Sustainable data analysis with Snakemake

Enantiomer Differentiation by Interaction-Specific Prediction of Residual Dipolar Couplings in Spherical-like Molecules

Residual Dipolar Couplings (RDCs) are averaged dipolar couplings between nuclear spins of atoms in a molecule that can be measured by nuclear magnetic resonance (NMR) spectroscopy upon partial alignment by a chiral alignment medium. The estimation of differences in alignment of enantiomers may, in principle, enable the determination of absolute configuration. Here, we use molecular dynamics (MD) simulations to mimic the alignment of chiral molecules (i.e., isopinocampheol, quinuclidin-3-ol, borneol, and camphor) to the chiral poly-γ-benzyl-L-glutamate (PBLG) polymer to predict RDCs in silico and compare calculated and experimentally measured residual dipolar couplings for the four enantiomeric pairs. The aim is to validate the computational scheme for the prediction of RDCs in chiral molecules and understand the interaction leading to the alignment in more detail. We determine the indispensable importance of hydrogen bonds between a chiral molecule and the alignment medium on the overall quality of the simulated alignment and interaction poses toward high agreement with experiments. A good correlation with experimental data is found for camphor and isopinocampheol, while the correlation for quinuclidin-3-ol and borneol is lower. We attribute this observation to the high difficulty of the RDC prediction for rather almost spherical molecules. The study reveals that the prediction of alignment with small enantiomeric differences is possible with an MD-based approach; however, extended simulation times (e.g., 50-100 μs) are required to sufficiently reduce the statistical uncertainty. This may be further used for the determination of the relative, as well as absolute, configuration of chiral molecules.

Re 2 Pair: Increasing the Scalability of RePair by Decreasing Memory Usage

The RePair compression algorithm produces a context-free grammar by iteratively substituting the most frequently occurring pair of consecutive symbols with a new symbol until all consecutive pairs of symbols appear only once in the compressed text. It is widely used in the settings of bioinformatics, machine learning, and information retrieval where random access to the original input text is needed. For example, in pangenomics, RePair is used for random access to a population of genomes. BigRePair improves the scalability of the original RePair algorithm by using Prefix-Free Parsing (PFP) to preprocess the text prior to building the RePair grammar. Despite the efficiency of PFP on repetitive text, there is a scalability issue with the size of the parse which causes a memory bottleneck in BigRePair. In this paper, we design and implement recursive RePair (denoted as Re 2 Pair), which builds the RePair grammar using recursive PFP. Our novel algorithm faces the challenge of constructing the RePair grammar without direct access to the parse of text, relying solely on the dictionary of the text and the parse and dictionary of the parse of the text. We compare Re 2 Pair to BigRePair using SARS-CoV-2 haplotypes and haplotypes from the 1000 Genomes Project. We show that our method Re 2 Pair achieves over a 40% peak memory reduction and a speed up ranging between 12% to 79% compared to BigRePair when compressing the largest input texts in all experiments. Re 2 Pair is made publicly available under the GNU public license here: https://github.com/jkim210/Recursive-RePair.

2012 ACM Subject Classification

Theory of computation → Formal languages and automata theory.

Island hitchhikers: pathogen agents of Madeira and Azores ticks

Ticks are blood-sucking arthropods that can transmit pathogens to their host. As insular ecosystems can enhance tick-host interactions, this study aimed to understand tick diversity, pathogen presence, and their respective associations in the Azores and Madeira archipelagos. Unfed or partially engorged ticks (n = 120) were collected from 58 cats and dogs in the Azores (n = 41 specimens) and Madeira (n = 79 specimens) from November 2018 to March 2019. Vector identification was based on morphology and molecular criteria. For pathogen sequencing, 18S gene fragment for Babesia/Hepatozoon and gltA for Rickettsia were performed. Sequence data was explored using BLAST and BLAST and phylogenetic inference tools. In the Azores, Ixodes hexagonus, I. ventalloi, and Rhipicephalus sanguineus (n = 6; 14.6%, n = 6; 14.6%, and n = 29; 70.7% respectively) were found and in Madeira I. ricinus and R. sanguineus (n = 78, 98.7%; and n = 1, 1.3%; respectively) were identified. Tick COI markers confirmed species highlighting confirmation of R. sanguineus s.s. and genotype A of I. ventalloi. In the Azores Islands, the detected Rickettsia massiliae was linked to R. sanguineus (dogs and cats) and I. hexagonus (dogs), and in Madeira Island, R. monacensis (dogs) and Hepatozoon silvestris (cats) were found associated with I. ricinus. Further, I. ventalloi presence in the Azores expands west its known range, and Hepatozoon silvestris in Madeira may suggest that I. ricinus could have a role as a potential vector. Finally, as R. massiliae and R. monacensis presence underlines public health risks, surveillance by health authorities is crucial as pathogen-tick interactions may drive disease spread, therefore monitoring remains pivotal for disease prevention.

UnCoVar: a reproducible and scalable workflow for transparent and robust virus variant calling and lineage assignment using SARS-CoV-2 as an example

BACKGROUND

At a global scale, the SARS-CoV-2 virus did not remain in its initial genotype for a long period of time, with the first global reports of variants of concern (VOCs) in late 2020. Subsequently, genome sequencing has become an indispensable tool for characterizing the ongoing pandemic, particularly for typing SARS-CoV-2 samples obtained from patients or environmental surveillance. For such SARS-CoV-2 typing, various in vitro and in silico workflows exist, yet to date, no systematic cross-platform validation has been reported.

RESULTS

In this work, we present the first comprehensive cross-platform evaluation and validation of in silico SARS-CoV-2 typing workflows. The evaluation relies on a dataset of 54 patient-derived samples sequenced with several different in vitro approaches on all relevant state-of-the-art sequencing platforms. Moreover, we present UnCoVar, a robust, production-grade reproducible SARS-CoV-2 typing workflow that outperforms all other tested approaches in terms of precision and recall.

CONCLUSIONS

In many ways, the SARS-CoV-2 pandemic has accelerated the development of techniques and analytical approaches. We believe that this can serve as a blueprint for dealing with future pandemics. Accordingly, UnCoVar is easily generalizable towards other viral pathogens and future pandemics. The fully automated workflow assembles virus genomes from patient samples, identifies existing lineages, and provides high-resolution insights into individual mutations. UnCoVar includes extensive quality control and automatically generates interactive visual reports. UnCoVar is implemented as a Snakemake workflow. The open-source code is available under a BSD 2-clause license at github.com/IKIM-Essen/uncovar.

The evolution of mammalian Rem2: unraveling the impact of purifying selection and coevolution on protein function, and implications for human disorders

Rad And Gem-Like GTP-Binding Protein 2 (Rem2), a member of the RGK family of Ras-like GTPases, is implicated in Huntington's disease and Long QT Syndrome and is highly expressed in the brain and endocrine cells. We examine the evolutionary history of Rem2 identified in various mammalian species, focusing on the role of purifying selection and coevolution in shaping its sequence and protein structural constraints. Our analysis of Rem2 sequences across 175 mammalian species found evidence for strong purifying selection in 70% of non-invariant codon sites which is characteristic of essential proteins that play critical roles in biological processes and is consistent with Rem2's role in the regulation of neuronal development and function. We inferred epistatic effects in 50 pairs of codon sites in Rem2, some of which are predicted to have deleterious effects on human health. Additionally, we reconstructed the ancestral evolutionary history of mammalian Rem2 using protein structure prediction of extinct and extant sequences which revealed the dynamics of how substitutions that change the gene sequence of Rem2 can impact protein structure in variable regions while maintaining core functional mechanisms. By understanding the selective pressures, protein- and gene - interactions that have shaped the sequence and structure of the Rem2 protein, we gain a stronger understanding of its biological and functional constraints.

Accurate, scalable, and fully automated inference of species trees from raw genome assemblies using ROADIES

Inference of species trees plays a crucial role in advancing our understanding of evolutionary relationships and has immense significance for diverse biological and medical applications. Extensive genome sequencing efforts are currently in progress across a broad spectrum of life forms, holding the potential to unravel the intricate branching patterns within the tree of life. However, estimating species trees starting from raw genome sequences is quite challenging, and the current cutting-edge methodologies require a series of error-prone steps that are neither entirely automated nor standardized. In this paper, we present ROADIES, a novel pipeline for species tree inference from raw genome assemblies that is fully automated, easy to use, scalable, free from reference bias, and provides flexibility to adjust the tradeoff between accuracy and runtime. The ROADIES pipeline eliminates the need to align whole genomes, choose a single reference species, or pre-select loci such as functional genes found using cumbersome annotation steps. Moreover, it leverages recent advances in phylogenetic inference to allow multi-copy genes, eliminating the need to detect orthology. Using the genomic datasets released from large-scale sequencing consortia across three diverse life forms (placental mammals, pomace flies, and birds), we show that ROADIES infers species trees that are comparable in quality with the state-of-the-art approaches but in a fraction of the time. By incorporating optimal approaches and automating all steps from assembled genomes to species and gene trees, ROADIES is poised to improve the accuracy, scalability, and reproducibility of phylogenomic analyses.

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.

Insights into gut microbiomes in stem cell transplantation by comprehensive shotgun long-read sequencing

The gut microbiome is a diverse ecosystem, dominated by bacteria; however, fungi, phages/viruses, archaea, and protozoa are also important members of the gut microbiota. Exploration of taxonomic compositions beyond bacteria as well as an understanding of the interaction between the bacteriome with the other members is limited using 16S rDNA sequencing. Here, we developed a pipeline enabling the simultaneous interrogation of the gut microbiome (bacteriome, mycobiome, archaeome, eukaryome, DNA virome) and of antibiotic resistance genes based on optimized long-read shotgun metagenomics protocols and custom bioinformatics. Using our pipeline we investigated the longitudinal composition of the gut microbiome in an exploratory clinical study in patients undergoing allogeneic hematopoietic stem cell transplantation (alloHSCT; n = 31). Pre-transplantation microbiomes exhibited a 3-cluster structure, characterized by Bacteroides spp. /Phocaeicola spp., mixed composition and Enterococcus abundances. We revealed substantial inter-individual and temporal variabilities of microbial domain compositions, human DNA, and antibiotic resistance genes during the course of alloHSCT. Interestingly, viruses and fungi accounted for substantial proportions of microbiome content in individual samples. In the course of HSCT, bacterial strains were stable or newly acquired. Our results demonstrate the disruptive potential of alloHSCTon the gut microbiome and pave the way for future comprehensive microbiome studies based on long-read metagenomics.

Fine-mapping genomic loci refines bipolar disorder risk genes

Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 22 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, YWHAE, DPH1, GSDMB, MED24, THRA, EEF1A2, and KCNQ2 in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).

Structural genomic variation in the inbred Scandinavian wolf population contributes to the realized genetic load but is positively affected by immigration

When populations decrease in size and may become isolated, genomic erosion by loss of diversity from genetic drift and accumulation of deleterious mutations is likely an inevitable consequence. In such cases, immigration (genetic rescue) is necessary to restore levels of genetic diversity and counteract inbreeding depression. Recent work in conservation genomics has studied these processes focusing on the genetic diversity of single nucleotide polymorphisms. In contrast, our knowledge about structural genomic variation (insertions, deletions, duplications and inversions) in endangered species is limited. We analysed whole-genome, short-read sequences from 212 wolves from the inbred Scandinavian population and from neighbouring populations in Finland and Russia, and detected >35,000 structural variants (SVs) after stringent quality and genotype frequency filtering; >26,000 high-confidence variants remained after manual curation. The majority of variants were shorter than 1 kb, with a distinct peak in the length distribution of deletions at 190 bp, corresponding to insertion events of SINE/tRNA-Lys elements. The site frequency spectrum of SVs in protein-coding regions was significantly shifted towards rare alleles compared to putatively neutral variants, consistent with purifying selection. The realized genetic load of SVs in protein-coding regions increased with inbreeding levels in the Scandinavian population, but immigration provided a genetic rescue effect by lowering the load and reintroducing ancestral alleles at loci fixed for derived SVs. Our study shows that structural variation comprises a common type of in part deleterious mutations in endangered species and that establishing gene flow is necessary to mitigate the negative consequences of loss of diversity.

Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue

The availability of target cells expressing the HIV receptors CD4 and CCR5 in genital tissue is a critical determinant of HIV susceptibility during sexual transmission. Quantification of immune cells in genital tissue is therefore an important outcome for studies on HIV susceptibility and prevention. Immunofluorescence microscopy allows for precise visualization of immune cells in mucosal tissues; however, this technique is limited in clinical studies by the lack of an accurate, unbiased, high-throughput image analysis method. Current pixel-based thresholding methods for cell counting struggle in tissue regions with high cell density and autofluorescence, both of which are common features in genital tissue. We describe a deep-learning approach using the publicly available StarDist method to count cells in immunofluorescence microscopy images of foreskin stained for nuclei, CD3, CD4, and CCR5. The accuracy of the model was comparable to manual counting (gold standard) and surpassed the capability of a previously described pixel-based cell counting method. We show that the performance of our deep-learning model is robust in tissue regions with high cell density and high autofluorescence. Moreover, we show that this deep-learning analysis method is both easy to implement and to adapt for the identification of other cell types in genital mucosal tissue.

Genotyping of SNPs in bread wheat at reduced cost from pooled experiments and imputation

KEY MESSAGE

Pooling and imputation are computational methods that can be combined for achieving cost-effective and accurate high-density genotyping of both common and rare variants, as demonstrated in a MAGIC wheat population. The plant breeding industry has shown growing interest in using the genotype data of relevant markers for performing selection of new competitive varieties. The selection usually benefits from large amounts of marker data, and it is therefore crucial to dispose of data collection methods that are both cost-effective and reliable. Computational methods such as genotype imputation have been proposed earlier in several plant science studies for addressing the cost challenge. Genotype imputation methods have though been used more frequently and investigated more extensively in human genetics research. The various algorithms that exist have shown lower accuracy at inferring the genotype of genetic variants occurring at low frequency, while these rare variants can have great significance and impact in the genetic studies that underlie selection. In contrast, pooling is a technique that can efficiently identify low-frequency items in a population, and it has been successfully used for detecting the samples that carry rare variants in a population. In this study, we propose to combine pooling and imputation and demonstrate this by simulating a hypothetical microarray for genotyping a population of recombinant inbred lines in a cost-effective and accurate manner, even for rare variants. We show that with an adequate imputation model, it is feasible to accurately predict the individual genotypes at lower cost than sample-wise genotyping and time-effectively. Moreover, we provide code resources for reproducing the results presented in this study in the form of a containerized workflow.

scTEA-db: a comprehensive database of novel terminal exon isoforms identified from human single cell transcriptomes

The usage of alternative terminal exons results in messenger RNA (mRNA) isoforms that differ in their 3' untranslated regions (3' UTRs) and often also in their protein-coding sequences. Alternative 3' UTRs contain different sets of cis-regulatory elements known to regulate mRNA stability, translation and localization, all of which are vital to cell identity and function. In previous work, we revealed that ∼25 percent of the experimentally observed RNA 3' ends are located within regions currently annotated as intronic, indicating that many 3' end isoforms remain to be uncovered. Also, the inclusion of not yet annotated terminal exons is more tissue specific compared to the already annotated ones. Here, we present the single cell-based Terminal Exon Annotation database (scTEA-db, www.scTEA-db.org) that provides the community with 12 063 so far not yet annotated terminal exons and associated transcript isoforms identified by analysing 53 069 publicly available single cell transcriptomes. Our scTEA-db web portal offers an array of features to find and explore novel terminal exons belonging to 5538 human genes, 110 of which are known cancer drivers. In summary, scTEA-db provides the foundation for studying the biological role of large numbers of so far not annotated terminal exon isoforms in cell identity and function.

Obtaining Detailed Phage Transcriptomes Using ONT-Cappable-Seq

Detailed transcription maps of bacteriophages are not usually explored, limiting our understanding of molecular phage biology and restricting their exploitation and engineering. The ONT-cappable-seq method described here brings phage transcriptomics to the accessible nanopore sequencing platform and provides an affordable and more detailed overview of transcriptional features compared to traditional RNA-seq experiments. With ONT-cappable-seq, primary transcripts are specifically capped, enriched, and prepared for long-read sequencing on the nanopore sequencing platform. This enables end-to-end sequencing of unprocessed transcripts covering both phage and host genome, thus providing insight on their operons. The subsequent analysis pipeline makes it possible to rapidly identify the most important transcriptional features such as transcription start and stop sites. The obtained data can thus provide a comprehensive overview of the transcription by your phage of interest.

kGWASflow: a modular, flexible, and reproducible Snakemake workflow for k-mers-based GWAS

Genome-wide association studies (GWAS) have been widely used to identify genetic variation associated with complex traits. Despite its success and popularity, the traditional GWAS approach comes with a variety of limitations. For this reason, newer methods for GWAS have been developed, including the use of pan-genomes instead of a reference genome and the utilization of markers beyond single-nucleotide polymorphisms, such as structural variations and k-mers. The k-mers-based GWAS approach has especially gained attention from researchers in recent years. However, these new methodologies can be complicated and challenging to implement. Here, we present kGWASflow, a modular, user-friendly, and scalable workflow to perform GWAS using k-mers. We adopted an existing kmersGWAS method into an easier and more accessible workflow using management tools like Snakemake and Conda and eliminated the challenges caused by missing dependencies and version conflicts. kGWASflow increases the reproducibility of the kmersGWAS method by automating each step with Snakemake and using containerization tools like Docker. The workflow encompasses supplemental components such as quality control, read-trimming procedures, and generating summary statistics. kGWASflow also offers post-GWAS analysis options to identify the genomic location and context of trait-associated k-mers. kGWASflow can be applied to any organism and requires minimal programming skills. kGWASflow is freely available on GitHub (https://github.com/akcorut/kGWASflow) and Bioconda (https://anaconda.org/bioconda/kgwasflow).

Tree-based differential testing using inferential uncertainty for RNA-Seq

Identifying differentially expressed transcripts poses a crucial yet challenging problem in transcriptomics. Substantial uncertainty is associated with the abundance estimates of certain transcripts which, if ignored, can lead to the exaggeration of false positives and, if included, may lead to reduced power. For a given set of RNA-Seq samples, TreeTerminus arranges transcripts in a hierarchical tree structure that encodes different layers of resolution for interpretation of the abundance of transcriptional groups, with uncertainty generally decreasing as one ascends the tree from the leaves. We introduce trenDi, which utilizes the tree structure from TreeTerminus for differential testing. The candidate nodes are determined in a data-driven manner to maximize the signal that can be extracted from the data while controlling for the uncertainty associated with estimating the transcript abundances. The identified candidate nodes can include transcripts and inner nodes, with no two nodes having an ancestor/descendant relationship. We evaluated our method on both simulated and experimental datasets, comparing its performance with other tree-based differential methods as well as with uncertainty-aware differential transcript/gene expression methods. Our method detects inner nodes that show a strong signal for differential expression, which would have been overlooked when analyzing the transcripts alone.

Epistasis between mutator alleles contributes to germline mutation spectra variability in laboratory mice

Maintaining germline genome integrity is essential and enormously complex. Although many proteins are involved in DNA replication, proofreading, and repair [1], mutator alleles have largely eluded detection in mammals. DNA replication and repair proteins often recognize sequence motifs or excise lesions at specific nucleotides. Thus, we might expect that the spectrum of de novo mutations - the frequencies of C>T, A>G, etc. - will differ between genomes that harbor either a mutator or wild-type allele. Previously, we used quantitative trait locus mapping to discover candidate mutator alleles in the DNA repair gene Mutyh that increased the C>A germline mutation rate in a family of inbred mice known as the BXDs [2,3]. In this study we developed a new method to detect alleles associated with mutation spectrum variation and applied it to mutation data from the BXDs. We discovered an additional C>A mutator locus on chromosome 6 that overlaps Ogg1, a DNA glycosylase involved in the same base-excision repair network as Mutyh [4]. Its effect depended on the presence of a mutator allele near Mutyh, and BXDs with mutator alleles at both loci had greater numbers of C>A mutations than those with mutator alleles at either locus alone. Our new methods for analyzing mutation spectra reveal evidence of epistasis between germline mutator alleles and may be applicable to mutation data from humans and other model organisms.

In vivo-like nearest neighbor parameters improve prediction of fractional RNA base-pairing in cells

We conducted a thermodynamic analysis of RNA stability in Eco80 artificial cytoplasm, which mimics in vivo conditions, and compared it to transcriptome-wide probing of mRNA. Eco80 contains 80% of Escherichia coli metabolites, with biological concentrations of metal ions, including 2 mM free Mg2+ and 29 mM metabolite-chelated Mg2+. Fluorescence-detected binding isotherms (FDBI) were used to conduct a thermodynamic analysis of 24 RNA helices and found that these helices, which have an average stability of -12.3 kcal/mol, are less stable by ΔΔGo37 ∼1 kcal/mol. The FDBI data was used to determine a set of Watson-Crick free energy nearest neighbor parameters (NNPs), which revealed that Eco80 reduces the stability of three NNPs. This information was used to adjust the NN model using the RNAstructure package. The in vivo-like adjustments have minimal effects on the prediction of RNA secondary structures determined in vitro and in silico, but markedly improve prediction of fractional RNA base pairing in E. coli, as benchmarked with our in vivo DMS and EDC RNA chemical probing data. In summary, our thermodynamic and chemical probing analyses of RNA helices indicate that RNA secondary structures are less stable in cells than in artificially stable in vitro buffer conditions.

aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow

Analysis of microbial data from archaeological samples is a growing field with great potential for understanding ancient environments, lifestyles, and diseases. However, high error rates have been a challenge in ancient metagenomics, and the availability of computational frameworks that meet the demands of the field is limited. Here, we propose aMeta, an accurate metagenomic profiling workflow for ancient DNA designed to minimize the amount of false discoveries and computer memory requirements. Using simulated data, we benchmark aMeta against a current state-of-the-art workflow and demonstrate its superiority in microbial detection and authentication, as well as substantially lower usage of computer memory.

Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides

Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

MOTIVATION

Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes.

RESULTS

We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data.

AVAILABILITY AND IMPLEMENTATION

The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.

Antiviral type III CRISPR signalling via conjugation of ATP and SAM

CRISPR systems are widespread in the prokaryotic world, providing adaptive immunity against mobile genetic elements1,2. Type III CRISPR systems, with the signature gene cas10, use CRISPR RNA to detect non-self RNA, activating the enzymatic Cas10 subunit to defend the cell against mobile genetic elements either directly, via the integral histidine-aspartate (HD) nuclease domain3-5 or indirectly, via synthesis of cyclic oligoadenylate second messengers to activate diverse ancillary effectors6-9. A subset of type III CRISPR systems encode an uncharacterized CorA-family membrane protein and an associated NrN family phosphodiesterase that are predicted to function in antiviral defence. Here we demonstrate that the CorA-associated type III-B (Cmr) CRISPR system from Bacteroides fragilis provides immunity against mobile genetic elements when expressed in Escherichia coli. However, B. fragilis Cmr does not synthesize cyclic oligoadenylate species on activation, instead generating S-adenosyl methionine (SAM)-AMP (SAM is also known as AdoMet) by conjugating ATP to SAM via a phosphodiester bond. Once synthesized, SAM-AMP binds to the CorA effector, presumably leading to cell dormancy or death by disruption of the membrane integrity. SAM-AMP is degraded by CRISPR-associated phosphodiesterases or a SAM-AMP lyase, potentially providing an 'off switch' analogous to cyclic oligoadenylate-specific ring nucleases10. SAM-AMP thus represents a new class of second messenger for antiviral signalling, which may function in different roles in diverse cellular contexts.

Cross-feeding affects the target of resistance evolution to an antifungal drug

Pathogenic fungi are a cause of growing concern. Developing an efficient and safe antifungal is challenging because of the similar biological properties of fungal and host cells. Consequently, there is an urgent need to better understand the mechanisms underlying antifungal resistance to prolong the efficacy of current molecules. A major step in this direction would be to be able to predict or even prevent the acquisition of resistance. We leverage the power of experimental evolution to quantify the diversity of paths to resistance to the antifungal 5-fluorocytosine (5-FC), commercially known as flucytosine. We generated hundreds of independent 5-FC resistant mutants derived from two genetic backgrounds from wild isolates of Saccharomyces cerevisiae. Through automated pin-spotting, whole-genome and amplicon sequencing, we identified the most likely causes of resistance for most strains. Approximately a third of all resistant mutants evolved resistance through a pleiotropic drug response, a potentially novel mechanism in response to 5-FC, marked by cross-resistance to fluconazole. These cross-resistant mutants are characterized by a loss of respiration and a strong tradeoff in drug-free media. For the majority of the remaining two thirds, resistance was acquired through loss-of-function mutations in FUR1, which encodes an important enzyme in the metabolism of 5-FC. We describe conditions in which mutations affecting this particular step of the metabolic pathway are favored over known resistance mutations affecting a step upstream, such as the well-known target cytosine deaminase encoded by FCY1. This observation suggests that ecological interactions may dictate the identity of resistance hotspots.

The five pillars of computational reproducibility: bioinformatics and beyond

Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.

Ten quick tips for building FAIR workflows

Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows-systematic executions of a series of computational tools-is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

Patient-derived xenograft models of ALK+ ALCL reveal preclinical promise for therapy with brigatinib

Anaplastic large-cell lymphoma (ALCL) is a T-cell malignancy predominantly driven by the oncogenic anaplastic lymphoma kinase (ALK), accounting for approximately 15% of all paediatric non-Hodgkin lymphoma. Patients with central nervous system (CNS) relapse are particularly difficult to treat with a 3-year overall survival of 49% and a median survival of 23.5 months. The second-generation ALK inhibitor brigatinib shows superior penetration of the blood-brain barrier unlike the first-generation drug crizotinib and has shown promising results in ALK+ non-small-cell lung cancer. However, the benefits of brigatinib in treating aggressive paediatric ALK+ ALCL are largely unknown. We established a patient-derived xenograft (PDX) resource from ALK+ ALCL patients at or before CNS relapse serving as models to facilitate the development of future therapies. We show in vivo that brigatinib is effective in inducing the remission of PDX models of crizotinib-resistant (ALK C1156Y, TP53 loss) ALCL and furthermore that it is superior to crizotinib as a second-line approach to the treatment of a standard chemotherapy relapsed/refractory ALCL PDX pointing to brigatinib as a future therapeutic option.

Distribution and abundance of tetraether lipid cyclization genes in terrestrial hot springs reflect pH

Many Archaea produce membrane-spanning lipids that enable life in extreme environments. These isoprenoid glycerol dibiphytanyl glycerol tetraethers (GDGTs) may contain up to eight cyclopentyl and one cyclohexyl ring, where higher degrees of cyclization are associated with more acidic, hotter or energy-limited conditions. Recently, the genes encoding GDGT ring synthases, grsAB, were identified in two Sulfolobaceae; however, the distribution and abundance of grs homologs across environments inhabited by these and related organisms remain a mystery. To address this, we examined the distribution of grs homologs in relation to environmental temperature and pH, from thermal springs across Earth, where sequences derive from metagenomes, metatranscriptomes, single-cell and cultivar genomes. The abundance of grs homologs shows a strong negative correlation to pH, but a weak positive correlation to temperature. Archaeal genomes and metagenome-assembled genomes (MAGs) that carry two or more grs copies are more abundant in low pH springs. We also find grs in 12 archaeal classes, with the most representatives in Thermoproteia, followed by MAGs of the uncultured Korarchaeia, Bathyarchaeia and Hadarchaeia, while several Nitrososphaeria encodes >3 copies. Our findings highlight the key role of grs-catalysed lipid cyclization in archaeal diversification across hot and acidic environments.

A maturational shift in the frontal cortex synaptic transcriptional landscape underlies schizophrenia-relevant behavioural traits: A congenital rat model

Disruption of brain development early in life may underlie the neurobiology behind schizophrenia. We have reported more immature synaptic spines in the frontal cortex (FC) of adult Roman High-Avoidance (RHA-I) rats, a behavioural model displaying schizophrenia-like traits. Here, we performed a whole transcriptome analysis in the FC of 4 months old male RHA-I (n=8) and its counterpart, the Roman Low-Avoidance (RLA-I) (n=8). We identified 203 significant genes with overrepresentation of genes involved in synaptic function. Next, we performed a gene set enrichment analysis (GSEA) for genes co-expressed during neurodevelopment. Gene networks were obtained by weighted gene co-expression network analysis (WGCNA) of a transcriptomic dataset containing human FC during lifespan (n=269). Out of thirty-one functional gene networks, six were significantly enriched in the RHA-I. These were differentially regulated during infancy and enriched in biological ontologies related to myelination, synaptic function, and immune response. We validated differential gene expression in a new cohort of adolescent (<=2 months old) and young-adult (>=3 months old) RHA-I and RLA-I rats. The results confirmed overexpression of Gsn, Nt5cd1, Ppp1r1b, and Slc9a3r1 in young-adult RHA-I, while Cables1, a regulator of Cdk5 phosphorylation in actin regulation and involved in synaptic plasticity and maturation, was significantly downregulated in adolescent RHA-I. This age-related expression change was also observed for presynaptic components Snap25 and Snap29. Our results show a different maturational expression profile of synaptic components in the RHA-I strain, supporting a shift in FC maturation underlying schizophrenia-like behavioural traits and adding construct validity to this strain as a neurodevelopmental model.

Rapid and Automated Ab Initio Metabolite Collisional Cross Section Prediction from SMILES Input

We implemented an ab initio CCS prediction workflow which incrementally refines generated structures using molecular mechanics, a deep learning potential, conformational clustering, and quantum mechanics (QM). Automating intermediate steps for a high performance computing (HPC) environment allows users to input the SMILES structure of small organic molecules and obtain a Boltzmann averaged collisional cross section (CCS) value as output. The CCS of a molecular species is a metric measured by ion mobility spectrometry (IMS) which can improve annotation of untargeted metabolomics experiments. We report only a minor drop in accuracy when we expedite the CCS calculation by replacing the QM geometry refinement step with a single-point energy calculation. Even though the workflow involves stochastic steps (i.e., conformation generation and clustering), the final CCS value was highly reproducible for multiple iterations on L-carnosine. Finally, we illustrate that the gas phase ensembles modeled for the workflow are intermediate files which can be used for the prediction of other properties such as aqueous phase nuclear magnetic resonance chemical shift prediction. The software is available at the following link: https://github.com/DasSusanta/snakemake_ccs.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well-described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred model for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Integrative Proteogenomics Using ProteomeGenerator2

Recent advances in nucleic acid sequencing now permit rapid and genome-scale analysis of genetic variation and transcription, enabling population-scale studies of human biology, disease, and diverse organisms. Likewise, advances in mass spectrometry proteomics now permit highly sensitive and accurate studies of protein expression at the whole proteome-scale. However, most proteomic studies rely on consensus databases to match spectra to peptide and protein sequences, and thus remain limited to the analysis of canonical protein sequences. Here, we develop ProteomeGenerator2 (PG2), based on the scalable and modular ProteomeGenerator framework. PG2 integrates genome and transcriptome sequencing to incorporate protein variants containing amino acid substitutions, insertions, and deletions, as well as noncanonical reading frames, exons, and other variants caused by genomic and transcriptomic variation. We benchmarked PG2 using synthetic data and genomic, transcriptomic, and proteomic analysis of human leukemia cells. PG2 can be integrated with current and emerging sequencing technologies, assemblers, variant callers, and mass spectral analysis algorithms, and is available open-source from https://github.com/kentsisresearchgroup/ProteomeGenerator2.

Compare_Genomes: A Comparative Genomics Workflow to Streamline the Analysis of Evolutionary Divergence Across Eukaryotic Genomes

The dawn of cost-effective genome assembly is enabling deep comparative genomics to address fundamental evolutionary questions by comparing the genomes of multiple species. However, comparative genomics analyses frequently deploy multiple, often purpose-built frameworks, limiting their transferability and replicability. Here, we present compare_genomes, a transferable and extensible comparative genomics workflow package we developed that streamlines the identification of orthologous families within and across eukaryotic genomes and tests for the presence of several mechanisms of evolution (gene family expansion or contraction and substitution rates within protein-coding sequences). The workflow is available for Linux, written as a Nextflow workflow that calls established genomics and phylogenetics tools to streamline the analysis and visualization of eukaryotic genome divergence. This workflow is freely available at https://github.com/jeffersonfparil/compare_genomes, distributed under the GNU General Public License version 3 (GPLv3). © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Comparative genomics with Nextflow and Conda.

Stringent and complex sequence constraints of an IGHV1-69 broadly neutralizing antibody to influenza HA stem

IGHV1-69 is frequently utilized by broadly neutralizing influenza antibodies to the hemagglutinin (HA) stem. These IGHV1-69 HA stem antibodies have diverse complementarity-determining region (CDR) H3 sequences. Besides, their light chains have minimal to no contact with the epitope. Consequently, sequence determinants that confer IGHV1-69 antibodies with HA stem specificity remain largely elusive. Using high-throughput experiments, this study revealed the importance of light chain sequence for the IGHV1-69 HA stem antibody CR9114, which is the broadest influenza antibody known to date. Moreover, we demonstrated that the CDR H3 sequences from many other IGHV1-69 antibodies, including those to HA stem, were incompatible with CR9114. Along with mutagenesis and structural analysis, our results indicate that light chain and CDR H3 sequences coordinately determine the HA stem specificity of IGHV1-69 antibodies. Overall, this work provides molecular insights into broadly neutralizing antibody responses to influenza virus, which have important implications for universal influenza vaccine development.

Biotite: new tools for a versatile Python bioinformatics library

BACKGROUND

Biotite is a program library for sequence and structural bioinformatics written for the Python programming language. It implements widely used computational methods into a consistent and accessible package. This allows for easy combination of various data analysis, modeling and simulation methods.

RESULTS

This article presents major functionalities introduced into Biotite since its original publication. The fields of application are shown using concrete examples. We show that the computational performance of Biotite for bioinformatics tasks is comparable to individual, special purpose software systems specifically developed for the respective single task.

CONCLUSIONS

The results show that Biotite can be used as program library to either answer specific bioinformatics questions and simultaneously allow the user to write entire, self-contained software applications with sufficient performance for general application.

Accelerating genomic workflows using NVIDIA Parabricks

BACKGROUND

As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to accelerate genomic workflows by orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster. We benchmarked six variant calling pipelines, including two germline callers (HaplotypeCaller and DeepVariant) and four somatic callers (Mutect2, Muse, LoFreq, SomaticSniper).

RESULTS

We achieved up to 65 × acceleration with germline variant callers, bringing HaplotypeCaller runtimes down from 36 h to 33 min on AWS, 35 min on GCP, and 24 min on the NVIDIA DGX. Somatic callers exhibited more variation between the number of GPUs and computing platforms. On cloud platforms, GPU-accelerated germline callers resulted in cost savings compared with CPU runs, whereas some somatic callers were more expensive than CPU runs because their GPU acceleration was not sufficient to overcome the increased GPU cost.

CONCLUSIONS

Germline variant callers scaled well with the number of GPUs across platforms, whereas somatic variant callers exhibited more variation in the number of GPUs with the fastest runtimes, suggesting that, at least with the version of Parabricks used here, these workflows are less GPU optimized and require benchmarking on the platform of choice before being deployed at production scales. Our study demonstrates that GPUs can be used to greatly accelerate genomic workflows, thus bringing closer to grasp urgent societal advances in the areas of biosurveillance and personalized medicine.

Implementing landscape genetics in molecular epidemiology to determine drivers of vector-borne disease: A malaria case study

This study employs landscape genetics to investigate the environmental drivers of a deadly vector-borne disease, malaria caused by Plasmodium falciparum, in a more spatially comprehensive manner than any previous work. With 1804 samples from 44 sites collected in western Kenya in 2012 and 2013, we performed resistance surface analysis to show that Lake Victoria acts as a barrier to transmission between areas north and south of the Winam Gulf. In addition, Mantel correlograms clearly showed significant correlations between genetic and geographic distance over short distances (less than 70 km). In both cases, we used an identity-by-state measure of relatedness tailored to find highly related individual parasites in order to focus on recent gene flow that is more relevant to disease transmission. To supplement these results, we performed conventional population genetics analyses, including Bayesian clustering methods and spatial ordination techniques. These analyses revealed some differentiation on the basis of geography and elevation and a cluster of genetic similarity in the lowlands north of the Winam Gulf of Lake Victoria. Taken as a whole, these results indicate low overall genetic differentiation in the Lake Victoria region, but with some separation of parasite populations north and south of the Winam Gulf that is explained by the presence of the lake as a geographic barrier to gene flow. We recommend similar landscape genetics analyses in future molecular epidemiology studies of vector-borne diseases to extend and contextualize the results of traditional population genetics.

Lineage tracing reveals fate bias and transcriptional memory in human B cells

We combined single-cell transcriptomics and lineage tracing to understand fate choice in human B cells. Using the antibody sequences of B cells, we tracked clones during in vitro differentiation. Clonal analysis revealed a subset of IgM+ B cells which were more proliferative than other B-cell types. Whereas the population of B cells adopted diverse states during differentiation, clones had a restricted set of fates available to them; there were two times more single-fate clones than expected given population-level cell-type diversity. This implicated a molecular memory of initial cell states that was propagated through differentiation. We then identified the genes which had strongest coherence within clones. These genes significantly overlapped known B-cell fate determination programs, suggesting the genes which determine cell identity are most robustly controlled on a clonal level. Persistent clonal identities were also observed in human plasma cells from bone marrow, indicating that these transcriptional programs maintain long-term cell identities in vivo. Thus, we show how cell-intrinsic fate bias influences differentiation outcomes in vitro and in vivo.

Metagenomic Analysis of the Abundance and Composition of Antibiotic Resistance Genes in Hospital Wastewater in Benin, Burkina Faso, and Finland

Antibiotic resistance is a global threat to human health, with the most severe effect in low- and middle-income countries. We explored the presence of antibiotic resistance genes (ARGs) in the hospital wastewater (HWW) of nine hospitals in Benin and Burkina Faso, two low-income countries in West Africa, with shotgun metagenomic sequencing. For comparison, we also studied six hospitals in Finland. The highest sum of the relative abundance of ARGs in the 68 HWW samples was detected in Benin and the lowest in Finland. HWW resistomes and mobilomes in Benin and Burkina Faso resembled each other more than those in Finland. Many carbapenemase genes were detected at various abundances, especially in HWW from Burkina Faso and Finland. The bla_GES genes, the most widespread carbapenemase gene in the Beninese HWW, were also found in water intended for hand washing and in a puddle at a hospital yard in Benin. mcr genes were detected in the HWW of all three countries, with mcr-5 being the most common mcr gene. These and other mcr genes were observed in very high relative abundances, even in treated wastewater in Burkina Faso and a street gutter in Benin. The results highlight the importance of wastewater treatment, with particular attention to HWW. IMPORTANCE The global emergence and increased spread of antibiotic resistance threaten the effectiveness of antibiotics and, thus, the health of the entire population. Therefore, understanding the resistomes in different geographical locations is crucial in the global fight against the antibiotic resistance crisis. However, this information is scarce in many low- and middle-income countries (LMICs), such as those in West Africa. In this study, we describe the resistomes of hospital wastewater in Benin and Burkina Faso and, as a comparison, Finland. Our results help to understand the hitherto unrevealed resistance in Beninese and Burkinabe hospitals. Furthermore, the results emphasize the importance of wastewater management infrastructure design to minimize exposure events between humans, HWW, and the environment, preventing the circulation of resistant bacteria and ARGs between humans (hospitals and community) and the environment.

The omnitig framework can improve genome assembly contiguity in practice

Despite the long history of genome assembly research, there remains a large gap between the theoretical and practical work. There is practical software with little theoretical underpinning of accuracy on one hand and theoretical algorithms which have not been adopted in practice on the other. In this paper we attempt to bridge the gap between theory and practice by showing how the theoretical safe-and-complete framework can be integrated into existing assemblers in order to improve contiguity. The optimal algorithm in this framework, called the omnitig algorithm, has not been used in practice due to its complexity and its lack of robustness to real data. Instead, we pursue a simplified notion of omnitigs, giving an efficient algorithm to compute them and demonstrating their safety under certain conditions. We modify two assemblers (wtdbg2 and Flye) by replacing their unitig algorithm with the simple omnitig algorithm. We test our modifications using real HiFi data from the Drosophilia melanogaster and the Caenorhabditis elegans genome. Our modified algorithms lead to a substantial improvement in alignment-based contiguity, with negligible computational costs and either no or a small increase in the number of misassemblies.

Mis-spliced transcripts generate de novo proteins in TDP-43-related ALS/FTD

Functional loss of TDP-43, an RNA-binding protein genetically and pathologically linked to ALS and FTD, leads to inclusion of cryptic exons in hundreds of transcripts during disease. Cryptic exons can promote degradation of affected transcripts, deleteriously altering cellular function through loss-of-function mechanisms. However, the possibility of de novo protein synthesis from cryptic exon transcripts has not been explored. Here, we show that mRNA transcripts harboring cryptic exons generate de novo proteins both in TDP-43 deficient cellular models and in disease. Using coordinated transcriptomic and proteomic studies of TDP-43 depleted iPSC-derived neurons, we identified numerous peptides that mapped to cryptic exons. Cryptic exons identified in iPSC models were highly predictive of cryptic exons expressed in brains of patients with TDP-43 proteinopathy, including cryptic transcripts that generated de novo proteins. We discovered that inclusion of cryptic peptide sequences in proteins altered their interactions with other proteins, thereby likely altering their function. Finally, we showed that these de novo peptides were present in CSF from patients with ALS. The demonstration of cryptic exon translation suggests new mechanisms for ALS pathophysiology downstream of TDP-43 dysfunction and may provide a strategy for novel biomarker development.

Integrative proteogenomics using ProteomeGenerator2

Recent advances in nucleic acid sequencing now permit rapid and genome-scale analysis of genetic variation and transcription, enabling population-scale studies of human biology, disease, and diverse organisms. Likewise, advances in mass spectrometry proteomics now permit highly sensitive and accurate studies of protein expression at the whole proteome-scale. However, most proteomic studies rely on consensus databases to match spectra to peptide and proteins sequences, and thus remain limited to the analysis of canonical protein sequences. Here, we develop ProteomeGenerator2 (PG2), based on the scalable and modular ProteomeGenerator framework. PG2 integrates genome and transcriptome sequencing to incorporate protein variants containing amino acid substitutions, insertions, and deletions, as well as non-canonical reading frames, exons, and other variants caused by genomic and transcriptomic variation. We benchmarked PG2 using synthetic data and genomic, transcriptomic, and proteomic analysis of human leukemia cells. PG2 can be integrated with current and emerging sequencing technologies, assemblers, variant callers, and mass spectral analysis algorithms, and is available open-source from https://github.com/kentsisresearchgroup/ProteomeGenerator2 .

Genomic selection for survival under naturally occurring Saprolegnia oomycete infection in farmed European whitefish Coregonus lavaretus

Saprolegnia oomycete infection causes serious economic losses and reduces fish health in aquaculture. Genomic selection based on thousands of DNA markers is a powerful tool to improve fish traits in selective breeding programs. Our goal was to develop a single nucleotide polymorphism (SNP) marker panel and to test its use in genomic selection for improved survival against Saprolegnia infection in European whitefish Coregonus lavaretus, the second most important farmed fish species in Finland. We used a double digest restriction site associated DNA (ddRAD) genotyping by sequencing method to produce a SNP panel, and we tested it analyzing data from a cohort of 1,335 fish, which were measured at different times for mortality to Saprolegnia oomycete infection and weight traits. We calculated the genetic relationship matrix (GRM) from the genome-wide genetic data, integrating it in multivariate mixed models used for the estimation of variance components and genomic breeding values (GEBVs), and to carry out Genome-Wide Association Studies for the presence of quantitative trait loci (QTL) affecting the phenotypes in analysis. We identified one major QTL on chromosome 6 affecting mortality to Saprolegnia infection, explaining 7.7% to 51.3% of genetic variance, and a QTL for weight on chromosome 4, explaining 1.8% to 5.4% of genetic variance. Heritability for mortality was 0.20 to 0.43 on the liability scale, and heritability for weight was 0.44 to 0.53. The QTL for mortality showed an additive allelic effect. We tested whether integrating the QTL for mortality as a fixed factor, together with a new GRM calculated excluding the QTL from the genetic data, would improve the accuracy estimation of GEBVs. This test was done through a cross-validation approach, which indicated that the inclusion of the QTL increased the mean accuracy of the GEBVs by 0.28 points, from 0.33 to 0.61, relative to the use of full GRM only. The area under the curve of the receiver-operator curve for mortality increased from 0.58 to 0.67 when the QTL was included in the model. The inclusion of the QTL as a fixed effect in the model increased the correlation between the GEBVs of early mortality with the late mortality, compared to a model that did not include the QTL. These results validate the usability of the produced SNP panel for genomic selection in European whitefish and highlight the opportunity for modeling QTLs in genomic evaluation of mortality due to Saprolegnia infection.

Cellsnake: a user-friendly tool for single-cell RNA sequencing analysis

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, nonexpert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines.

RESULTS

We have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples.

CONCLUSION

As an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.

Genetic Drift Shapes the Evolution of a Highly Dynamic Metapopulation

The dynamics of extinction and (re)colonization in habitat patches are characterizing features of dynamic metapopulations, causing them to evolve differently than large, stable populations. The propagule model, which assumes genetic bottlenecks during colonization, posits that newly founded subpopulations have low genetic diversity and are genetically highly differentiated from each other. Immigration may then increase diversity and decrease differentiation between subpopulations. Thus, older and/or less isolated subpopulations are expected to have higher genetic diversity and less genetic differentiation. We tested this theory using whole-genome pool-sequencing to characterize nucleotide diversity and differentiation in 60 subpopulations of a natural metapopulation of the cyclical parthenogen Daphnia magna. For comparison, we characterized diversity in a single, large, and stable D. magna population. We found reduced (synonymous) genomic diversity, a proxy for effective population size, weak purifying selection, and low rates of adaptive evolution in the metapopulation compared with the large, stable population. These differences suggest that genetic bottlenecks during colonization reduce effective population sizes, which leads to strong genetic drift and reduced selection efficacy in the metapopulation. Consistent with the propagule model, we found lower diversity and increased differentiation in younger and also in more isolated subpopulations. Our study sheds light on the genomic consequences of extinction-(re)colonization dynamics to an unprecedented degree, giving strong support for the propagule model. We demonstrate that the metapopulation evolves differently from a large, stable population and that evolution is largely driven by genetic drift.

A small bacteriophage protein determines the hierarchy over co-residential jumbo phage in Bacillus thuringiensis serovar israelensis

Bacillus thuringiensis serovar israelensis is the most widely used biopesticide against insects, including vectors of animal and human diseases. Among several extrachromosomal elements, this endospore-forming entomopathogen harbors two bacteriophages: a linear DNA replicon named GIL01 that does not integrate into the chromosome during lysogeny and a circular-jumbo prophage known as pBtic235. Here, we show that GIL01 hinders the induction of cohabiting prophage pBtic235. The GIL01-encoded small protein, gp7, which interacts with the host LexA repressor, is a global transcription regulator and represses the induction of pBtic235 after DNA damage to presumably allow GIL01 to multiply first. In a complex with host LexA in stressed cells, gp7 down-regulates the expression of more than 250 host and pBtic235 genes, many of which are involved in the cellular functions of genome maintenance, cell-wall transport, and membrane and protein stability. We show that gp7 homologs that are found exclusively in bacteriophages act in a similar fashion to enhance LexA's binding to DNA, while likely also affecting host gene expression. Our results provide evidence that GIL01 influences both its host and its co-resident bacteriophage.

Plastaumatic: Automating plastome assembly and annotation

Plastome sequence data is most often extracted from plant whole genome sequencing data and need to be assembled and annotated separately from the nuclear genome sequence. In projects comprising multiple genomes, it is labour intense to individually process the plastomes as it requires many steps and software. This study developed Plastaumatic - an automated pipeline for both assembly and annotation of plastomes, with the scope of the researcher being able to load whole genome sequence data with minimal manual input, and therefore a faster runtime. The main structure of the current automated pipeline includes trimming of adaptor and low-quality sequences using fastp, de novo plastome assembly using NOVOPlasty, standardization and quality checking of the assembled genomes through a custom script utilizing BLAST+ and SAMtools, annotation of the assembled genomes using AnnoPlast, and finally generating the required files for NCBI GenBank submissions. The pipeline is demonstrated with 12 potato accessions and three soybean accessions.

A pseudovirus system enables deep mutational scanning of the full SARS-CoV-2 spike

A major challenge in understanding SARS-CoV-2 evolution is interpreting the antigenic and functional effects of emerging mutations in the viral spike protein. Here we describe a new deep mutational scanning platform based on non-replicative pseudotyped lentiviruses that directly quantifies how large numbers of spike mutations impact antibody neutralization and pseudovirus infection. We demonstrate this new platform by making libraries of the Omicron BA.1 and Delta spikes. These libraries each contain ~7000 distinct amino-acid mutations in the context of up to ~135,000 unique mutation combinations. We use these libraries to map escape mutations from neutralizing antibodies targeting the receptor binding domain, N-terminal domain, and S2 subunit of spike. Overall, this work establishes a high-throughput and safe approach to measure how ~10 5 combinations of mutations affect antibody neutralization and spike-mediated infection. Notably, the platform described here can be extended to the entry proteins of many other viruses.

Emergence of Tn1999.7, a New Transposon in blaOXA-48-Harboring Plasmids Associated with Increased Plasmid Stability

OXA-48 is the most common carbapenemase in Enterobacterales in Germany and many other European countries. Depending on the genomic location of bla_OXA-48, OXA-48-producing isolates vary in phenotype and intra- and interspecies transferability of bla_OXA-48. In most bacterial isolates, bla_OXA-48 is located on one of seven variants of Tn1999 (Tn1999.1 to Tn1999.6 and invTn1999.2). Here, a novel Tn1999 variant, Tn1999.7, is described, which was identified in 11 clinical isolates from 2016 to 2020. Tn1999.7 differs from Tn1999.1 by the insertion of the 8,349-bp Tn3 family transposon Tn7442 between the lysR gene and bla_OXA-48 open reading frame. Tn7442 carries genes coding for a restriction endonuclease and a DNA methyltransferase as cargo, forming a type III restriction modification system. Tn1999.7 was carried on an ~71-kb IncL plasmid in 9/11 isolates. In one isolate, Tn1999.7 was situated on an ~76-kb plasmid, harboring an additional insertion sequence in the plasmid backbone. In one isolate, the plasmid size is only ~63 kb due to a deletion adjacent to Tn7442 that extends into the plasmid backbone. Mean conjugation rates of the Tn1999.7-harboring plasmids in J53 ranged from 4.47 × 10^-5 to 2.03 × 10^-2, similar to conjugation rates of other pOXA-48-type IncL plasmids. The stability of plasmids with Tn1999.7 was significantly higher than that of a Tn1999.2-harboring plasmid in vitro. This increase in stability could be related to the insertion of a restriction-modification system, which can promote postsegregational killing. The increased plasmid stability associated with Tn1999.7 could contribute to the further spread of OXA-48.

Global and precise identification of functional miRNA targets in mESCs by integrative analysis

MicroRNA (miRNA) loaded Argonaute (AGO) complexes regulate gene expression via direct base pairing with their mRNA targets. Previous works suggest that up to 60% of mammalian transcripts might be subject to miRNA‐mediated regulation, but it remains largely unknown which fraction of these interactions are functional in a specific cellular context. Here, we integrate transcriptome data from a set of miRNA‐depleted mouse embryonic stem cell (mESC) lines with published miRNA interaction predictions and AGO‐binding profiles. Using this integrative approach, combined with molecular validation data, we present evidence that < 10% of expressed genes are functionally and directly regulated by miRNAs in mESCs. In addition, analyses of the stem cell‐specific miR‐290‐295 cluster target genes identify TFAP4 as an important transcription factor for early development. The extensive datasets developed in this study will support the development of improved predictive models for miRNA‐mRNA functional interactions.