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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2020

The fully resolved genome of Bacillus thuringiensis HER1410 reveals a cry-containing chromosome, two megaplasmids & an integrative plasmidial prophage

Bacillus thuringiensis is the most used biopesticide in agriculture. Its entomopathogenic capacity stems from the possession of plasmid-borne insecticidal crystal genes (cry), traditionally used as discriminant taxonomic feature for that species. As such, crystal and plasmid identification are key to the characterization of this species. To date, about 600 B. thuringiensis genomes have been reported, but less than 5% have been resolved, while the other draft genomes are incomplete, precluding plasmid delineation. Here we present the complete genome of Bacillus thuringiensis HER1410, a strain closely related to B. thuringiensis entomocidus and a known host for a variety of Bacillus phages. The combination of short and long-reads techniques allowed fully resolving the genome and delineation of three plasmids. This enabled the accurate detection of an unusual location of a unique cry gene, cry1Ba4, located in a genomic island near the chromosome replication origin. Two megaplasmids, pLUSID1 and pLUSID2 could be delineated: pLUSID1 (368kb), a likely conjugative plasmid involved in virulence, and pLUSID2 potentially related to the sporulation process. A smaller plasmidial prophage pLUSID3, with a dual lifestyle whose integration within the chromosome, causes the disruption of a flagellar key component. Finally, phylogenetic analysis located this strain within a clade comprising members from the B. thuringiensis serovar thuringiensis and other serovars and with B.cereus s. s. This highlights the intermingled taxonomy of B. cereus sensu lato group, where genomics alone does not support the present taxonomy between B. cereus s. s. and B. thuringiensis as species designation currently relies solely on the presence of entomocidal genes. ImportanceBacillus cereus group species have been extensively studied due to their economical and clinical relevance. This importance originally set the basis for B. cereus group members classification which are commonly based on phenotypical criteria. Sequencing era has shed light about genomic characterization of these species, showing their chromosomal genomic similarity and highlighting the role of mobile genetic elements, especially megaplasmids, in the classification and characterization of this group. However, only the 5% of the sequenced B. thuringiensis genomes have been fully resolved. Thus, here we addressed efficiently the study B. thuringiensis HER1410 genomic features by the use of a combination of short and long-reads sequencing. This methodology resulted in the high-quality assembly, which led to the identification of an uncommon location of a cry gene close to the chromosomal origin, as well as three fully resolved extrachromosomal elements, two megaplasmids and an integrative plasmidial prophage.

microbiology↗
bioRxiv2020

Metagenomic association analysis of gut symbiont Lactobacillus reuteri without host-specific genome isolation

Lactobacillus reuteri is a model symbiont colonizing the guts of vertebrates used for studies on host adaptation of the gut symbiont. Previous studies investigated host-specific phylogenetic and functional properties by isolating its genomic sequence. This dependency on genome isolation is a significant bottleneck. Here we propose a method to study the association between L. reuteri and its hosts directly from metagenomic reads without strain isolation by using pan-genomes. We characterized the host-specificity of L. reuteri in metagenomic samples not only in the previously studied organisms (mice and pigs) but additionally in dogs. For each sample, two types of profiles were generated: (1) genome-based strain type abundance profiles and (2) gene composition profiles. Our profiles showed host-association of L. reuteri in both phylogenetic and functional aspects without depending on the host-specific genome isolation. We could observe not only the presence of host-specific lineages but also the dominant lineages associated with the different hosts. Furthermore, we show that metagenome-assembled genomes provide detailed insights into the host-specificity of L. reuteri. We could infer evolutionary trajectories of host-associative L. reuteri strains in the metagenomic samples by placing the metagenome-assembled genomes into a phylogenetic tree and identify novel host-specific genes which were unannotated in existing pan-genome databases. Our pan-genomic approach drops the need for time-consuming and expensive host-specific genome isolation while producing consistent results with previous host-association findings in mice and pigs. Additionally, we could predict associations that have not yet been studied in dogs.

microbiology↗
bioRxiv2020

Revealing the impact of genomic alterations on cancer cell signaling with a partially transparent deep learning model

Cancer is a disease of aberrant cellular signaling and tumor-specific aberrations in signaling systems determine the aggressiveness of a cancer and response to therapy. Identifying such abnormal signaling pathways causing a patients cancer would enable more patient-specific and effective treatments. We interpret the cellular signaling system as a causal graphical model, where it is known that genomic alterations cause changes in the functions of signaling proteins, and the propagation of signals among proteins eventually leads to changed gene expression. To represent such a system, we developed a deep learning model, referred to as a redundant input neural network (RINN), with a redundant input architecture and an L1 regularized objective function to find causal relationships between input, latent, and output variables--when it is known a priori that input variables cause output variables. We hypothesize that training RINN on cancer omics data will enable us to map the functional impacts of genomic alterations to latent variables in a deep learning model, allowing us to discover the hierarchical causal relationships between variables perturbed by different genomic alterations. Importantly, the direct connections between all input and all latent variables in RINN make the latent variables partially interpretable, as they can be easily mapped to input space. We show that gene expression can be predicted from genomic alterations with reasonable accuracy when measured as the area under ROC curves (AUROCs). We also show that RINN is able to discover the shared functional impact of genomic alterations that perturb a common cancer signaling pathway, especially relationships in the PI3K, Nrf2, and TGF{beta} pathways, including some causal relationships. However, despite high regularization, the learned causal relationships were somewhat too dense to be easily and directly interpretable as causal graphs. We suggest promising future directions for RINN, including differential regularization, autoencoder pretrained representations, and constrained evolutionary strategies. Author summaryA modified deep learning model (RINN with L1 regularization) can be used to capture cancer signaling pathway relationships within its hidden variables and weights. We found that genomic alterations impacting the same known cancer pathway had interactions with a similar set of RINN latent variables. Having genomic alterations (input variables) directly connected to all latent variables in the RINN model allowed us to label the latent variables with a set of genomic alterations, making the latent variables partially interpretable. With this labeling, we were able to visualize RINNs as causal graphs and capture at least some of the causal relationships in known cancer signaling pathways. However, the graphs learned by RINN were somewhat too dense (despite large amounts of regularization) to compare directly to known cancer signaling pathways. We also found that differential expression can be predicted from genomic alterations by a RINN with reasonably high AUROCs, especially considering the very high dimensionality of the prediction task relative to the number of input variables and instances in the dataset. These are encouraging results for the future of deep learning models trained on cancer genomic data.

bioinformatics↗
bioRxiv2020

Large scale metagenome assembly reveals novel animal-associated microbial genomes, biosynthetic gene clusters, and other genetic diversity

Large-scale metagenome assemblies of human microbiomes have produced a vast catalogue of previously unseen microbial genomes; however, comparatively few microbial genomes derive from other vertebrates. Here, we generated 5596 metagenome-assembled genomes from the gut metagenomes of 180 predominantly wild animal species representing 5 classes, in addition to 14 existing animal gut metagenome datasets. The MAGs comprised 1522 species-level genome bins (SGBs); most of which were novel at the species, genus, or family levels, and the majority were enriched in host versus environment metagenomes. Many traits distinguished SGBs enriched in host or environmental biomes, including the number of antimicrobial resistance genes. We identified 1986 diverse biosynthetic gene clusters; only 23 clustered with any MIBiG database references. Gene-based assembly revealed tremendous gene diversity, much of it host- or environment-specific. Our MAG and gene datasets greatly expand the microbial genome repertoire and provide a broad view of microbial adaptations to the vertebrate gut. ImportanceMicrobiome studies on a select few mammalian species (e.g., humans, mice, and cattle) have revealed a great deal of novel genomic diversity in the gut microbiome. However, little is known of the microbial diversity in the gut of other vertebrates. We studied the gut microbiome of a large set of mostly wild animal species consisting of mammals, birds, reptiles, amphibians, and fish. Unfortunately, we found that existing reference databases commonly used for metagenomic analyses failed to capture the microbiome diversity among vertebrates. To increase database representation, we applied advanced metagenome assembly methods to our animal gut data and to many public gut metagenome datasets that had not been used to obtain microbial genomes. Our resulting genome and gene cluster collections comprised a great deal of novel taxonomic and genomic diversity, which we extensively characterized. Our findings substantially expand what is known of microbial genomic diversity in the vertebrate gut.

microbiology↗
bioRxiv2020

Global population genomic analysis of Mycoplasma bovis isolates reveals transcontinental variations and potential virulence genes

Among more than twenty species belonging to the class Mollecutes, Mycoplasma bovis is the most common cause of bovine mycoplasmosis in North America and Europe. Bovine mycoplasmosis causes significant economic loss in the cattle industry. The number of M. bovis positive herds has recently increased in North America and Europe. Since antibiotic treatment is ineffective and no efficient vaccine is available, M. bovis-induced mycoplasmosis is primarily controlled by herd management measures such as the restriction of moving infected animals out of the herds and culling of infected animals or shedders. To better understand the population structure and genomic factors that may contribute to its transmission, we sequenced 147 M. bovis strains isolated from four different countries and hosts, primarily cattle. We performed a large-scale comparative analysis of M. bovis genomes by integrating 104 publicly available genomes and our dataset (251 total genomes). A whole genome-single nucleotide polymorphism (SNP)-based phylogeny revealed that M. bovis population structure is composed of five clades with one of the isolates clustering with the outgroup M. agalactiae. These isolates were found to cluster with those from Canada, Israel, Lithuania, and Switzerland, suggesting trans-continental transmission of the strains. We also validated a previous report suggesting minimum divergence in isolates of Australian origin, which grouped within a single clade along with strains from China and Israel. However, no observable pattern of host association in M. bovis genomes was found in this study. Our comparative genome analysis also revealed that M. bovis has an open pangenome with a large breadth of unexplored diversity of genes. Analysis of vsp gene-host association revealed a single vsp significantly associated with bovine isolates that may be targeted for diagnostics or vaccine development. Our study also found that M. bovis genome harbors a large number of IS elements, including a novel 1624 bp IS element, and ISMbov9. Collectively, the genome data and the whole genome-based population analysis in this study may help to develop control measures to reduce the incidence of M. bovis-induced mycoplasmosis in cattle and/or to identify candidate genes for vaccine development.

microbiology↗
bioRxiv2020

Experimental evolution confirms signatures of sexual selection in genomic divergence.

Comparative genomics has contributed to the growing evidence that sexual selection is an important component of evolutionary divergence and speciation. Divergence by sexual selection is implicated in faster rates of divergence of the X chromosome and of genes thought to underlie sexually selected traits, including genes that are sex-biased in expression. However, accurately inferring the relative importance of complex and interacting forms of natural selection, demography and neutral processes which occurred in the evolutionary past is challenging. Experimental evolution provides an opportunity to apply controlled treatments for multiple generations and examine the consequent genomic divergence. Here we altered sexual selection intensity, elevating sexual selection in polyandrous lines and eliminating it in monogamous lines, and examined patterns of divergence in the genome of Drosophila pseudoobscura after more than 160 generations of experimental evolution. Divergence is not uniform across the genome but concentrated in "islands", many of which contain candidate genes implicated in mating behaviours and other sexually selected phenotypes. These are more often seen on the X chromosome, which shows divergence greater than neutral expectations. There are characteristic signatures of selection seen in these regions, with lower diversity and greater FST on the X chromosome than the autosomes, and differences in diversity on the autosomes between selection regimes. Reduced Tajimas D implies that selective sweeps have occurred within some of the divergent regions, despite considerable recombination. These changes are associated with both differential gene expression between the lines and sex-biased gene expression within the lines. Our results are very similar to those thought to implicate sexual selection in divergence in natural populations, and hence provide experimental support for the likely role of sexual selection in driving such types of genetic divergence, but also illustrate how variable outcomes can be for different genomic regions. Impact SummaryHow does sexual selection contribute to the divergence of genomes? It is often thought that sexual selection is a potent force in evolutionary divergence, but finding signatures of sexual selection in the genome is not straight-forward, and has been quite controversial recently. Here we used experimental evolution to allow replicate populations of fruit fly to evolve under relaxed or strengthened sexual selection for over 160 generations, then sequenced their genomes to see how they had diverged. The features we find are very similar to those reported in populations of natural species thought to be under strong sexual selection. We found that genomic divergence was concentrated in small patches of the genome rather than widespread. These are more often seen on the X chromosome, which overall shows especially elevated divergence. There are also characteristic signatures of selection seen in these regions, with lower genetic diversity suggesting that selection was strong in these regions. The changes are associated with both differential gene expression between the lines and sex-biased gene expression within the lines. Many of the patches of divergence also contain candidate genes implicated in mating behaviours and other sexually selected phenotypes. Our results provide experimental support for the likely role of sexual selection in driving such types of genetic divergence.

evolutionary biology↗
bioRxiv2020

An Interpreted Atlas of Biosynthetic Gene Clusters from 1000 Fungal Genomes

Fungi are prolific producers of natural products, compounds which have had a large societal impact as pharmaceuticals, mycotoxins, and agrochemicals. Despite the availability of over 1000 fungal genomes and several decades of compound discovery efforts from fungi, the biosynthetic gene clusters (BGCs) encoded by these genomes and the associated chemical space have yet to be analyzed systematically. Here we provide detailed annotation and analyses of fungal biosynthetic and chemical space to enable genome mining and discovery of fungal natural products. Using 1037 genomes from species across the fungal kingdom (e.g., Ascomycota, Basidiomycota, and non-Dikarya taxa), 36,399 predicted BGCs were organized into a network of 12,067 gene cluster families (GCFs). Anchoring these GCFs with reference BGCs enabled automated annotation of 2,026 BGCs with predicted metabolite scaffolds. We performed parallel analyses of the chemical repertoire of Fungi, organizing 15,213 fungal compounds into 2,945 molecular families (MFs). The taxonomic landscape of fungal GCFs is largely species-specific, though select families such as the equisetin GCF are present across vast phylogenetic distances with parallel diversifications in the GCF and MF. We compare these fungal datasets with a set of 5,453 bacterial genomes and their BGCs and 9,382 bacterial compounds, revealing dramatic differences between bacterial and fungal biosynthetic logic and chemical space. These genomics and cheminformatics analyses reveal the large extent to which fungal and bacterial sources represent distinct compound reservoirs. With a >10-fold increase in the number of interpreted strains and annotated BGCs, this work better regularizes the biosynthetic potential of fungi for rational compound discovery. Significance StatementFungi represent an underexploited resource for new compounds with applications in the pharmaceutical and agriscience industries. Despite the availability of >1000 fungal genomes, our knowledge of the biosynthetic space encoded by these genomes is limited and ad hoc. We present results from systematically organizing the biosynthetic content of 1037 fungal genomes, providing a resource for data-driven genome mining and large-scale comparison of the genetic and molecular repertoires produced in fungi and compare to those present in bacteria.

biochemistry↗
bioRxiv2020

The economics of endosymbiotic gene transfer and the evolution of organellar genomes

The endosymbiosis of the bacterial progenitors of mitochondrion and the chloroplast are landmark events in the evolution of life on earth. While both organelles have retained substantial proteomic and biochemical complexity, this complexity is not reflected in the content of their genomes. Instead, the organellar genomes encode fewer than 5% of genes found in living relatives of their ancestors. While many of the 95% of missing organellar genes have been discarded, others have been transferred to the host nuclear genome through a process known as endosymbiotic gene transfer. Here we demonstrate that the difference in the per-cell copy number of the organellar and nuclear genomes presents an energetic incentive to the cell to either delete genes or transfer them to the nuclear genome. We show that, for the majority transferred genes, the energy saved by nuclear-transfer exceeds the costs incurred from importing the encoded protein into the organelle where it can provide its function. Finally, we show that the net energy saved by endosymbiotic gene transfer can constitute an appreciable proportion of total cellular energy budgets, and is therefore sufficient to impart a selectable advantage to the cell. Thus, reduced cellular cost and improved energy efficiency likely played a role in the reductive evolution of mitochondrial and chloroplast genomes and the transfer of organellar genes to the nuclear genome. Significance statementThe endosymbioses of the mitochondrion and the chloroplast were each followed by substantial gene loss and transfer of organellar genes to the nuclear genome. Here we show that the high per-cell copy number of these organellar genomes creates an energetic incentive for the cell to discard genes or transfer them to the nuclear genome. Thus, organellar gene loss and endosymbiotic gene transfer can be intrinsically advantageous to the cell.

evolutionary biology↗
bioRxiv2020

Evolutionary rates are correlated between Buchnera endosymbionts and mitochondrial genomes of their aphid hosts

The evolution of bacterial endosymbiont genomes is strongly influenced by host-driven selection. Factors affecting host genome evolution will potentially affect endosymbiont genomes in similar ways. One potential outcome is correlations in molecular rates between the genomes of the symbiotic partners. Recently, we presented the first evidence of such a correlation between the mitochondrial genomes of cockroaches and the genome of their endosymbiont (Blattabacterium cuenoti). Here we investigate whether similar patterns are found in additional host-symbiont partners. We use partial genome data from multiple strains of bacterial endosymbionts, B. aphidicola and Sulcia mulleri, and the mitochondrial genomes of their sap-feeding insect hosts. Both endosymbionts show phylogenetic congruence with the mitochondria of their hosts, a result that is expected due to their identical mode of inheritance. We compared root-to-tip distances and branch lengths of phylogenetically independent species pairs. Both analyses show a highly significant correlation of molecular rates between genomes of Buchnera and mitochondrial genomes of their hosts. A similar correlation was detected between Sulcia and their hosts, but was not statistically significant. Our results indicate that evolutionary rate correlations between hosts and long-term symbionts may be a widespread phenomenon.

evolutionary biology↗
bioRxiv2020

Dissecting genomic determinants of positive selection with an evolution-guided regression model

In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as gene expression level, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald-Kreitman test, are available to examine the association between genomic features and the rate of adaptation, we currently lack a statistical approach to disentangle the independent effect of a genomic feature from the effects of other correlated genomic features. To address this problem, I present a novel statistical model, the MK regression, which augments the McDonald-Kreitman test with a generalized linear model. Analogous to the classical multiple regression model, the MK regression can analyze multiple genomic features simultaneously to infer the independent effect of a genomic feature, holding constant all other genomic features. Using the MK regression, I identify numerous genomic features driving positive selection in chimpanzees. These features include well-known ones, such as local mutation rate, residue exposure level, tissue specificity, and immune genes, as well as new features not previously reported, such as gene expression level and metabolic genes. In particular, I show that highly expressed genes may have a higher adaptation rate than their weakly expressed counterparts, even though a higher expression level may impose stronger negative selection. Also, I show that metabolic genes may have a higher adaptation rate than their non-metabolic counterparts, possibly due to recent changes in diet in primate evolution. Overall, the MK regression is a powerful approach to elucidate the genomic basis of adaptation.

evolutionary biology↗
bioRxiv2020

Detection of plasmid contigs in draft genome assemblies using customized Kraken databases

Plasmids play an important role in bacterial evolution and mediate horizontal transfer of genes including virulence and antimicrobial resistance genes. Although short-read sequencing technologies have enabled large-scale bacterial genomics, the resulting draft genome assemblies are often fragmented into hundreds of discrete contigs. Several tools and approaches have been developed to identify plasmid sequences in such assemblies, but require trade-off between sensitivity and specificity. Here we propose using the Kraken classifier, together with a custom Kraken database comprising known chromosomal and plasmid sequences of Klebsiella pneumoniae species complex (KpSC), to identify plasmid-derived contigs in draft assemblies. We assessed performance using Illumina-based draft genome assemblies for 82 KpSC isolates, for which complete genomes were available to supply ground truth. When benchmarked against five other classifiers (Centrifuge, RFPlasmid, mlplasmids, PlaScope, and Platon), Kraken showed balanced performance in terms of overall sensitivity and specificity (90.8% and 99.4%, respectively for contig count; 96.5% and >99.9%, respectively for cumulative contig length), and the highest accuracy (96.8% vs 91.8%-96.6% for contig count; 99.8% vs 99.0%-99.7% for cumulative contig length), and F1-score (94.5% vs 84.5%-94.1%, for contig count; 98.0% vs 88.9%-96.7% for cumulative contig length). Kraken also achieved consistent performance across our genome collection. Furthermore, we demonstrate that expanding the Kraken database with additional known chromosomal and plasmid sequences can further improve classification performance. Although we have focused here on the KpSC, this methodology could easily be applied to other species with a sufficient number of completed genomes. IMPACT STATEMENTThe assembly of bacterial genomes using short-read data often results in hundreds of discrete contigs due to the presence of repeat sequences in those genomes. Separating plasmid contigs from chromosomal contigs in such assemblies is required, e.g., to assess the mobility of antimicrobial resistance genes. Although several tools have been developed for that purpose, they often suffer from low sensitivity or specificity. Here, we propose that the Kraken classifier coupled with a custom Kraken database comprising plasmid-free chromosomal sequences and complete plasmid sequences can be used for detection of plasmid contigs in draft genome assemblies. We showed that Kraken achieved balanced and higher performance compared with other methods (Centrifuge, RFPlasmid, mlplasmids, PlaScope, and Platon). We therefore consider that the Kraken classifier can be the best option for predicting the origin of contigs for species with a suitable number of completed chromosomal and plasmid sequences. DATA SUMMARYTable S1: Complete chromosomes used for creating the base Kraken database. Plasmid-free chromosomal sequences and complete plasmid sequences used for creating the base Kraken database are also available via Figshare at https://doi.org/10.6084/m9.figshare.13289564. Table S2: Sequence data used for benchmarking. Draft assemblies of these 82 KpSC strains are available via Figshare at https://doi.org/10.6084/m9.figshare.13553432. The corresponding sequence read files and complete genomes were deposited in the NCBI SRA and GenBank under BioProjects PRJEB6891, PRJNA351909, PRJNA486877, and PRJNA646837 (individual BioSample IDs listed in Table S2). Kraken output files are available via Figshare at https://doi.org/10.6084/m9.figshare.13553789.

microbiology↗
bioRxiv2021

Large scale genomic and evolutionary study reveals SARS-CoV-2 virus isolates from Bangladesh strongly correlate with European origin and not with China.

RationaleThe global public health is in serious crisis due to emergence of SARS-CoV-2 virus. Studies are ongoing to reveal the genomic variants of the virus circulating in various parts of the world. However, data generated from low- and middle-income countries are scarce due to resource limitation. This study was focused to perform whole genome sequencing of 151 SARS-CoV-2 isolates from COVID-19 positive Bangladeshi patients. The goal of this study was to identify the genomic variants among the SARS-CoV-2 virus isolates in Bangladesh, to determine the molecular epidemiology and to develop a relationship between host clinical trait with the virus genomic variants. MethodSuspected patients were tested for COVID-19 using one step commercial qPCR kit for SARS-CoV-2 Virus. Viral RNA was extracted from positive patients, converted to cDNA which was amplified using Ion AmpliSeq SARS-CoV-2 Research Panel. Massive parallel sequencing was carried out using Ion AmpliSeq Library Kit Plus. Assembly of raw data is done by aligning the reads to a pre-defined reference genome (NC_045512.2) while retaining the unique variations of the input raw data by creating a consensus genome. A random forest-based association analysis was carried out to correlate the viral genomic variants with the clinical traits present in the host. ResultAmong the 151 viral isolates, we observed the 413 unique variants. Among these 8 variants occurred in more than 80 % of cases which include 241C to T, 1163A to T, 3037C to T,14408C to T, 23403A to G, 28881G to A, 28882 G to A, and finally the 28883G to C. Phylogenetic analysis revealed a predominance of variants belonging to GR clade, which have a strong geographical presence in Europe, indicating possible introduction of the SARS-CoV-2 virus into Bangladesh through a European channel. However, other possibilities like a route of entry from China cannot be ruled out as viral isolate belonging to L clade with a close relationship to Wuhan reference genome was also detected. We observed a total of 37 genomic variants to be strongly associated with clinical symptoms such as fever, sore throat, overall symptomatic status, etc. (Fishers Exact Test p-value<0.05). The most mention-worthy among those were the 3916CtoT (associated with causing sore throat, p-value 0.0005), the 14408C to T (associated with protection from developing cough, p-value= 0.027), and the 28881G to A, 28882G to A, and 28883G to C variant (associated with causing chest pain, p-value 0.025). ConclusionTo our knowledge, this study is the first large scale phylogenomic studies of SARS-CoV-2 virus circulating in Bangladesh. The observed epidemiological and genomic features may inform future research platform for disease management, vaccine development and epidemiological study.

genetics↗
bioRxiv2021

FILER: large-scale, harmonized FunctIonaL gEnomics Repository

MotivationQuerying massive collections of functional genomic and annotation data, linking and summarizing the query results across data sources and data types are important steps in high-throughput genomic and genetic analytical workflows. However, accomplishing these steps is difficult because of the heterogeneity and breadth of data sources, experimental assays, biological conditions (e.g., tissues, cell types), data types, and file formats. ResultsFunctIonaL gEnomics Repository (FILER) is a large-scale, harmonized functional genomics data catalog uniquely providing: 1) streamlined access to >50,000 harmonized, annotated functional genomic and annotation datasets across >20 integrated data sources, >1,100 biological conditions/tissues/cell types, and >20 experimental assays; 2) a scalable, indexing-based genomic querying interface; 3) ability for users to analyze and annotate their own experimental data against reference datasets. This rich resource spans >17 Billion genomic records for both GRCh37/hg19 and GRCh38/hg38 genome builds. FILER scales well with the experimental (query) data size and the number of reference datasets and data sources. When evaluated on large-scale analysis tasks, FILER demonstrated great efficiency as the observed running time for querying 1000x more genomic intervals (106 vs. 103) against all 7x109 hg19 FILER records increased sub-linearly by only a factor of 15x. Together, these features facilitate reproducible research and streamline querying, integrating, and utilizing large-scale functional genomics and annotation data. Availability and implementationFILER can be 1) freely accessed at https://lisanwanglab.org/FILER, 2) deployed on cloud or local servers (https://bitbucket.org/wanglab-upenn/FILER), and 3) integrated with other pipelines using provided scripts. Contactlswang@pennmedicine.upenn.edu

bioinformatics↗
bioRxiv2021

Next-generation sequencing-based bulked segregant analysis without sequencing the parental genomes

The genomic region(s) that controls a trait of interest can be rapidly identified using BSA-Seq, a technology in which next-generation se-quencing (NGS) is applied to bulked segregant analysis (BSA). We recently developed the significant structural variant method for BSA-Seq data analysis that exhibits higher detection power than standard BSA-Seq analysis methods. Our original algorithm was developed to analyze BSA-Seq data in which genome sequences of one parent served as the reference sequences in genotype calling, and thus required the availability of high-quality assembled parental genome sequences. Here we modified the original script to allow for the effective detection of the genomic region-trait associations using only bulk genome sequences. We analyzed a public BSA-Seq dataset using our modified method and the standard allele frequency and G-statistic methods with and without the aid of the parental genome sequences. Our results demonstrate that the genomic region(s) associated with the trait of interest could be reliably identified only via the significant structural variant method without using the parental genome sequences. Significance StatementBSA-Seq can be utilized to rapidly identify structural varianttrait associations, and our modified significant structural variant method allows the detection of such associations without sequencing the parental genomes, leading to further lower the sequencing cost and making BSA-Seq more accessible to the research community and more applicable to the species with a large genome.

bioinformatics↗
bioRxiv2021

Comparative genomics analysis of Chryseobacterium sp. KMC2 reveals metabolic pathways involved in keratinous utilization and natural product biosynthesis

Several efforts have been made to valorize keratinous materials, an abundant and renewable resource. Despite these attempts to valorize products generated from keratin hydrolysate, either via chemical or microbial conversion, they generally remain with an overall low value. In this study, a promising keratinolytic strain from the genus Chryseobacterium (Chryseobacterium sp. KMC2) was investigated using comparative genomic tools against publicly available reference genomes to reveal the metabolic potential for biosynthesis of valuable secondary metabolites. Genome and metabolic features of four species were compared, shows different gene numbers but similar functional categories. We successfully mined eleven different secondary metabolite gene clusters of interest from the four genomes, including five common ones shared across all genomes. Among the common metabolites, we identified gene clusters involved in biosynthesis of flexirubin-type pigment, microviridin, and siderophore, all showing remarkable conservation across the four genomes. Unique secondary metabolite gene clusters were also discovered, for example, ladderane from Chryseobacterium sp. KMC2. Additionally, this study provides a more comprehensive understanding of the potential metabolic pathways of keratin utilization in Chryseobacterium sp. KMC2, with the involvement of amino acid metabolism, TCA cycle, glycolysis/gluconeogenesis, propanoate metabolism, and sulfate reduction. This work uncovers the biosynthesis of secondary metabolite gene clusters from four keratinolytic Chryseobacterium spp. and shades lights on the keratinolytic potential of Chryseobacterium sp. KMC2 from a genome-mining perspective, providing alternatives to valorize keratinous materials into high-value natural products. ImportanceKeratin is an abundant and renewable resource from slaughterhouses or the poultry industry. Low-value products such as animal feed and fertilizer were generated from these feedstocks based on conventional processing like chemical conversion. In fact, microorganisms possess the potential to synthesize valuable natural products. In this work, we explored the metabolic potential of Chryseobacterium sp. KMC2, which was isolated with efficient keratinolytic capacity from a previous study. Comparative genomics analysis displayed similar functional categories against three publicly available reference genomes of keratin-degrading Chryseobacterium spp.. Eleven different secondary metabolite gene clusters of interest were mined among four genomes, including five common and unique ones. Furthermore, we provide a more comprehensive understanding of metabolic pathways on keratin utilization in Chryseobacterium sp. KMC2, with the involvement of amino acid assimilation and sulfate reduction. These findings contribute to expanding the application of Chryseobacterium sp. KMC2 on the valorization of keratinous materials.

microbiology↗
bioRxiv2021

ChromeBat: A Bio-Inspired Approach to 3D Genome Reconstruction

BackgroundWith the advent of Next Generation Sequencing and the Hi-C experiment, high quality genome-wide contact data is becoming increasingly available. This data represents an empirical measure of how a genome interacts inside the nucleus. Genome conformation is of particular interest as it has been experimentally shown to be a driving force for many genomic functions from regulation to transcription. Thus, the Three Dimensional Genome Reconstruction Problem seeks to take Hi-C data and produce the complete physical genome structure as it appears in the nucleus for genomic analysis. ResultsWe propose and develop a novel method to solve the Chromosome and Genome Reconstruction problem based on the Bat Algorithm which we called ChromeBat. We demonstrate on real Hi-C data that ChromeBat is capable of state of the art performance. Additionally, the domain of Genome Reconstruction has been criticized for lacking algorithmic diversity, and the bio-inspired nature of ChromeBat contributes algorithmic diversity to the problem domain. ConclusionsChromeBat is an effective approach at solving the Genome Reconstruction Problem. The source code and usage guide can be found here: https://github.com/OluwadareLab/ChromeBat.

bioinformatics↗
bioRxiv2021

Gene-gene relationships in an Escherichia coli accessory genome are linked to function and mobility

The pangenome contains all genes encoded by a species, with the core genome present in all strains and the accessory genome in only a subset. Coincident gene relationships are expected within the accessory genome, where the presence or absence of one gene is influenced by the presence or absence of another. Here, we analysed the accessory genome of an Escherichia coli pangenome consisting of 400 genomes from 20 sequence types to identify genes that display significant co-occurrence or avoidance patterns with one another. We present a complex network of genes that are either found together or that avoid one another more often than would be expected by chance, and show that these relationships vary by lineage. We demonstrate that genes co-occur by function, and that several highly connected gene relationships are linked to mobile genetic elements. We find that genes are more likely to co-occur with, rather than avoid, another gene, suggesting that cooperation is more common than conflict in the accessory genome. This work furthers our understanding of the dynamic nature of prokaryote pangenomes and implicates both function and mobility as drivers of gene relationships. Data summaryAll Supplementary Data files and the Python scripts used in the analyses are available at doi.org/10.17639/nott.7103. Impact statementThe pangenome of a species encompasses the core genes encoded by all genomes, as well as the accessory genes found in only a subset. Much remains to be understood about the relationships and interactions between accessory genes; in particular, what drives pairs of genes to appear together in the same genome, or what prevents them from being in the same genome together, more often than expected by chance. How these co-occurrence and avoidance relationships develop, and what effect they have on the dynamics and evolution of the pangenome as a whole, is largely unknown. Here, we present a springboard for understanding prokaryote pangenome evolution by uncovering significant gene relationships in a model Escherichia coli pangenome. We identify mobile genetic elements and the sharing of common function as possible driving forces behind the co-occurrence of accessory genes. Furthermore, this work offers an extensive dataset from which gene relationships could be identified for any gene of interest in this E. coli accessory genome, providing a rich resource for the community.

evolutionary biology↗
bioRxiv2021

Soil, ocean, hot spring, and host-associated environments reveal unique selection pressures on genomic features of bacteria in microbial communities

Free-living bacteria in nutrient limited environments often exhibit small genomes which curb the cost of reproduction - a phenomenon known as genomic streamlining. Streamlining has been associated with a suite of traits such as reduced GC content, fewer 16S rRNA copies, and a lower abundance of regulatory genes, such as sigma ({sigma}) factors. Here, we analyzed these traits from 116 publicly available metagenomes derived from marine, soil, host associated, and thermophilic communities. In marine and thermophilic communities, genome size and GC content declined in parallel, but GC content was higher in thermophilic communities. In soils, the relationship between genome size and GC content was negative, suggesting a different selection pressure on genome size and GC content in soil bacteria. The abundance of {sigma}-factors varied with average genome size, ecosystem type, and the specific functions regulated by the sigma factor. In marine environments, housekeeping and heat-shock {sigma}-factor genes (rpoD and rpoH respectively) increased as genome size declined, and {sigma}-factor responsible for flagella biosynthesis (fliA) decreased, suggesting a trade-off between nutrient conservation and chemotaxis. In soils, a high abundance of fliA and the stress response {sigma}-factor gene (rpoS) was associated with smaller average genome size and often located in harsh and/or carbon-limited environments such as deserts or agricultural fields - suggesting an increased capacity for stress response and mobility in nutrient-poor soils. This work showcases how ecosystem-specific environmental constraints force trade-offs which are then embedded in the genomic features of bacteria in microbial communities, specifically genome size, GC content, and regulatory genes, and further highlights the importance of considering these features in microbial community analysis.

microbiology↗