since September 2021, with CO2 degassing and occurrence of long period and very long period events. We interrogated a fiber-optic telecommunication cable on-shore and off-shore linking Vulcano Island to Sicily. We explore various approaches to automatically detect seismo-volcanic events both adapting conventional algorithms and using machine learning techniques. During one month of acquisition, we found 1488 events with a great variety of waveforms composed of two main frequency bands (from 0.1 to 0.2 Hz and from 3 to 5 Hz) with various relative amplitudes. On the basis of spectral signature and family classification, we propose a model in which gas accumulates in the hydrothermal system and is released through a series of resonating fractures until the surface. Our findings demonstrate that fiber optic telecom cables in association with cutting-edge machine learning algorithms contribute to a better understanding and monitoring of volcanic hydrothermal systems.

The Lichen Flavin-Dependent Halogenase, DnHal: Identification, Heterologous Expression and Functional Characterization.

Enzymatic halogenation captures scientific interest considering its feasibility in modifying compounds for chemical diversity. Currently, majority of flavin-dependent halogenases (F-Hals) were reported from bacterial origin, and as far as we know, none from lichenized fungi. Fungi are well-known producers of halogenated compounds, so using available transcriptomic dataset of Dirinaria sp., we mined for putative gene encoding for F-Hal. Phylogenetic-based classification of the F-Hal family suggested a non-tryptophan F-Hals, similar to other fungal F-Hals, which mainly act on aromatic compounds. However, after the putative halogenase gene from Dirinaria sp., dnhal was codon-optimized, cloned, and expressed in Pichia pastoris, the ~63 kDa purified enzyme showed biocatalytic activity towards tryptophan and an aromatic compound methyl haematommate, which gave the tell-tale isotopic pattern of a chlorinated product at m/z 239.0565 and 241.0552; and m/z 243.0074 and 245.0025, respectively.This study is the start of understanding the complexities of lichenized fungal F-hals and its ability to halogenate tryptophan and other aromatic. compounds which can be used as green alternatives for biocatalysis of halogenated compounds.

Structural and Functional Classification of G-Quadruplex Families within the Human Genome.

G-quadruplexes (G4s) are short secondary DNA structures located throughout genomic DNA and transcribed RNA. Although G4 structures have been shown to form in vivo, no current search tools that examine these structures based on previously identified G-quadruplexes and filter them based on similar sequence, structure, and thermodynamic properties are known to exist. We present a framework for clustering G-quadruplex sequences into families using the CD-HIT, MeShClust, and DNACLUST methods along with a combination of Starcode and BLAST. Utilizing this framework to filter and annotate clusters, 95 families of G-quadruplex sequences were identified within the human genome. Profiles for each family were created using hidden Markov models to allow for the identification of additional family members and generate homology probability scores. The thermodynamic folding energy properties, functional annotation of genes associated with the sequences, scores from different prediction algorithms, and transcription factor binding motifs within a family were used to annotate and compare the diversity within and across clusters. The resulting set of G-quadruplex families can be used to further understand how different regions of the genome are regulated by factors targeting specific structures common to members of a specific cluster.

Identification of Trehalose-6-Phosphate Synthase (TPS) Genes Associated with Both Source-/Sink-Related Yield Traits and Drought Response in Rapeseed (Brassica napus L.).

Trehalose-6-phosphate synthase (TPS) is an important enzyme for the synthesis of Trehalose-6-phosphate (T6P). In addition to being a signaling regulator of carbon allocation that improves crop yields, T6P also plays essential roles in desiccation tolerance. However, comprehensive studies, such as evolutionary analysis, expression analysis, and functional classification of the TPS family in rapeseed (Brassica napus L.) are lacking. Here, we identified 35 BnTPSs, 14 BoTPSs, and 17 BrTPSs in cruciferous plants, which were classified into three subfamilies. Phylogenetic and syntenic analysis of TPS genes in four cruciferous species indicated that only gene elimination occurred during their evolution. Combined phylogenetic, protein property, and expression analysis of the 35 BnTPSs suggested that changes in gene structures might have led to changes in their expression profiles and further functional differentiation during their evolution. In addition, we analyzed one set of transcriptome data from Zhongshuang11 (ZS11) and two sets of data from extreme materials associated with source-/sink-related yield traits and the drought response. The expression levels of four BnTPSs (BnTPS6, BnTPS8, BnTPS9, and BnTPS11) increased sharply after drought stress, and three differentially expressed genes (BnTPS1, BnTPS5, and BnTPS9) exhibited variable expression patterns among source and sink tissues between yield-related materials. Our findings provide a reference for fundamental studies of TPSs in rapeseed and a framework for future functional research of the roles of BnTPSs in both yield and drought resistance.

Genome-Wide Identification, Classification, and Expression Analyses of the CsDGAT Gene Family in Cannabis sativa L. and Their Response to Cold Treatment.

Hempseed is a nutrient-rich natural resource, and high levels of hempseed oil accumulate within hemp seeds, consisting primarily of different triglycerides. Members of the diacylglycerol acyltransferase (DGAT) enzyme family play critical roles in catalyzing triacylglycerol biosynthesis in plants, often governing the rate-limiting step in this process. As such, this study was designed to characterize the Cannabis sativa DGAT (CsDGAT) gene family in detail. Genomic analyses of the C. sativa revealed 10 candidate DGAT genes that were classified into four families (DGAT1, DGAT2, DGAT3, WS/DGAT) based on the features of different isoforms. Members of the CsDGAT family were found to be associated with large numbers of cis-acting promoter elements, including plant response elements, plant hormone response elements, light response elements, and stress response elements, suggesting roles for these genes in key processes such as development, environmental adaptation, and abiotic stress responses. Profiling of these genes in various tissues and varieties revealed varying spatial patterns of CsDGAT expression dynamics and differences in expression among C. sativa varieties, suggesting that the members of this gene family likely play distinct functional regulatory functions CsDGAT genes were upregulated in response to cold stress, and significant differences in the mode of regulation were observed when comparing roots and leaves, indicating that CsDGAT genes may play positive roles as regulators of cold responses in hemp while also playing distinct roles in shaping the responses of different parts of hemp seedlings to cold exposure. These data provide a robust basis for further functional studies of this gene family, supporting future efforts to screen the significance of CsDGAT candidate genes to validate their functions to improve hempseed oil composition.

Generative adversarial networks and image-based malware classification

For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. In this paper, we extract features from malware executable files and represent them as images using various approaches. We then focus on generative adversarial networks (GAN) for multiclass classification and compare our GAN results to other popular machine learning techniques, including support vector machine (SVM), XGBoost, and restricted Boltzmann machines (RBM). We find that the AC-GAN discriminator is generally competitive with other machine learning techniques. We also evaluate the utility of the GAN generative model for adversarial attacks on image-based malware detection. While AC-GAN generated images are visually impressive, we find that they are easily distinguished from real malware images using any of several learning techniques. This result indicates that our GAN generated images are of surprisingly little value in adversarial attacks.

Digital soil mapping of Italy to map derived soil profiles with neural networks

In recent years there has been an increased demand for digital soil mapping (DSM) products. DSM has become the ultimate soil spatial representation framework due to its quantitative results, replicability, and uncertainty analysis. The present study aimed to map the probability distribution of the derived soil profiles (DSPs) of soil typological units (STUs). DSPs are statistical representation of the properties of the soil profiles belonging to STUs. STUs aggregate individual profiles into a group. The criteria used for grouping were homogeneity for World Reference Base (WRB) reference soil group (WRB-RSG), qualifiers (WRB-qu), and Soil Taxonomy particle size for the family classification (USDA-PS), and the belonging to a specific Soil Region. The European Soil Regions have been suggested as the primary grouping criteria for soil mapping at the European continental scale since they define continental-scale soilscapes, distinguished mainly by their climate and geology. To map DSPs, we firstly mapped STUs. The grouping criteria of STUs were mapped at 500 m spatial resolution, using a Neural Network trained on 18,707 georeferenced and analyzed soil profiles selected from the Italian national soil database. A 10% of the soil profiles were randomly sampled using a stratified sampling approach for validation. In particular, the procedure consisted of: i) mapping the grouping criteria WRB-RSG, WRB-qu, and USDA-PS, on a 500 m national grid, through Neural Network; ii) grouping soil profiles on the base of the combinations of grouping criteria (WRB-RSG, WRB-qu, USDA-PS, and Soil Regions) as mapped with the first step at each grid node, to produce a map of Soil Typological Units (STUs); iii) calculating statistics for the soil parameters of the groups of soil profiles created, to produce a map of Derived Soil Profiles (DSPs). DSPs statistics (average, standard deviation, and sample numerosity) were elaborated for the following parameters: soil rooting depth, pH (in water), soil organic carbon, clay, silt, sand, coarse fragments, and cation exchange capacity. The maps obtained were validated against the test set. The same test set was used for the comparison with the National benchmark map (Soils Map of Italy 1:1,000,000) and with the global scale SoilGrids at 250 m spatial resolution. The overall accuracy was 45.98% for the WRB-RSG map compared with the 30.74% of WRB-RSG as mapped with the Soil Map of Italy, and 28.79% as mapped with SoilGrids; 33.07% for WRB-qu compared with the 15.69% of WRB-qu as mapped with the Soil Map of Italy, and 12.45% as mapped with SoilGrids, and 45.48% for USDA-PS, not comparable with the National and Global benchmarks. Tau statistics showed a higher accuracy Kappa of our approach than in others, due to the unbalanced classes numerosity. The predictive ability in the validation of DSPs parameters resulted in a R2 of 0.35 for clay (0.16 with SoilGrids), 0.28 for sand (0.08 with SoilGrids), 0.18 for pH in water (0.21 with SoilGrids). The proposed approach produced harmonized soil type maps with higher accuracy than the previous generation of conventional field-based soil maps for the national benchmark and the calculation of the uncertainty. The STUs express variability of soil properties between groups so their knowledge might improve our understanding of the soil distribution, the planning of their management, monitoring, and the decisions for further surveys. A future challenge will be including more dynamic parameters in the criteria used to create STU, to help monitoring soil management effects.

Structural and Functional Annotation of Hypothetical Proteins from the Microsporidia Species Vittaforma corneae ATCC 50505 Using in silico Approaches.

Microsporidia are spore-forming eukaryotes that are related to fungi but have unique traits that set them apart. They have compact genomes as a result of evolutionary gene loss associated with their complete dependency on hosts for survival. Despite having a relatively small number of genes, a disproportionately high percentage of the genes in microsporidia genomes code for proteins whose functions remain unknown (hypothetical proteins-HPs). Computational annotation of HPs has become a more efficient and cost-effective alternative to experimental investigation. This research developed a robust bioinformatics annotation pipeline of HPs from Vittaforma corneae, a clinically important microsporidian that causes ocular infections in immunocompromised individuals. Here, we describe various steps to retrieve sequences and homologs and to carry out physicochemical characterization, protein family classification, identification of motifs and domains, protein-protein interaction network analysis, and homology modelling using a variety of online resources. Classification of protein families produced consistent findings across platforms, demonstrating the accuracy of annotation utilizing in silico methods. A total of 162 out of 2034 HPs were fully annotated, with the bulk of them categorized as binding proteins, enzymes, or regulatory proteins. The protein functions of several HPs from Vittaforma corneae were accurately inferred. This improved our understanding of microsporidian HPs despite challenges related to the obligate nature of microsporidia, the absence of fully characterized genes, and the lack of homologous genes in other systems.

Phylogenetic and AlphaFold predicted structure analyses provide insights for A1 aspartic protease family classification in Arabidopsis.

Aspartic proteases are widely distributed in animals, plants, fungi and other organisms. In land plants, A1 aspartic protease family members have been implicated to play important and varied roles in growth, development and defense. Thus a robust classification of this family is important for understanding their gene function and evolution. However, current A1 family members in Arabidopsis are less well classified and need to be re-evaluated. In this paper, 70 A1 aspartic proteases in Arabidopsis are divided into four groups (group I-IV) based on phylogenetic and gene structure analyses of 1200 A1 aspartic proteases which are obtained from 12 Embryophyta species. Group I-III members are further classified into 2, 4 and 7 subgroups based on the AlphaFold predicted structures. Furthermore, unique insights of A1 aspartic proteases have been unraveled by AlphaFold predicted structures. For example, subgroup II-C members have a unique II-C specific motif in the C-extend domain, and subgroup IV is a Spermatophyta conserved group without canonical DTGS/DSGT active sites. These results prove that AlphaFold combining phylogenetic analysis is a promising solution for complex gene family classification.

KinFams: De-Novo Classification of Protein Kinases Using CATH Functional Units.

Protein kinases are important targets for treating human disorders, and they are the second most targeted families after G-protein coupled receptors. Several resources provide classification of kinases into evolutionary families (based on sequence homology); however, very few systematically classify functional families (FunFams) comprising evolutionary relatives that share similar functional properties. We have developed the FunFam-MARC (Multidomain ARchitecture-based Clustering) protocol, which uses multi-domain architectures of protein kinases and specificity-determining residues for functional family classification. FunFam-MARC predicts 2210 kinase functional families (KinFams), which have increased functional coherence, in terms of EC annotations, compared to the widely used KinBase classification. Our protocol provides a comprehensive classification for kinase sequences from >10,000 organisms. We associate human KinFams with diseases and drugs and identify 28 druggable human KinFams, i.e., enriched in clinically approved drugs. Since relatives in the same druggable KinFam tend to be structurally conserved, including the drug-binding site, these KinFams may be valuable for shortlisting therapeutic targets. Information on the human KinFams and associated 3D structures from AlphaFold2 are provided via our CATH FTP website and Zenodo. This gives the domain structure representative of each KinFam together with information on any drug compounds available. For 32% of the KinFams, we provide information on highly conserved residue sites that may be associated with specificity.

Malware Family Classification Based on Vision Transformer

<p>Cybersecurity worries intensify as Big Data, the Internet of Things, and 5G technologies develop. Based on code reuse technologies, malware creators are producing new malware quickly, and new malware is continually endangering the effectiveness of existing detection methods. We propose a vision transformer-based approach for malware picture identification because, in contrast to CNN, Transformer’s self-attentive process is not constrained by local interactions and can simultaneously compute long-range mine relationships. We use ViT-B/16 weights pre-trained on the ImageNet21k dataset to improve model generalization capability and fine-tune them for the malware image classification task. This work demonstrates that (i) a pure attention mechanism applies to malware recognition, and (ii) the Transformer can be used instead of traditional CNN for malware image recognition. We train and assess our models using the MalImg dataset and the BIG2015 dataset in this paper. Our experimental evaluation found that the recognition accuracy of transfer learning-based ViT for MalImg samples and BIG2015 samples is 99.14% and 98.22%, respectively. This study shows that training ViT models using transfer learning can perform better than CNN in malware family classification.</p> <p> </p>

On Deceiving Malware Classification with Section Injection

We investigate how to modify executable files to deceive malware classification systems. This work’s main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decrease classification accuracy but also as a defensive method, augmenting the data available for training. It respects the operating system file format to make sure the malware will still execute after our injection and will not change its behavior. We reproduced five state-of-the-art malware classification approaches to evaluate our injection scheme: one based on Global Image Descriptor (GIST) + K-Nearest-Neighbors (KNN), three Convolutional Neural Network (CNN) variations and one Gated CNN. We performed our experiments on a public dataset with 9339 malware samples from 25 different families. Our results show that a mere increase of 7% in the malware size causes an accuracy drop between 25% and 40% for malware family classification. They show that an automatic malware classification system may not be as trustworthy as initially reported in the literature. We also evaluate using modified malware alongside the original ones to increase networks robustness against the mentioned attacks. The results show that a combination of reordering malware sections and injecting random data can improve the overall performance of the classification. All the code is publicly available.

Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies.

Protein language models, trained on millions of biologically observed sequences, generate feature-rich numerical representations of protein sequences. These representations, called sequence embeddings, can infer structure-functional properties, despite protein language models being trained on primary sequence alone. While sequence embeddings have been applied toward tasks such as structure and function prediction, applications toward alignment-free sequence classification have been hindered by the lack of studies to derive, quantify and evaluate relationships between protein sequence embeddings. Here, we develop workflows and visualization methods for the classification of protein families using sequence embedding derived from protein language models. A benchmark of manifold visualization methods reveals that Neighbor Joining (NJ) embedding trees are highly effective in capturing global structure while achieving similar performance in capturing local structure compared with popular dimensionality reduction techniques such as t-SNE and UMAP. The statistical significance of hierarchical clusters on a tree is evaluated by resampling embeddings using a variational autoencoder (VAE). We demonstrate the application of our methods in the classification of two well-studied enzyme superfamilies, phosphatases and protein kinases. Our embedding-based classifications remain consistent with and extend upon previously published sequence alignment-based classifications. We also propose a new hierarchical classification for the S-Adenosyl-L-Methionine (SAM) enzyme superfamily which has been difficult to classify using traditional alignment-based approaches. Beyond applications in sequence classification, our results further suggest NJ trees are a promising general method for visualizing high-dimensional data sets.

An updated classification of cyanobacterial orders and families based on phylogenomic and polyphasic analysis.

Cyanobacterial taxonomy is facing a period of rapid changes thanks to the ease of 16S rRNA gene sequencing and established workflows for description of new taxa. Since the last comprehensive review of the cyanobacterial system in 2014 until 2021, at least 273 species in 140 genera were newly described. These taxa were mainly placed into previously defined orders and families although several new families were proposed. However, the classification of most taxa still relied on hierarchical relationships inherited from the classical morphological taxonomy. Similarly, the obviously polyphyletic orders such as Synechococcales and Oscillatoriales were left unchanged. In this study, the rising number of genomic sequences of cyanobacteria and well-described reference strains allowed us to reconstruct a robust phylogenomic tree for taxonomic purposes. A less robust but better sampled 16S rRNA gene phylogeny was mapped to the phylogenomic backbone. Based on both these phylogenies, a polyphasic classification throughout the whole phylum of Cyanobacteria was created, with ten new orders and fifteen new families. The proposed system of cyanobacterial orders and families relied on a phylogenomic tree but still employed phenotypic apomorphies where possible to make it useful for professionals in the field. It was, however, confirmed that morphological convergence of phylogenetically distant taxa was a frequent phenomenon in cyanobacteria. Moreover, the limited phylogenetic informativeness of the 16S rRNA gene, resulting in ambiguous phylogenies above the genus level, emphasized the integration of genomic data as a prerequisite for the conclusive taxonomic placement of a vast number of cyanobacterial genera in the future.

β-LacFamPred: An online tool for prediction and classification of β-lactamase class, subclass, and family.

β-Lactams are a broad class of antimicrobial agents with a high safety profile, making them the most widely used class in clinical, agricultural, and veterinary setups. The widespread use of β-lactams has induced the extensive spread of β-lactamase hydrolyzing enzymes known as β-lactamases (BLs). To neutralize the effect of β-lactamases, newer generations of β-lactams have been developed, which ultimately led to the evolution of a highly diverse family of BLs. Based on sequence homology, BLs are categorized into four classes: A-D in Ambler's classification system. Further, each class is subdivided into families. Class B is first divided into subclasses B1-B3, and then each subclass is divided into families. The class to which a BL belongs gives a lot of insight into its hydrolytic profile. Traditional methods of determining the hydrolytic profile of BLs and their classification are time-consuming and require resources. Hence we developed a machine-learning-based in silico method, named as β-LacFamPred, for the prediction and annotation of Ambler's class, subclass, and 96 families of BLs. During leave-one-out cross-validation, except one all β-LacFamPred model HMMs showed 100% accuracy. Benchmarking with other BL family prediction methods showed β-LacFamPred to be the most accurate. Out of 60 penicillin-binding proteins (PBPs) and 57 glyoxalase II proteins, β-LacFamPred correctly predicted 56 PBPs and none of the glyoxalase II sequences as non-BLs. Proteome-wide annotation of BLs by β-LacFamPred showed a very less number of false-positive predictions in comparison to the recently developed BL class prediction tool DeepBL. β-LacFamPred is available both as a web-server and standalone tool at http://proteininformatics.org/mkumar/blacfampred and GitHub repository https://github.com/mkubiophysics/B-LacFamPred respectively.