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6th International Conference on Algorithms for Computational Biology, AlCoB 2019

作者： Shlemov, Alexander Korobeynikov, Anton Center for Algorithmic Biotechnology Saint Petersburg State University Saint Petersburg198504 Russia

ISBN: (纸本)9783030181734

Recently large databases containing profile Hidden Markov Models (pHMMs) emerged. These pHMMs may represent the sequences of antibiotic resistance genes, or allelic variations amongst highly conserved housekeeping genes used for strain typing, etc. The typical application of such a database includes the alignment of contigs to pHMM hoping that the sequence of gene of interest is located within the single contig. Such a condition is often violated for metagenomes preventing the effective use of such databases. We present PathRacer—a novel standalone tool that aligns profile HMM directly to the assembly graph (performing the codon translation on fly for amino acid pHMMs). The tool provides the set of most probable paths traversed by a HMM through the whole assembly graph, regardless whether the sequence of interested is encoded on the single contig or scattered across the set of edges, therefore significantly improving the recovery of sequences of interest even from fragmented metagenome assemblies. © 2019, Springer Nature Switzerland AG.

关键词： Database systems

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Assessing the significance of peptide spectrum match scores 17

Assessing the significance of peptide spectrum match scores

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17th International Workshop on Algorithms in Bioinformatics, WABI 2017

作者： Abramova, Anastasiia Korobeynikov, Anton Department of Statistical Modeling Saint Petersburg State University Saint Petersburg Russia Center for Algorithmic Biotechnology Saint Petersburg State University Saint Petersburg Russia

ISBN: (纸本)9783959770507

Peptidic Natural Products (PNPs) are highly sought after bioactive compounds that include many antibiotic, antiviral and antitumor agents, immunosuppressors and toxins. Even though recent advancements in mass-spectrometry have led to the development of accurate sequencing methods for nonlinear (cyclic and branch-cyclic) peptides, requiring only picograms of input material, the identification of PNPs via a database search of mass spectra remains problematic. This holds particularly true when trying to evaluate the statistical significance of Peptide Spectrum Matches (PSM) especially when working with non-linear peptides that often contain non-standard amino acids, modifications and have an overall complex structure. In this paper we describe a new way of estimating the statistical significance of a PSM, defined by any peptide (including linear and non-linear), by using state-of-the-art Markov Chain Monte Carlo methods. In addition to the estimate itself our method also provides an uncertainty estimate in the form of confidence bounds, as well as an automatic simulation stopping rule that ensures that the sample size is sufficient to achieve the desired level of result accuracy. © Anastasiia Abramova and Anton Korobeynikov.

关键词： Mass spectrometry

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Unravelling the enzymatic wood decay repertoire of Cerrena zonata: A multi-omics approach

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Microbiological Research 2025年 298卷 128214页

作者： Broel, Niklas Daumüller, Franziska Ali, Arslan Lemanschick, Jasmin Maibach, Katharina Mewe, Carolin Bunk, Boyke Spröer, Cathrin Baschien, Christiane Zorn, Holger Schlüter, Hartmut Rühl, Martin Janssen, Stefan Gand, Martin Justus Liebig University Giessen Institute of Food Chemistry and Food Biotechnology Heinrich-Buff-Ring 17 Giessen35392 Germany Justus Liebig University Giessen Algorithmic Bioinformatics Ludwigsplatz 13-15 Giessen35390 Germany Dr. Panjwani Center for Molecular Medicine and Drug Research International Center for Chemical and Biological Sciences University of Karachi University Road Karachi75270 Pakistan University Medical Center Hamburg-Eppendorf Institute of Clinical Chemistry and Laboratory Medicine Diagnostic Center Section Mass Spectrometry & Proteomics Campus Research Martinistr. 52 N27 Hamburg20246 Germany Fraunhofer Institute for Molecular Biology and Applied Ecology IME Ohlebergsweg 12 Giessen35392 Germany Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures Inhoffenstraße 7B Braunschweig38124 Germany University of Hamburg Institute of Biochemistry and Molecular Biology Martin Luther King Platz 6 Hamburg20146 Germany

Lignocellulosic biomass (LCB), such as wheat straw, bagasse, or wood, is a cost-effective, sustainable carbon source but remains challenging to utilize due to the recalcitrance of lignin, which hinders efficient carbohydrate hydrolysis. Effective LCB degradation demands a wide range of enzymes, and commercial enzyme cocktails often require physical or chemical pretreatments. A fully enzymatic degradation could drastically improve the efficiency of these processes. Basidiomycota fungi naturally possess diverse enzymes suited for LCB breakdown. The white-rot fungus Cerrena zonata, a member of the phylum Basidiomycota, was analyzed for its Carbohydrate-Active Enzymes (CAZymes) using a multi-omics approach. Genomic and transcriptomic analyses of C. zonata identified 20,816 protein-encoding genes, including 487 CAZymes (2.3 %). Cultivating C. zonata with and without LCB addition revealed a total of 147 proteins, of which 36 were CAZymes (13 auxiliary activities (AA), 3 carbohydrate esterases, and 20 glycoside hydrolases). In accordance, laccase, manganese peroxidase (MnP) as well as versatile peroxidase (VP) activities were detected in the fungal culture supernatants. Furthermore, relevant enzymes were visualized via zymography. Consistent with these results, five putative peroxidases (AA2) and three putative laccases (AA1_1) were identified in all –omics dimensions. Further structure and sequence analysis of AA2 proteins supports that two proteins were classified as VPs and three as MnPs, based on their active and Mn2 + binding sites. In summary, C. zonata possesses a broad enzyme spectrum expressed under varied conditions, highlighting its potential for identifying efficient lignin-degrading enzymes for enzymatic pretreatment of food industry side streams and other LCBs. © 2025 The Authors

关键词： Binding sites

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algorithmic INFORMATION DISTORTIONS AND INCOMPRESSIBILITY IN UNIFORM MULTIDIMENSIONAL NETWORKS

arXiv

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arXiv 2018年

作者： Abrahão, Felipe S. Wehmuth, Klaus Zenil, Hector Ziviani, Artur Brazil Petropolis RJ Brazil for the Natural and Digital Sciences Paris France Machine Learning Group Department of Chemical Engineering and Biotechnology The University of Cambridge United Kingdom Oxford Immune Algorithmics United Kingdom Algorithmic Dynamics Lab Unit of Computational Medicine Department of Medicine Solna Center for Molecular Medicine Karolinska Institute Stockholm Sweden

This article presents a theoretical investigation of generalized encoded forms of networks in a uniform multidimensional space. First, we study encoded networks with (finite) arbitrary node dimensions (or aspects), such as time instants or layers. In particular, we study these networks that are formalized in the form of multiaspect graphs. In the context of node-aligned non-uniform (or node-unaligned non-uniform and uniform) multidimensional spaces, previous results has shown that, unlike classical graphs, the algorithmic information of a multidimensional network is not in general dominated by the algorithmic information of the binary sequence that determines the presence or absence of edges. In the present work, first we demonstrate the existence of such algorithmic information distortions for node-aligned uniform multidimensional networks. Secondly, we show that there are particular cases of infinite nesting families of finite uniform multidimensional networks such that each member of these families is incompressible. From these results, we also recover the network topological properties and equivalences in irreducible information content of multidimensional networks in comparison to their isomorphic classical graph counterpart in the previous literature. These results together establish a universal algorithmic approach and set limitations and conditions for irreducible information content analysis in comparing arbitrary networks with a large number of dimensions, such as multilayer *** Codes 05C82, 68Q30, 68P30, 94A29, 68R10, 05C75, 94A16, 03D32, 05C80, 05C60, 11U05, 68T09, 68Q01, 94A15, 05C30, 05C78, 62R07 Copyright © 2018, The Authors. All rights reserved.

关键词： Binary sequences

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A SIMPLICITY BUBBLE PROBLEM IN FORMAL-THEORETIC LEARNING SYSTEMS

arXiv

引用

arXiv 2021年

作者： Abrahão, Felipe S. Zenil, Hector Porto, Fabio Winter, Michael Wehmuth, Klaus D'Ottaviano, Itala M.L. Brazil RJ Petropolis Brazil for the Natural and Digital Sciences Paris France Machine Learning Group Department of Chemical Engineering and Biotechnology The University of Cambridge United Kingdom Oxford Immune Algorithmics England United Kingdom Algorithmic Dynamics Lab Unit of Computational Medicine Department of Medicine Solna Center for Molecular Medicine Karolinska Institute Stockholm Sweden

When mining large datasets in order to predict new data, limitations of the principles behind statistical machine learning pose a serious challenge not only to the Big Data deluge, but also to the traditional assumptions that data generating processes are biased toward low algorithmic complexity. Even when one assumes an underlying algorithmic-informational bias toward simplicity in finite dataset generators, we show that current approaches to machine learning (including deep learning, or any formal-theoretic hybrid mix of top-down AI and statistical machine learning approaches), can always be deceived, naturally or artificially, by sufficiently large datasets. In particular, we demonstrate that, for every learning algorithm (with or without access to a formal theory), there is a sufficiently large dataset size above which the algorithmic probability of an unpredictable deceiver is an upper bound (up to a multiplicative constant that only depends on the learning algorithm) for the algorithmic probability of any other larger dataset. In other words, very large and complex datasets can deceive learning algorithms into a "simplicity bubble" as likely as any other particular non-deceiving dataset. These deceiving datasets guarantee that any prediction effected by the learning algorithm will unpredictably diverge from the high-algorithmic-complexity globally optimal solution while converging toward the low-algorithmic-complexity locally optimal solution, although the latter is deemed a global one by the learning algorithm. We discuss the framework and additional empirical conditions to be met in order to circumvent this deceptive phenomenon, moving away from statistical machine learning towards a stronger type of machine learning based on, and motivated by, the intrinsic power of algorithmic information theory and computability theory. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

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Reconstructing antibody repertoires from error-prone immunosequencing datasets

arXiv

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arXiv 2017年

作者： Shlemov, Alexander Bankevich, Sergey Bzikadze, Andrey Turchaninova, Maria A. Safonova, Yana Pevzner, Pavel A. Center for Algorithmic Biotechnology Institute for Translational Biomedicine St. Petersburg University St. Petersburg Department of Statistical Modelling St. Petersburg University St. Petersburg Russia Institute of Bioorganic Chemistry Russian Academy of Sciences Moscow Russia Department of Computer Science and Engineering University of California San Diego United States

Transforming error-prone immunosequencing datasets into antibody repertoires is a fundamental problem in immunogenomics, and a prerequisite for studies of immune responses. Although various repertoire reconstruction algorithms were released in the last three years, it remains unclear how to benchmark them and how to assess the accuracy of the reconstructed repertoires. We describe a novel IGREC algorithm for constructing antibody repertoires from high-throughput immunosequencing datasets and a new framework for assessing the quality of reconstructed repertoires. Benchmarking IGREC against the existing antibody repertoire reconstruction tools has demonstrated that it results in highly accurate repertoire reconstructions. Surprisingly, antibody repertoires constructed by IGREC from barcoded immunosequencing datasets in blind mode (without using unique molecular identifiers information) improved upon the repertoires constructed by the state-of-the-art tools that use barcoding. This finding suggests that IGREC may alleviate the need to generate repertoires using the barcoding technology (the workhorse of current immunogenomics efforts) because our computational approach to error correction of immunosequencing data ends up being nearly as powerful as the experimental approach based on barcoding. Copyright © 2017, The Authors. All rights reserved.

关键词： Antibodies

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Correction to: CDSnake: Snakemake pipeline for retrieval of annotated OTUs from paired-end reads using CD-HIT utilities

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BMC bioinformatics 2020年第1期21卷 362页

作者： Yulia Kondratenko Anton Korobeynikov Alla Lapidus Center for Algorithmic Biotechnology Institute for Translational Biomedicine St. Petersburg State University St. Petersburg Russia 199004. jdkondratenko@***. Center for Algorithmic Biotechnology Institute for Translational Biomedicine St. Petersburg State University St. Petersburg Russia 199004. Department of Statistical Modelling St. Petersburg State University St. Petersburg Russia 198515. Center for Algorithmic Biotechnology Institute for Translational Biomedicine St. Petersburg State University St. Petersburg Russia 199004. piterlabs@***.

An amendment to this paper has been published and can be accessed via the original article.

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Complete mitochondrial genomes of Baikal oilfishes (Perciformes: Cottoidei), earth’s deepest-swimming freshwater fishes

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Mitochondrial DNA Part B 2017年第2期2卷 773-775页

作者： Michael W. Sandel Andres Aguilar Kayla Fast Stephen O’Brien Alla Lapidus David B. Allison Veronika Teterina Sergei Kirilchik Department of Biological and Environmental Sciences The University of West Alabama Livingston AL USA Department of Biological Sciences California State University Los Angeles Los Angeles CA USA Theodosius Dobzhansky Center for Genome Bioinformatics St. Petersburg State University St. Petersburg RussiaHalmos College of Natural Sciences and Oceanography Nova Southeastern University Fort Lauderdale FL USA Centre for Algorithmic Biotechnology St. Petersburg State University St. Petersburg Russia Department of Epidemiology and Biostatistics School of Public Health Indiana University Bloomington IN USA Limnological Institute of the Russian Academy of Sciences Irkutsk Russia

Sculpins are predominantly benthic sit-and-wait predators that inhabit marine and freshwaters of the Northern Hemisphere. In striking contrast to riverine relatives, sculpins endemic to Lake Baikal have diversified in both form and function, with multiple taxa having adaptations for pelagic and bathyal niches within the world’s deepest lake. Baikal Oilfishes (Comephorusspp.) represent a highly apomorphic taxon with unique skeletal morphology, soft anatomy, and reproductive ecology. Selection for novel behavior and life history may be evident in genes responsible for organismal energy balance, including those encoding subunits of the electron transport chain. Complete mitochondrial genomes were sequenced for the Big Baikal Oilfish (Comephorus baicalensis) and Little Baikal Oilfish (Comephorus dybowskii). Mitochondrial genomes encode genes essential for electron transport, and data provided here will complement ongoing investigations of genome-to-phenome maps for teleost respiration and metabolism. Phylogenetic analyses including oilfish mitogenomes and all publicly available cottoid representative sequences are largely concordant with previous studies.

关键词： Comephorus baicalensis Comephorus dybowskii Golomyanka oilfish mtDNA mitochondrial genome

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Hi-C Metagenomics Facilitate Comparative Genome Analysis of Bacteria and Yeast from Spontaneous Beer and Cider

SSRN

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SSRN 2024年

作者： Sonets, Ignat V. Solovyev, Mikhail A. Ivanova, Valeriia A. Vasiluev, Petr A. Kachalkin, Aleksey V. Ochkalova, Sofia D. Korobeynikov, Anton I. Razin, Sergey V. Ulianov, Sergey V. Tyakht, Alexander V. Institute of Gene Biology Russian Academy of Sciences Moscow Russia Faculty of Biology Lomonosov Moscow State University Moscow Russia Research Center for Medical Genetics Moscow Russia Department of Soil Biology Faculty of Soil Science Lomonosov Moscow State University Moscow Russia G.K. Skryabin Institute of Biochemistry and Physiology of Microorganisms of RAS Pushchino Russia Applied Genomics Laboratory SCAMT Institute ITMO University Saint Petersburg197101 Russia Center for Algorithmic Biotechnology Saint Petersburg State University Saint Petersburg199004 Russia Department of Statistical Modelling Saint Petersburg State University Saint Petersburg199004 Russia Center for Precision Genome Editing and Genetic Technologies for Biomedicine Institute of Gene Biology Russian Academy of Sciences Moscow Russia

Sequence-based analysis of fermented foods and beverages' microbiomes offers insights into their impact on taste and health. High-throughput metagenomics offers superior taxonomic and functional community profiling compared to amplicon sequencing, there remains room for improvement in bacterial and yeast genome reconstruction and mobile genetic elements *** established an experimental and bioinformatic pipeline for exploring fermented foods microbiomes using metagenomics coupled with chromosome conformation capture (Hi-C metagenomics). The approach was applied to analyze a collection of spontaneously fermented beers (n=5) and ciders (n=7). The Hi-C reads were used to reconstruct the metagenome-assembled genomes (MAGs) of bacteria and yeasts facilitating subsequent comparative genomic analysis, assembly scaffolding and exploration of "bacteria-plasmids" links. For a subset of beverages, yeasts were isolated and characterized *** reconstructed Hi-C MAGs primarily belonged to the Lactobacillaceae family in beers, along with Acetobacteraceae and Enterobacteriaceae in ciders, exhibiting improved quality compared to conventional metagenomic MAGs. Comparative genomic analysis of Lactobacillaceae Hi-C MAGs revealed phylogenetic clustering by niche and suggested genetic determinants of survival and probiotic potential. Using Pediococcus damnosus as an example, we demonstrated how Hi-C-based networks of contigs can enhance the linkage of host bacteria with plasmids. Additionally, analyzing phylogeny and accessory genes in the context of known reference genomes from various sources offered insights into the niche specialization of beer lactobacilli. The subspecies-level genomic diversity of Tatumella spp. from cider was disentangled using a Hi-C-based graph. We obtained highly complete yeast Hi-C MAGs primarily represented by Brettanomyces and Saccharomyces. The case of Brettanomyces bruxellensis exemplified how a chromosome conformation signal can provi

关键词： Yeast

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Microbiome analyses of blood and tissues suggest cancer diagnostic approach

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Nature 2020年第7800期579卷 567-574页

作者： Gregory D Poore Evguenia Kopylova Qiyun Zhu Carolina Carpenter Serena Fraraccio Stephen Wandro Tomasz Kosciolek Stefan Janssen Jessica Metcalf Se Jin Song Jad Kanbar Sandrine Miller-Montgomery Robert Heaton Rana Mckay Sandip Pravin Patel Austin D Swafford Rob Knight Department of Bioengineering University of California San Diego La Jolla CA USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Clarity Genomics Beerse Belgium. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. Malopolska Centre of Biotechnology Jagiellonian University in Krakow Krakow Poland. Algorithmic Bioinformatics Department of Biology and Chemistry Justus Liebig University Gießen Gießen Germany. Department of Animal Sciences Colorado State University Fort Collins CO USA. Department of Medicine University of California San Diego La Jolla CA USA. Department of Psychiatry University of California San Diego La Jolla CA USA. Moores Cancer Center University of California San Diego Health La Jolla CA USA. Department of Bioengineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Pediatrics University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu.

Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions, we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia-IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration.

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