Biological Sequences Research Articles

Growth Factors: Key Biological Mediators Involved in Periodontal Regeneration

Healing of periodontal tissue is a complex process which involves various molecular and biological sequence of co-ordinated events. Growth factors plays a vital role in periodontal regeneration. The following review gives insight into role of various growth factors involved in periodontal regeneration. Keywords: Cellular Biology, Growth factors, Healing, Regeneration.

Remediation of arsenic-containing ferrihydrite in soil using iron- and sulfate-reducing bacteria: Implications for microbially-assisted clean technology

The iron-reducing bacterium (IRB) Shewanella oneidensis MR-1 and a sulfate-reducing bacterium (SRB) Desulfovibrio vlugaris miyazaki, were used in varying sequences for batch and column arsenic extraction experiments to assess their use in alleviating arsenic pollution from arsenic-containing ferrihydrite in soil. Both batch (four conditions) and column experiments show variations in biological sequence affect the arsenic release and species. The results of arsenic release from batch conditions are: IRB-SRB (2.28 mg/g) > SRB-IRB (1.72 mg/g) > SRB+IRB (0.55 mg/g) > Control group (pure water, 0.04 mg/g). Further cycle operations from batch experiments showed that IRB-SRB operation always obtained the best performance of releasing arsenic. The column experiments achieved greater arsenic release: SRB+IRB (10.87 mg/g) > IRB-SRB (7.54 mg/g) > SRB-IRB (6.48 mg/g) relative to arsenic in the pristine samples (36.6 mg/g). Flow methods could obtain more releasing performance than batch experiment. The hydraulic conductivity in the column experiments was best in the SRB-IRB operation (average water difference: 0.39 psi for SRB-IRB, 0.86 psi for IRB-SRB and 1.07 psi for SRB+IRB). After the biological operation, chemical treatment (0.5 M, (NH4)2HPO4) only desorbed 11.14–39.81% of the total arsenic quantity of each operation. Reductive dissolution is the main driving reason for arsenic release, though the formation of FeS was found to hinder arsenic release. Analysis of the solids showed that the SRB+IRB operation results in 58.07% arsenic released. These results suggest that microbially-assisted reduction using iron- and sulfate-reducing bacteria may be an effective technology for remediating arsenic-bearing soil e.g. paddy soil using systems such as an in-situ injection and pump.

Management of DNA reference libraries for barcoding and metabarcoding studies with the R package refdb.

DNA barcoding and metabarcoding are revolutionizing the study and survey of biodiversity. In order to assign taxonomic labels to the DNA sequence data retrieved, these methods are strongly dependent on comprehensive and accurate reference databases. Producing reliable databases linking biological sequences and taxonomic data can be-and often has been-done using mainstream tools such as spreadsheet software. However, spreadsheets quickly become insufficient when the amount of data increases to thousands of taxa and sequences to be matched, and validation operations become more complex and are error prone if done in a manual way. Thus, there is a clear need for providing scientists with user-friendly, reliable and powerful tools to manipulate and manage DNA reference databases in tractable, sound and efficient ways. Here, we introduce the R package refdb as an environment for semi-automatic and assisted construction of DNA reference libraries. The refdb package is a reference database manager offering a set of powerful functions to import, organize, clean, filter, audit and export the data. It is broadly applicable in metabarcoding data generally obtained in biodiversity and biomonitoring studies. We present the main features of the package and outline how refdb can speed up reference database generation, management and handling, and thus contribute to standardization and repeatability in barcoding and metabarcoding studies.

Multifarious aspects of the chaos game representation and its applications in biological sequence analysis

Chaos game representation (CGR) has been successfully applied in bioinformatics for over 30 years. Since then, many further extensions were announced. Numerical encoding of biological sequences is especially convenient in the visualisation process, free-alignment methods and input preparation for machine learning techniques. The development and applications of CGR have embraced mainly linear nucleotide sequences. However, there were also some attempts to create a representation of proteins. The latter need to be more sophisticated, as arbitrary coordinates for amino acids do not reflect their properties which is crucial during the encoding process. In this paper, the authors summarised various variations of CGRs and their limitations. We began by studying the PROSITE motifs and showed the immense number of amino acid properties employed by different proteins. To this aim, we harnessed the Principal Component Analysis (PCA) and studied the relation between explained variance and the number of features that describe them. It appeared that even after many reductions, about 50 features are non-redundant. This was the reason we introduced an embedding concept from natural language processing which enables adjusting features for a given list of sequences. We presented a simple neural network architecture with one hidden layer and one neuron within it and showed it provides satisfactory results in phylogenetic tree construction in ND5 and SPARC protein cases. To this aim, we transformed CGR representations for all considered sequences using Discrete Fourier Transform (DFT) and applied Unweighted Pair Group Method with Arithmetic Mean (UPGMA) algorithm. Moreover, we indicated some similarities between CGR and Recurrent Neural Networks (RNN). In the end, we attempted to include information about the RNA secondary structure and defined some measures to validate biological significance. We studied their properties and showed on ALMV-3 example its usefulness.

Integrated analysis of miRNAs and mRNA profiling reveals the potential roles of miRNAs in sheep hair follicle development

BackgroundMerino sheep exhibit high wool production and excellent wool quality. The fleece of Merino sheep is predominantly composed of wool fibers grown from hair follicles (HFs). The HF is a complex biological system involved in a dynamic process governed by gene regulation, and gene expression is regulated by microRNAs (miRNAs). miRNA inhibits posttranscriptional gene expression by specifically binding to target messenger RNA (mRNA) and plays an important role in regulating gene expression, the cell cycle and biological development sequences. The purpose of this study was to examine mRNA and miRNA binding to identify key miRNAs and target genes related to HF development. This will provide new and important insights into fundamental mechanisms that regulate cellular activity and cell fate decisions within and outside of the skin.ResultsWe analyzed miRNA data in skin tissues collected from 18 Merino sheep on four embryonic days (E65, E85, E105 and E135) and two postnatal days (D7 and D30) and identified 87 differentially expressed miRNAs (DE-miRNAs). These six stages were further divided into two longer developmental stages based on heatmap cluster analysis, and the results showed that DE-mRNAs in Stage A were closely related to HF morphogenesis. A coanalysis of Stage A DE-mRNAs and DE-miRNAs revealed that 9 DE-miRNAs and 17 DE-mRNAs presented targeting relationships in Stage A. We found that miR-23b and miR-133 could target and regulate ACVR1B and WNT10A. In dermal fibroblasts, the overexpression of miR-133 significantly reduced the mRNA and protein expression levels of ACVR1B. The overexpression of miR-23b significantly reduced the mRNA and protein expression levels of WNT10A.ConclusionThis study provides a new reference for understanding the molecular basis of HF development and lays a foundation for further improving sheep HF breeding. miRNAs and target genes related to hair follicular development were found, which provided a theoretical basis for molecular breeding for the culture of fine-wool sheep.

Protein language models trained on multiple sequence alignments learn phylogenetic relationships

Self-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, including MSA Transformer and AlphaFold’s EvoFormer, take multiple sequence alignments (MSAs) of evolutionarily related proteins as inputs. Simple combinations of MSA Transformer’s row attentions have led to state-of-the-art unsupervised structural contact prediction. We demonstrate that similarly simple, and universal, combinations of MSA Transformer’s column attentions strongly correlate with Hamming distances between sequences in MSAs. Therefore, MSA-based language models encode detailed phylogenetic relationships. We further show that these models can separate coevolutionary signals encoding functional and structural constraints from phylogenetic correlations reflecting historical contingency. To assess this, we generate synthetic MSAs, either without or with phylogeny, from Potts models trained on natural MSAs. We find that unsupervised contact prediction is substantially more resilient to phylogenetic noise when using MSA Transformer versus inferred Potts models.

The dynseq browser track shows context-specific features at nucleotide resolution.

High-throughput experimental platforms have revolutionized the ability to profile biochemical and functional properties of biological sequences such as DNA, RNA and proteins. By collating several data modalities with customizable tracks rendered using intuitive visualizations, genome browsers enable an interactive and interpretable exploration of diverse types of genome profiling experiments and derived annotations. However, existing genome browser tracks are not well suited for intuitive visualization of high-resolution DNA sequence features such as transcription factor motifs. Typically, motif instances in regulatory DNA sequences are visualized as BED-based annotation tracks, which highlight the genomic coordinates of the motif instances but do not expose their specific sequences. Instead, a genome sequence track needs to be cross-referenced with the BED track to identify sequences of motif hits. Even so, quantitative information about the motif instances such as affinity or conservation as well as differences in base resolution from the consensus motif are not immediately apparent. This makes interpretation slow and challenging. This problem is compounded when analyzing several cellular states and/or molecular readouts (such as ATAC-seq and ChIP–seq) simultaneously, as coordinates of enriched regions (peaks) and the set of active transcription factor motifs vary across cell states.

Relation is an option for processing context information.

Attention mechanisms are one of the most frequently used architectures in the development of artificial intelligence because they can process contextual information efficiently. Various artificial intelligence architectures, such as Transformer for processing natural language, image data, etc., include the Attention. Various improvements have been made to enhance its performance since Attention is a powerful component to realize artificial intelligence. The time complexity of Attention depends on the square of the input sequence length. Developing methods to improve the time complexity of Attention is one of the most popular research topics. Attention is a mechanism that conveys contextual information of input sequences to downstream networks. Thus, if one wants to improve the performance of processing contextual information, the focus should not be confined only on improving Attention but also on devising other similar mechanisms as possible alternatives. In this study, we devised an alternative mechanism called “Relation” that can understand the context information of sequential data. Relation is easy to implement, and its time complexity depends only on the length of the sequences; a comparison of the performance of Relation and Attention on several benchmark datasets showed that the context processing capability of Relation is comparable to that of Attention but with less computation time. Processing contextual information at high speeds would be useful because natural language processing and biological sequence processing sometimes deal with very long sequences. Hence, Relation is an ideal option for processing context information.

Function-based classification of hazardous biological sequences: Demonstration of a new paradigm for biohazard assessments.

Bioengineering applies analytical and engineering principles to identify functional biological building blocks for biotechnology applications. While these building blocks are leveraged to improve the human condition, the lack of simplistic, machine-readable definition of biohazards at the function level is creating a gap for biosafety practices. More specifically, traditional safety practices focus on the biohazards of known pathogens at the organism-level and may not accurately consider novel biodesigns with engineered functionalities at the genetic component-level. This gap is motivating the need for a paradigm shift from organism-centric procedures to function-centric biohazard identification and classification practices. To address this challenge, we present a novel methodology for classifying biohazards at the individual sequence level, which we then compiled to distinguish the biohazardous property of pathogenicity at the whole genome level. Our methodology is rooted in compilation of hazardous functions, defined as a set of sequences and associated metadata that describe coarse-level functions associated with pathogens (e.g., adherence, immune subversion). We demonstrate that the resulting database can be used to develop hazardous “fingerprints” based on the functional metadata categories. We verified that these hazardous functions are found at higher levels in pathogens compared to non-pathogens, and hierarchical clustering of the fingerprints can distinguish between these two groups. The methodology presented here defines the hazardous functions associated with bioengineering functional building blocks at the sequence level, which provide a foundational framework for classifying biological hazards at the organism level, thus leading to the improvement and standardization of current biosecurity and biosafety practices.

SODA: a TypeScript/JavaScript library for visualizing biological sequence annotation.

We present SODA, a lightweight and open-source visualization library for biological sequence annotations that enables straightforward development of flexible, dynamic and interactive web graphics. SODA is implemented in TypeScript and can be used as a library within TypeScript and JavaScript.

E. coli 6S RNA complexed to RNA polymerase maintains product RNA synthesis at low cellular ATP levels by initiation with noncanonical initiator nucleotides.

The E. coli 6S RNA is an RNA polymerase (RNAP) inhibitor that competes with σ70-dependent DNA promoters for binding to RNAP holoenzyme (RNAP:σ70). The 6S RNA when bound is then used as a template to synthesize a short product RNA (pRNA; usually 13-nt-long). This pRNA changes the 6S RNA structure, triggering the 6S RNA:pRNA complex to release and allowing DNA-dependent housekeeping gene expression to resume. In high nutrient conditions, 6S RNA turnover is extremely rapid but becomes very slow in low nutrient environments. This leads to a large accumulation of inhibited RNAP:σ70 in stationary phase. As pRNA initiates synthesis with ATP, we and others have proposed that the 6S RNA release rate strongly depends on ATP levels as a proxy for sensing the cellular metabolic state. By purifying endogenous 6S RNA:pRNA complexes using RNA Mango and using reverse transcriptase to generate pRNA-cDNA chimeras, we demonstrate that 6S RNA:pRNA formation can be simultaneous with 6S RNA 5' maturation. More importantly, we find a dramatic accumulation of capped pRNAs during stationary phase. This indicates that ATP levels in stationary phase are low enough for noncanonical initiator nucleotides (NCINs) such as NAD+ and NADH to initiate pRNA synthesis. In vitro, mutation of the conserved 6S RNA template sequence immediately upstream of the pRNA transcriptional start site can increase or decrease the pRNA capping efficiency, suggesting that evolution has tuned the biological 6S RNA sequence for an optimal capping rate. NCIN-initiated pRNA synthesis may therefore be essential for cell viability in low nutrient conditions.

Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy

In recent years, there has been an exponential growth in sequencing projects due to accelerated technological advances, leading to a significant increase in the amount of data and resulting in new challenges for biological sequence analysis. Consequently, the use of techniques capable of analyzing large amounts of data has been explored, such as machine learning (ML) algorithms. ML algorithms are being used to analyze and classify biological sequences, despite the intrinsic difficulty in extracting and finding representative biological sequence methods suitable for them. Thereby, extracting numerical features to represent sequences makes it statistically feasible to use universal concepts from Information Theory, such as Tsallis and Shannon entropy. In this study, we propose a novel Tsallis entropy-based feature extractor to provide useful information to classify biological sequences. To assess its relevance, we prepared five case studies: (1) an analysis of the entropic index q; (2) performance testing of the best entropic indices on new datasets; (3) a comparison made with Shannon entropy and (4) generalized entropies; (5) an investigation of the Tsallis entropy in the context of dimensionality reduction. As a result, our proposal proved to be effective, being superior to Shannon entropy and robust in terms of generalization, and also potentially representative for collecting information in fewer dimensions compared with methods such as Singular Value Decomposition and Uniform Manifold Approximation and Projection.

Improving language model of human genome for DNA-protein binding prediction based on task-specific pre-training.

The DNA-protein binding plays a pivotal role in regulating gene expression and evolution, and computational identification of DNA-protein has drawn more and more attention in bioinformatics. Recently, variants of BERT are also used to capture the semantic information of DNA sequences for predicting DNA-protein bindings. In this study, we leverage a task-specific pre-training strategy on BERT using large-scale multi-source DNA-protein binding data and present TFBert. TFBert treats DNA sequences as natural sentences and k-mer nucleotides as words. It can effectively extract upstream and downstream nucleotide context information by pre-training the 690 unlabeled ChIP-seq datasets. Experiments show that the pre-trained model can achieve promising performance on every single dataset in the 690 ChIP-seq datasets after simple fine tuning, especially on small datasets. The average AUC is 94.7%, outperforming existing popular methods. In conclusion, this study provides a variant of BERT based on pre-training and achieved state-of-the-art results in predicting DNA-protein bindings. We believe that TFBert can provide insights into other biological sequence classification problems.

ENA Source Attribute Helper: An Application Programming Interface to facilitate accurate reference to biological source data

Background: Metadata attributes of sequences that accurately reference their biological sources, as specimens or other materials of origin, and link with natural history collections, are essential to facilitate the connections between different fields in life sciences and promote reusability of data. However, metadata used to reference the biological source of sequences available within the molecular data repositories are not always well structured or comprehensive. Methods: Within the scope of the Horizon 2020 project Biodiversity Community Integrated Knowledge Library (BiCIKL), we have developed a tool, the European Nucleotide Archive (ENA) Source Attribute Helper Application Programming Interface (API), to help users accurately report biological source-related sequence and sample attributes. This tool currently focuses on the attributes in which specimens, cultures or other materials are identified, from which the sequence data were derived, and uses curated data to obtain the unique codes for the institutions and collections holding the vouchers. The API's main functions include the presentation of metadata associated with queried institutions or collections, validation of institution and collection codes in the attribute strings provided by the user, and the construction of an attribute string based on user-entered data. The API does not however support the search of voucher specimen codes, as these need to be obtained directly from the voucher institutions. We describe the API and discuss use cases for its different endpoints. The API is available at https://www.ebi.ac.uk/ena/sah/api/. Conclusions: We expect the API to promote and support the initial submission and any subsequent curation of biological source attributes, and hereby contribute to better links between sequence data and natural history collections, and hence on to taxonomy and biodiversity research, towards increasing the discoverability, reusability and impact of data.

Differentiable Learning of Sequence-Specific Minimizer Schemes with DeepMinimizer.

Minimizers are widely used to sample representative k-mers from biological sequences in many applications, such as read mapping and taxonomy prediction. In most scenarios, having the minimizer scheme select as few k-mer positions as possible (i.e., having a low density) is desirable to reduce computation and memory cost. Despite the growing interest in minimizers, learning an effective scheme with optimal density is still an open question, as it requires solving an apparently challenging discrete optimization problem on the permutation space of k-mer orderings. Most existing schemes are designed to work well in expectation over random sequences, which have limited applicability to many practical tools. On the other hand, several methods have been proposed to construct minimizer schemes for a specific target sequence. These methods, however, only approximate the original objective with likewise discrete surrogate tasks that are not able to significantly improve the density performance. This article introduces the first continuous relaxation of the density minimizing objective, DeepMinimizer, which employs a novel Deep Learning twin architecture to simultaneously ensure both validity and performance of the minimizer scheme. Our surrogate objective is fully differentiable and, therefore, amenable to efficient gradient-based optimization using GPU computing. Finally, we demonstrate that DeepMinimizer discovers minimizer schemes that significantly outperform state-of-the-art constructions on human genomic sequences.

Morganella Phage Mecenats66 Utilizes an Evolutionarily Distinct Subtype of Headful Genome Packaging with a Preferred Packaging Initiation Site.

Both recognized species from the genus Morganella (M. morganii and M. psychrotolerans) are Gram-negative facultative anaerobic rod-shaped bacteria that have been documented as sometimes being implicated in human disease. Complete genomes of seven Morganella-infecting phages are publicly available today. Here, we report on the genomic characterization of an insect associated Morganella sp. phage, which we named Mecenats66, isolated from dead worker honeybees. Phage Mecenats66 was propagated, purified, and subjected to whole-genome sequencing with subsequent complete genome annotation. After the genome de novo assembly, it was noted that Mecenats66 might employ a headful packaging with a preferred packaging initiation site, although its terminase amino acid sequence did not fall within any of the currently recognized headful packaging subtype employing phage (that had their packaging strategy experimentally verified) with clusters on a terminase sequence phylogenetic tree. The in silico predicted packaging strategy was verified experimentally, validating the packaging initiation site and suggesting that Mecenats66 represents an evolutionarily distinct headful genome packaging with a preferred packaging initiation site strategy subtype. These findings can possibly be attributed to several of the phages already found within the public biological sequence repositories and could aid newly isolated phage packaging strategy predictions in the future.

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

BackgroundCurrent methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics.ResultsHere, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods.ConclusionsThis work illustrates how integrated temporal gene expression modalities may be leveraged for predicting cellular trajectories and sample-associated perturbation and disease phenotypes. Additionally, this study provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.

A survey on improving pattern matching algorithms for biological sequences

SummaryPattern matching is a highly useful procedure in several stages of the computational pipelines. Furthermore, some research trends in this research domain contributed to growing biological databases and updated them throughout time. This article proposes an comparison and analysis of different algorithms for match equivalent pattern matching like complexity, efficiency, and techniques. Which algorithm is best for which DNA sequence and why? This describes the different algorithms for various activities that include pattern matching as an important aspect of functionality. This article shows that BM, Horspool, ZT, QS, FS, Smith, and SSABS methods employ the bad character preprocessing function. In addition, BM, SSABS, TVSBS, and BRFS methods are using two approaches in the preprocessing stage, which decreases the preprocessing time. Furthermore, KR, QS, SSABS, BRFS, and Shift‐Or are not recommended for the long pattern, whereas ZT, FS, d‐BM, Raita, and Smith are not recommended for the short pattern. This is because they are time‐consuming and certain algorithms, such as ZT and DCPM, use a lot of time and space during the matching and search process, while others, such as d‐BM and TSW, save space and time. Although DCPM, BRFS, and QS are quicker than other algorithms, FLPM, PAPM, and LFPM rank highest in terms of complexity time.

Developmental Trajectories of Student Self-Perception over a Yearlong Introductory Biology Sequence.

Student self-perception is related to persistence in science. Yet how self-perception develops over time is less clear. We examined student self-perception trajectories and their relationship with gender, persons excluded due to ethnicity or race (PEER) status, and first-generation college student (FGCS) status across a yearlong introductory biology sequence. While we found similar rates of change in self-efficacy and science identity for all groups, females and PEER students had lower initial scores that failed to "catch up" to male and non-PEER scores by the end of the year. Students grouped into either high and stable or lower and decreasing trajectories for scientific community values, with first-generation college students overrepresented in the latter group. Additionally, we found no evidence for intersectionality of subgroups. We did find evidence that the relationship between gender and PEER status and science identity is likely mediated via self-efficacy. Taken together, our results suggest that introductory biology students develop self-efficacy and science identity at similar rates regardless of gender, PEER status, or FGCS status and that interventions targeting scientific community values for all students and self-efficacy of female and PEER students may be fruitful areas to pursue to increase persistence of students in the sciences and to reduce score differences between groups.

SEMgraph: an R package for causal network inference of high-throughput data with structural equation models.

With the advent of high-throughput sequencing in molecular biology and medicine, the need for scalable statistical solutions for modeling complex biological systems has become of critical importance. The increasing number of platforms and possible experimental scenarios raised the problem of integrating large amounts of new heterogeneous data and current knowledge, to test novel hypotheses and improve our comprehension of physiological processes and diseases. Combining network analysis and causal inference within the framework of structural equation modeling (SEM), we developed the R package SEMgraph. It provides a fully automated toolkit, managing complex biological systems as multivariate networks, ensuring robustness and reproducibility through data-driven evaluation of model architecture and perturbation, which is readily interpretable in terms of causal effects among system components. SEMgraph package is available at https://cran.r-project.org/web/packages/SEMgraph. Supplementary data are available at Bioinformatics online.