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Borodovsky, Mark

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Author: Borodovsky, Mark × Author: Lomsadze, Alexandre ×

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    The genome of the polar eukaryotic microalga coccomyxa subellipsoidea reveals traits of cold adaptation
    (Georgia Institute of Technology, 2012) Blanc, Guillaume ; Agarkova, Irina ; Grimwood, Jane ; Kuo, Alan ; Brueggeman, Andrew ; Dunigan, David D. ; Gurnon, James ; Ladunga, Istvan ; Lindquist, Erika ; Lucas, Susan ; Pangilinan, Jasmyn ; Pröschold, Thomas ; Salamov, Asaf ; Schmutz, Jeremy ; Weeks, Donald ; Yamada, Takashi ; Lomsadze, Alexandre ; Borodovsky, Mark ; Claverie, Jean-Michel ; Grigoriev, Igor V. ; Van Etten, James L.
    Background: Little is known about the mechanisms of adaptation of life to the extreme environmental conditions encountered in polar regions. Here we present the genome sequence of a unicellular green alga from the division chlorophyta, Coccomyxa subellipsoidea C-169, which we will hereafter refer to as C-169. This is the first eukaryotic microorganism from a polar environment to have its genome sequenced. Results: The 48.8 Mb genome contained in 20 chromosomes exhibits significant synteny conservation with the chromosomes of its relatives Chlorella variabilis and Chlamydomonas reinhardtii. The order of the genes is highly reshuffled within synteny blocks, suggesting that intra-chromosomal rearrangements were more prevalent than inter-chromosomal rearrangements. Remarkably, Zepp retrotransposons occur in clusters of nested elements with strictly one cluster per chromosome probably residing at the centromere. Several protein families overrepresented in C. subellipsoidae include proteins involved in lipid metabolism, transporters, cellulose synthases and short alcohol dehydrogenases. Conversely, C-169 lacks proteins that exist in all other sequenced chlorophytes, including components of the glycosyl phosphatidyl inositol anchoring system, pyruvate phosphate dikinase and the photosystem 1 reaction center subunit N (PsaN). Conclusions: We suggest that some of these gene losses and gains could have contributed to adaptation to low temperatures. Comparison of these genomic features with the adaptive strategies of psychrophilic microbes suggests that prokaryotes and eukaryotes followed comparable evolutionary routes to adapt to cold environments.
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    The Genome Sequence of the North-European Cucumber (Cucumis sativus L.) Unravels Evolutionary Adaptation Mechanisms in Plants
    (Georgia Institute of Technology, 2011) Wóycicki, Rafał ; Witkowicz, Justyna ; Gawroński, Piotr ; Dąbrowska, Joanna ; Lomsadze, Alexandre ; Pawełkowicz, Magdalena ; Siedlecka, Ewa ; Yagi, Kohei ; Pląder, Wojciech ; Seroczyńska, Anna ; Śmiech, Mieczysław ; Gutman, Wojciech ; Niemirowicz-Szczytt, Katarzyna ; Bartoszewski, Grzegorz ; Tagashira, Norikazu ; Hoshi, Yoshikazu ; Borodovsky, Mark ; Karpiński, Stanisław ; Malepszy, Stefan ; Przybecki, Zbigniew
    Cucumber (Cucumis sativus L.), a widely cultivated crop, has originated from Eastern Himalayas and secondary domestication regions includes highly divergent climate conditions e.g. temperate and subtropical. We wanted to uncover adaptive genome differences between the cucumber cultivars and what sort of evolutionary molecular mechanisms regulate genetic adaptation of plants to different ecosystems and organism biodiversity. Here we present the draft genome sequence of the Cucumis sativus genome of the North-European Borszczagowski cultivar (line B10) and comparative genomics studies with the known genomes of: C. sativus (Chinese cultivar – Chinese Long (line 9930)), Arabidopsis thaliana, Populus trichocarpa and Oryza sativa. Cucumber genomes show extensive chromosomal rearrangements, distinct differences in quantity of the particular genes (e.g. involved in photosynthesis, respiration, sugar metabolism, chlorophyll degradation, regulation of gene expression, photooxidative stress tolerance, higher non-optimal temperatures tolerance and ammonium ion assimilation) as well as in distributions of abscisic acid-, dehydration- and ethylene-responsive cis-regulatory elements (CREs) in promoters of orthologous group of genes, which lead to the specific adaptation features. Abscisic acid treatment of non-acclimated Arabidopsis and C. sativus seedlings induced moderate freezing tolerance in Arabidopsis but not in C. sativus. This experiment together with analysis of abscisic acid-specific CRE distributions give a clue why C. sativus is much more susceptible to moderate freezing stresses than A. thaliana. Comparative analysis of all the five genomes showed that, each species and/or cultivars has a specific profile of CRE content in promoters of orthologous genes. Our results constitute the substantial and original resource for the basic and applied research on environmental adaptations of plants, which could facilitate creation of new crops with improved growth and yield in divergent conditions.
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    Ab initio Gene Identification in Metagenomic Sequences
    (Georgia Institute of Technology, 2010) Zhu, Wenhan ; Lomsadze, Alexandre ; Borodovsky, Mark
    We describe an algorithm for gene identification in DNA sequences derived from shotgun sequencing of microbial communities. Accurate ab initio gene prediction in a short nucleotide sequence of anonymous origin is hampered by uncertainty in model parameters. While several machine learning approaches could be proposed to bypass this difficulty, one effective method is to estimate parameters from dependencies, formed in evolution, between frequencies of oligonucleotides in protein-coding regions and genome nucleotide composition. Original version of the method was proposed in 1999 and has been used since for (i) reconstructing codon frequency vector needed for gene finding in viral genomes and (ii) initializing parameters of self-training gene finding algorithms. With advent of new prokaryotic genomes en masse it became possible to enhance the original approach by using direct polynomial and logistic approximations of oligonucleotide frequencies, as well as by separating models for bacteria and archaea. These advances have increased the accuracy of model reconstruction and, subsequently, gene prediction. We describe the refined method and assess its accuracy on known prokaryotic genomes split into short sequences. Also, we show that as a result of application of the new method, several thousands of new genes could be added to existing annotations of several human and mouse gut metagenomes
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    Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training
    (Georgia Institute of Technology, 2008-12) Ter-Hovhannisyan,Vardges ; Lomsadze, Alexandre ; Chernoff, Yury O. ; Borodovsky, Mark
    We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects
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    Gene identification in novel eukaryotic genomes by self-training algorithm
    (Georgia Institute of Technology, 2005) Lomsadze, Alexandre ; Ter-Hovhannisyan, Vardges ; Chernoff, Yury O. ; Borodovsky, Mark
    Finding new protein-coding genes is one of the most important goals of eukaryotic genome sequencing projects. However, genomic organization of novel eukaryotic genomes is diverse and ab initio gene finding tools tuned up for previously studied species are rarely suitable for efficacious gene hunting inDNA sequences of a new genome. Gene identification methods based on cDNA and expressed sequence tag (EST) mapping to genomic DNA or those using alignments to closely related genomes rely either on existence of abundant cDNA and EST data and/ or availability on reference genomes. Conventional statistical ab initio methods require large training sets of validated genes for estimating gene model parameters. In practice, neither one of these types of data may be available in sufficient amount until rather late stages of the novel genome sequencing. Nevertheless, we have shown that gene finding in eukaryotic genomes could be carried out in parallel with statistical models estimation directly from yet anonymous genomic DNA. The suggested method of parallelization of gene prediction with the model parameters estimation follows the path of the iterative Viterbi training. Rounds of genomic sequence labeling into coding and non-coding regions are followed by the rounds of model parameters estimation. Several dynamically changing restrictions on the possible range of model parameters are added to filter out fluctuations in the initial steps of the algorithm that could redirect the iteration process away from the biologically relevant point in parameter space. Tests on well-studied eukaryotic genomes have shown that the new method performs comparably or better than conventional methods where the supervised model training precedes the gene prediction step. Several novel genomes have been analyzed and biologically interesting findings are discussed. Thus, a self-training algorithm that had been assumed feasible only for prokaryotic genomes has now been developed for ab initio eukaryotic gene identification.