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      Nuclear genome of Bulinus truncatus, an intermediate host of the carcinogenic human blood fluke Schistosoma haematobium

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          Abstract

          Some snails act as intermediate hosts (vectors) for parasitic flatworms (flukes) that cause neglected tropical diseases, such as schistosomiases. Schistosoma haematobium is a blood fluke that causes urogenital schistosomiasis and induces bladder cancer and increased risk of HIV infection. Understanding the molecular biology of the snail and its relationship with the parasite could guide development of an intervention approach that interrupts transmission. Here, we define the genome for a key intermediate host of S. haematobium—called Bulinus truncatus—and explore protein groups inferred to play an integral role in the snail’s biology and its relationship with the schistosome parasite. Bu. truncatus shared many orthologous protein groups with Biomphalaria glabrata—the key snail vector for S. mansoni which causes hepatointestinal schistosomiasis in people. Conspicuous were expansions in signalling and membrane trafficking proteins, peptidases and their inhibitors as well as gene families linked to immune response regulation, such as a large repertoire of lectin-like molecules. This work provides a sound basis for further studies of snail-parasite interactions in the search for targets to block schistosomiasis transmission.

          Abstract

          The snail Bulinus truncatus is an intermediate host of the carcinogenic human blood fluke Schistosoma haematobium. Here the authors report the genome of Bu. truncatus, explore protein groups inferred to play a role in its interaction with the schistosome parasite, and identify expansions in gene families linked to immune response regulation.

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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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              Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype

              Rapid advances in next-generation sequencing technologies have dramatically changed our ability to perform genome-scale analyses. The human reference genome used for most genomic analyses represents only a small number of individuals, limiting its usefulness for genotyping. We designed a novel method, HISAT2, for representing and searching an expanded model of the human reference genome, in which a large catalogue of known genomic variants and haplotypes is incorporated into the data structure used for searching and alignment. This strategy for representing a population of genomes, along with a fast and memory-efficient search algorithm, enables more detailed and accurate variant analyses than previous methods. We demonstrate two initial applications of HISAT2: HLA typing, a critical need in human organ transplantation, and DNA fingerprinting, widely used in forensics. These applications are part of HISAT-genotype, with performance not only surpassing earlier computational methods, but matching or exceeding the accuracy of laboratory-based assays.
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                Author and article information

                Contributors
                nyoung@unimelb.edu.au
                robinbg@unimelb.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                21 February 2022
                21 February 2022
                2022
                : 13
                : 977
                Affiliations
                [1 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Faculty of Veterinary and Agricultural Sciences, , The University of Melbourne, ; Parkville, VIC Australia
                [2 ]GRID grid.418352.9, NIH-NIAID Schistosomiasis Resource Center, Biomedical Research Institute (BRI), ; Rockville, MD USA
                [3 ]GRID grid.48004.38, ISNI 0000 0004 1936 9764, Department of Parasitology, , Liverpool School of Tropical Medicine, ; Liverpool, UK
                [4 ]GRID grid.35937.3b, ISNI 0000 0001 2270 9879, Department of Life Sciences, , Natural History Museum, ; London, UK
                [5 ]GRID grid.512598.2, London Centre for Neglected Tropical Disease Research, ; London, UK
                Author information
                http://orcid.org/0000-0001-8756-229X
                http://orcid.org/0000-0002-9957-4674
                http://orcid.org/0000-0002-9370-3420
                http://orcid.org/0000-0003-1999-1716
                http://orcid.org/0000-0002-4423-1690
                Article
                28634
                10.1038/s41467-022-28634-9
                8861042
                35190553
                b7858c79-25b5-4610-848a-709ddca2116e
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 June 2021
                : 2 February 2022
                Funding
                Funded by: Australian Research Council (ARC): LP180101334, LP180101085 and LP160101299)
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                genome,parasite genomics,zoology
                Uncategorized
                genome, parasite genomics, zoology

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