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      Resolving the Microalgal Gene Landscape at the Strain Level: a Novel Hybrid Transcriptome of Emiliania huxleyi CCMP3266

      1 , 2 , 1
      Applied and Environmental Microbiology
      American Society for Microbiology

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          Abstract

          Microalgae are key players in the ecology and biogeochemistry of our oceans. Efforts to implement genomic and transcriptomic tools in laboratory studies involving microalgae suffer from the lack of published genomes.

          ABSTRACT

          Microalgae are key ecological players with a complex evolutionary history. Genomic diversity, in addition to limited availability of high-quality genomes, challenge studies that aim to elucidate molecular mechanisms underlying microalgal ecophysiology. Here, we present a novel and comprehensive transcriptomic hybrid approach to generate a reference for genetic analyses and resolve the microalgal gene landscape at the strain level. The approach is demonstrated for a strain of the coccolithophore microalga Emiliania huxleyi , which is a species complex with considerable genome variability. The investigated strain is commonly studied as a model for algal-bacterial interactions and was therefore sequenced in the presence of bacteria to elicit the expression of interaction-relevant genes. We applied complementary PacBio Iso-Seq full-length cDNA and poly(A)-independent Illumina total RNA sequencing, which resulted in a de novo -assembled, near-complete hybrid transcriptome. In particular, hybrid sequencing improved the reconstruction of long transcripts and increased the recovery of full-length transcript isoforms. To use the resulting hybrid transcriptome as a reference for genetic analyses, we demonstrate a method that collapses the transcriptome into a genome-like data set, termed “synthetic genome” (sGenome). We used the sGenome as a reference to visually confirm the robustness of the CCMP3266 gene assembly, to conduct differential gene expression analysis, and to characterize novel E. huxleyi genes. The newly identified genes contribute to our understanding of E. huxleyi genome diversification and are predicted to play a role in microbial interactions. Our transcriptomic toolkit can be implemented in various microalgae to facilitate mechanistic studies on microalgal diversity and ecology.

          IMPORTANCE Microalgae are key players in the ecology and biogeochemistry of our oceans. Efforts to implement genomic and transcriptomic tools in laboratory studies involving microalgae suffer from the lack of published genomes. In the case of coccolithophore microalgae, the problem has long been recognized; the model species Emiliania huxleyi is a species complex with genomes composed of a core and a large variable portion. To study the role of the variable portion in niche adaptation, and specifically in microbial interactions, strain-specific genetic information is required. Here, we present a novel transcriptomic hybrid approach, and generated strain-specific genome-like information. We demonstrate our approach on an E. huxleyi strain that is cocultivated with bacteria. By constructing a “synthetic genome,” we generated comprehensive gene annotations that enabled accurate analyses of gene expression patterns. Importantly, we unveiled novel genes in the variable portion of E. huxleyi that play putative roles in microbial interactions.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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                Author and article information

                Contributors
                Journal
                Applied and Environmental Microbiology
                Appl Environ Microbiol
                American Society for Microbiology
                0099-2240
                1098-5336
                January 25 2022
                January 25 2022
                : 88
                : 2
                Affiliations
                [1 ]Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
                [2 ]Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
                Article
                10.1128/AEM.01418-21
                8788742
                34757817
                ea9801e1-3b1e-46f4-99bc-f7dac98f5e3a
                © 2022

                https://doi.org/10.1128/ASMCopyrightv2

                https://journals.asm.org/non-commercial-tdm-license

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