Set Of Sequences Research Articles

ICOR: improving codon optimization with recurrent neural networks

BackgroundIn protein sequences—as there are 61 sense codons but only 20 standard amino acids—most amino acids are encoded by more than one codon. Although such synonymous codons do not alter the encoded amino acid sequence, their selection can dramatically affect the expression of the resulting protein. Codon optimization of synthetic DNA sequences is important for heterologous expression. However, existing solutions are primarily based on choosing high-frequency codons only, neglecting the important effects of rare codons. In this paper, we propose a novel recurrent-neural-network based codon optimization tool, ICOR, that aims to learn codon usage bias on a genomic dataset of Escherichia coli. We compile a dataset of over 7,000 non-redundant, high-expression, robust genes which are used for deep learning. The model uses a bidirectional long short-term memory-based architecture, allowing for the sequential context of codon usage in genes to be learned. Our tool can predict synonymous codons for synthetic genes toward optimal expression in Escherichia coli.ResultsWe demonstrate that sequential context achieved via RNN may yield codon selection that is more similar to the host genome. Based on computational metrics that predict protein expression, ICOR theoretically optimizes protein expression more than frequency-based approaches. ICOR is evaluated on 1,481 Escherichia coli genes as well as a benchmark set of 40 select DNA sequences whose heterologous expression has been previously characterized. ICOR’s performance is measured across five metrics: the Codon Adaptation Index, GC-content, negative repeat elements, negative cis-regulatory elements, and codon frequency distribution.ConclusionsThe results, based on in silico metrics, indicate that ICOR codon optimization is theoretically more effective in enhancing recombinant expression of proteins over other established codon optimization techniques. Our tool is provided as an open-source software package that includes the benchmark set of sequences used in this study.

Learning to Read and Write in the Language of Proteins

Learning to Read and Write in the Language of Proteins

Menshov-Type Theorem for Divergence Sets of Sequences of Localized Operators

Menshov-Type Theorem for Divergence Sets of Sequences of Localized Operators

Regulatory Networks of lncRNAs, miRNAs, and mRNAs in Response to Heat Stress in Wheat (Triticum Aestivum L.): An Integrated Analysis.

Climate change has become a major source of concern, particularly in agriculture, because it has a significant impact on the production of economically important crops such as wheat, rice, and maize. In the present study, an attempt has been made to identify differentially expressed heat stress-responsive long non-coding RNAs (lncRNAs) in the wheat genome using publicly available wheat transcriptome data (24 SRAs) representing two conditions, namely, control and heat-stressed. A total of 10,965 lncRNAs have been identified and, among them, 153, 143, and 211 differentially expressed transcripts have been found under 0 DAT, 1 DAT, and 4 DAT heat-stress conditions, respectively. Target prediction analysis revealed that 4098 lncRNAs were targeted by 119 different miRNA responses to a plethora of environmental stresses, including heat stress. A total of 171 hub genes had 204 SSRs (simple sequence repeats), and a set of target sequences had SNP potential as well. Furthermore, gene ontology analysis revealed that the majority of the discovered lncRNAs are engaged in a variety of cellular and biological processes related to heat stress responses. Furthermore, the modeled three-dimensional (3D) structures of hub genes encoding proteins, which had an appropriate range of similarity with solved structures, provided information on their structural roles. The current study reveals many elements of gene expression regulation in wheat under heat stress, paving the way for the development of improved climate-resilient wheat cultivars.

Підвищення ефективності максимальної потужності сигналів NOMA покоління 5G і вище з використанням SLM алгоритму

В роботі представлено новий підхід для послаблення шкідливих впливів великого значення відношення пікового рівня потужності до середнього PAPR (Peak-to-Average Power Ratio), притаманних сигналам неортогональної системи множинного доступу NOMA (Non-Orthogonal Multiple Access), при застосуванні техніки вибіркового відображення SLM (Selective Mapping). NOMA — добре відома схема множинного доступу в сучасних системах безпроводового зв’язку, яка сприяє одночасній передачі сигналів багатьох користувачів за допомогою одного частотно-часового ресурсу. Однак система NOMA чутлива до підвищених рівнів PAPR, що викликає погіршення ккд та міжсимвольні перешкоди. Пропонований метод використовує SLM для подолання піків PAPR в сигналах NOMA. Шляхом формування набору різноманітних послідовностей фаз, SLM створю альтернативні варіанти оригінального сигналу NOMA. Тоді фазова послідовність, яка призводить до найнижчого значення PAPR, вибирається для передачі. Ця динамічна адаптація суттєво зменшує піки переданого сигналу і таким чином підвищує ккд, мінімізує спотворення, та зменшу ризик нелінійного підсилення. Проведено моделювання для оцінки ккд процедури, що розробляється. Результати показали значне зниження PAPR в сигналах NOMA у порівнянні з традиційними методами передачі. Крім того, цей метод зберігає якість сигналу та зменшує рівень бітових помилок BER (bit error rate), покращу спектральну густину потужності PSD (power spectral density) та підвищу загальну надійність структури радіомовлення. Ця стаття приводить до висновку про реалістичність інтегрування SLM в існуючі системи на основі NOMA, пропонуючи перспективний шлях для оптимізації ефективності сучасних радіомереж.

Topologicality of the Whitney topology on function spaces in the ωμ-metric framework

Let X be a Tychonoff space, (Yd) a metric space and C(X,Y) the set of all continuous functions from X to Y. In the classical Whitney topology on C(X,Y) the approximation of functions is done by the means of continuous positive functions from X to the Euclidean real line. It is known that, whenever X is paracompact, the Whitney topology is independent of the metric d, in fact being a topological character. An analogous result holds whenever (Y,d) is replaced by an ωμ-metric space (Y,ρ,G), where ωμ is a regular ordinal number, ρ:Y×Y→G is a distance sharing the usual formal properties with real metrics but valued in an additive totally ordered Abelian group G admitting a strictly decreasing ωμ-sequence converging to zero in the order topology, or, the same, of character ωμ. Precisely, whenever ωμ is an uncountable ordinal and X is paracompact and ωμ-additive, then the Whitney topology on C(X,Y) is independent of the ωμ-metric ρ, in fact being again a topological character. The purpose of this paper is to fill the gap in the case the basic groups are of countable character but eventually non Archimedean. It comes up as very natural the preliminary question: how does a totally ordered Abelian group G look like? The Hahn's Embedding Theorem and the Hahn's Completeness Theorem provide models so making easier the investigation of their order topology in the distinguished two cases of countable character: the basic group G has finitely many Archimedean classes or infinitely many, then countably many, Archimedean classes. In the former, any basic group G with n,n≥2, Archimedean classes can be identified up to a bijective monotone homomorphism with a topological subgroup of Rn, which is a one-dimensional topological group when equipped with the pointwise addition and the lexicographic absolute value metric. In the latter, any basic group G with countably many Archimedean classes can be identified up to a bijective monotone homomorphism with a topological subgroup of Rℵ0, the set of all real sequences, which is a zero-dimensional topological group when equipped with the pointwise addition and the lexicographic absolute value metric. In the former case, we establish the following result including the classic metric one: Let X be paracompact. If(Y,ρ)is a G-metric with G of countable character and with finitely many Archimedean classes, eventually a real metric space, then the Whitney topology onC(X,Y)relative to ρ and G is independent of both ρ and G, in fact being a topological character. And in the latter one, we achieve the general following result: Let X be paracompact and(Y,ρ)a G of countable character and with infinitely many Archimedean classes. Then, the Whitney topology onC(X,Y), when different from the relative uniform topology, is independent of both ρ and G, so agreeing with the classical one, if and only if any real valued positive continuous function on X can be minorized with a positive locally constant function.1

Exploration of O-GlcNAc transferase glycosylation sites reveals a target sequence compositional bias

O-GlcNAc transferase (OGT) is an essential glycosylating enzyme that catalyzes the addition of N-acetylglucosamine to serine or threonine residues of nuclear and cytoplasmic proteins. The enzyme glycosylates a broad range of peptide sequences and the prediction of glycosylation sites has proven challenging. The lack of an experimentally verified set of polypeptide sequences that are not glycosylated by OGT has made prediction of legitimate glycosylation sites more difficult. Here, we tested a number of intrinsically disordered protein regions as substrates of OGT to establish a set of sequences that are not glycosylated by OGT. The negative data set suggests an amino acid compositional bias for OGT targets. This compositional bias was validated by modifying the amino acid composition of the protein fused in sarcoma (FUS) to enhance glycosylation. NMR experiments demonstrate that the tetratricopeptide repeat region of OGT can bind FUS and that glycosylation-promoting mutations enhance binding. These results provide evidence that the tetratricopeptide repeat region recognizes disordered segments of substrates with particular compositions to promote glycosylation, providing insight into the broad specificity of OGT.

Extensive Cryptic Diversity and Ecological Associations Uncovered among Mexican and Global Collections of Naegleria and Vermamoeba Species by 18S Ribosomal DNA, Internal Transcribed Spacer, and Cytochrome Oxidase Subunit I Sequence Analysis.

Free-living amoebae (FLA) are phagocytic protists that play crucial roles in microbial communities as significant microbial grazers. However, our current knowledge of their diversity, ecology, and population genetic structures is marginal due to the shallow and biased sampling of ecosystems and the use of few, poorly resolving molecular markers. Thirty-two FLA were isolated from soil and water samples collected across representative ecosystems of the State of Morelos in Central Mexico, including the drinking water distribution system (DWDS) from the state capital. We classified our isolates as members of Acanthamoeba, Vermamoeba, Naegleria, and Tetramitus by 18S ribosomal DNA (rDNA) sequencing. Vermamoeba isolates were recovered exclusively from the DWDS samples. In contrast, Naegleria strains displayed a broad distribution in soil and water samples across the natural ecosystems. We used a combination of phylogenetic and population genetic analyses of internal transcribed spacer (ITS) and cytochrome oxidase subunit I (COI) sequences from our isolates and a comprehensive set of reference sequences to analyze the currently known diversity of Naegleria spp. Significant associations were uncovered between the most prevalent lineages of Naegleria and Vermamoeba and broad ecological and geographical variables at regional and global levels. The population structure and cryptic diversity within the Naegleria galeacystis-Naegleria americana and Vermamoeba vermiformis species complexes were thoroughly analyzed. Our results prove that the genus Vermamoeba, which was previously thought to consist of only one species, actually encompasses at least seven widely distributed species, as indicated by consistent evidence from Bayesian phylogenetics, two species-delimitation programs, and population genetics analyses. IMPORTANCE Our study sheds new light on the population genetic structure of V. vermiformis and diverse Naegleria species. Using improved molecular markers and advanced analytical approaches, we discovered that N. americana, previously considered a single species, actually contains multiple distinct lineages, as revealed by COI sequencing. These lineages are highly differentiated, with little gene flow between them. Our findings demonstrate that the genus Vermamoeba holds multiple cryptic species, requiring a significant taxonomic revision in light of multilocus sequence analyses. These results advance our understanding of the ecology, molecular systematics, and biogeography of these genera and species complexes at both regional and global scales. This study has significant implications for diagnosing amoebal infections and evaluating health risks associated with FLA in domestic and recreational waters.

Ritualization of Online Worship During COVID-19 Pandemic: A Case Study in GKMI ARK

The COVID-19 outbreak that first happened in the city of Wuhan, China on January 2020 did not take long until it reached other parts of the world and then declared by World Health Organization as a pandemic within two month. In Indonesia, Mr. President Joko Widodo urged every citizen to do academic, economical, and religious activities at home in order to slow the spread of the virus. Christian congregations are among religious groups that had been affected by government’s measures to reduce the Coronavirus transmission. While online learning and remote working have been known and practiced by some, online worship was imaginable by none. Located within the “red area” where Coronavirus transmission is high, Gereja Kristen Muria Indonesia Anugerah Rayon Kembangan (GKMI ARK) was among the first churches in Indonesia to switch from on-site to fully online worship. The pastor and elders gave new set of rituals for online worship, to which the congregation must adapt, regardless of their preparedness. Ritual can be broadly defined as a set of meaningful sequence of actions produced by ritualization. This paper aims to explore the ritualization of online worship of GKMI ARK congregation during the COVID-19 pandemic.

Limit Bipolar Sequences for Patchwork-Based Robust Digital Audio Watermarking

Ensuring the robustness of digital audio watermarking under the influence of interference, various transformations and possible attacks is an urgent problem. One of the most used and fairly stable marking methods is the patchwork method. Its robustness is ensured by the use of expanding bipolar numerical sequences in the formation and embedding of a watermark in a digital audio and correlation detection in the detection and extraction of a watermark. An analysis of the patchwork method showed that the absolute values of the ratio of the maximum of the autocorrelation function (ACF) to its minimum for expanding bipolar sequences and extended marker sequences used in traditional digital watermarking approach 2 with high accuracy. This made it possible to formulate criteria for searching for special expanding bipolar sequences, which have improved correlation properties and greater robustness. The article developed a mathematical apparatus for searching and constructing limit-expanding bipolar sequences used in solving the problem of robust digital audio watermarking using the patchwork method. Limit bipolar sequences are defined as sequences whose autocorrelation functions have the maximum possible ratios of maximum to minimum in absolute value. Theorems and corollaries from them are formulated and proved: on the existence of an upper bound on the minimum values of autocorrelation functions of limit bipolar sequences and on the values of the first and second petals of the ACF. On this basis, a rigorous mathematical definition of limit bipolar sequences is given. A method for searching for the complete set of limit bipolar sequences based on rational search and a method for constructing limit bipolar sequences of arbitrary length using generating functions are developed. The results of the computer simulation of the assessment of the values of the absolute value of the ratio of the maximum to the minimum of the autocorrelation and cross-correlation functions of the studied bipolar sequences for blind reception are presented. It is shown that the proposed limit bipolar sequences are characterized by better correlation properties in comparison with the traditionally used bipolar sequences and are more robust.

A CP-based approach for mining sequential patterns with quantities

This paper addresses the problem of mining sequential patterns (SPM) from data represented as a set ofsequences. In this work, we are interested in sequences of items in which each item is associated with its quantity.To the best of our knowledge, existing approaches don’t allow to handle this kind of sequences under constraints.In the other hand, several proposals show the efficiency of constraint programming (CP) to solve SPM problemdealing with several kind of constraints. However, in this paper, we propose the global constraint QSPM whichis an extension of the two CP-based approaches proposed in [5] and [7]. Experiments on real-life datasets showthe efficiency of our approach allowing to specify many constraints like size, membership and regular expressionconstraints.

SCRAPT: an iterative algorithm for clustering large 16S rRNA gene data sets.

16S rRNA gene sequence clustering is an important tool in characterizing the diversity of microbial communities. As 16S rRNA gene data sets are growing in size, existing sequence clustering algorithms increasingly become an analytical bottleneck. Part of this bottleneck is due to the substantial computational cost expended on small clusters and singleton sequences. We propose an iterative sampling-based 16S rRNA gene sequence clustering approach that targets the largest clusters in the data set, allowing users to stop the clustering process when sufficient clusters are available for the specific analysis being targeted. We describe a probabilistic analysis of the iterative clustering process that supports the intuition that the clustering process identifies the larger clusters in the data set first. Using real data sets of 16S rRNA gene sequences, we show that the iterative algorithm, coupled with an adaptive sampling process and a mode-shifting strategy for identifying cluster representatives, substantially speeds up the clustering process while being effective at capturing the large clusters in the data set. The experiments also show thatSCRAPT(Sample, Cluster, Recruit, AdaPt and iTerate) is able to produce operational taxonomic units that are less fragmented than popular tools: UCLUST, CD-HIT and DNACLUST. The algorithm is implemented in the open-source package SCRAPT. The source code used to generate the results presented in this paper is available at https://github.com/hsmurali/SCRAPT.

Pleistocene glaciations in southern Poland – a revision

A revised stratigraphy the Lower and Middle Pleistocene in southern Poland is presented, based on geological evidence from key sites, better constraints on the number and age of Scandinavian ice sheet advances, and the stratigraphic setting of the interglacial sequences. Identification of the palaeomagnetic Brunhes/Matuyama boundary and an admixture of glacially derived material of northern provenance in fluvial deposits demonstrate that the oldest Scandinavian ice sheet advance (Nidanian Glaciation) in Poland and the first warming of the oldest interglacial (Podlasian) occurred in the late Early Pleistocene. The first ice sheet advance in the Oowiecim Basin is recorded by a till of the Nidanian Glaciation. A single till and its geological setting relative to deposits of the Podlasian and Ferdynandovian interglacials show that, contrary to previous opinions, the ice sheet advance of the early Middle Pleistocene Sanian 1 Glaciation was a single, the most extensive glacial event in the Sandomierz Basin and in western Ukraine. The Sanian 2 and the Odranian ice sheets were considerably less extensive in eastern Poland. The revised Pleistocene stratigraphy and palaeogeography of southern Poland is a significant step forward towards reliable correlation of the Polish climatostratigraphic units with the European stratigraphy

Efficient Pause Extraction and Encode Strategy for Alzheimer's Disease Detection Using Only Acoustic Features from Spontaneous Speech.

Clinical studies have shown that speech pauses can reflect the cognitive function differences between Alzheimer's Disease (AD) and non-AD patients, while the value of pause information in AD detection has not been fully explored. Herein, we propose a speech pause feature extraction and encoding strategy for only acoustic-signal-based AD detection. First, a voice activity detection (VAD) method was constructed to detect pause/non-pause feature and encode it to binary pause sequences that are easier to calculate. Then, an ensemble machine-learning-based approach was proposed for the classification of AD from the participants' spontaneous speech, based on the VAD Pause feature sequence and common acoustic feature sets (ComParE and eGeMAPS). The proposed pause feature sequence was verified in five machine-learning models. The validation data included two public challenge datasets (ADReSS and ADReSSo, English voice) and a local dataset (10 audio recordings containing five patients and five controls, Chinese voice). Results showed that the VAD Pause feature was more effective than common feature sets (ComParE: 6373 features and eGeMAPS: 88 features) for AD classification, and that the ensemble method improved the accuracy by more than 5% compared to several baseline methods (8% on the ADReSS dataset; 5.9% on the ADReSSo dataset). Moreover, the pause-sequence-based AD detection method could achieve 80% accuracy on the local dataset. Our study further demonstrated the potential of pause information in speech-based AD detection, and also contributed to a more accessible and general pause feature extraction and encoding method for AD detection.

From sequences to species: Charting the phytoplasma classification and taxonomy in the era of taxogenomics.

Phytoplasma taxonomy has been a topic of discussion for the last two and half decades. Since the Japanese scientists discovered the phytoplasma bodies in 1967, the phytoplasma taxonomy was limited to disease symptomology for a long time. The advances in DNA-based markers and sequencing improved phytoplasma classification. In 2004, the International Research Programme on Comparative Mycoplasmology (IRPCM)- Phytoplasma/Spiroplasma Working Team - Phytoplasma taxonomy group provided the description of the provisional genus 'Candidatus Phytoplasma' with guidelines to describe the new provisional phytoplasma species. The unintentional consequences of these guidelines led to the description of many phytoplasma species where species characterization was restricted to a partial sequence of the 16S rRNA gene alone. Additionally, the lack of a complete set of housekeeping gene sequences or genome sequences, as well as the heterogeneity among closely related phytoplasmas limited the development of a comprehensive Multi-Locus Sequence Typing (MLST) system. To address these issues, researchers tried deducing the definition of phytoplasma species using phytoplasmas genome sequences and the average nucleotide identity (ANI). In another attempts, a new phytoplasma species were described based on the Overall Genome relatedness Values (OGRI) values fetched from the genome sequences. These studies align with the attempts to standardize the classification and nomenclature of 'Candidatus' bacteria. With a brief historical account of phytoplasma taxonomy and recent developments, this review highlights the current issues and provides recommendations for a comprehensive system for phytoplasma taxonomy until phytoplasma retains 'Candidatus' status.

First report of Odontoglossum ringspot virus in Vanilla (Orchidaceae) in Madagascar.

Vanilla (Vanilla planifolia, Orchidaceae) is Madagascar's leading agricultural export resource which provides 80% of world's consumption. During a phytosanitary survey conducted from November 2019 to March 2021 in the main vanilla production regions of Madagascar, 250 plots were indexed for cymbidium mosaic virus (CymMV, Potexvirus genus) and odontoglossum ringspot virus (ORSV, Tobamovirus genus) the two most prevalent viruses of cultivated orchids worldwide (Zettler et al., 1990). For each plot, bulk samples (ten leaves taken at random) were assayed using Immunostrips (AGDIA, ISK 13301). A quarter of the plots (63/250) tested positive for CymMV. The highest prevalence of CymMV was observed in the SAVA region (57 out of 153 plots = 37,2%) where the virus has been reported since 1997 (Grisoni et al., 2010). Six plots located in the district of Mahanoro (Atsinanana) tested positive for ORSV. A few plants in these plots showed chlorotic often annular spots on their leaves. They were individually tested positive for ORSV, and negative for CymMV and potyviruses (Immunostrips AGDIA ISK 27200), the other two viruses reported so far in vanilla in Madagascar. To confirm the diagnosis of ORSV, leaf samples from five of the six infected plots were analysed by Tube Capture-RT-PCR (Grisoni et al., 2017) using two pairs of primers flanking the ORSV coat protein (CP) gene: OrCP1 (GGTCGGTAATGGTGTTAG) / OrCP2 (TGCATTATCGTATGCTCC), and CPOR-F(ATGTCTTACACTATTACAGACC) / CPOR-R(TTAGGAAGAGGTCCAAGTAAG). The five samples gave amplicons of the expected size (820 nt and 476 nt, respectively) and were sequenced with Sanger technology (Macrogen, The Netherlands). The ORSV-CP sequences of the Mahanoro isolates showed very close similarity to 198 ORSV-CP sequences from GenBank (95.8% to 99.6% nucleotide and 94.5 to 100% amino-acid identities), and less than 75.4% nucleotide (80.1% amino-acid) identities with Bell pepper mosaic virus (DQ355023), the tobamovirus closest to ORSV. The five ORSV-CP sequences from vanilla were deposited in GenBank under accessions numbers OM847399 to OM847403. These data confirmed that ORSV infects vanilla vines in Madagascar. To our knowledge, this is the first report of this virus in Madagascar and of its ability to infect symptomatically V. planifolia. The five ORSV isolates from vanilla had more than 98.7 % nucleotide identities of CP gene and clustered into a monophyletic group in maximum likelihood phylogenetic tree, suggesting a single origin of these isolates. To further investigate the origin of ORSV in Madagascar, we made use of RNA sequences isolated at different points in time to infer the timing of evolutionary events (Rieux et al., 2016). We estimated the CP gene substitution rate to 4.8E-4 subst/site/year [95%HPD 2.1E-4 - 8.7E-4] which is close to the estimate of He et al. (2019) based on a slightly different sequences set (1.25E-3 subst/site/year). We dated the initial contamination of vanilla plts by ORSV between 2004 and 2013. Both ORSV and CymMV have deleterious effects on many ornamental orchids, and the pathogenicity of CymMV is exacerbated when co-infecting with ORSV (Lee et al., 2021). Therefore, ORSV represents a new threat to the Malagasy vanilla crop, especially in regions where CymMV is already rife. Given the economic importance of vanilla cultivation in the country, the implementation of prophylactic measures aimed at preventing the spread of ORSV, in particular through the sanitary control of cuttings, should be a priority for the vanilla industry.

The difference an explanation makes: Verbal debrief effects when children cope with uncertainty

Previous research showed that uncertain, stochastic feedback drastically reduces children’s performance. Here, 145 children from 7 to 11 years learned sets of sequences of four left-right button presses, each press followed by a red/green signal. After each of the 15% randomly false feedback trials, children received a verbal debrief that it was either (1) a mistake, or (2) a lie, or (3) received a reassuring comment for 85% correct trials. The control group received no verbal debrief. In the stochastic condition children reflected more on previous trials than with 100% correct feedback. Verbal debriefs helped children to overcome the deterioration of the first two repetitions. Mistakes were discarded and therefore the most helpful comment. Lie debriefs yielded the most reflection on previous experience. Reassurance comments were not quite as efficient.

Machine learning for the identification of respiratory viral attachment machinery from sequences data.

At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral "spike" proteins from sequence can significantly accelerate the development of medical countermeasures. For six families of respiratory viruses, covering the vast majority of airborne and droplet-transmitted diseases, host cell entry is mediated by the binding of viral surface glycoproteins that interact with a host cell receptor. In this report it is shown that sequence data for an unknown virus belonging to one of the six families above provides sufficient information to identify the protein(s) responsible for viral attachment. Random forest models that take as input a set of respiratory viral sequences can classify the protein as "spike" vs. non-spike based on predicted secondary structure elements alone (with 97.3% correctly classified) or in combination with N-glycosylation related features (with 97.0% correctly classified). Models were validated through 10-fold cross-validation, bootstrapping on a class-balanced set, and an out-of-sample extra-familial validation set. Surprisingly, we showed that secondary structural elements and N-glycosylation features were sufficient for model generation. The ability to rapidly identify viral attachment machinery directly from sequence data holds the potential to accelerate the design of medical countermeasures for future pandemics. Furthermore, this approach may be extendable for the identification of other potential viral targets and for viral sequence annotation in general in the future.

The birth of a gene

The availability of an extensive set of vertebrate genome sequences, together with large-scale transcriptome studies, has allowed the discovery of numerous non-canonical ORFs (usually quite short) with evidence of transcription, translation and functional involvement. Orthologs for these ORFs can be detected in many vertebrates, and the time of appearance of a functional mini-gene can be ascertained. Some of these have appeared quite recently in evolution and have already very specific expression patterns in humans.

Profiling DNA Ligase Substrate Specificity with a Pacific Biosciences Single-Molecule Real-Time Sequencing Assay.

DNA ligases catalyze the joining of breaks in nucleic acid backbones and are essential enzymes for in vivo genome replication and repair across all domains of life. These enzymes are also critically important to in vitro manipulation of DNA in applications such as cloning, sequencing, and molecular diagnostics. DNA ligases generally catalyze the formation of a phosphodiester bond between an adjacent 5'-phosphate and 3'-hydroxyl in DNA, but they exhibit different substrate structure preferences, sequence-dependent biases in reaction kinetics, and variable tolerance for mismatched base pairs. Information on substrate structure and sequence specificity can inform both biological roles and molecular biology applications of these enzymes. Given the high complexity of DNA sequence space, testing DNA ligase substrate specificity on individual nucleic acid sequences in parallel rapidly becomes impractical when a large sequence space is investigated. Here, we describe methods for investigating DNA ligase sequence bias and mismatch discrimination using Pacific Biosciences Single-Molecule Real-Time (PacBio SMRT) sequencing technology. Through its rolling-circle amplification methodology, SMRT sequencing can give multiple reads of the same insert. This feature permits high-quality top- and bottom-strand consensus sequences to be determined while preserving information on top-bottom strand mismatches that can be obfuscated or lost when using other sequencing methods. Thus, PacBio SMRT sequencing is uniquely suited to measuring substrate bias and enzyme fidelity through multiplexing a diverse set of sequences in a single reaction. The protocols describe substrate synthesis, library preparation, and data analysis methods suitable for measuring fidelity and bias of DNA ligases. The methods are easily adapted to different nucleic acid substrate structures and can be used to characterize many enzymes under a variety of reaction conditions and sequence contexts in a rapid and high-throughput manner. © 2023 New England Biolabs and The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Preparation of overhang DNA substrates for ligation Basic Protocol 2: Preparation of ligation fidelity libraries Support Protocol 1: Preparation of ligation libraries for PacBio Sequel II sequencing Support Protocol 2: Loading and sequencing of a prepared library on the Sequel II instrument Basic Protocol 3: Computational processing of ligase fidelity sequencing data.