High-throughput Sequencing Datasets Research Articles

RNAdt: An online tutorial and data portal for the RNA structurome era

RNA is not only a passive transporter of genetic information, but also a pivotal player in all domains of life. RNA can regulate gene expression because of its involvement in transcription, mRNA modification and processing, and translation. RNA also possesses other intricate functions such as catalysis, ligand sensing, interaction with biomolecules, response to environment stresses, and information storage. The primary structure of RNA is single stranded, but it always folds into complex secondary and tertiary structures owing to base pairing and effects from the cellular environment. The importance of structure has been increasingly recognized in understanding the myriad functions of RNA. After decades of development, there is a wide range of RNA structure probing techniques. The marriage between structure probing and high-throughput sequencing (HTS) especially enables the measurement of RNA structure on a transcriptomic scale, advancing the advent of the RNA structurome era. Dozens of HTS-associated RNA structure probing methods have been published, so it is urgent to provide a user-friendly and easy-to-use resource for users who are perplexed by selecting the most suitable method for their experiments. Motivated by this demand, we collected currently available HTS-associated RNA structure probing methods and then developed RNAdt (freely accessible at http://www.zhounan.org/rnadt). RNAdt can be used as a web-based tutorial to learn fundamental knowledge of HTS-associated RNA structure probing methods. RNAdt can also be used as a data portal to access HTS data sets from previous RNA structurome studies. At the end of this work, we also provided perspectives on future development of RNA structure probing methods. Our study is expected to facilitate RNA structure probing and ultimately elucidate the connection between RNA structure and biological functions.

Data, time and money: evaluating the best compromise for inferring molecular phylogenies of non-model animal taxa

For over a decade now, High Throughput sequencing (HTS) approaches have revolutionized phylogenetics, both in terms of data production and methodology. While transcriptomes and (reduced) genomes are increasingly used, generating and analyzing HTS datasets remain expensive, time consuming and complex for most non-model taxa. Indeed, a literature survey revealed that 74% of the molecular phylogenetics trees published in 2018 are based on data obtained through Sanger sequencing. In this context, our goal was to identify the strategy that would represent the best compromise among costs, time and robustness of the resulting tree. We sequenced and assembled 32 transcriptomes of the marine mollusk family Turridae, considered as a typical non-model animal taxon. From these data, we extracted the loci most commonly used in gastropod phylogenies (cox1, 12S, 16S, 28S, h3 and 18S), full mitogenomes, and a reduced nuclear transcriptome representation. With each dataset, we reconstructed phylogenies and compared their robustness and accuracy. We discuss the impact of missing data and the use of statistical tests, tree metrics, and supertree and supermatrix methods to further improve phylogenetic data acquisition pipelines. We evaluated the overall costs (time and money) in order to identify the best compromise for phylogenetic data sampling in non-model animal taxa. Although sequencing full mitogenomes seems to constitute the best compromise both in terms of costs and node support, they are known to induce biases in phylogenetic reconstructions. Rather, we recommend to systematically include loci commonly used for phylogenetics and taxonomy (i.e. DNA barcodes, rRNA genes, full mitogenomes, etc.) among the other loci when designing baits for capture.

Balance between community assembly processes mediates species coexistence in agricultural soil microbiomes across eastern China.

Revealing the linkages between community assembly and species coexistence, which is crucial for the understanding of ecosystem diversity and functioning, is a fundamental but rarely investigated subject in microbial ecology. Here we examined archaeal, bacterial, and fungal community assembly in adjacent pairs of maize (water-unsaturated) and rice (water-saturated) fields across different habitats and regions throughout Eastern China. The high-throughput sequencing dataset was analyzed by variation partitioning, null model, and neutral community model analyses. We demonstrated that microbial community assembly was governed more by species sorting than by dispersal limitation in maize fields, and to a lesser extent in rice fields. The relative importance of species sorting in maize soils was greater at low latitudes than at high latitudes, while rice soils exhibited an opposite trend. Microbial co-occurrence associations tended to be higher when communities were primarily driven by dispersal limitation relative to species sorting. There were greater community dissimilarities between maize and rice soils in low-latitude regions, which was consistent with the higher proportion of negative edges in the correlation networks. The results indicate that a balance between species sorting and dispersal limitation mediates species coexistence in soil microbiomes. This study enhances our understanding of contemporary coexistence theory in microbial ecosystems.

Metaviromics Reveals Unknown Viral Diversity in the Biting Midge Culicoides impunctatus.

Biting midges (Culicoides species) are vectors of arboviruses and were responsible for the emergence and spread of Schmallenberg virus (SBV) in Europe in 2011 and are likely to be involved in the emergence of other arboviruses in Europe. Improved surveillance and better understanding of risks require a better understanding of the circulating viral diversity in these biting insects. In this study, we expand the sequence space of RNA viruses by identifying a number of novel RNA viruses from Culicoides impunctatus (biting midge) using a meta-transcriptomic approach. A novel metaviromic pipeline called MetaViC was developed specifically to identify novel virus sequence signatures from high throughput sequencing (HTS) datasets in the absence of a known host genome. MetaViC is a protein centric pipeline that looks for specific protein signatures in the reads and contigs generated as part of the pipeline. Several novel viruses, including an alphanodavirus with both segments, a novel relative of the Hubei sobemo-like virus 49, two rhabdo-like viruses and a chuvirus, were identified in the Scottish midge samples. The newly identified viruses were found to be phylogenetically distinct to those previous known. These findings expand our current knowledge of viral diversity in arthropods and especially in these understudied disease vectors.

Identification of potential key genes in esophageal adenocarcinoma using bioinformatics.

Esophageal adenocarcinoma (EAC) is the predominant pathological subtype of esophageal cancer in Europe and the USA. The present bioinformatics study analyzed a high-throughput sequencing dataset, GSE94869, to determine differentially expressed genes (DEGs) in order to identify key genes, biological processes and pathways associated with EAC. Functional enrichment analysis was performed using the Database for Annotation Visualization and Integrated Discovery. The co-expression network of the DEGs was established using Weighted Gene Co-Expression Network Analysis and visualized using Cytoscape. A Kaplan-Meier analysis based on The Cancer Genome Atlas (TCGA) database was used to identify prognosis-associated genes. Univariate and multivariate Cox proportional hazard models were used to identify genes with a prognostic value regarding relapse-free survival (RFS), while validation of the differential expression of prognosis-associated genes was performed using a box plot based on data from TCGA and another microarray dataset, GSE26886. A total of 130 DEGs, comprising 82 upregulated and 48 downregulated genes, were identified. The upregulated DEGs were significantly associated with extracellular matrix organization, disassembly, and the phosphoinositide-3 kinase/AKT, Rap1 and Ras signaling pathways, while the downregulated genes were associated with the Wnt signalling pathway. Subsequently, two co-expression modules were established and 20 hub genes were identified. The blue module was associated with the Rap1 signaling pathway, while the turquoise module was associated with the Ras and Rap1 signaling pathways. Among them, methyltransferase like 7B (METTL7B) was associated with RFS. Furthermore, the overexpression of METTL7B in EAC was successfully validated using data from TCGA and GSE26886. The present study identified key genes and provides potential biomarkers for the diagnosis and treatment of EAC.

Improving eDNA‐based protist diversity assessments using networks of amplicon sequence variants

Effective and precise grouping of highly similar sequences remains a major bottleneck in the evaluation of high-throughput sequencing datasets. Amplicon sequence variants (ASVs) offer a promising alternative that may supersede the widely used operational taxonomic units (OTUs) in environmental sequencing studies. We compared the performance of a recently developed pipeline based on the algorithm DADA2 for obtaining ASVs against a pipeline based on the algorithm SWARM for obtaining OTUs. Illumina-sequencing of 29 individual ciliate species resulted in up to 11 ASVs per species, while SWARM produced up to 19 OTUs per species. To improve the congruency between species diversity and molecular diversity, we applied sequence similarity networks (SSNs) for second-level sequence grouping into network sequence clusters (NSCs). At 100% sequence similarity in SWARM-SSNs, NSC numbers decreased from 7.9-fold overestimation without abundance filter, to 4.5-fold overestimation when an abundance filter was applied. For the DADA2-SSN approach, NSC numbers decreased from 3.5-fold to 3-fold overestimation. Rand index cluster analyses predicted best binning results between 97% and 94% sequence similarity for both DADA2-SSNs and SWARM-SSNs. Depending on the ecological questions addressed in an environmental sequencing study with protists we recommend ASVs as replacement for OTUs, best in combination with SSNs.

Proficiency Testing of Virus Diagnostics Based on Bioinformatics Analysis of Simulated In Silico High-Throughput Sequencing Data Sets.

Quality management and independent assessment of high-throughput sequencing-based virus diagnostics have not yet been established as a mandatory approach for ensuring comparable results. The sensitivity and specificity of viral high-throughput sequence data analysis are highly affected by bioinformatics processing using publicly available and custom tools and databases and thus differ widely between individuals and institutions. Here we present the results of the COMPARE [Collaborative Management Platform for Detection and Analyses of (Re-)emerging and Foodborne Outbreaks in Europe] in silico virus proficiency test. An artificial, simulated in silico data set of Illumina HiSeq sequences was provided to 13 different European institutes for bioinformatics analysis to identify viral pathogens in high-throughput sequence data. Comparison of the participants' analyses shows that the use of different tools, programs, and databases for bioinformatics analyses can impact the correct identification of viral sequences from a simple data set. The identification of slightly mutated and highly divergent virus genomes has been shown to be most challenging. Furthermore, the interpretation of the results, together with a fictitious case report, by the participants showed that in addition to the bioinformatics analysis, the virological evaluation of the results can be important in clinical settings. External quality assessment and proficiency testing should become an important part of validating high-throughput sequencing-based virus diagnostics and could improve the harmonization, comparability, and reproducibility of results. There is a need for the establishment of international proficiency testing, like that established for conventional laboratory tests such as PCR, for bioinformatics pipelines and the interpretation of such results.

Actinobacteria and Cyanobacteria Diversity in Terrestrial Antarctic Microenvironments Evaluated by Culture-Dependent and Independent Methods.

Bacterial diversity from McMurdo Dry Valleys in Antarctica, the coldest desert on earth, has become more easily assessed with the development of High Throughput Sequencing (HTS) techniques. However, some of the diversity remains inaccessible by the power of sequencing. In this study, we combine cultivation and HTS techniques to survey actinobacteria and cyanobacteria diversity along different soil and endolithic micro-environments of Victoria Valley in McMurdo Dry Valleys. Our results demonstrate that the Dry Valleys actinobacteria and cyanobacteria distribution is driven by environmental forces, in particular the effect of water availability and endolithic environments clearly conditioned the distribution of those communities. Data derived from HTS show that the percentage of cyanobacteria decreases from about 20% in the sample closest to the water source to negligible values on the last three samples of the transect with less water availability. Inversely, actinobacteria relative abundance increases from about 20% in wet soils to over 50% in the driest samples. Over 30% of the total HTS data set was composed of actinobacterial strains, mainly distributed by 5 families: Sporichthyaceae, Euzebyaceae, Patulibacteraceae, Nocardioidaceae, and Rubrobacteraceae. However, the 11 actinobacterial strains isolated in this study, belonged to Micrococcaceae and Dermacoccaceae families that were underrepresented in the HTS data set. A total of 10 cyanobacterial strains from the order Synechococcales were also isolated, distributed by 4 different genera (Nodosilinea, Leptolyngbya, Pectolyngbya, and Acaryochloris-like). In agreement with the cultivation results, Leptolyngbya was identified as dominant genus in the HTS data set. Acaryochloris-like cyanobacteria were found exclusively in the endolithic sample and represented 44% of the total 16S rRNA sequences, although despite our efforts we were not able to properly isolate any strain from this Acaryochloris-related group. The importance of combining cultivation and sequencing techniques is highlighted, as we have shown that culture-dependent methods employed in this study were able to retrieve actinobacteria and cyanobacteria taxa that were not detected in HTS data set, suggesting that the combination of both strategies can be usefull to recover both abundant and rare members of the communities.

Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets

BackgroundMethods to read out naturally occurring or experimentally introduced nucleic acid modifications are emerging as powerful tools to study dynamic cellular processes. The recovery, quantification and interpretation of such events in high-throughput sequencing datasets demands specialized bioinformatics approaches.ResultsHere, we present Digital Unmasking of Nucleotide conversions in K-mers (DUNK), a data analysis pipeline enabling the quantification of nucleotide conversions in high-throughput sequencing datasets. We demonstrate using experimentally generated and simulated datasets that DUNK allows constant mapping rates irrespective of nucleotide-conversion rates, promotes the recovery of multimapping reads and employs Single Nucleotide Polymorphism (SNP) masking to uncouple true SNPs from nucleotide conversions to facilitate a robust and sensitive quantification of nucleotide-conversions. As a first application, we implement this strategy as SLAM-DUNK for the analysis of SLAMseq profiles, in which 4-thiouridine-labeled transcripts are detected based on T > C conversions. SLAM-DUNK provides both raw counts of nucleotide-conversion containing reads as well as a base-content and read coverage normalized approach for estimating the fractions of labeled transcripts as readout.ConclusionBeyond providing a readily accessible tool for analyzing SLAMseq and related time-resolved RNA sequencing methods (TimeLapse-seq, TUC-seq), DUNK establishes a broadly applicable strategy for quantifying nucleotide conversions.

Identification and quantification of defective virus genomes in high throughput sequencing data using DVG-profiler, a novel post-sequence alignment processing algorithm.

Most viruses are known to spontaneously generate defective viral genomes (DVG) due to errors during replication. These DVGs are subgenomic and contain deletions that render them unable to complete a full replication cycle in the absence of a co-infecting, non-defective helper virus. DVGs, especially of the copyback type, frequently observed with paramyxoviruses, have been recognized to be important triggers of the antiviral innate immune response. DVGs have therefore gained interest for their potential to alter the attenuation and immunogenicity of vaccines. To investigate this potential, accurate identification and quantification of DVGs is essential. Conventional methods, such as RT-PCR, are labor intensive and will only detect primer sequence-specific species. High throughput sequencing (HTS) is much better suited for this undertaking. Here, we present an HTS-based algorithm called DVG-profiler to identify and quantify all DVG sequences in an HTS data set generated from a virus preparation. DVG-profiler identifies DVG breakpoints relative to a reference genome and reports the directionality of each segment from within the same read. The specificity and sensitivity of the algorithm was assessed using both in silico data sets as well as HTS data obtained from parainfluenza virus 5, Sendai virus and mumps virus preparations. HTS data from the latter were also compared with conventional RT-PCR data and with data obtained using an alternative algorithm. The data presented here demonstrate the high specificity, sensitivity, and robustness of DVG-profiler. This algorithm was implemented within an open source cloud-based computing environment for analyzing HTS data. DVG-profiler might prove valuable not only in basic virus research but also in monitoring live attenuated vaccines for DVG content and to assure vaccine lot to lot consistency.

The emerging importance of noncoding RNAs in the insecticide tolerance, with special emphasis on Plutella xylostella (Lepidoptera: Plutellidae).

Recently generated high-throughput sequencing data sets have shed light on the important regulatory roles of noncoding RNA (ncRNA) molecules in the development of higher organisms. Nowadays it is well-known that regulatory ncRNAs can bind complementary RNA or DNA sequences and recruit chromatin remodelers to selectively modulate gene expression. Consequently, genome sequencing and transcriptomics technologies are now being used to reveal hidden associations among ncRNAs and distinct biological mechanisms. This is the case for the diamondback moth Plutella xylostella, a worldwide pest known to infest cruciferous crops and to display resistance to most insecticides, including Bacillus thuringiensis (Bt) based biopesticides. In P. xylostella, it is thought that ncRNAs could play important roles in both development and insecticide resistance. This review will highlight recent insights into the roles of ncRNAs in P. xylostella and related lepidopterans, and will outline genetic engineering technologies which might be used to design efficient ncRNA-based pest control strategies. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

NGS‐Integrator: A Tool for Combining Information from Multiple Genome‐Wide NGS Data Tracks Using Minimum Bayes Factors

Genome‐wide studies that generate multiple high‐throughput next‐generation sequencing (NGS) datasets to identify genomic DNA elements for gene transcription require data integration methods to minimize complexity and false‐positive findings. This can involve integration of multiple genome‐wide data generated from same type or different types of high‐throughput NGS techniques. Since several strategies to integrate multiple genome‐wide NGS datasets based on peak calling tools have been developed, these conventional methods are typically applied to individual replicates and not the aggregate data from multiple data.NGS‐integrator, a Java‐based tool, integrates genome‐wide NGS datasets into a single data track for a genome browser based on minimum Bayes Factor (MBF) calculated from the signal‐to‐noise ratio. NGS‐integrator consists of two elements, “Calculator” and “Integrator”. The “Calculator” element calculates the complement of MBF at each genomic position, estimating the median of the noise values across a sliding window straddling the position at which signal value is being calculated. This calculation assumes that the features being detected are relatively sparsely represented along the genome, allowing the median to be representative of the true noise level. The “Integrator” element calculates the joint probability between the multiple NGS data tracks based on the complement of MBF calculated with ‘Calculator’. We used NGS‐integrator to integrate data from three types of data generated in our laboratory in cultured mpkCCD kidney cells, viz. ChIP‐Seq for the transcription factor ELF1, ATAC‐Seq, and ChIP‐Seq for the histone modification H3K27Ac and RNA polymerase II.Support or Funding InformationThe work was supported by the Division of Intramural Research, National Heart, Lung, and Blood Institute projects ZIA‐HL001285 (to MAK) and ZIA‐HL006129 (to MAK). BW was supported by the NHLBI Summer Internship Program (June–August 2018).This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Amino acid based de Bruijn graph algorithm for identifying complete coding genes from metagenomic and metatranscriptomic short reads.

Metagenomic studies, greatly promoted by the fast development of next-generation sequencing (NGS) technologies, uncover complex structures of microbial communities and their interactions with environment. As the majority of microbes lack information of genome sequences, it is essential to assemble prokaryotic genomes ab initio aiming to retrieve complete coding genes from various metabolic pathways. The complex nature of microbial composition and the burden of handling a vast amount of metagenomic data, bring great challenges to the development of effective and efficient bioinformatic tools. Here we present a protein assembler (MetaPA), based on de Bruijn graph searching on oligopeptide spaces and can be applied on both metagenomic and metatranscriptomic sequencing data. When public homologous protein sequences are involved to guide the assembling procedures, MetaPA assembles 85% of total proteins in complete sequences with high precision of 83% on real high-throughput sequencing datasets. Application of MetaPA on metatranscriptomic data successfully identifies the majority of actively transcribed genes validated in related studies. The results suggest that MetaPA has a good potential in both metagenomic and metatranscriptomic studies to characterize the composition and abundance of microbiota.

Moving Just Enough Deep Sequencing Data to Get the Job Done.

Motivation:As the size of high-throughput DNA sequence datasets continues to grow, the cost of transferring and storing the datasets may prevent their processing in all but the largest data centers or commercial cloud providers. To lower this cost, it should be possible to process only a subset of the original data while still preserving the biological information of interest.Results:Using 4 high-throughput DNA sequence datasets of differing sequencing depth from 2 species as use cases, we demonstrate the effect of processing partial datasets on the number of detected RNA transcripts using an RNA-Seq workflow. We used transcript detection to decide on a cutoff point. We then physically transferred the minimal partial dataset and compared with the transfer of the full dataset, which showed a reduction of approximately 25% in the total transfer time. These results suggest that as sequencing datasets get larger, one way to speed up analysis is to simply transfer the minimal amount of data that still sufficiently detects biological signal.Availability:All results were generated using public datasets from NCBI and publicly available open source software.

SA71 - RARE AND DE NOVO VARIANTS IN OBSESSIVE COMPULSIVE DISORDER

Background Obsessive Compulsive Disorder (OCD) is a complex neuropsychiatric disorder that affects approximately 1% of the population. It is likely that the genetic architecture underlying the disorder is complex, and that furthermore some subset of it involves rare and de novo variants of relatively large effect size. Methods We have generated what to our knowledge is the largest high throughput sequencing dataset for this disorder presently in existence. The data consist of over 550 sporadic OCD trios and quartets selected for whole exome sequencing, as well as 150 representative affected members of multiplex families for OCD that were selected for whole genome sequencing. We have carried out an analysis of rare and de novo coding variation within this cohort in order to assess how much it contributes to OCD risk, as well as if there are any single genes with exome-wide significant levels of damaging variant burden. Results In assessing de novo mutation burden in trios, while we don’t observe a marked departure in mutation rate from what is expected, we do note an elevation of damaging de novo variant burden in cases relative to controls (odds ratio=1.3, p=0.097), as well as in individuals with an early age at onset (e.g., Discussion In general, the sequence data we have generated provides further support for a complex genetic architecture in OCD, as well as the need for a larger number of samples for adequate resolution.

TRNA-Derived Small RNAs: Biogenesis, Modification, Function and Potential Impact on Human Disease Development.

Transfer RNAs (tRNAs) are abundant small non-coding RNAs that are crucially important for decoding genetic information. Besides fulfilling canonical roles as adaptor molecules during protein synthesis, tRNAs are also the source of a heterogeneous class of small RNAs, tRNA-derived small RNAs (tsRNAs). Occurrence and the relatively high abundance of tsRNAs has been noted in many high-throughput sequencing data sets, leading to largely correlative assumptions about their potential as biologically active entities. tRNAs are also the most modified RNAs in any cell type. Mutations in tRNA biogenesis factors including tRNA modification enzymes correlate with a variety of human disease syndromes. However, whether it is the lack of tRNAs or the activity of functionally relevant tsRNAs that are causative for human disease development remains to be elucidated. Here, we review the current knowledge in regard to tsRNAs biogenesis, including the impact of RNA modifications on tRNA stability and discuss the existing experimental evidence in support for the seemingly large functional spectrum being proposed for tsRNAs. We also argue that improved methodology allowing exact quantification and specific manipulation of tsRNAs will be necessary before developing these small RNAs into diagnostic biomarkers and when aiming to harness them for therapeutic purposes.

Structural diversity across arbuscular mycorrhizal, ectomycorrhizal, and endophytic plant\u2013fungus networks

BackgroundBelow-ground linkage between plant and fungal communities is one of the major drivers of terrestrial ecosystem dynamics. However, we still have limited knowledge of how such plant–fungus associations vary in their community-scale properties depending on fungal functional groups and geographic locations.MethodsBy compiling a high-throughput sequencing dataset of root-associated fungi in eight forests along the Japanese Archipelago, we performed a comparative analysis of arbuscular mycorrhizal, ectomycorrhizal, and saprotrophic/endophytic associations across a latitudinal gradient from cool-temperate to subtropical regions.ResultsIn most of the plant–fungus networks analyzed, host–symbiont associations were significantly specialized but lacked “nested” architecture, which has been commonly reported in plant–pollinator and plant–seed disperser networks. In particular, the entire networks involving all functional groups of plants and fungi and partial networks consisting of ectomycorrhizal plant and fungal species/taxa displayed “anti-nested” architecture (i.e., negative nestedness scores) in many of the forests examined. Our data also suggested that geographic factors affected the organization of plant–fungus network structure. For example, the southernmost subtropical site analyzed in this study displayed lower network-level specificity of host–symbiont associations and higher (but still low) nestedness than northern localities.ConclusionsOur comparative analyses suggest that arbuscular mycorrhizal, ectomycorrhizal, and saprotrophic/endophytic plant–fungus associations often lack nested network architecture, while those associations can vary, to some extent, in their community-scale properties along a latitudinal gradient. Overall, this study provides a basis for future studies that will examine how different types of plant–fungus associations collectively structure terrestrial ecosystems.

Coming up short: Identifying substrate and geographic biases in fungal sequence databases

Abstract Insufficient reference database coverage is a widely recognized limitation of molecular ecology approaches which are reliant on database matches for assignment of function or identity. Here, we use data from 65 amplicon high-throughput sequencing (HTS) datasets targeting the internal transcribed spacer (ITS) region of fungal rDNA to identify substrates and geographic areas whose underrepresentation in the available reference databases could have meaningful impact on our ability to draw ecological conclusions. A total of 14 different substrates were investigated. Database representation was particularly poor for the fungal communities found in aquatic (freshwater and marine) and soil ecosystems. Aquatic ecosystems are identified as priority targets for the recovery of novel fungal lineages. A subset of the data representing soil samples with global distribution were used to identify geographic locations and terrestrial biomes with poor database representation. Database coverage was especially poor in tropical, subtropical, and Antarctic latitudes, and the Amazon, Southeast Asia, Australasia, and the Indian subcontinent are identified as priority areas for improving database coverage in fungi.

Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding.

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

TranSurVeyor: an improved database-free algorithm for finding non-reference transpositions in high-throughput sequencing data.

Transpositions transfer DNA segments between different loci within a genome; in particular, when a transposition is found in a sample but not in a reference genome, it is called a non-reference transposition. They are important structural variations that have clinical impact. Transpositions can be called by analyzing second generation high-throughput sequencing datasets. Current methods follow either a database-based or a database-free approach. Database-based methods require a database of transposable elements. Some of them have good specificity; however this approach cannot detect novel transpositions, and it requires a good database of transposable elements, which is not yet available for many species. Database-free methods perform de novo calling of transpositions, but their accuracy is low. We observe that this is due to the misalignment of the reads; since reads are short and the human genome has many repeats, false alignments create false positive predictions while missing alignments reduce the true positive rate. This paper proposes new techniques to improve database-free non-reference transposition calling: first, we propose a realignment strategy called one-end remapping that corrects the alignments of reads in interspersed repeats; second, we propose a SNV-aware filter that removes some incorrectly aligned reads. By combining these two techniques and other techniques like clustering and positive-to-negative ratio filter, our proposed transposition caller TranSurVeyor shows at least 3.1-fold improvement in terms of F1-score over existing database-free methods. More importantly, even though TranSurVeyor does not use databases of prior information, its performance is at least as good as existing database-based methods such as MELT, Mobster and Retroseq. We also illustrate that TranSurVeyor can discover transpositions that are not known in the current database.