Unknown Sequence Research Articles

Exploring the Astrovirome of Shellfish Matrices Using Nanopore Sequencing.

Astroviruses are important human enteric pathogens transmissible with contaminated food and water. Astroviruses have also been identified in mammals, birds, lower vertebrates and invertebrates. The genetic diversity of human and animal astroviruses poses a challenge for diagnostics and taxonomy. As a proof of concept, we used a panastrovirus consensus primer set, able to amplify in a nested RT-PCR protocol a 400-nt-long fragment of the RNA-dependent RNA polymerase of most members of the Astroviridae family, in conjunction with a nanopore sequencing platform, to generate information on the astrovirome in filter-feeding mollusks. Amplicons generated from bivalve samples were used to generate libraries for deep sequencing. In three samples, only one unique RdRp sequence type was obtained. However, in seven samples and in three barcodes with eleven pooled samples, we identified a variety of known and unknown RdRp sequence types, in most cases distantly related to astrovirus sequences available in the databases. In total, 37 different sequence contigs were generated. Avian-origin astrovirus sequences were predominant, likely due to contamination of shellfish harvesting waters by marine birds. Astroviruses of the aquatic eco-system were also identified, whereas human astroviruses were not detected.

Ion-pumping microbial rhodopsin protein classification by machine learning approach

BackgroundRhodopsin is a seven-transmembrane protein covalently linked with retinal chromophore that absorbs photons for energy conversion and intracellular signaling in eukaryotes, bacteria, and archaea. Haloarchaeal rhodopsins are Type-I microbial rhodopsin that elicits various light-driven functions like proton pumping, chloride pumping and Phototaxis behaviour. The industrial application of Ion-pumping Haloarchaeal rhodopsins is limited by the lack of full-length rhodopsin sequence-based classifications, which play an important role in Ion-pumping activity. The well-studied Haloarchaeal rhodopsin is a proton-pumping bacteriorhodopsin that shows promising applications in optogenetics, biosensitized solar cells, security ink, data storage, artificial retinal implant and biohydrogen generation. As a result, a low-cost computational approach is required to identify Ion-pumping Haloarchaeal rhodopsin sequences and its subtype.ResultsThis study uses a support vector machine (SVM) technique to identify these ion-pumping Haloarchaeal rhodopsin proteins. The haloarchaeal ion pumping rhodopsins viz., bacteriorhodopsin, halorhodopsin, xanthorhodopsin, sensoryrhodopsin and marine prokaryotic Ion-pumping rhodopsins like actinorhodopsin, proteorhodopsin have been utilized to develop the methods that accurately identified the ion pumping haloarchaeal and other type I microbial rhodopsins. We achieved overall maximum accuracy of 97.78%, 97.84% and 97.60%, respectively, for amino acid composition, dipeptide composition and hybrid approach on tenfold cross validation using SVM. Predictive models for each class of rhodopsin performed equally well on an independent data set. In addition to this, similar results were achieved using another machine learning technique namely random forest. Simultaneously predictive models performed equally well during five-fold cross validation. Apart from this study, we also tested the own, blank, BLAST dataset and annotated whole-genome rhodopsin sequences of PWS haloarchaeal isolates in the developed methods. The developed web server (https://bioinfo.imtech.res.in/servers/rhodopred) can identify the Ion Pumping Haloarchaeal rhodopsin proteins and their subtypes. We expect this web tool would be useful for rhodopsin researchers.ConclusionThe overall performance of the developed method results show that it accurately identifies the Ionpumping Haloarchaeal rhodopsin and their subtypes using known and unknown microbial rhodopsin sequences. We expect that this study would be useful for optogenetics, molecular biologists and rhodopsin researchers.

Meta-Viromic Sequencing Reveals Virome Characteristics of Mosquitoes and Culicoides on Zhoushan Island, China.

Mosquitoes and biting Culicoides species are arbovirus vectors. Effective virome profile surveillance is essential for the prevention and control of insect-borne diseases. From June to September 2021, we collected eight species of female mosquito and Culicoides on Zhoushan Island, China, and used meta-viromic sequencing to analyze their virome compositions and characteristics. The classified virus reads were distributed in 191 genera in 66 families. The virus sequences in mosquitoes with the largest proportions were Iflaviridae (30.03%), Phasmaviridae (23.09%), Xinmoviridae (21.82%), Flaviviridae (13.44%), and Rhabdoviridae (8.40%). Single-strand RNA+ viruses formed the largest proportions of viruses in all samples. Blood meals indicated that blood-sucking mosquito hosts were mainly chicken, duck, pig, and human, broadly consistent with the habitats where the mosquitoes were collected. Novel viruses of the Orthobunyavirus, Narnavirus, and Iflavirus genera were found in Culicoides by de-novo assembly. The viruses with vertebrate hosts carried by mosquitoes and Culicoides also varied widely. The analysis of unclassified viruses and deep-learning analysis of the "dark matter" in the meta-viromic sequencing data revealed the presence of a large number of unknown viruses. IMPORTANCE The monitoring of the viromes of mosquitoes and Culicoides, widely distributed arbovirus transmission vectors, is crucial to evaluate the risk of infectious disease transmission. In this study, the compositions of the viromes of mosquitoes and Culicoides on Zhoushan Island varied widely and were related mainly to the host species, with different host species having different core viromes. and many unknown sequences in the Culicoides viromes remain to be annotated, suggesting the presence of a large number of unknown viruses.

Characterization and genome analysis of Vibrio phage vB_VhaP_PG11, representing a new viral genus

Vibrio is a kind of common gram-negative bacteria, which is widely distributed in marine and estuarine environments. In the study, a novel marine phage vB_VhaP_PG11, infecting Vibrio hangzhouensis, was isolated from the offshore waters of Qingdao, China. vB_VhaP_PG11 is a double-stranded DNA phage. The whole genome proteomic tree shows that vB_VhaP_PG11 phage is related to two Vibrio phages, Vibrio phage 1.238.A._10N.261.52.F10 and Vibrio phage 1.245.O._10N.261.54.C7, but with low homology. Their amino acids identity with vB_VhaP_PG11 is 42.77 and 41.49% respectively. The prediction results of genome-blast distance phylogeny (GBDP) and the analysis gene-sharing network indicate that vB_VhaP_PG11 belongs to a new genus in Schitoviridae, named Qingschitovirus. The study of Vibrio phage vB_VhaP_PG11 provides basic information contributing to a better understanding of interactions between Vibrio phages and their hosts and helps analyze unknown viral sequences in the metagenomic database.

DeepAProt: Deep learning based abiotic stress protein sequence classification and identification tool in cereals.

The impact of climate change has been alarming for the crop growth. The extreme weather conditions can stress the crops and reduce the yield of major crops belonging to Poaceae family too, that sustains 50% of the world's food calorie and 20% of protein intake. Computational approaches, such as artificial intelligence-based techniques have become the forefront of prediction-based data interpretation and plant stress responses. In this study, we proposed a novel activation function, namely, Gaussian Error Linear Unit with Sigmoid (SIELU) which was implemented in the development of a Deep Learning (DL) model along with other hyper parameters for classification of unknown abiotic stress protein sequences from crops of Poaceae family. To develop this models, data pertaining to four different abiotic stress (namely, cold, drought, heat and salinity) responsive proteins of the crops belonging to poaceae family were retrieved from public domain. It was observed that efficiency of the DL models with our proposed novel SIELU activation function outperformed the models as compared to GeLU activation function, SVM and RF with 95.11%, 80.78%, 94.97%, and 81.69% accuracy for cold, drought, heat and salinity, respectively. Also, a web-based tool, named DeepAProt (http://login1.cabgrid.res.in:5500/) was developed using flask API, along with its mobile app. This server/App will provide researchers a convenient tool, which is rapid and economical in identification of proteins for abiotic stress management in crops Poaceae family, in endeavour of higher production for food security and combating hunger, ensuring UN SDG goal 2.0.

Signaling landscape of mitochondrial non-coding RNAs

Human mitochondria are the vital cell organelle acting as a storehouse of energy generation and diverse regulatory functions. Mitochondrial DNA comprises 93% coding region and 7% non-coding regions, in which the non-coding region hypothesized as responsible for signaling is our specific interest. Here, we explored the unknown functions of mitochondrial non-coding RNAs by studying their respective signaling pathways. We retrieved conserved motifs of interactions from known experimental protein-RNA complexes to model unknown mitochondrial ncRNA sequences. Our results provide the ncRNAs list and show their involvement in four crucial pathways, such as (i) Processing of Capped Intron-Containing Pre-mRNA, (ii) Spliceosome, (iii) Spliceosomal assembly, and (iv) RNA Polymerase II Transcription, respectively. The interactome analysis revealed that the SRSF2 and U2AF2 proteins interact with ncRNAs. Further, we have simulated the selected ncRNA-protein complexes in cell-like environmental conditions and found them stable in terms of energetics. Through our study, we have identified an apparent interaction of mitochondrial ncRNAs with proteins and their role in critical signaling pathways, providing new insights into mitochondrial ncRNA-dependent gene regulation. Communicated by Ramaswamy H. Sarma

AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data.

Low-complexity data analysis is the area that addresses the search and quantification of regions in sequences of elements that contain low-complexity or repetitive elements. For example, these can be tandem repeats, inverted repeats, homopolymer tails, GC-biased regions, similar genes, and hairpins, among many others. Identifying these regions is crucial because of their association with regulatory and structural characteristics. Moreover, their identification provides positional and quantity information where standard assembly methodologies face significant difficulties because of substantial higher depth coverage (mountains), ambiguous read mapping, or where sequencing or reconstruction defects may occur. However, the capability to distinguish low-complexity regions (LCRs) in genomic and proteomic sequences is a challenge that depends on the model's ability to find them automatically. Low-complexity patterns can be implicit through specific or combined sources, such as algorithmic or probabilistic, and recurring to different spatial distances-namely, local, medium, or distant associations. This article addresses the challenge of automatically modeling and distinguishing LCRs, providing a new method and tool (AlcoR) for efficient and accurate segmentation and visualization of these regions in genomic and proteomic sequences. The method enables the use of models with different memories, providing the ability to distinguish local from distant low-complexity patterns. The method is reference and alignment free, providing additional methodologies for testing, including a highly flexible simulation method for generating biological sequences (DNA or protein) with different complexity levels, sequence masking, and a visualization tool for automatic computation of the LCR maps into an ideogram style. We provide illustrative demonstrations using synthetic, nearly synthetic, and natural sequences showing the high efficiency and accuracy of AlcoR. As large-scale results, we use AlcoR to unprecedentedly provide a whole-chromosome low-complexity map of a recent complete human genome and the haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar. The AlcoR method provides the ability of fast sequence characterization through data complexity analysis, ideally for scenarios entangling the presence of new or unknown sequences. AlcoR is implemented in C language using multithreading to increase the computational speed, is flexible for multiple applications, and does not contain external dependencies. The tool accepts any sequence in FASTA format. The source code is freely provided at https://github.com/cobilab/alcor.

Um dýpdarboringina í Vestmanna og í Lopra

A 1:50.000 geological map of the Faeroe Islands was published in 1969 with a shortened English edition in 1970. It was based on strictly chronostratigraphic principles. A total sequence of 3 km of tholeiitic basalt lavas was divided into a lower basalt series (about 900 m); a coal bearing sediment series (alout 10—15 m); a middle basalt series (about 1250 m) initiated by explosive volcanism producing agglomerates and tuffs, the tuffagglomerate zone; and an upper basalt series (about 675 m). Minor intrusions occur in form of dikes, sills and irregular intrusive bodies. The plateau has a low-angle easterly dip and the lavas of the lower basalt series are updomed in two areas, Vágar—Mykines and Suðuroy.
 Geological and geophysical investigations were continued out on the shelf around the islands and on the Faeroe bank, Bill Baily bank and Lousy bank to the west.
 Further geological information was obtained from two bore holes, one drilled in the summer 1980 at Vestmanna on Streymoy and the second in the summer and autumn of 1981 near the village of Lopra on Suðuroy (Fig- !)•
 The faeroese government organized, and was economically responsible for the drill project, which was carried out in collaboration with the Geological Survey of Denmark. The drilling work was entrusted to the icelandic »National Energy Authority« (Orkustofnun). The drilling project was supported by the Carlsberg Foundation because of its scientific significance,The bore hole at Vestmanna was drilled to determine if the lower basalt series and the coal bearing sediment series from Vágar could be traced down dip below sea level as far east as Streymoy, and to get information on the regional thermal gradient.
 The bore hoie at Vestmanna (Fig. 2) was drilled from just above sea level with full core to a depth of 660 m (Fig. 3). The coal bearing sedimentary series from Vágar was found at 556 m. Here it was 3 m thick and contained basaltic sand and pebbles, but not coal. The tuff-agglomerate zone did not occur at the drill site in Vestmanna. The drilling continued 101 m into the upper part of the lower basalt series. The thermal gradíent was 40°C/km.
 The Lopra bore hole was planned to penetrate at least 2 km into the unknown sequence below the known part of the lower basalt series and to obtain further thermal gradient data. The drill site was located west of the village Lopra a few m above sealevel, as low in the known part of the lower basalt series as possible (Fig. 4). A section through the bore hole reaching 2178 m below sea level is shown on Fig. 5. Six cores were taken (one core lost) at different levels and cuttings collected for each two m drilled. The drilled sequence consist of about 120 subaerially erupted lava flows indicating a subsidence of more than 2000 m. The lava flows average about 20 m in thickness while the intercalated sediments are up to 5 m. The drilled sequence consists ofaphyric to feldsparphyric tholeiitic basalts and must be considered as a continuation af the lower basalt series. The thickness of the total lava pile on the Faeroes is therefore at least 5 km and the early Tertiary basalt plateau on the European side of the North Atlantic, the Faeroe Islands, then reaches a thickness which is directly comparable with that on the East Greenland side of that ocean. The thermal gradient was about the samt as in Vestmanna.
 Final scientific results will be published in Fróðskaparrit (supplementa) in 2 years.

Characterisation of Streptococcus suis Isolates in the Czech Republic Collected from Diseased Pigs in the Years 2018-2022.

As in other countries, in the Czech Republic, Streptococcus suis infection in pigs is considered an economically significant disease for the pig industry, though little is known about its population structure. We collected S. suis isolates from 144 farms in the years 2018-2022. All samples were taken from animals suffering from symptoms indicating possible S. suis infection. Serotyping revealed the presence of 23 different serotypes, and 18.94% were non-typable strains. The most common was S7 (14.96%), while other serotypes had frequencies of less than 10%. Sequence typing identified 56 different sequence types, including 31 newly assigned sequence types together with 41 new alleles in genes in the MLST schema. A large portion of isolates (25.70%) were of unknown sequence type. The most common sequence types were ST29 (14.77%) and ST28 (10.04%); the other sequence types had frequencies of less than 10%. In total, 100 different combinations of serotypes and sequence types were identified. Among them, S7ST29 was found in 72 isolates, representing 13.63% of all isolates, and was significantly associated with the central nervous system. Many other isolates of particular serotype and sequence type combinations were found in a few cases, and a number of isolates were non-typable.

Identifying unknown specimens using predictive phylogenies for remote forensic education.

One significant impact of the COVID-19 pandemic for educators in forensic science was adapting what is traditionally a very applied field to a virtual learning environment. Because of this, science classes with a practical laboratory component had to implement significant adjustments to ensure that student learning objectives were still met, including practical elements. To provide learners with an alternative to a traditional lab exercise in forensic entomology, we designed an exercise to be conducted completely remotely to instruct students in how to identify a real unknown specimen using a hypothetical phylogenetic tree they create. Students retrieved data and make predictions using public databases, generate phylogenies based off sequence data, and finally determine the identity of the unknown sequence provided. This lab replacement exercise has been adapted to be accessible to learners from different academic levels and takes the length of one two-and-a-half-hour lab period to complete. Through creating this resource, we have been able to link molecular systematics to forensic education and provide learners who download this publicly available exercise insight into how forensic questions are answered.

Wiener Index of Some Brooms

In the field of chemical graph theory, a Wiener (topological) index is a type of a molecular descriptor that is calculated based on the molecular graph of alkanes. It gives the sum of geodesic distances (or shortest paths) between all pairs of vertices of the graph. We found and prove the Wiener indices of some Brooms, which are Caterpillars, giving several unknown sequences that are now added to the collection of the largest Online Encyclopedia of Integer Sequences.

Transposable element finder (TEF): finding active transposable elements from next generation sequencing data

BackgroundDetection of newly transposed events by transposable elements (TEs) from next generation sequence (NGS) data is difficult, due to their multiple distribution sites over the genome containing older TEs. The previously reported Transposon Insertion Finder (TIF) detects TE transpositions on the reference genome from NGS short reads using end sequences of target TE. TIF requires the sequence of target TE and is not able to detect transpositions for TEs with an unknown sequence.ResultThe new algorithm Transposable Element Finder (TEF) enables the detection of TE transpositions, even for TEs with an unknown sequence. TEF is a finding tool of transposed TEs, in contrast to TIF as a detection tool of transposed sites for TEs with a known sequence. The transposition event is often accompanied with a target site duplication (TSD). Focusing on TSD, two algorithms to detect both ends of TE, TSDs and target sites are reported here. One is based on the grouping with TSDs and direct comparison of k-mers from NGS without similarity search. The other is based on the junction mapping of TE end sequence candidates. Both methods succeed to detect both ends and TSDs of known active TEs in several tests with rice, Arabidopsis and Drosophila data and discover several new TEs in new locations. PCR confirmed the detected transpositions of TEs in several test cases in rice.ConclusionsTEF detects transposed TEs with TSDs as a result of TE transposition, sequences of both ends and their inserted positions of transposed TEs by direct comparison of NGS data between two samples. Genotypes of transpositions are verified by counting of junctions of head and tail, and non-insertion sequences in NGS reads. TEF is easy to run and independent of any TE library, which makes it useful to detect insertions from unknown TEs bypassed by common TE annotation pipelines.

Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

Detached off-grids, subject to the generated renewable energy (RE), need to balance and compensate the unstable power supply dependent on local source potential. Power quality (PQ) is a set of EU standards that state acceptable deviations in the parameters of electrical power systems to guarantee their operability without dropout. Optimization of the estimated PQ parameters in a day-horizon is essential in the operational planning of autonomous smart grids, which accommodate the norms for the specific equipment and user demands to avoid malfunctions. PQ data for all system states are not available for dozens of connected / switched on household appliances, defined by their binary load series only, as the number of combinations grows exponentially. The load characteristics and eventual RE contingent supply can result in system instability and unacceptable PQ events. Models, evolved by Artificial Intelligence (AI) methods using self-optimization algorithms, can estimate unknown cases and states in autonomous systems contingent on self-supply of RE power related to chaotic and intermitted local weather sources. A new multilevel extension procedure designed to incrementally improve the applicability and adaptability to training data. The initial AI model starts with binary load series only, which are insufficient to represent complex data patterns. The input vector is progressively extended with correlated PQ parameters at the next estimation level to better represent the active demand of the power consumer. Historical data sets comprise training samples for all PQ parameters, but only the load sequences of the switch-on appliances are available in the next estimation states. The most valuable PQ parameters are selected and estimated in the previous algorithm stages to be used as supplementary series in the next more precise computing. More complex models, using the previous PQ-data approximates, are formed at the secondary processing levels to estimate the target PQ-output in better quality. The new added input parameters allow us to evolve a more convenient model form. The proposed multilevel refinement algorithm can be generally applied in modelling of unknown sequence states of dynamical systems, initially described by binary series or other insufficient limited-data variables, which are inadequate in a problem representation. Most AI computing techniques can adapt this strategy to improve their adaptive learning and model performance.

Genome-Wide Identification of miRNAs and Its Downstream Transcriptional Regulatory Network during Seed Maturation in Tilia tuan

Seed maturation not only determines the qualities and yields of seeds, but also affects seed storage and quality preservation. MicroRNAs (miRNAs) are a ubiquitous regulatory factor of gene expression in eukaryotes, which participate in the complex regulatory network of gene expression during seed maturation. However, miRNAs involved in maturation of Tilia tuan are still unknown. To reveal the role of miRNAs in T. tuan, small RNAs were profiled by high-throughput sequencing during seed maturation at five developmental stages. By predicting the target genes of miRNAs, the expression patterns of miRNAs during seed maturation were analyzed to identify those related to seed maturation. A total of 187 known miRNAs belonging to 42 miRNA families were found at five different seed maturation stages. Based on the analysis of unknown sequences, eight novel miRNAs were identified; 11,775 targets of 195 miRNAs were identified. Large numbers of miRNAs with diverse expression patterns, multiple-targeting and co-targeting of many miRNAs, and a complex regulatory network of miRNA-target genes were identified during seed maturation. These miRNAs and their targets may be involved in fatty acid, ABA, and lignin biosynthesis. Our study provides more information about the miRNA regulatory network and deepens our understanding of the function of miRNAs in T. tuan. miRNAs are revealed to be crucial during seed maturation, which provides a basis for further study of the regulatory role of miRNAs during seed maturation.

Fusion primer driven racket PCR: A novel tool for genome walking.

The limitations of the current genome-walking strategies include strong background and cumbersome experimental processes. Herein, we report a genome-walking method, fusion primer-driven racket PCR (FPR-PCR), for the reliable retrieval of unknown flanking DNA sequences. Four sequence-specific primers (SSP1, SSP2, SSP3, and SSP4) were sequentially selected from known DNA (5'→3') to perform FPR-PCR. SSP3 is the fragment that mediates intra-strand annealing (FISA). The FISA fragment is attached to the 5' end of SSP1, generating a fusion primer. FPR-PCR comprises two rounds of amplification reactions. The single-fusion primary FPR-PCR begins with the selective synthesis of the target first strand, then allows the primer to partially anneal to some place(s) on the unknown region of this strand, producing the target second strand. Afterward, a new first strand is synthesized using the second strand as the template. The 3' end of this new first strand undergoes intra-strand annealing to the FISA site, followed by the formation of a racket-like DNA by a loop-back extension. This racket-like DNA is exponentially amplified in the secondary FPR-PCR performed using SSP2 and SSP4. We validated this FPR-PCR method by identifying the unknown flanks of Lactobacillus brevis CD0817 glutamic acid decarboxylase genes and the rice hygromycin gene.

RFfiller: a robust and fast statistical algorithm for gap filling in draft genomes.

Numerous published genomes contain gaps or unknown sequences. Gap filling is a critical final step in de novo genome assembly, particularly for large genomes. While certain computational approaches partially address the problem, others have shortcomings regarding the draft genome’s dependability and correctness (high rates of mis-assembly at gap-closing sites and high error rates). While it is well established that genomic repeats result in gaps, many sequence reads originating from repeat-related gaps are typically missed by existing approaches. A fast and reliable statistical algorithm for closing gaps in a draft genome is presented in this paper. It utilizes the alignment statistics between scaffolds, contigs, and paired-end reads to generate a Markov chain that appropriately assigns contigs or long reads to scaffold gap regions (only corrects candidate regions), resulting in accurate and efficient gap closure. To reconstruct the missing component between the two ends of the same insert, the RFfiller meticulously searches for valid overlaps (in repeat regions) and generates transition tables for similar reads, allowing it to make a statistical guess at the missing sequence. Finally, in our experiments, we show that the RFfiller’s gap-closing accuracy is better than that of other publicly available tools when sequence data from various organisms are used. Assembly benchmarks were used to validate RFfiller. Our findings show that RFfiller efficiently fills gaps and that it is especially effective when the gap length is longer. We also show that the RFfiller outperforms other gap closing tools currently on the market.

DAR-PCR: a new tool for efficient retrieval of unknown flanking genomic DNA

Various PCR-based genome-walking methods have been developed to acquire unknown flanking DNA sequences. However, the specificity and efficacy levels, and the operational processes, of the available methods are unsatisfactory. This work proposes a novel walking approach, termed differential annealing-mediated racket PCR (DAR-PCR). The key to DAR-PCR is the use of primer-mediated intra-strand annealing (ISA). An ISA primer consists of a 5’ root homologous to the known sequence and a heterologous 3’ bud. In the single low-stringency cycle, the ISA primer anneals to a site on an unknown region and extends towards the sequence-specific primer (SSP) 1 site, thereby forming a target single-stranded DNA bound by the SSP1 complement and the ISA primer. In the subsequent more stringent cycles, its complementary strand is accumulated, owing to the differential annealing between the moderate-stringency ISA primer and the high-stringency SSP1. The accumulation of this strand provides an opportunity for ISA mediated by the ISA primer root. A loop-back extension subsequent to ISA occurs, creating a racket-like DNA with the known region positioned at both ends of the unknown sequence. This DNA is exponentially amplified during the secondary PCR driven by an SSP pair inner to SSP1. DAR-PCR was validated as an efficient walking method by determining unknown flanking sequences in Lactobacillus brevis and Oryza sativa.

BOA: A partitioned view of genome assembly

SummaryDe novo genome assembly is a fundamental problem in computational molecular biology that aims to reconstruct an unknown genome sequence from a set of short DNA sequences (or reads) obtained from the genome. The relative ordering of the reads along the target genome is not known a priori, which is one of the main contributors to the increased complexity of the assembly process. In this article, with the dual objective of improving assembly quality and exposing a high degree of parallelism, we present a partitioning-based approach. Our framework, BOA (bucket-order-assemble), uses a bucketing alongside graph- and hypergraph-based partitioning techniques to produce a partial ordering of the reads. This partial ordering enables us to divide the read set into disjoint blocks that can be independently assembled in parallel using any state-of-the-art serial assembler of choice. Experimental results show that BOA improves both the overall assembly quality and performance.

Metabarcoding as an effective complement of microscopic studies in revealing the composition of the diatom community – a case study of an oxbow lake of Tisza River (Hungary) with the description of a new Mayamaea species

Diatoms are valuable bioindicators and their traditional classification and identification are mainly based on the morphological characteristics of their frustules. However, in recent years, DNA-based methods have been proposed and are rapidly growing in the scientific literature as a complementary tool to assess the ecological status of freshwaters. Diatom-based ecological status assessment uses indices calculated from sensitivity and tolerance values as well as relative abundance of species. Correct assessment requires an accurate identification of species. In the present study, diatom assemblages of an oxbow lake were investigated using light and scanning electron microscopy as well as metabarcoding using rbcL marker, and the identification results were compared, intending to match barcode sequences of species that are currently missing in the diatom reference database. The investigated oxbow is an important wetland for bird conservation, although it is impacted by land use. Taxon lists based on morphology and metabarcoding considerably differed when bioinformatics analysis involved DADA2 pipeline with Diat.barcode database. Previously unknown sequence variants of four pennate species were found with additional BLAST search. Using phylogeny and p-distance calculations sequences could be matched to three small-celled naviculoid species that were found under a microscope. One of them was found to be a new species of the genus Mayamaea and was described as a new species, Mayamaea ectorii. Additionally, spatial distribution maps for several small-celled naviculoid species are provided for the Hungarian territory.

Organizing the bacterial annotation space with amino acid sequence embeddings

BackgroundDue to the ever-expanding gap between the number of proteins being discovered and their functional characterization, protein function inference remains a fundamental challenge in computational biology. Currently, known protein annotations are organized in human-curated ontologies, however, all possible protein functions may not be organized accurately. Meanwhile, recent advancements in natural language processing and machine learning have developed models which embed amino acid sequences as vectors in n-dimensional space. So far, these embeddings have primarily been used to classify protein sequences using manually constructed protein classification schemes.ResultsIn this work, we describe the use of amino acid sequence embeddings as a systematic framework for studying protein ontologies. Using a sequence embedding, we show that the bacterial carbohydrate metabolism class within the SEED annotation system contains 48 clusters of embedded sequences despite this class containing 29 functional labels. Furthermore, by embedding Bacillus amino acid sequences with unknown functions, we show that these unknown sequences form clusters that are likely to have similar biological roles.ConclusionsThis study demonstrates that amino acid sequence embeddings may be a powerful tool for developing more robust ontologies for annotating protein sequence data. In addition, embeddings may be beneficial for clustering protein sequences with unknown functions and selecting optimal candidate proteins to characterize experimentally.