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      Draft Genome Sequence of the Astaxanthin-Producing Microalga Haematococcus lacustris Strain NIES-144

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          Abstract

          Haematococcus lacustris is an industrially important eukaryotic microalga that is thought to be a great source of natural astaxanthin with strong antioxidant activity. Here, we report the draft assembly and annotation results of the genome of H. lacustris NIES-144. These data will expand our knowledge of the molecular biological features of this microalga.

          ABSTRACT

          Haematococcus lacustris is an industrially important eukaryotic microalga that is thought to be a great source of natural astaxanthin with strong antioxidant activity. Here, we report the draft assembly and annotation results of the genome of H. lacustris NIES-144. These data will expand our knowledge of the molecular biological features of this microalga.

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          Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources

          Mario Stanke, Oliver Schöffmann, Burkhard Morgenstern … (2006)
          Background In order to improve gene prediction, extrinsic evidence on the gene structure can be collected from various sources of information such as genome-genome comparisons and EST and protein alignments. However, such evidence is often incomplete and usually uncertain. The extrinsic evidence is usually not sufficient to recover the complete gene structure of all genes completely and the available evidence is often unreliable. Therefore extrinsic evidence is most valuable when it is balanced with sequence-intrinsic evidence. Results We present a fairly general method for integration of external information. Our method is based on the evaluation of hints to potentially protein-coding regions by means of a Generalized Hidden Markov Model (GHMM) that takes both intrinsic and extrinsic information into account. We used this method to extend the ab initio gene prediction program AUGUSTUS to a versatile tool that we call AUGUSTUS+. In this study, we focus on hints derived from matches to an EST or protein database, but our approach can be used to include arbitrary user-defined hints. Our method is only moderately effected by the length of a database match. Further, it exploits the information that can be derived from the absence of such matches. As a special case, AUGUSTUS+ can predict genes under user-defined constraints, e.g. if the positions of certain exons are known. With hints from EST and protein databases, our new approach was able to predict 89% of the exons in human chromosome 22 correctly. Conclusion Sensitive probabilistic modeling of extrinsic evidence such as sequence database matches can increase gene prediction accuracy. When a match of a sequence interval to an EST or protein sequence is used it should be treated as compound information rather than as information about individual positions.
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            Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.

            Vardges Ter-Hovhannisyan, Alexandre Lomsadze, Yury Chernoff … (2008)
            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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              Genome and Transcriptome Sequencing of the Astaxanthin-Producing Green Microalga, Haematococcus pluvialis

              Qiulan Luo, Chao Bian, Ming Tao … (2018)
              Abstract Haematococcus pluvialis is a freshwater species of Chlorophyta, family Haematococcaceae. It is well known for its capacity to synthesize high amounts of astaxanthin, which is a strong antioxidant that has been utilized in aquaculture and cosmetics. To improve astaxanthin yield and to establish genetic resources for H. pluvialis, we performed whole-genome sequencing, assembly, and annotation of this green microalga. A total of 83.1 Gb of raw reads were sequenced. After filtering the raw reads, we subsequently generated a draft assembly with a genome size of 669.0 Mb, a scaffold N50 of 288.6 kb, and predicted 18,545 genes. We also established a robust phylogenetic tree from 14 representative algae species. With additional transcriptome data, we revealed some novel potential genes that are involved in the synthesis, accumulation, and regulation of astaxanthin production. In addition, we generated an isoform-level reference transcriptome set of 18,483 transcripts with high confidence. Alternative splicing analysis demonstrated that intron retention is the most frequent mode. In summary, we report the first draft genome of H. pluvialis. These genomic resources along with transcriptomic data provide a solid foundation for the discovery of the genetic basis for theoretical and commercial astaxanthin enrichment.
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                Author and article information

                Contributors
                Antonis Rokas: Role: Editor
                Journal
                Journal ID (nlm-ta): Microbiol Resour Announc
                Journal ID (iso-abbrev): Microbiol Resour Announc
                Journal ID (hwp): ga
                Journal ID (pmc): mra
                Journal ID (publisher-id): MRA
                Title: Microbiology Resource Announcements
                Publisher: American Society for Microbiology (1752 N St., N.W., Washington, DC )
                ISSN (Electronic): 2576-098X
                Publication date (Electronic): 4 June 2020
                Publication date Collection: June 2020
                Volume: 9
                Issue: 23
                Electronic Location Identifier: e00128-20
                Affiliations
                [a ]Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto, Japan
                Vanderbilt University
                Author notes
                Address correspondence to Daichi Morimoto, morimoto.daichi.65r@ 123456kyoto-u.jp , or Shigeki Sawayama, sawayama.shigeki.g22@ 123456kyoto-u.jp .

                Citation Morimoto D, Yoshida T, Sawayama S. 2020. Draft genome sequence of the astaxanthin-producing microalga Haematococcus lacustris strain NIES-144. Microbiol Resour Announc 9:e00128-20. https://doi.org/10.1128/MRA.00128-20.

                Article
                Publisher ID: MRA00128-20
                DOI: 10.1128/MRA.00128-20
                PMC ID: 7272542
                PubMed ID: 32499361
                SO-VID: 9ff9e3d1-de61-4363-981f-61dcb4586633
                Copyright © Copyright © 2020 Morimoto et al.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                Date received : 1 April 2020
                Date accepted : 14 May 2020
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 17, Pages: 2, Words: 1447
                Categories
                Subject: Genome Sequences
                Custom metadata
                cover-date June 2020

                Data availability:

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