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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

Interpretations of microbial community studies are biased by the selected 16S rRNA gene amplicon sequencing pipeline.

One of the major methods to identify microbial community composition, to unravel microbial population dynamics, and to explore microbial diversity in environmental samples is DNA- or RNA-based 16S rRNA (gene) amplicon sequencing. Subsequent bioinformatics analyses are required to extract valuable information from the high-throughput sequencing approach. However, manifold bioinformatics tools complicate their choice and might cause differences in data interpretation, making the selection of the pipeline a crucial step. Here, we compared the performance of most widely used 16S rRNA gene amplicon sequencing analysis tools (i.e. Mothur, QIIME1, QIIME2, and MEGAN) using mock datasets and environmental samples from contrasting terrestrial and freshwater sites. Our results showed that QIIME2 outcompeted all other investigated tools in sequence recovery (>10 times less false positives), taxonomic assignments (>22% better F-score) and diversity estimates (>5% better assessment), while there was still room for improvement e.g. imperfect sequence recovery (recall up to 87%) or detection of additional false sequences (precision up to 72%). Furthermore, we found that microbial diversity estimates and highest abundant taxa varied among analysis pipelines (i.e. only one in five genera was shared among all analysis tools) when analyzing environmental samples, which might skew biological conclusions. Our findings were subsequently implemented in a high-performance computing conformant workflow following the FAIR (Findable, Accessible, Interoperable, and Re-usable) principle, allowing reproducible 16S rRNA gene amplicon sequence analysis starting from raw sequence files. Our presented workflow can be utilized for future studies, thereby facilitating the analysis of high-throughput DNA- or RNA-based 16S rRNA (gene) sequencing data substantially. ImportanceMicroorganisms play an essential role in biogeochemical cycling events across the globe. Phylogenetic marker gene analysis is a widely used method to explore microbial community dynamics in space and time, to predict the ecological relevance of microbial populations, or to identify microbial key players in biogeochemical cycles. Several computational analysis methods were developed to aid 16S rRNA gene analysis but choosing the best method is not trivial. In this study, we compared popular analysis methods (i.e. Mothur, QIIME1 and 2, and MEGAN) using samples with known microbial composition (i.e. mock community samples) and environmental samples from contrasting habitats (i.e. groundwater, soil, sediment, and river water). Our findings provide guidance for choosing the currently optimal 16S rRNA gene sequencing analysis method and we implemented our recommended pipeline into a reproducible workflow, which follows highest bioinformatics standards and is open source and free to use.

microbiology↗
bioRxiv2020

Genome-Wide Sequencing as a First-Tier Screening Test for Short Tandem Repeat Expansions

Short tandem repeat (STR) expansions cause several neurological and neuromuscular disorders. Screening for STR expansions in genome-wide (exome and genome) sequencing data can enable diagnosis, optimal clinical management/treatment, and accurate genetic counselling of patients with repeat expansion disorders. We assessed the performance of lobSTR, HipSTR, RepeatSeq, ExpansionHunter, TREDPARSE, GangSTR, STRetch, and exSTRa - bioinformatics tools that have been developed to detect and/or genotype STR expansions - on experimental and simulated genome sequence data with known STR expansions aligned using two different aligners, Isaac and BWA. We then adjusted the parameter settings to optimize the sensitivity and specificity of the STR tools and fed the optimized results into a machine-learning decision tree classifier to determine the best combination of tools to detect full mutation expansions with high diagnostic sensitivity and specificity. The decision tree model supported using ExpansionHunters full mutation calls with those of either STRetch or exSTRa for detection of full mutations with precision, recall, and F1-score of 90%, 100%, and 95%, respectively. We used this pipeline to screen the BWA-aligned exome or genome sequence data of 306 families of children with suspected genetic disorders for pathogenic expansions of known disease STR loci. We identified 27 samples, 17 with an apparent full-mutation expansion of the AR, ATXN1, ATXN2, ATXN8, DMPK, FXN, HTT, or TBP locus, nine with an intermediate or premutation allele in the FMR1 locus, and one with a borderline allele in the ATXN2 locus. We report the concordance between our bioinformatics findings and the clinical PCR results in a subset of these samples. Implementation of our bioinformatics workflow can improve the detection of disease STR expansions in exome and genome sequence diagnostics and enhance clinical outcomes for patients with repeat expansion disorders.

genetics↗
bioRxiv2020

ISSRseq: an extensible, low-cost, and efficient method for reduced representation sequencing

O_LIThe capability to generate densely sampled single nucleotide polymorphism (SNP) data is essential in diverse subdisciplines of biology, including crop breeding, pathology, forensics, forestry, ecology, evolution, and conservation. However, the wet-lab expertise and bioinformatics training required to conduct genome-scale variant discovery remain limiting factors for investigators with limited resources. C_LIO_LIHere we present ISSRseq, a PCR-based method for reduced representation of genomic variation using simple sequence repeats as priming sites to sequence inter simple sequence repeat (ISSR) regions. Briefly, ISSR regions are amplified with single primers, pooled, used to construct sequencing libraries with a commercially-available kit, and sequenced on the Illumina platform. We also present a flexible bioinformatic pipeline that assembles ISSR loci, calls and hard filters variants, outputs data matrices in common formats, and conducts population analyses using R. C_LIO_LIUsing three angiosperm species as case studies, we demonstrate that ISSRseq is highly repeatable, necessitates only simple wet-lab skills and commonplace instrumentation, is flexible in terms of the number of single primers used, and can generate genomic-scale variant discovery on par with existing RRS methods which require more complex wet lab procedures. C_LIO_LIISSRseq represents a straightforward approach to SNP genotyping in any organism, and we predict that this method will be particularly useful for those studying population genomics and phylogeography of non-model organisms. Furthermore, the ease of ISSRseq relative to other RRS methods should prove useful to those lacking advanced expertise in wet lab methods or bioinformatics. C_LI

genomics↗
bioRxiv2021

Transparent exploration of machine learning for biomarker discovery from proteomics and omics data

Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy, but they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery, but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become indispensable for this purpose, however, it is sometimes applied in an opaque manner, generally requires expert knowledge and complex and expensive software. To enable easy access to ML for biomarker discovery without any programming or bioinformatic skills, we developed OmicLearn (https://OmicLearn.com), an open-source web-based ML tool using the latest advances in the Python ML ecosystem. We host a web server for the exploration of the researchers results that can readily be cloned for internal use. Output tables from proteomics experiments are easily uploaded to the central or a local webserver. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental datasets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/434053v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@7b95corg.highwire.dtl.DTLVardef@11a5357org.highwire.dtl.DTLVardef@15586c2org.highwire.dtl.DTLVardef@2357f_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIOmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discovery C_LIO_LIThe ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledge C_LIO_LIOmicLearns web-based interface provides an easy-to-follow platform for classification and gaining insights into the dataset C_LIO_LISeveral algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integrated C_LIO_LIAll results, settings and method text can be exported in publication-ready formats C_LI

biochemistry↗
bioRxiv2021

High-throughput Interpretation of Killer-cell Immunoglobulin-like Receptor Short-read Sequencing Data with PING

The killer-cell immunoglobulin-like receptor (KIR) complex on chromosome 19 encodes receptors that modulate the activity of natural killer cells, and variation in these genes has been linked to infectious and autoimmune disease, as well as having bearing on pregnancy and transplant outcomes. The medical relevance and high variability of KIR genes makes short-read sequencing an attractive technology for interrogating the region, providing a high-throughput, high-fidelity sequencing method that is cost-effective. However, because this gene complex is characterized by extensive nucleotide polymorphism, structural variation including gene fusions and deletions, and a high level of homology between genes, its interrogation at high resolution has been thwarted by bioinformatic challenges, with most studies limited to examining presence or absence of specific genes. Here, we present the PING (Pushing Immunogenetics to the Next Generation) pipeline, which incorporates empirical data, novel alignment strategies and a custom alignment processing workflow to enable high-throughput KIR sequence analysis from short-read data. PING provides KIR gene copy number classification functionality for all KIR genes through use of a comprehensive alignment reference. The gene copy number determined per individual enables an innovative genotype determination workflow using genotype-matched references. Together, these methods address the challenges imposed by the structural complexity and overall homology of the KIR complex. To determine copy number and genotype determination accuracy, we applied PING to European and African validation cohorts and a synthetic dataset. PING demonstrated exceptional copy number determination performance across all datasets and robust genotype determination performance. Finally, an investigation into discordant genotypes for the synthetic dataset provides insight into misaligned reads, advancing our understanding in interpretation of short-read sequencing data in complex genomic regions. PING promises to support a new era of studies of KIR polymorphism, delivering high-resolution KIR genotypes that are highly accurate, enabling high-quality, high-throughput KIR genotyping for disease and population studies. Author summaryKiller cell immunoglobulin-like receptors (KIR) serve a critical role in regulating natural killer cell function. They are encoded by highly polymorphic genes within a complex genomic region that has proven difficult to interrogate owing to structural variation and extensive sequence homology. While methods for sequencing KIR genes have matured, there is a lack of bioinformatic support to accurately interpret KIR short-read sequencing data. The extensive structural variation of KIR, both the small-scale nucleotide insertions and deletions and the large-scale gene duplications and deletions, coupled with the extensive sequence similarity among KIR genes presents considerable challenges to bioinformatic analyses. PING addressed these issues through a highly-dynamic alignment workflow, which constructs individualized references that reflect the determined copy number and genotype makeup of a sample. This alignment workflow is enabled by a custom alignment processing pipeline, which scaffolds reads aligned to all reference sequences from the same gene into an overall gene alignment, enabling processing of these alignments as if a single reference sequence was used regardless of the number of sequences or of any insertions or deletions present in the component sequences. Together, these methods provide a novel and robust workflow for the accurate interpretation of KIR short-read sequencing data.

immunology↗
bioRxiv2021

Commensal Streptococcus mitis produces two different lipoteichoic acids of type I and type IV

The opportunistic pathogen Streptococcus mitis possesses, like other members of the Mitis group of viridans streptococci, phosphorylcholine (P-Cho)-containing teichoic acids (TAs) in its cell wall. Bioinformatic analyses predicted the presence of TAs that are almost identical with those identified in the pathogen S. pneumoniae, but a detailed analysis of S. mitis lipoteichoic acid (LTA) was not performed to date. Here we determined the structures of LTA from two S. mitis strains, the high-level beta-lactam and multiple antibiotic resistant strain B6 and the penicillin-sensitive strain NCTC10712. In agreement with bioinformatic predictions we found that the structure of one LTA (type IV) was like pneumococcal LTA, except the exchange of a glucose moiety with a galactose within the repeating units. Further genome comparisons suggested that the majority of S. mitis strains should contain the same type IV LTA as S. pneumoniae, providing a more complete understanding of the biosynthesis of these P-Cho-containing TAs in members of the Mitis group of streptococci. Remarkably, we observed besides type IV LTA an additional polymer belonging to LTA type I in both investigated S. mitis strains. This LTA consists of {beta}-galactofuranosyl-(1,3)-diacylglycerol as glycolipid anchor and a poly-glycerol-phosphate chain at the O-6 position of the furanosidic galactose. Hence, these bacteria are capable of synthesizing two different LTA polymers, most likely produced by distinct biosynthesis pathways. Our bioinformatics analysis revealed the prevalence of the LTA synthase LtaS, most probably responsible for the second LTA version (type I), amongst S. mitis and S. pseudopneumoniae strains.

microbiology↗
bioRxiv2021

Dataset complexity impacts both MOTU delimitation and biodiversity estimates in eukaryotic 18S rRNA metabarcoding studies

How does the evolution of bioinformatics tools impact the biological interpretation of high-throughput sequencing datasets? For eukaryotic metabarcoding studies, in particular, researchers often rely on tools originally developed for the analysis of 16S ribosomal RNA (rRNA) datasets. Such tools do not adequately account for the complexity of eukaryotic genomes, the ubiquity of intragenomic variation in eukaryotic metabarcoding loci, or the differential evolutionary rates observed across eukaryotic genes and taxa. Recently, metabarcoding workflows have shifted away from the use of Operational Taxonomic Units (OTUs) towards delimitation of Amplicon Sequence Variants (ASVs). We assessed how the choice of bioinformatics algorithm impacts the downstream biological conclusions that are drawn from eukaryotic 18S rRNA metabarcoding studies. We focused on four workflows including UCLUST and VSearch algorithms for OTU clustering, and DADA2 and Deblur algorithms for ASV delimitation. We used two 18S rRNA datasets to further evaluate whether dataset complexity had a major impact on the statistical trends and ecological metrics: a "high complexity" (HC) environmental dataset generated from community DNA in Arctic marine sediments, and a "low complexity" (LC) dataset representing individually-barcoded nematodes. Our results indicate that ASV algorithms produce more biologically realistic metabarcoding outputs, with DADA2 being the most consistent and accurate pipeline regardless of dataset complexity. In contrast, OTU clustering algorithms inflate the metabarcoding-derived estimates of biodiversity, consistently returning a high proportion of "rare" Molecular Operational Taxonomic Units (MOTUs) that appear to represent computational artifacts and sequencing errors. However, species-specific MOTUs with high relative abundance are often recovered regardless of the bioinformatics approach. We also found high concordance across pipelines for downstream ecological analysis based on beta-diversity and alpha-diversity comparisons that utilize taxonomic assignment information. Analyses of LC datasets and rare MOTUs are especially sensitive to the choice of algorithms and better software tools may be needed to address these scenarios.

ecology↗
bioRxiv2021

Characterization of the radiation desiccation response regulon of the radioresistant bacterium Deinococcus radiodurans by integrative genomic analyses.

Numerous genes are overexpressed in the radioresistant bacterium Deinococcus radiodurans after exposure to radiation or prolonged desiccation. The DdrO and IrrE proteins play a major role in regulating the expression of approximately predicted twenty of these genes. The transcriptional repressor DdrO blocks the expression of these genes under normal growth conditions. After exposure to genotoxic agents, the IrrE metalloprotease cleaves DdrO and relieves gene repression. Bioinformatic analyzes showed that this mechanism seems to be conserved in several species of Deinococcus, but many questions remain as such the number of genes regulated by DdrO. Here, by RNA-seq and CHiP-seq assays performed at a genome-wide scale coupled with bioinformatic analyses, we show that, the DdrO regulon in D. radiodurans includes many other genes than those previously described. These results thus pave the way to better understand the radioresistance mechanisms encoded by this bacterium. Author SummaryThe main response pathway to genotoxic conditions in the radioresistant bacterium Deinococcus radiodurans is regulated by the constitutively expressed metalloprotease IrrE that cleaves the transcriptional repressor DdrO, leading to the expression of the genes repressed by DdrO. One of the major goals to better understand how pathways involved in radioresistance are coordinated into this fascinating bacterium is to highlight genes regulated by DdrO. In this study, we mapped in vivo the DdrO regulon in D. radiodurans by using two genome-scale approaches, ChIP-seq and RNA-seq analyses, coupled with bioinformatic analyses. As homologs of these two proteins are also found in many other bacteria, these results also pave the way to compare the stress-induced responses mediated by this couple of proteins in diverse bacteria.

genomics↗
bioRxiv2021

Unraveling phylogenetic relationships, reticulate evolution, and genome composition of polyploid plant complexes by RAD-Seq and Hyb-Seq

Complex genome evolution of young polyploid complexes is poorly understood. Besides challenges caused by hybridization, polyploidization, and incomplete lineage sorting, bioinformatic analyses are often exacerbated by missing information on progenitors, ploidy, and reproduction modes. By using a comprehensive, self-developed bioinformatic pipeline integrating phylogenetic, structure, network, and SNP-origin analyses, we for the first time unraveled polyploid phylogenetic relationships and genome evolution within the large Eurasian Ranunculus auricomus species complex comprising more than 840 taxa. Our results rely on 97,312 genomic RAD-Seq loci, target enrichment of 576 nuclear genes (48 phased), and 71 plastid regions (Hyb-Seq; OMICS-data) derived from the 75 most widespread polyploid apomictic taxa and four di- and one tetraploid potential sexual progenitor species. Phylogenetic tree and structure analyses consistently showed 3-5 supported polyploid groups, each containing sexual progenitor species. In total, analyses revealed four diploid sexual progenitors and a one unknown, probably extinct progenitor, contributing to the genome composition of R. auricomus polyploids. Phylogenetic network, structure, and SNP-origin analyses based on RAD-Seq loci and phased nuclear genes completed by plastid data demonstrated predominantly allopolyploid origins, each involving 2-3 different diploid sexual subgenomes. Allotetraploid genomes were characterized by subgenome dominance and large proportions of interspecific, non-hybrid SNPs, indicating an enormous degree of post-origin evolution (i.e., Mendelian segregation of the diploid hybrid generations, back-crossings, and gene flow due to facultative sexuality of apomicts), but only low proportions of lineage-specific SNPs. The R. auricomus model system is the first large European polyploid species complex studied with reduced representation OMICS data. Our bioinformatic pipeline underlines the importance of combining different approaches and datasets to successfully unveil how reticulate evolution and post-origin processes shape the diversity of polyploid plant complexes.

evolutionary biology↗
bioRxiv2021

Experience-induced remodeling of the hippocampal post-synaptic proteome and phosphoproteome

The post synaptic density (PSD) of excitatory synapses contains a highly organized protein network with thousands of proteins and is key node in the regulation of synaptic plasticity. To gain new mechanistic insight into experience-induced changes in the PSD, we examined the global dynamics of the PSD proteome and phosphoproteome in mice following various treatments. Mice were trained using an inhibitory avoidance (IA) task and hippocampal PSD fractions were isolated for quantitative proteomic and phosphoproteomics analysis. We used a sequential enrichment strategy to explore the concurrent events of protein expression and phosphorylation in the hippocampal PSD following IA training (IA) or immediate shock (Shock). We identified more than 6,200 proteins and 3,000 phosphoproteins in the sequential strategy covering a total of 7,429 proteins. On the phosphoproteins we identified a total of 9,589 phosphosites. Strikingly, of the significantly IA-regulated proteins and phosphoproteins, a large fraction of the proteins displayed an overall decrease in phosphorylation level. Bioinformatic analysis of proteins and phosphoproteins that were regulated by IA were annotated for an involvement in regulation of glutamate receptor functionality, calcium signaling, and synaptic plasticity. We also identified synaptic kinases, phosphatases and their respective phosphosites regulated by IA training or immediate shock. Furthermore, we found that AMPA receptor surface expression was regulated by protein phosphatase, Mg2+/Mn2+ dependent 1H (Ppm1h). Together, these results unravel the dynamic remodeling of the PSD upon IA learning or immediate shock and serve as a resource for elucidating the synaptic proteome dynamics induced by experience-dependent plasticity. HighlightsO_LIThe proteome and phosphoproteome of mouse hippocampal PSD fractions were examined using quantitative phosphoproteomics and bioinformatics following inhibitory avoidance training or non-associative immediate shock. C_LIO_LIApproximately 6,200 proteins and 3,000 phosphoproteins were identified and quantified in the hippocampal PSD fractions. C_LIO_LIIA mediates widespread decreases in the abundance and phosphorylation of proteins in the hippocampal PSD fraction. C_LIO_LIKinases, phosphatases and their phosphorylation status were dynamically and significantly regulated by IA and immediate shock. C_LIO_LIFunctional validation shows that the protein phosphatase Ppm1h is linked to the regulation of synaptic plasticity in vitro and in vivo. C_LI In BriefQuantitative proteomics and phosphoproteomics combined with subcellular protein fractionation and bioinformatic analysis identifies a highly dynamic regulation of synaptic protein phosphorylation at the postsynaptic density following IA training and immediate shock.

neuroscience↗
bioRxiv2021

Copy number alterations and epithelial-mesenchymal transition genes in diffuse and intestinal gastric cancers in Mexican patients

Gastric cancer (GC) is a malignancy with the highest mortality among diseases of the digestive system worldwide. The study of GC-alterations is crucial to understand tumor biology, to establish important aspects of cancer prognosis and treatment response. Here, we purified DNA and performed whole-genome analysis with high-density arrays in samples from Mexican patients diagnosed with GC: diffuse (DGC) or intestinal (IGC), or non-atrophic gastritis (NAG) samples that served as controls. We identified shared and unique copy number alterations (CNA) between these altered tissues involving key genes and signaling pathways associated with cancer, allowing their molecular distinction and identification of the most relevant molecular functions impacted. When focused on epithelial-mesenchymal transition (EMT) genes, our bioinformatic analysis revealed that the altered network associated with chromosomal alterations included 11 genes shared between DGC, IGC, and NAG, as well as 19 DGC- and 7 IGC-exclusive genes, whose main molecular functions included adhesion, angiogenesis, migration, metastasis, morphogenesis, proliferation, and survival. This study presents the first whole-genome high-density array study in GC from Mexican patients and reveals shared and exclusive CNA-genes in DGC and IGC. In addition, we provide a bioinformatically predicted network focused on CNA-altered genes involved in the EMT, associated with the hallmarks of cancer, as well as precancerous alterations that could lead to gastric cancer. ImplicationsMolecular signatures of diffuse and intestinal GC, predicted bioinformatically, involve common and distinct CNA-EMT genes related to the hallmarks of cancer that are potential candidates for screening GC biomarkers, including early stages.

cancer biology↗
bioRxiv2021

A target capture approach for phylogenomic analyses at multiple evolutionary timescales in rosewoods (Dalbergia spp.) and the legume family (Fabaceae)

Understanding the genetic changes associated with the evolution of biological diversity is of fundamental interest to molecular ecologists. The assessment of genetic variation at hundreds or thousands of unlinked genetic loci forms a sound basis to address questions ranging from micro-to macro-evolutionary timescales, and is now possible thanks to advances in sequencing technology. Major difficulties are associated with i) the lack of genomic resources for many taxa, especially from tropical biodiversity hotspots, ii) scaling the numbers of individuals analyzed and loci sequenced, and iii) building tools for reproducible bioinformatic analyses of such datasets. To address these challenges, we developed a set of target capture probes for phylogenomic studies of the highly diverse, pantropically distributed and economically significant rosewoods (Dalbergia spp.), explored the performance of an overlapping probe set for target capture across the legume family (Fabaceae), and built a general-purpose bioinformatics pipeline. Phylogenomic analyses of Dalbergia species from Madagascar yielded highly resolved and well supported hypotheses of evolutionary relationships. Population genomic analyses identified differences between closely related species and revealed the existence of a potentially new species, suggesting that the diversity of Malagasy Dalbergia species has been underestimated. Analyses at the family level corroborated previous findings by the recovery of monophyletic subfamilies and many well-known clades, as well as high levels of gene tree discordance, especially near the root of the family. The new genomic and bioinformatics resources will hopefully advance systematics and ecological genetics research in legumes, and promote conservation of the highly diverse and endangered Dalbergia rosewoods.

genomics↗
bioRxiv2022

LENS - Landscape of Effective Neoantigens Software

MotivationElimination of cancer cells by T cells is a critical mechanism of anti-tumor immunity and cancer immunotherapy response. T cells recognize cancer cells by engagement of T cell receptors with peptide epitopes presented by major histocompatibility complex (MHC) molecules on the cancer cell surface. Peptide epitopes can be derived from antigen proteins coded for by multiple genomic sources. Bioinformatics tools used to identify tumor-specific epitopes via analysis of DNA and RNA sequencing data have largely focused on epitopes derived from somatic variants, though a smaller number have evaluated potential antigens from other genomic sources. ResultsWe report here an open-source workflow utilizing the Nextflow DSL2 workflow manager, Landscape of Effective Neoantigen Software (LENS), which predicts tumor-specific and tumor-associated antigens from single nucleotide variants, insertions and deletions, fusion events, splice variants, cancer testis antigens, overexpressed self-antigens, viruses, and endogenous retroviruses. The primary advantage of LENS is that it expands the breadth of genomic sources of discoverable tumor antigens using genomics data. Other advantages include modularity, extensibility, ease of use, and harmonization of relative expression level and immunogenicity prediction across multiple genomic sources. We present an analysis of 115 acute myeloid leukemia (AML) samples to demonstrate the utility of LENS. We expect LENS will be a valuable platform and resource for T cell epitope discovery bioinformatics, especially in cancers with few somatic variants where tumor-specific epitopes from alternative genomic sources are an elevated priority. AvailabilityMore information about LENS, including workflow documentation and instructions, can be found at https://gitlab.com/landscape-of-effective-neoantigens-software Contactsteven_vensko@med.unc.edu, benjamin_vincent@med.unc.edu Supplementary informationSupplementary data are available at Bioinformatics online.

cancer biology↗
bioRxiv2022

The Universal Endurance Microbiome?

Billions of microbial cells sculpt the gut ecosystem, playing essential roles in human physiology. Since endurance athletes performance is often physiology-limited, understanding the composition and interactions within these athletes gut microbiomes could lead to improved performance. Previous studies describe differences in the relative abundance of bacterial taxa when comparing athletes versus controls or athletes before and after an endurance event, suggesting the existence of an "endurance microbiome". However, there are inconsistencies among studies in which taxa correlate with extended physical exertion. Although these studies employed similar barcoding methods, variation in downstream bioinformatic analyses makes it difficult to determine whether inconsistencies are due to methodological differences or biological factors. Herein, we created a metagenomic bioinformatics workflow reanalyzing four 16S rDNA sequence datasets reflecting endurance athletes gut microbiomes, looking at alpha diversity, changes in relative abundance of gut microbiome genera, changes in pairwise correlations between bacterial genera and compared bacterial association networks. There were no significant differences in alpha diversity between any of the four treatment group comparisons. For relative abundance, there were no consistent differences in all four datasets, and only two genera were significantly different in 50% of the datasets. Although many genera showed changes in pairwise correlations in endurance microbiome samples from individual datasets, none were consistent across datasets. Collectively, these results suggest that either there is no universal endurance microbiome, or that it remains elusive even after controlling for the bioinformatic workflow and statistical analyses. Using this data, a power analysis indicates that sample sizes 150- to 800-fold larger than these published studies would be necessary to detect a 10% difference in relative abundance. Furthermore, 10- to 20-fold more samples will be needed to control for the multitude of covariates (genetic, metabolic, dietary, environmental, and pharmacological factors) that mold the gut microbiome of athletes and non-athletes alike. Im going to work so that its a pure guts race at the end, and if it is, I am the only one who can win it. - Steve Prefontaine

microbiology↗
bioRxiv2022

An assessment of Nano-RECall: Interpretation of Oxford Nanopore sequence data for HIV-1 drug resistance testing

IntroductionOxford Nanopore Technologies (ONT) offer sequencing with low-capital-layout sequencing options, which could assist in expanding HIV drug resistance testing to resource limited settings. However, sequence analysis remains time time-consuming and reliant on skilled personnel. Moreover, current ONT bioinformatic pipelines provide a single consensus sequence that is not equivalent to Sanger sequencing, as drug resistance is often detected in mixed populations. We have therefore investigated an integrated bioinformatic pipeline, Nano-RECall, for seamless drug resistance of low read coverage ONT sequence data from affordable Flongle or MinION flow cells. MethodsWe compared Sanger sequencing to ONT sequencing of the same HIV-1 subtype C polymerase chain reaction (PCR) amplicons, respectively using RECall and the novel Nano-RECall bioinformatics pipelines. Amplicons were from separate assays a) Applied Biosystems HIV-1 Genotyping Kit (ThermoFisher) spanning protease (PR) to reverse transcriptase (RT) (PR-RT) (n=46) and b) homebrew integrase (IN) (n=21). We investigated optimal read-depth by assessing the coefficient of variation (CV) of nucleotide proportions for various read-depths; and between replicates of 400 reads. The agreement between Sanger sequences and ONT sequences were assessed at nucleotide level, and at codon level for Stanford HIV drug resistance database mutations. ResultsThe coefficient of variation of ONT minority variants plateaued after a read depth of 400-fold implying limited benefit of additional depth and replicates of 400 reads showed a CV of [~]6 % for a representative position. The average sequence similarity between ONT and Sanger sequences was 99.3% (95% CI: 99.1-99.4%) for PR-RT and 99.6% (95% CI: 99.4-99.7%) for INT. Drug resistance mutations did not differ for 21 IN sequences; 16 mutations were detected by both ONT- and Sanger sequencing. For the 46 PR and RT sequences, 245 mutations were detected by either ONT or Sanger, of these 238 (97.1%) were detected by both. ConclusionsThe Nano-RECall pipeline, freely available as a downloadable application on a Windows computer, provides Sanger-equivalent HIV drug resistance interpretation. This novel pipeline combined with a simple workflow and multiplexing samples on ONT flow-cells would contribute to making HIV drug resistance sequencing feasible for resource limited settings.

microbiology↗
bioRxiv2023

Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations - application to HIV-1 quasispecies

Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies. Author SummaryThere is a great need to understand the genetic diversity of pathogens in an accurate and timely manner, but many errors can be introduced during the sample handling and sequencing steps which may prevent accurate analyses. In some cases, the errors introduced during these steps can be indistinguishable from real genetic variation and prevent analyses from identifying true sequence variation present in the pathogen population. There are established methods which can help to prevent these types of errors, but can involve many different steps and variables, all of which must be optimized and tested together to ensure the desired effect. Here we show results from testing different methods on a set of HIV+ blood plasma samples and arrive at a streamlined laboratory protocol and bioinformatic pipeline which prevents or corrects for different types of errors that can arise in sequence datasets. These methods should be an accessible starting point for anyone wanting accurate sequencing without extensive optimizations.

molecular biology↗
bioRxiv2023

Biofoundry-scale DNA assembly validation using cost-effective high-throughput long read sequencing

Biofoundries are automated high-throughput facilities specialising in the design, construction and testing of engineered/synthetic DNA constructs (plasmids), often from genetic parts. A critical step of this process is assessing the fidelity of the assembled DNA construct to the desired design. Current methods utilised for this purpose are restriction digest or PCR followed by fragment analysis, and sequencing. The Edinburgh Genome Foundry (EGF) has recently established a single-molecule sequencing quality control step using the Oxford Nanopore sequencing technology, along with a companion Nextflow pipeline and a Python package to perform in-depth analysis and generate a detailed report. Our software enables biofoundry scientists and end-users to rapidly analyse sequencing data, without specialised bioinformatics knowledge. In conclusion, we have created a laboratory and software protocol that validates assembled, cloned or edited plasmids, using Nanopore long reads, which can serve as a useful resource for the genetics, synthetic biology and sequencing communities. Author informationAll authors contributed to the design of the sequencing quality control step and pipeline, and the preparation of the manuscript. P.V. wrote the manuscript, designed and implemented the bioinformatics pipeline and interpreted results. S.D. wrote the manuscript, implemented the laboratory protocol and interpreted results. G.S. designed the bioinformatics pipeline. R.F. wrote the manuscript and contributed to the design of the laboratory protocol and pipeline. Address: Edinburgh Genome Foundry (University of Edinburgh), Michael Swann Building, Max Born Crescent, Edinburgh, EH9 3BF, United Kingdom Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/558498v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@a819e5org.highwire.dtl.DTLVardef@3c34ceorg.highwire.dtl.DTLVardef@aef5d8org.highwire.dtl.DTLVardef@8e5d2f_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗
bioRxiv2023

Multi-omics resources for the Australian southern stuttering frog (Mixophyes australis) reveal assorted antimicrobial peptides

The number of genome-level resources for non-model species continues to rapidly expand. However, frog species remain underrepresented, with up to 90% of frog genera having no genomic or transcriptomic data. Here, we assemble the first genomic and transcriptomic resources for the recently described southern stuttering frog (Mixophyes australis). The southern stuttering frog is ground-dwelling, inhabiting naturally vegetated riverbanks in south-eastern Australia. Using PacBio HiFi long-read sequencing and Hi-C scaffolding, we generated a high-quality genome assembly, with a scaffold N50 of 369.3 Mb and 95.1% of the genome contained in twelve scaffolds. Using this assembly, we identified the mitochondrial genome, and assembled six tissue-specific transcriptomes. We also bioinformatically characterised novel sequences of two families of antimicrobial peptides (AMPs) in the southern stuttering frog, the cathelicidins and {beta}-defensins. While traditional peptidomic approaches to peptide discovery have typically identified one or two AMPs in a frog species from skin secretions, our bioinformatic approach discovered 12 cathelicidins and two {beta}-defensins that were expressed in a range of tissues. We investigated the novelty of the peptides and found diverse predicted activities. Our bioinformatic approach highlights the benefits of multi-omics resources in peptide discovery and contributes valuable genomic resources in an under-represented taxon.

genomics↗