Biology subjects

Nagarajan, N.

Publications and source records attributed to Nagarajan, N..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes

BackgroundWith the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to reduce the impact of higher sequencing error rates. While alignment-based methods are generally slower, k-mer-based taxonomic classifiers can overcome this limitation, potentially at the expense of lower sensitivity for strains and species that are not in the database. ResultsWe present MetageNN, a memory-efficient long-read taxonomic classifier that is robust to sequencing errors and missing genomes. MetageNN is a neural network model that uses short k-mer profiles of sequences to reduce the impact of distribution shifts on error-prone long reads. Benchmarking MetageNN against other machine learning approaches for taxonomic classification (GeNet) showed substantial improvements with long-read data (20% improvement in F1 score). By utilizing nanopore sequencing data, MetageNN exhibits improved sensitivity in situations where the reference database is incomplete. It surpasses the alignment-based MetaMaps and MEGAN-LR, as well as the k-mer-based Kraken2 tools, with improvements of 100%, 36%, and 23% respectively at the read-level analysis. Notably, at the community level, MetageNN consistently demonstrated higher sensitivities than the previously mentioned tools. Furthermore, MetageNN requires < 1/4th of the database storage used by Kraken2, MEGAN-LR and MMseqs2 and is >7x faster than MetaMaps and GeNet and >2x faster than MEGAN-LR and MMseqs2. ConclusionThis proof of concept work demonstrates the utility of machine-learning-based methods for taxonomic classification using long reads. MetageNN can be used on sequences not classified by conventional methods and offers an alternative approach for memory-efficient classifiers that can be optimized further.

bioinformatics↗

bioRxiv2023

Comparison of tet(X4)-containing contigs from metagenomic sequencing data with plasmid sequences of isolates from a cohort of healthy subjects.

The recently discovered tigecycline-inactivating enzyme Tet(X4) can confer high-level tigecycline resistance on its hosts, which makes it a public health concern. This study focused on detection, analysis, and characterization of Tet(X4)-positive Enterobacteriaceae from the gut microbiota of a healthy cohort of individuals in Singapore using cultivation-dependent and cultivation-independent approaches. Twelve Tet(X4)-positive Enterobacteriaceae strains that were previously obtained from the cohort were fully genome-sequenced and comparatively analysed. A metagenomic sequencing (MS) dataset of the same samples was mined for contigs that harboured the tet(X4) resistance gene. The sequences of tet(X4)-containing contigs and plasmids sequences were compared. The presence of the resistance genes floR and catD (also annotated as estT) was detected in the same cassette in 10 and 12 out of the 12 tet(X4)-carrying plasmids, respectively. MS detected tet(X4)-containing contigs in two out of 109 subjects, while cultivation-dependent analysis previously reported a prevalence of 10.1%. Contig sequences are relatively short ([~]14-33 kb) but show high similarity to the respective plasmid sequences of the isolates. The frequent co-occurrence of florR and catD with tet(X4) corroborates the hypothesis that the transmission of tet(X4) may have originated from the veterinary sector. Our findings show that MS can complement efforts in the surveillance of antibiotic resistance genes for clinical samples, while it has a lower sensitivity than a cultivation-based method when the target organism have a low abundance. Further optimisation is required if MS is to be utilised in antibiotic resistance surveillance.

microbiology↗

bioRxiv2023

Tip of the iceberg? Three novel TOPLESS interacting effectors of the gall-inducing fungus Ustilago maydis

O_LIUstilago maydis is a biotrophic pathogen causing smut disease in maize. It secretes a cocktail of effector proteins during its biotrophic stages in the host plant, which target different host proteins. One such class of proteins we identified previously is TOPLESS (TPL) and TOPLESS RELATED (TPR) transcriptional corepressors. C_LIO_LIHere we screen 297 U. maydis effector candidates for their ability to interact with maize TPL protein RAMOSA 1 ENHANCER LOCUS 2 Like 2 (RELK2) and their ability to induce auxin signaling and thereby identified three novel TPL /TPR interacting effector proteins (Tip6, Tip7 and Tip8). Two of them, Tip6 and Tip7 contain a classical ethylene-responsive element binding factor-associated amphiphilic repression (EAR) motif and interact with maize TPL protein RELK2 in nuclear compartments, whereas Tip8 lacks known TPL interaction motifs and its overexpression in non-host plant leads to cell death indicating recognition of the effector. C_LIO_LIBy using structural modeling, we show an interaction of Tip6 and Tip7 with the previously crystallized EAR motif binding domain of RELK2. Furthermore, by infection assays with an octuple deletion mutant of U. maydis, we demonstrate a role of Tips in U. maydis virulence. Our findings suggest the TOPLESS class of corepressors as a major hub of U. maydis effector proteins. C_LI

plant biology↗

bioRxiv2023

Dissecting the Ca2+ dependence of Mycobacterium tuberculosis DesA1 function

Mycobacterium tuberculosis (M. tb) has a complex cell wall, largely composed of mycolic acids and long-chain fatty acids that play a crucial role in maintaining its integrity and permeability. This complex lipid structure has a role in abrogating the process of phagosome-lysosome fusion and infection establishment. The M. tb desaturase A1 (DesA1) catalyzes the introduction of position-specific double bonds, a key step in the biosynthesis of a diverse range of mycolic acids. We have previously demonstrated that M. tb DesA1 is a Ca2+-binding protein, belonging to the extended {beta}{gamma}-crystallin superfamily. Using a combination of biophysical and genetic approaches, we investigated the structural and functional significance of Ca2+ binding on DesA1 activity. A protein unfolding assay of the protein in the presence and absence of Ca2+ shows that Ca2+ binding imparts structural stability to DesA1. To identify the role of Ca2+, we introduced mutations at key residues in the identified Ca2+-binding motif of DesA1 and generated F303A, E304Q, and F303A-E304Q variants of DesA1. We identified F303 as a hot point which disables the protein for Ca2+ binding. Two other mutations E304Q and F303A-E304Q showed reduced Ca2+ binding. Complementation of a conditionally complemented desA1 deletion mutant strain of Mycobacterium smegmatis with these mutants, either failed to complement its growth phenotype or led to a compromise in complementation. In addition, the F303A and F303A-E304Q complements exhibit increased sensitivity to isoniazid, a first-line anti-tubercular drug, pointing to a cell wall permeability defect in these strains. Our findings highlight the critical importance of Ca2+ in the functioning of DesA1 and its implicit role in the maintenance of mycobacterial cellular integrity.

microbiology↗

bioRxiv2022

16S rRNA sequencing for profiling microbial communities in idiopathic granulomatous mastitis

BackgroundIdiopathic granulomatous mastitis (IGM) is a rare, chronic, and benign inflammatory breast disease with ambiguous aetiology. Contrastingly, lactational mastitis (LM) is commonly diagnosed in breastfeeding women. To investigate IGM aetiology, we profiled the microbial flora of pus and skin in IGM and LM patients. MethodsIGM patients were recruited from 2018 to 2020; LM patients were recruited as controls. 16S rRNA sequencing libraries were constructed from 16S rRNA gene amplified from total DNA extracted from pus and skin swabs. Metagenomic analysis was conducted using modified microbiome abundance analysis-suite customised R-resource for paired pus and skin samples. Microbiome multivariable association analyses were performed using linear models. Results21 IGM and 3 LM paired pus and skin samples underwent metagenomic analysis. Bray-Curtis ecological dissimilarity distance showed dissimilarity across four sample types (IGM-pus, IGM-skin, LM-pus, LM-skin; PERMANOVA, p<0.001). No characteristic dominant genus was observed across IGM samples. IGM pus samples were more diverse than corresponding IGM skin samples (Shannon and Simpson index; Wilcoxon paired signed-rank tests, p=0.022 and p=0.07). Corynebacterium kroppenstedtii, reportedly associated with IGM in literature, was higher in IGM pus samples than paired skin sample (Wilcoxon, p=0.022). Three other species and nineteen genera were statistically significant in paired IGM pus-skin comparison after antibiotic treatment adjustment, and multiple comparisons correction. ConclusionsMicrobial profiles are unique between IGM and LM patients. Inter-patient variability and polymicrobial IGM pus samples cannot implicate specific genus or species as an infectious cause for IGM.

microbiology↗

bioRxiv2022

No evidence for a common blood microbiome based on a population study of 9,770 healthy humans

Human blood is conventionally considered sterile. Recent studies have challenged this, suggesting the presence of a blood microbiome in healthy humans. We present the largest investigation to date of microbes in blood, based on shotgun sequencing libraries from 9,770 healthy subjects. Leveraging the availability of data from multiple cohorts, we stringently filtered for laboratory contaminants to identify 117 microbial species detected in the blood of sampled individuals, some of which had signatures of DNA replication. These primarily comprise of commensals associated with human body sites such as the gut (n=40), mouth (n=32), and genitourinary tract (n=18), which are species that are distinct from common pathogens detected in clinical blood cultures based on more than a decade of records from a tertiary hospital. Contrary to the expectations of a shared blood microbiome, no species were detected in 84% of individuals, while only a median of one microbial species per individual was detected in the remaining 16%. Futhermore, microbes of the same species were detected in <5% of individuals, no co-occurrence patterns similar to microbiomes in other body sites was observed, and no associations between host phenotypes (e.g. demographics and blood parameters) and microbial species could be established. Overall, these results do not support the hypothesis of a consistent core microbiome endogenous to human blood. Rather, our findings support the transient and sporadic translocation of commensal microbes, or their DNA, from other body sites into the bloodstream.

microbiology↗

bioRxiv2022

Long-term ecological and evolutionary dynamics in the gut microbiomes of carbapenemase-producing Enterobacteriaceae colonized subjects

Long-term colonization of the gut microbiome by carbapenemase-producing Enterobacteriaceae (CPE) is a growing area of public health concern as it can lead to community transmission and rapid increase in cases of life-threatening CPE infections. Leveraging the observation that many subjects are decolonized without interventions within a year, we used longitudinal shotgun metagenomics (up to 12 timepoints) for detailed characterization of ecological and evolutionary dynamics in the gut microbiome of a cohort of CPE-colonized subjects and family members (n=46; 361 samples). Subjects who underwent decolonization exhibited a distinct ecological shift marked by recovery of microbial diversity, key commensals and anti-inflammatory pathways. In addition, colonization was marked by elevated but unstable Enterobacteriaceae abundances, which exhibited distinct strain-level dynamics for different species (Escherichia coli and Klebsiella pneumoniae). Finally, comparative analysis with whole genome sequencing data from CPE isolates (n=159) helped identify sub-strain variation in key functional genes and the presence of highly similar E. coli and K. pneumoniae strains with variable resistance profiles and plasmid sharing. These results provide an enhanced view into how colonization by multi-drug resistant bacteria associates with altered gut ecology and can enable transfer of resistance genes, even in the absence of overt infection and antibiotic usage.

genomics↗

bioRxiv2022

Genome-centric analysis of short and long read metagenomes reveals uncharacterized microbiome diversity in Southeast Asians

Despite extensive efforts to address it, the vastness of uncharacterized dark matter microbial genetic diversity can impact short-read sequencing based metagenomic studies. Population-specific biases in genomic reference databases can further compound this problem. Leveraging advances in long-read and Hi-C technologies, we deeply characterized 109 gut microbiomes from three ethnicities in Singapore to comprehensively reconstruct 4,497 medium and high-quality metagenome assembled genomes, 1,708 of which were missing in short-read only analysis and with >28x N50 improvement. Species-level clustering identified 70 (>10% of total) novel gut species out of 685, improved reference genomes for 363 species (53% of total), and discovered 3,413 strains that are unique to these populations. Among the top 10 most abundant gut bacteria in our study, one of the species and >80% of all strains were not represented in existing databases. Annotation of biosynthetic gene clusters (BGCs) uncovered more than 27,000 BGCs with a large fraction (36-88%) not represented in current databases, and with several unique clusters predicted to produce bacteriocins that could significantly alter microbiome community structure. These results reveal the significant uncharacterized gut microbial diversity in Southeast Asian populations and highlight the utility of hybrid metagenomic references for bioprospecting and disease-focused studies.

genomics↗

bioRxiv2022

Genomic patterns of divergence in the early and late steps of speciation of the deep-sea vent thermophilic worms of the genus Alvinella.

BackgroundThe transient and fragmented nature of the deep-sea hydrothermal environment made of ridge subduction, plate collision and the emergence of new rifts is currently acting to the separation of vent populations, local adaptation and is likely to promote bursts of speciation and species specialization. The tube-dwelling worms Alvinella pompejana called the Pompeii worm and its sister species A. caudata live syntopically on the hottest part of deep-sea hydrothermal chimneys along the East Pacific Rise. They are exposed to extreme thermal and chemical gradients, which vary greatly in space and time, and thus represent ideal candidates for understanding the evolutionary mechanisms at play in the vent fauna evolution. ResultsIn the present study, we explored genomic patterns of divergence in the early and late steps of speciation of these emblematic worms using transcriptome assemblies and the first draft genome to better understand the relative role of geographic isolation and habitat preference in their genome evolution. Analyses were conducted on allopatric populations of Alvinella pompejana (early stage of separation) and between A. pompejana and its syntopic species Alvinella caudata (late stage of speciation). We first identified divergent genomic regions and targets of selection as well as their position in the genome over collections of orthologous genes and, then, described the speciation dynamics by documenting the annotation of the most divergent and/or positively selected genes involved in the isolation process. Gene mapping clearly indicated that divergent genes associated with the early stage of speciation, although accounting for nearly 30% of genes, are highly scattered in the genome without any island of divergence and not involved in gamete recognition or mito-nuclear incompatibilities. By contrast, genomes of A. pompejana and A. caudata are clearly separated with nearly all genes (96%) exhibiting high divergence. This congealing effect however seems to be linked to habitat specialization and still allows positive selection on genes involved in gamete recognition, as a possible long-duration process of species reinforcement. ConclusionOur analyses pointed out the non-negligible role of natural selection on both the early and late stages of speciation in the emblematic thermophilic worms living on the walls of deep-sea hydrothermal chimneys. They shed ligth on the evolution of gene divergence during the process of speciation and species specialization over a very long period of time.

evolutionary biology↗

bioRxiv2021

Critical Assessment of Metagenome Interpretation - the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from [~]1,700 novel and known microbial genomes, as well as [~]600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results. Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

bioinformatics↗

bioRxiv2021

Whole-genome analysis of antimicrobial-resistant Escherichia coli in human gut microbiota reveals its origin and flexibility in transmitting mcr-1

Multidrug resistant (MDR) Escherichia coli strains that carry extended-spectrum {beta}-lactamases (ESBLs) or colistin resistance gene mcr-1 have been identified in the human gut at an increasing incidence worldwide. In this study, we sampled and characterized MDR Enterobacteriaceae from the gut microbiota of healthy Singaporeans and show that the prevalence of ESBL-producing and mcr-positive Enterobacteriaceae is 26.6% and 7.3%, respectively. Whole-genome sequencing of 37 E. coli isolates identified 25 sequence types and assigned them into six different phylogroups, suggesting that the human intestinal MDR E. coli strains are highly diverse. In addition, we found that E. coli isolates belonging to phylogroup D, B2 and F carry a higher number of virulence genes, whereas isolates of phylogroup A, B1 and E carry fewer virulence factor genes but are frequent carriers of florfenicol resistance gene floR and colistin resistance gene mcr-1. Comparison of the seven mcr-1-positive E. coli isolates revealed that mcr-1 is carried by conjugative plasmids or embedded in composite transposons, which could potentially mobilize mcr-1 to other pathogenic Enterobacteriaceae strains or MDR plasmids. Finally, we found that 12 out of the 37 MDR E. coli isolates in this study show high similarity to ESBL-producing E. coli isolates from raw meats from local markets, suggesting a potential transmission of MDR E. coli from meat products to the human gut microbiota. Our findings show diverse antibiotic resistance and virulence profiles of intestinal E. coli and call for better countermeasures to block the transmission of MDR E. coli via the food chain. ImportanceThe human gut can harbor both antibiotic resistant and virulent E. coli which may subsequently cause infections. In this study, the antibiotic resistance and virulence traits of antibiotic-resistant E. coli isolates from human gut microbiota of healthy subjects were investigated. The isolated E. coli strains carry a diverse range of antibiotic resistance mechanisms and virulence factor genes, are highly diverse to each other, and are likely to originate from raw meat products from the local markets. Of particular concern are seven E. coli isolates which carry colistin resistance gene mcr-1. This gene can be mobilized into other pathogens and MDR plasmids, thereby spreading resistance to the last-resort antibiotic colistin. Our findings also suggest that raw meat could serve as important source to transmit MDR bacteria into the human gut microbiota.

microbiology↗

bioRxiv2021

Ancient migrations - The first complete genome assembly, annotation and variants of the Zoroastrian-Parsi community of India

With the advent of Next Generation Sequencing, many population specific whole genome sequences published thus far, predominantly represent individuals of European ancestry. While sequencing efforts of underrepresented communities in genomes datasets, like the Yoruba West-African, Han Chinese, Tibetan, South Korean, Egyptian and Japanese have recently added to the public genomic repositories, a comprehensive understanding of human genomic diversity and discovery of trait-associated variants necessitates the need for additional population specific analysis. In this context, the genomics of the population from the Indian sub-continent, given its genetic heterogeneity needs further elucidation. In this context, the endogamous Zoroastrian-Parsi community of India, offer an exceptional insight into a homogenous population that has culturally, socially, and genetically remained intact, for 13 centuries amidst the genomic, social and cultural Indian landscape, consequent to their migration from the ancient Persian plateau. Notwithstanding longevity as a trait, this endangered community is highly susceptible to cancers, rare genetic disorders, and display a documented high incidence of neurodegenerative and autoimmune conditions. The community as a matter of cultural practice abstains from smoking. Here, we describe the assembly and annotation of the genome of an adult female, Zoroastrian-Parsi individual sequenced at a high depth of 173X using a combination of short Illumina reads (160X) and long nanopore reads (13X). Using a combination of hybrid assemblers, we created a new, population-specific human reference genome, The Zoroastrian-Parsi Genome Reference Female, AGENOME-ZPGRF, contains 2,778,216,114 nucleotides as compared to 3,096,649,726 in GRCh38 constituting 93.235% of the total genomic fraction. Annotation identified 20833 genomic features, of which 14996 are almost identical to their counterparts on GRCh38 while 5837 genomic features were covered in partial. AGENOME-ZPGRF contained 5,426,310 variants of which the majority were SNPs (4,291,601) and 960,867 SNPs were AGENOME-ZPGRF specific personal variants not listed in dbSNP. We present, AGENOME-ZPGRF as a whole reference for any genetic studies involving Zoroastrian-Parsi individuals extending their application to identify disease relevant prognostic biomarkers and variants in global population genomics studies.

genomics↗

bioRxiv2020

Dynamic Methylome Modification associated with mutational signatures in ageing and etiology of disease

Reversible epigenetic changes within the loci of genes that regulate critical cell processes have recently emerged as important biomarkers of disease pathology. It is then natural to consider the consequences for population health risk of such epigenetic changes during the aging process. Specifically, the interplay between dynamic methylation changes that accompany aging and mutations that accrue in an individuals genome over time need further investigation. The current study investigated the role of dynamic methylation acting together with gene variants in an individual over time to gain insight into the evolving epigenome-genome interplay that affects biochemical pathways controlling physiological processes during aging. We completed a whole-genome methylation and variant analysis in a non-smoking Zoroastrian-Parsi individual, collecting two samples, 12 years apart (at 53 and 65 years respectively) (ZPMetG-Hv2a-1A (old, t0), ZPMetG-Hv2a-1B (recent, t0+12)) and analysing them using a GridION Nanopore sequencer at 13X genome coverage overall. We further identified the single nucleotide variants (SNVs) and indels in known CpG islands by employing the Genome Analysis Tool Kit (GATK) and MuTect2 variant-caller pipeline with the GRCh37 (patch 13) human genome as reference. We found 5258 disease-relevant genes that had been differentially methylated in this individual over 12 years. Employing the GATK pipeline, we found 24,948 genes, corresponding to 4,58,148 variants, specific to ZPMetG-Hv2a-1B, indicating the presence of variants that had accrued over time. A fraction of the gene variants (242/24948) occurred within the CpG regions that were differentially methylated, with 67/247 exactly coincident with a CpG site. Our analysis yielded a critical cluster of 10 genes that were each significantly methylated and had variants at the CpG site or the {+/-}4 bp CpG region window. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment network analysis as well as Reactome and STRING analysis of gene-specific variants indicated an impact on biological processes regulating the immune system, disease networks implicated in cancer and neurodegenerative diseases, and transcriptional control of processes regulating cellular senescence and longevity. Additional analysis of mutational signatures indicated a majority of C>T transitions followed by T>C transitions in the more recent sample, ZPMetG-Hv2a-1B. Our current study provides additional insight into the aging methylome over time and the interplay between different methylation and gene variants in the etiology of disease.

genomics↗

bioRxiv2020

TUGDA: Task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in vitro to in vivo settings

Over the last decade, large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning predictive models for this include the high-dimensionality of omics data, limitations in the number of data points available, and heterogeneity in biological and clinical factors affecting patient response. The use of multi-task learning (MTL) techniques has been widely explored to address dataset limitations for in vitro drug response models, while domain adaptation (DA) has been employed to extend them to predict in vivo response. In both of these transfer learning settings, noisy data for some tasks (or domains) can substantially reduce the performance for others compared to single-task (domain) learners, i.e. lead to negative transfer (NT). We describe a novel multi-task unsupervised domain adaptation method (TUGDA) that addresses these limitations in a unified framework by quantifying uncertainty in predictors and weighting their influence on shared domain/task feature representations. TUGDAs ability to rely more on predictors with low-uncertainty allowed it to notably reduce cases of negative transfer for in vitro models (63% for drugs with limited data and 94% overall) compared to state-of-the-art methods. For domain adaptation to in vivo settings, TUGDA improved performance for 6 out of 12 drugs in patient-derived xenografts, and 7 out of 22 drugs in TCGA patient datasets, despite being trained in an unsupervised fashion. TUGDAs ability to avoid negative transfer thus provides a key capability as we try to integrate diverse drug-response datasets to build consistent predictive models with in vivo utility. Availabilityhttps://github.com/CSB5/TUGDA

bioinformatics↗

bioRxiv2020

Benchmarking metagenomic classification tools for long-read sequencing data

BackgroundLong reads have gained popularity in the analysis of metagenomics data. Therefore, we comprehensively assessed metagenomics classification tools on the species taxonomic level. We analysed kmer-based tools, mapping-based tools and two general-purpose long reads mappers. We evaluated more than 20 pipelines which use either nucleotide or protein databases and selected 13 for an extensive benchmark. We prepared seven synthetic datasets to test various scenarios, including the presence of a host, unknown species and related species. Moreover, we used available sequencing data from three well-defined mock communities, including a dataset with abundance varying from 0.0001% to 20% and six real gut microbiomes. ResultsGeneral-purpose mappers Minimap2 and Ram achieved similar or better accuracy on most testing metrics than best-performing classification tools. They were up to ten times slower than the fastest kmer-based tools requiring up to four times less RAM. All tested tools were prone to report organisms not present in datasets, except CLARK-S, and they underperformed in the case of the high presence of the hosts genetic material. Tools which use a protein database performed worse than those based on a nucleotide database. Longer read lengths made classification easier, but due to the difference in read length distributions among species, the usage of only the longest reads reduced the accuracy. The comparison of real gut microbiome datasets shows a similar abundance profiles for the same type of tools but discordance in the number of reported organisms and abundances between types. Most assessments showed the influence of database completeness on the reports. ConclusionThe findings indicate that kmer-based tools are well-suited for rapid analysis of long reads data. However, when heightened accuracy is essential, off-the-shelf mappers demonstrate slightly superior performance, albeit at a considerably slower pace. Nevertheless, a combination of diverse categories of tools and databases will likely be necessary to analyse complex samples. Discrepancies observed among tools when applied to real gut datasets, as well as a reduced performance in cases where unknown species or a significant proportion of the host genome is present in the sample, highlight the need for continuous improvement of existing tools. Additionally, regular updates and curation of databases are important to ensure their effectiveness.

bioinformatics↗

bioRxiv2020

Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

While understanding heterogeneity in molecular signatures across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Single-cell RNA-seq (scRNA-seq) technologies have facilitated investigations into the role of intra-tumor transcriptomic heterogeneity (ITTH) in tumor biology and evolution, but their application to in silico models of drug response has not been explored. Based on large-scale analysis of cancer omics datasets, we highlight the utility of ITTH for predicting clinical outcomes. We then show that heterogeneous gene expression signatures obtained from scRNA-seq data can be accurately analyzed (80%) in a recommender system framework (CaDRReS-Sc) for in silico drug response prediction. Patient-derived cell lines capturing transcriptomic heterogeneity from primary and metastatic tumors were used as in vitro proxies for validating monotherapy predictions (Pearson r>0.6), as well as optimal drug combinations to target different subclonal populations (>10% improvement). Applying CaDRReS-Sc to the increasing number of publicly available tumor scRNA-seq datasets can serve as an in silico screen for further in vitro and in vivo drug repurposing studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=191 SRC="FIGDIR/small/389676v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1aa5795org.highwire.dtl.DTLVardef@5c2323org.highwire.dtl.DTLVardef@106e074org.highwire.dtl.DTLVardef@7a0834_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LILarge-scale analysis to establish the impact of transcriptomic heterogeneity within tumors on clinical outcomes C_LIO_LICalibrated recommender system for drug response prediction based on single-cell RNA-seq data (CaDRReS-Sc) C_LIO_LIPrediction of drug response in patient-derived cell lines with transcriptomic heterogeneity C_LIO_LIIn silico identification of drug combinations that work based on clonal vulnerabilities C_LI

bioinformatics↗

bioRxiv2020

BEEM-Static: Accurate inference of ecological interactions from cross-sectional metagenomic data

MotivationThe structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of metagenomic sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional metagenomic datasets for unravelling ecological structure in a scalable manner thus remains an open problem. MethodsWe present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. ResultsBenchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n=4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species. ConclusionBEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of metagenomic data.

bioinformatics↗

bioRxiv2020

Direct RNA sequencing reveals structural differences between transcript isoforms

The ability to correctly assign structure information to an individual transcript in a continuous and phased manner is critical to understanding RNA function. RNA structure play important roles in every step of an RNAs lifecycle, however current short-read high throughput RNA structure mapping strategies are long, complex and cannot assign unique structures to individual gene-linked isoforms in shared sequences. To address these limitations, we present an approach that combines structure probing with SHAPE-like compound NAI-N3, nanopore direct RNA sequencing, and one-class support vector machines to detect secondary structures on near full-length RNAs (PORE-cupine). PORE-cupine provides rapid, direct, accurate and robust structure information along known RNAs and recapitulates global structural features in human embryonic stem cells. The majority of gene-linked isoforms showed structural differences in shared sequences both local and distal to the alternative splice site, highlighting the importance of long-read sequencing for phasing of structures. Structural differences between gene-linked isoforms are associated with differential translation efficiencies globally, highlighting the role of structure as a pervasive mechanism for regulating isoform-specific gene expression inside cells.

biochemistry↗