scholarly journals RedOak: a reference-free and alignment-free structure for indexing a collection of similar genomes

2020 ◽  
Author(s):  
Clement Agret ◽  
Annie Chateau ◽  
Gaetan Droc ◽  
Gautier Sarah ◽  
Alban Mancheron ◽  
...  
Keyword(s):  
Software Package ◽  
High Throughput Sequencing ◽  
Source Code ◽  
Free Software ◽  
Large Collection ◽  
Sequencing Technologies ◽  
Alignment Free ◽  
The Cost ◽  
New Algorithms ◽  
Extract Information

AbstractBackgroundAs the cost of DNA sequencing decreases, high-throughput sequencing technologies become increasingly accessible to many laboratories. Consequently, new issues emerge that require new algorithms, including tools for indexing and compressing hundred to thousands of complete genomes.ResultsThis paper presents RedOak, a reference-free and alignment-free software package that allows for the indexing of a large collection of similar genomes.RedOak can also be applied to reads from unassembled genomes, and it provides a nucleotide sequence query function. This software is based on a k-mer approach and has been developed to be heavily parallelized and distributed on several nodes of a cluster. The source code of our RedOak algorithm is available at https://gitlab.info-ufr.univ-montp2.fr/DoccY/RedOak.ConclusionsRedOak may be really useful for biologists and bioinformaticians expecting to extract information from large sequence datasets.

scholarly journals CRAFT: Compact genome Representation towards large-scale Alignment-Free daTabase

2020 ◽  
Author(s):  
Yang Young Lu ◽  
Jiaxing Bai ◽  
Yiwen Wang ◽  
Ying Wang ◽  
Fengzhu Sun
Keyword(s):  
Dna Sequences ◽  
Sequence Comparison ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Practical Interest ◽  
Supplementary Information ◽  
Computationally Efficient ◽  
Sequencing Technologies ◽  
Alignment Free

AbstractMotivationRapid developments in sequencing technologies have boosted generating high volumes of sequence data. To archive and analyze those data, one primary step is sequence comparison. Alignment-free sequence comparison based on k-mer frequencies offers a computationally efficient solution, yet in practice, the k-mer frequency vectors for large k of practical interest lead to excessive memory and storage consumption.ResultsWe report CRAFT, a general genomic/metagenomic search engine to learn compact representations of sequences and perform fast comparison between DNA sequences. Specifically, given genome or high throughput sequencing (HTS) data as input, CRAFT maps the data into a much smaller embedding space and locates the best matching genome in the archived massive sequence repositories. With 102 – 104-fold reduction of storage space, CRAFT performs fast query for gigabytes of data within seconds or minutes, achieving comparable performance as six state-of-the-art alignment-free measures.AvailabilityCRAFT offers a user-friendly graphical user interface with one-click installation on Windows and Linux operating systems, freely available at https://github.com/jiaxingbai/[email protected]; [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Discovering the Unknown: Improving Detection of Novel Species and Genera from Short Reads

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Gail L. Rosen ◽  
Robi Polikar ◽  
Diamantino A. Caseiro ◽  
Steven D. Essinger ◽  
Bahrad A. Sokhansanj
Keyword(s):  
High Throughput Sequencing ◽  
Mine Drainage ◽  
Novel Species ◽  
Classification Performance ◽  
Species Level ◽  
Metagenomic Data ◽  
Sequencing Technologies ◽  
Novel Taxa ◽  
New Algorithms ◽  
Better Than

High-throughput sequencing technologies enable metagenome profiling, simultaneous sequencing of multiple microbial species present within an environmental sample. Since metagenomic data includes sequence fragments (“reads”) from organisms that are absent from any database, new algorithms must be developed for the identification and annotation of novel sequence fragments. Homology-based techniques have been modified to detect novel species and genera, but, composition-based methods, have not been adapted. We develop a detection technique that can discriminate between “known” and “unknown” taxa, which can be used with composition-based methods, as well as a hybrid method. Unlike previous studies, we rigorously evaluate all algorithms for their ability to detect novel taxa. First, we show that the integration of a detector with a composition-based method performs significantly better than homology-based methods for the detection of novel species and genera, with best performance at finer taxonomic resolutions. Most importantly, we evaluate all the algorithms by introducing an “unknown” class and show that the modified version of PhymmBL has similar or better overall classification performance than the other modified algorithms, especially for the species-level and ultrashort reads. Finally, we evaluate theperformance of several algorithms on a real acid mine drainage dataset.

scholarly journals SALTS – SURFR (sncRNA) And LAGOOn (lncRNA) Transcriptomics Suite

2021 ◽  
Author(s):  
Mohan V Kasukurthi ◽  
Dominika Houserova ◽  
Yulong Huang ◽  
Addison A. Barchie ◽  
Justin T. Roberts ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Protein Coding ◽  
Web Based ◽  
Functional Roles ◽  
Sequencing Technologies ◽  
Active Research ◽  
The Cost ◽  
Analysis Platform ◽  
Transcriptional Output

ABSTRACTThe widespread utilization of high-throughput sequencing technologies has unequivocally demonstrated that eukaryotic transcriptomes consist primarily (>98%) of non-coding RNA (ncRNA) transcripts significantly more diverse than their protein-coding counterparts.ncRNAs are typically divided into two categories based on their length. (1) ncRNAs less than 200 nucleotides (nt) long are referred as small non-coding RNAs (sncRNAs) and include microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs), transfer ribonucleic RNAs (tRNAs), etc., and the majority of these are thought to function primarily in controlling gene expression. That said, the full repertoire of sncRNAs remains fairly poorly defined as evidenced by two entirely new classes of sncRNAs only recently being reported, i.e., snoRNA-derived RNAs (sdRNAs) and tRNA-derived fragments (tRFs). (2) ncRNAs longer than 200 nt long are known as long ncRNAs (lncRNAs). lncRNAs represent the 2nd largest transcriptional output of the cell (behind only ribosomal RNAs), and although functional roles for several lncRNAs have been reported, most lncRNAs remain largely uncharacterized due to a lack of predictive tools aimed at guiding functional characterizations.Importantly, whereas the cost of high-throughput transcriptome sequencing is now feasible for most active research programs, tools necessary for the interpretation of these sequencings typically require significant computational expertise and resources markedly hindering widespread utilization of these datasets. In light of this, we have developed a powerful new ncRNA transcriptomics suite, SALTS, which is highly accurate, markedly efficient, and extremely user-friendly. SALTS stands for SURFR (sncRNA) And LAGOOn (lncRNA) Transcriptomics Suite and offers platforms for comprehensive sncRNA and lncRNA profiling and discovery, ncRNA functional prediction, and the identification of significant differential expressions among datasets. Notably, SALTS is accessed through an intuitive Web-based interface, can be used to analyze either user-generated, standard next-generation sequencing (NGS) output file uploads (e.g., FASTQ) or existing NCBI Sequence Read Archive (SRA) data, and requires absolutely no dataset pre-processing or knowledge of library adapters/oligonucleotides.SALTS constitutes the first publically available, Web-based, comprehensive ncRNA transcriptomic NGS analysis platform designed specifically for users with no computational background, providing a much needed, powerful new resource capable of enabling more widespread ncRNA transcriptomic analyses. The SALTS WebServer is freely available online at http://salts.soc.southalabama.edu.

scholarly journals Deep embeddings to comprehend and visualize microbiome protein space

2021 ◽  
Author(s):  
Krzysztof Odrzywolek ◽  
Zuzanna Karwowska ◽  
Jan Majta ◽  
Aleksander Byrski ◽  
Kaja Milanowska-Zabel ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Language Model ◽  
Structure And Function ◽  
Metagenomic Data ◽  
Use Case ◽  
Sequencing Technologies ◽  
Alignment Free ◽  
And Function ◽  
Clinical Potential ◽  
Protein Space

Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that our model (ArdiMiPE) manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies such as ArdiMiPE that contextualize metagenomic data are a promising direction to deeply understand the microbiome.

scholarly journals Fast Inference of Individual Admixture Coefficients Using Geographic Data

2016 ◽  
Author(s):  
Kevin Caye ◽  
Flora Jay ◽  
Olivier Michel ◽  
Olivier François
Keyword(s):  
High Throughput Sequencing ◽  
R Package ◽  
Geographic Proximity ◽  
Geographic Space ◽  
Estimation Algorithms ◽  
Sequencing Technologies ◽  
Genotype Frequencies ◽  
Ancestry Estimation ◽  
Ancestry Proportions ◽  
New Algorithms

Accurately evaluating the distribution of genetic ancestry across geographic space is one of the main questions addressed by evolutionary biologists. This question has been commonly addressed through the application of Bayesian estimation programs allowing their users to estimate individual admixture proportions and allele frequencies among putative ancestral populations. Following the explosion of high-throughput sequencing technologies, several algorithms have been proposed to cope with computational burden generated by the massive data in those studies. In this context, incorporating geographic proximity in ancestry estimation algorithms is an open statistical and computational challenge. In this study, we introduce new algorithms that use geographic information to estimate ancestry proportions and ancestral genotype frequencies from population genetic data. Our algorithms combine matrix factorization methods and spatial statistics to provide estimates of ancestry matrices based on least-squares approximation. We demonstrate the benefit of using spatial algorithms through extensive computer simulations, and we provide an example of application of our new algorithms to a set of spatially referenced samples for the plant species Arabidopsis thaliana. Without loss of statistical accuracy, the new algorithms exhibit runtimes that are much shorter than those observed for previously developed spatial methods. Our algorithms are implemented in the R package, tess3r.

scholarly journals Reassortment of Genome Segments Creates Stable Lineages Among Strains of Orchid Fleck Virus Infecting Citrus in Mexico

2020 ◽  
Vol 110 (1) ◽  
pp. 106-120 ◽  
Author(s):  
Avijit Roy ◽  
Andrew L. Stone ◽  
Gabriel Otero-Colina ◽  
Gang Wei ◽  
Ronald H. Brlansky ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Sensu Stricto ◽  
Genome Segment ◽  
Rt Pcr ◽  
Sequence Comparisons ◽  
Orchid Fleck Virus ◽  
Reverse Transcription Pcr ◽  
Sequencing Technologies ◽  
Negative Sense

The genus Dichorhavirus contains viruses with bipartite, negative-sense, single-stranded RNA genomes that are transmitted by flat mites to hosts that include orchids, coffee, the genus Clerodendrum, and citrus. A dichorhavirus infecting citrus in Mexico is classified as a citrus strain of orchid fleck virus (OFV-Cit). We previously used RNA sequencing technologies on OFV-Cit samples from Mexico to develop an OFV-Cit–specific reverse transcription PCR (RT-PCR) assay. During assay validation, OFV-Cit–specific RT-PCR failed to produce an amplicon from some samples with clear symptoms of OFV-Cit. Characterization of this virus revealed that dichorhavirus-like particles were found in the nucleus. High-throughput sequencing of small RNAs from these citrus plants revealed a novel citrus strain of OFV, OFV-Cit2. Sequence comparisons with known orchid and citrus strains of OFV showed variation in the protein products encoded by genome segment 1 (RNA1). Strains of OFV clustered together based on host of origin, whether orchid or citrus, and were clearly separated from other dichorhaviruses described from infected citrus in Brazil. The variation in RNA1 between the original (now OFV-Cit1) and the new (OFV-Cit2) strain was not observed with genome segment 2 (RNA2), but instead, a common RNA2 molecule was shared among strains of OFV-Cit1 and -Cit2, a situation strikingly similar to OFV infecting orchids. We also collected mites at the affected groves, identified them as Brevipalpus californicus sensu stricto, and confirmed that they were infected by OFV-Cit1 or with both OFV-Cit1 and -Cit2. OFV-Cit1 and -Cit2 have coexisted at the same site in Toliman, Queretaro, Mexico since 2012. OFV strain-specific diagnostic tests were developed.

scholarly journals Application of Oxford Nanopore Technology to Plant Virus Detection

Viruses ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1424
Author(s):  
Lia W. Liefting ◽  
David W. Waite ◽  
Jeremy R. Thompson
Keyword(s):  
Plant Virus ◽  
High Throughput Sequencing ◽  
Virus Detection ◽  
Diagnostic Methods ◽  
Plant Virus Detection ◽  
Sequencing Technologies ◽  
Oxford Nanopore ◽  
Virus Diagnostics ◽  
Post Entry ◽  
Read Accuracy

The adoption of Oxford Nanopore Technologies (ONT) sequencing as a tool in plant virology has been relatively slow despite its promise in more recent years to yield large quantities of long nucleotide sequences in real time without the need for prior amplification. The portability of the MinION and Flongle platforms combined with lowering costs and continued improvements in read accuracy make ONT an attractive method for both low- and high-scale virus diagnostics. Here, we provide a detailed step-by-step protocol using the ONT Flongle platform that we have developed for the routine application on a range of symptomatic post-entry quarantine and domestic surveillance plant samples. The aim of this methods paper is to highlight ONT’s feasibility as a valuable component to the diagnostician’s toolkit and to hopefully stimulate other laboratories towards the eventual goal of integrating high-throughput sequencing technologies as validated plant virus diagnostic methods in their own right.

scholarly journals Assessing genotyping errors in mammalian museum study skins using high-throughput genotyping-by-sequencing

2021 ◽  
Author(s):  
Stella C. Yuan ◽  
Eric Malekos ◽  
Melissa T. R. Hawkins
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Massively Parallel Sequencing ◽  
Massively Parallel ◽  
Museum Specimens ◽  
Museum Specimen ◽  
Genotyping Errors ◽  
Allelic Dropout ◽  
Parallel Sequencing ◽  
Sequencing Technologies

AbstractThe use of museum specimens held in natural history repositories for population and conservation genetic research is increasing in tandem with the use of massively parallel sequencing technologies. Short Tandem Repeats (STRs), or microsatellite loci, are commonly used genetic markers in wildlife and population genetic studies. However, they traditionally suffered from a host of issues including length homoplasy, high costs, low throughput, and difficulties in reproducibility across laboratories. Massively parallel sequencing technologies can address these problems, but the incorporation of museum specimen derived DNA suffers from significant fragmentation and exogenous DNA contamination. Combatting these issues requires extra measures of stringency in the lab and during data analysis, yet there have not been any high-throughput sequencing studies evaluating microsatellite allelic dropout from museum specimen extracted DNA. In this study, we evaluate genotyping errors derived from mammalian museum skin DNA extracts for previously characterized microsatellites across PCR replicates utilizing high-throughput sequencing. We found it useful to classify samples based on DNA concentration, which determined the rate by which genotypes were accurately recovered. Longer microsatellites performed worse in all museum specimens. Allelic dropout rates across loci were dependent on sample quantity, with high concentration museum specimens performing as well and recovering quality metrics nearly as high as the frozen tissue sample. Based on our results, we provide a set of best practices for quality assurance and incorporation of reliable genotypes from museum specimens.

scholarly journals Multi-omics approach to precision medicine for immune-mediated diseases

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Mineto Ota ◽  
Keishi Fujio
Keyword(s):  
Treatment Response ◽  
High Throughput Sequencing ◽  
Disease Risk ◽  
Clinical Information ◽  
Clinical Settings ◽  
Social Significance ◽  
Sequencing Technologies ◽  
Immune Mediated ◽  
Recent Innovation ◽  
Future Direction

AbstractRecent innovation in high-throughput sequencing technologies has drastically empowered the scientific research. Consequently, now, it is possible to capture comprehensive profiles of samples at multiple levels including genome, epigenome, and transcriptome at a time. Applying these kinds of rich information to clinical settings is of great social significance. For some traits such as cardiovascular diseases, attempts to apply omics datasets in clinical practice for the prediction of the disease risk have already shown promising results, although still under way for immune-mediated diseases. Multiple studies have tried to predict treatment response in immune-mediated diseases using genomic, transcriptomic, or clinical information, showing various possible indicators. For better prediction of treatment response or disease outcome in immune-mediated diseases, combining multi-layer information together may increase the power. In addition, in order to efficiently pick up meaningful information from the massive data, high-quality annotation of genomic functions is also crucial. In this review, we discuss the achievement so far and the future direction of multi-omics approach to immune-mediated diseases.

scholarly journals Profiling DNA Methylation Based on Next-Generation Sequencing Approaches: New Insights and Clinical Applications

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 429 ◽  
Author(s):  
Daniela Barros-Silva ◽  
C. Marques ◽  
Rui Henrique ◽  
Carmen Jerónimo
Keyword(s):  
Dna Methylation ◽  
Next Generation Sequencing ◽  
High Throughput Sequencing ◽  
Epigenetic Modification ◽  
Response To Therapy ◽  
Next Generation ◽  
Sequencing Technologies ◽  
Prognosis And Prediction ◽  
Novel Biomarkers ◽  
Generation Sequencing

DNA methylation is an epigenetic modification that plays a pivotal role in regulating gene expression and, consequently, influences a wide variety of biological processes and diseases. The advances in next-generation sequencing technologies allow for genome-wide profiling of methyl marks both at a single-nucleotide and at a single-cell resolution. These profiling approaches vary in many aspects, such as DNA input, resolution, coverage, and bioinformatics analysis. Thus, the selection of the most feasible method according with the project’s purpose requires in-depth knowledge of those techniques. Currently, high-throughput sequencing techniques are intensively used in epigenomics profiling, which ultimately aims to find novel biomarkers for detection, diagnosis prognosis, and prediction of response to therapy, as well as to discover new targets for personalized treatments. Here, we present, in brief, a portrayal of next-generation sequencing methodologies’ evolution for profiling DNA methylation, highlighting its potential for translational medicine and presenting significant findings in several diseases.

Sign in / Sign up

Export Citation Format

Share Document