Biology subjects

Remm, M.

Publications and source records attributed to Remm, M..

9 recordsLinked to original sources

bioRxiv2023

GeneToCN: An Alignment-Free Method for Gene Copy Number Estimation Directly from Next-Generation Sequencing Reads

Genomes exhibit large regions with segmental copy number variation, many of which include entire genes and are multiallelic. We have developed a computational method GeneToCN that counts the frequencies of gene-specific k-mers in FASTQ files and uses this information to infer copy number of the gene. We validated the copy number predictions for amylase genes (AMY1, AMY2A, AMY2B) using experimental data from digital droplet PCR (ddPCR) on 39 individuals and observed a strong correlation (R=0.99) between GeneToCN predictions and experimentally determined copy numbers. We further tested the method on three different genomic regions (SMN, NPY4R, and LPA Kringle IV-2 domain). Predicted copy number distributions of these genes in a set of 500 individuals from the Estonian Biobank were in good agreement with the previously published studies. In addition, we investigated the possibility to use GeneToCN on sequencing data generated by different technologies by comparing copy number predictions from Illumina, PacBio, and Oxford Nanopore data of the same sample. Despite the differences in variability of k-mer frequencies, all three sequencing technologies give similar predictions with GeneToCN.

bioinformatics↗

bioRxiv2022

Development of a dedicated Golden Gate Assembly platform (RtGGA) for Rhodotorula toruloides

Rhodotorula toruloides is a potential chassis to microbial cell factories as this yeast can metabolise different substrates into a diverse range of natural products, but the lack of efficient synthetic biology tools hinders its applicability. In this study, the modular, versatile and efficient Golden Gate DNA assembly system was adapted to the first basidiomycete, an oleaginous yeast R. toruloides (RtGGA). R. toruloides CCT 0783 was sequenced, and used for the RtGGA design. The DNA fragments were assembled with predesigned 4-nt overhangs and a library of standardized parts was created containing promoters, genes, terminators, insertional regions, and resistance genes. The library was combined to create cassettes for the characterization of promoters strength and to overexpress the carotenoid production pathway. A variety of reagents, plasmids, and strategies were used and the RtGGA proved to be robust. The RtGGA was used to build three versions of the carotenoid overexpression cassette were built by using different promoter combinations. The cassettes were transformed into R. toruloides and the three new strains were characterized. Total carotenoid concentration increased by 41%. The dedicated GGA platform fills a gap in the advanced genome engineering toolkit for R. toruloides, enabling the efficient design of complex metabolic pathways.

synthetic biology↗

bioRxiv2020

KATK: fast genotyping of rare variants directly from unmapped sequencing reads

MotivationKATK is a fast and accurate software tool for calling variants directly from raw NGS reads. It uses predefined k-mers to retrieve only the reads of interest from the FASTQ file and calls genotypes by aligning retrieved reads locally. KATK does not use data about known polymorphisms and has NC (No Call) as default genotype. The reference or variant allele is called only if there is sufficient evidence for their presence in data. Thus it is not biased against rare variants or de novo mutations. ResultsWith simulated datasets, we achieved a false negative rate of 0.23% (sensitivity 99.77%) and a false discovery rate of 0.19%. Calling all human exonic regions with KATK requires 1-2 h, depending on sequencing coverage. AvailabilityKATK is distributed under the terms of GNU GPL v3. The k-mer databases are distributed under the Creative Commons CC BY-NC-SA license. The source code is available at GitHub as part of Genometester4 package (https://github.com/bioinfo-ut/GenomeTester4/). The binaries of KATK package and k-mer databases described in the current paper are available on http://bioinfo.ut.ee/KATK/.

bioinformatics↗

bioRxiv2020

Competitiveness prediction for nodule colonization in Sinorhizobium meliloti through combined in vitro tagged strain characterization and genome-wide association analysis

Associations between leguminous plants and symbiotic nitrogen fixing bacteria (rhizobia) are a classical example of mutualism between a eukaryotic host and a specific group of prokaryotic microbes. Though being in part species-specific, different strains may colonize the same plant symbiotic structure (nodule). It is known that some rhizobial strains are better competitor than others, but detailed analyses aimed to predict from the rhizobial genome its competitive abilities are still scarce. Here we performed a bacterial genome wide association (GWAS) analysis to define the genomic determinants related to the competitive capabilities in the model rhizobial species Sinorhizobium meliloti. Thirteen tester strains were GFP-tagged and assayed against three reference competitor strains RFP-tagged (Rm1021, AK83 and BL225C) in a Medicago sativa nodule occupancy test. Competition data in combination with strains genomic sequences were used to build-up a model for GWAS based on k-mers. The model was then trained and applied for competition capabilities prediction. The model was able to well predict the competition abilities against two partners, BL225C, Rm1021 with coefficient of determination of 0.96 and 0.84, respectively. Four strains showing the highest competition phenotypes (> 60% single strain nodule occupancy; GR4, KH35c, KH46 and SM11) versus BL225C were used to identify k-mers associated with competition. The k-mers with highest scores mapped on the symbiosis-related megaplasmid pSymA and on genes coding for transporters, proteins involved in the biosynthesis of cofactors and proteins related to metabolism (i.e. glycerol, fatty acids) suggesting that competition abilities reside in multiple genetic determinants comprising several cellular components. IMPORTANCEDecoding the competitive pattern that occurs in the rhizosphere is challenging in the study of bacterial social interaction strategies. To date, single-gene approach has been mainly used to uncover the bases of nodulation, but there is still a gap about the main features that a priori turn out rhizobial strains able to outcompete indigenous rhizobia. Therefore, tracking down which traits make different rhizobial strains able to win the competition for plant infection over other indigenous rhizobia will allow ameliorating strain selection and consequently plant yield in sustainable agricultural production systems. We have proven that a k-mer based GWAS approach can effectively predict the competition abilities of a panel of strains, which were analyzed for their plant tissue occupancy by using double fluorescent labeling. The reported strategy could be used for detailed studies on the genomic aspects of the evolution of bacterial symbiosis and for an extensive evaluation of rhizobial inoculants.

microbiology↗

bioRxiv2020

Pseudomonas putida chromosomal toxin-antitoxin systems carry neither clear fitness benefits nor big costs

Chromosomal toxin-antitoxin (TA) systems are widespread genetic elements among bacteria, yet, despite extensive studies in the last decade, their biological importance remains ambivalent. The ability of TA-encoded toxins to affect stress tolerance supports the hypothesis of TA systems being associated with stress adaptation. However, the deletion of TA genes has usually no fitness consequences, supporting the selfish elements hypothesis. Here, we aimed to evaluate the cost and benefits of chromosomal TA systems to Pseudomonas putida. We show that multiple TA systems do not confer fitness benefits to this bacterium as deletion of 13 TA loci does not influence stress tolerance, persistence and biofilm formation. Our results instead show that TA loci are costly and decrease the competitive fitness of P. putida. Still, the cost of multiple TA systems is low and detectable in certain conditions only. Construction of antitoxin deletion strains showed that only five TA systems code for toxic proteins, while other TA loci have evolved towards reduced toxicity and encode non-toxic or moderately potent proteins. Analysis of P. putida TA systems homologs among fully sequenced Pseudomonads suggests that the TA loci have been subjected to purifying selection and that TA systems spread among bacteria by horizontal gene transfer.

microbiology↗

bioRxiv2019

Muropeptides Stimulate Growth Resumption from Stationary Phase in Escherichia coli

When nutrients run out, bacteria enter a dormant metabolic state. This low or undetectable metabolic activity helps bacteria to preserve their scant reserves for future, but also diminishes their ability to trace the environment for new growth-promoting substrates. However, neighboring microbial growth is a sure indicator of favorable environment and thus, can serve as a cue for exiting the dormancy. Here we report that for Escherichia coli this cue is the basic peptidoglycan unit (i.e. muropeptide). We show that several forms of muropeptides can stimulate growth resumption of dormant E. coli cells, but the sugar - peptide bond is crucial for activity. We also demonstrate that muropeptides from several different species can induce growth resumption of E. coli and also Pseudomonas aeruginosa. These results, together with the previous identification of muropeptides as germination signal for bacterial spores, makes muropeptides rather universal cue for bacterial growth.

microbiology↗

bioRxiv2019

Gene content of the fish-hunting cone snail Conus consors

BackgroundConus consors is a fish-hunting cone snail that lives in the tropical waters of the Indo-Pacific region. Cone snails have attracted scientific interest for the amazing potency of their venom, which consists of a complex mixture of small proteins known as conopeptides, many of which act as ion channel and receptor modulators with high selectivity.\n\nResultsWe have analysed publicly available transcriptomic sequences from 8 tissues of Conus consors and complemented the transcriptome data with the data from genomic DNA reads. We identified 17,715 full-length protein sequences from the transcriptome. In addition, we predicted 168 full-length or partial conopeptide sequences and characterized gene structures of several conopeptide superfamilies.

genomics↗

bioRxiv2019

AluMine: alignment-free method for the discovery of polymorphic Alu element insertions

BackgroundRecently, alignment-free sequence analysis methods have gained popularity in the field of personal genomics. These methods are based on counting frequencies of short k-mer sequences, thus allowing faster and more robust analysis compared to traditional alignment-based methods.\n\nResultsWe have created a fast alignment-free method, AluMine, to analyze polymorphic insertions of Alu elements in the human genome. We tested the method on 2,241 individuals from the Estonian Genome Project and identified 28,962 potential polymorphic Alu element insertions. Each tested individual had on average 1,574 Alu element insertions that were different from those in the reference genome. In addition, we propose an alignment-free genotyping method that uses the frequency of insertion/deletion-specific 32-mer pairs to call the genotype directly from raw sequencing reads. Using this method, the concordance between the predicted and experimentally observed genotypes was 98.7%. The running time of the discovery pipeline is approximately 2 hours per individual. The genotyping of potential polymorphic insertions takes between 0.4 and 4 hours per individual, depending on the hardware configuration.\n\nConclusionsAluMine provides tools that allow discovery of novel Alu element insertions and/or genotyping of known Alu element insertions from personal genomes within few hours.

genomics↗

bioRxiv2018

A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria.

We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) generates a k-mer-based statistical model for predicting a given phenotype and (b) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 460 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets performed with 88% accuracy on the K. pneumoniae test set, 88% on the P. aeruginosa test set and 96.5% on the C. difficile test set. Prediction accuracy was the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets.\n\nPhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).\n\nSummaryPredicting phenotypic properties of bacterial isolates from their genomic sequences has numerous potential applications. A good example would be prediction of antimicrobial resistance and virulence phenotypes for use in medical diagnostics. We have developed a method that is able to predict phenotypes of interest from the genomic sequence of the isolate within seconds. The method uses statistical model that can be trained automatically on isolates with known phenotype. The method is implemented in Python programming language and can be run on low-end Linux server and/or on laptop computers.

bioinformatics↗