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      High quality genome assembly of the amitochondriate eukaryote Monocercomonoides exilis

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          Abstract

          Monocercomonoides exilis is considered the first known eukaryote to completely lack mitochondria. This conclusion is based primarily on a genomic and transcriptomic study which failed to identify any mitochondrial hallmark proteins. However, the available genome assembly has limited contiguity and around 1.5 % of the genome sequence is represented by unknown bases. To improve the contiguity, we re-sequenced the genome and transcriptome of M. exilis using Oxford Nanopore Technology (ONT). The resulting draft genome is assembled in 101 contigs with an N50 value of 1.38 Mbp, almost 20 times higher than the previously published assembly. Using a newly generated ONT transcriptome, we further improve the gene prediction and add high quality untranslated region (UTR) annotations, in which we identify two putative polyadenylation signals present in the 3′UTR regions and characterise the Kozak sequence in the 5′UTR regions. All these improvements are reflected by higher BUSCO genome completeness values. Regardless of an overall more complete genome assembly without missing bases and a better gene prediction, we still failed to identify any mitochondrial hallmark genes, thus further supporting the hypothesis on the absence of mitochondrion.

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            Highly accurate protein structure prediction with AlphaFold

            Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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              IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

              Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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                Author and article information

                Journal
                Microb Genom
                Microb Genom
                mgen
                mgen
                Microbial Genomics
                Microbiology Society
                2057-5858
                2021
                24 December 2021
                24 December 2021
                : 7
                : 12
                : 000745
                Affiliations
                [ 1] departmentDepartment of Parasitology , Faculty of Science, Charles University, BIOCEV, Průmyslová 595 , 252 42 Vestec, Czech Republic
                [ 2] departmentInstitute of Evolutionary Biology , Faculty of Biology, Biological and Chemical Research Centre, University of Warsaw , Warsaw, Poland
                Author notes
                *Correspondence: Sebastian Cristian Treitli, sebastian.treitli@ 123456yahoo.com
                *Correspondence: Vladimír Hampl, vlada@ 123456natur.cuni.cz
                Author information
                https://orcid.org/0000-0001-8000-1780
                https://orcid.org/0000-0002-9428-6737
                https://orcid.org/0000-0002-9555-0675
                https://orcid.org/0000-0003-3709-7873
                Article
                000745
                10.1099/mgen.0.000745
                8767320
                34951395
                c68fbb93-55b8-4c2e-ae37-e3b9b2af53a8
                © 2021 The Authors

                This is an open-access article distributed under the terms of the Creative Commons Attribution License.

                History
                : 09 September 2021
                : 15 November 2021
                Funding
                Funded by: European Molecular Biology Organization
                Award Recipient : AnnaKarnkowska
                Funded by: H2020 European Research Council
                Award ID: 771592
                Award Recipient : VladimirHampl
                Categories
                Research Articles
                Genomic Methodologies
                Custom metadata
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                monocercomonoides,amitochondriate,genome,nanopore
                monocercomonoides, amitochondriate, genome, nanopore

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