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      De novo assembly, annotation, marker discovery, and genetic diversity of the Stipa breviflora Griseb. (Poaceae) response to grazing

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          Abstract

          Grassland is one of the most widely-distributed ecosystems on Earth and provides a variety of ecosystem services. Grasslands, however, currently suffer from severe degradation induced by human activities, overgrazing pressure and climate change. In the present study, we explored the transcriptome response of Stipa breviflora, a dominant species in the desert steppe, to grazing through transcriptome sequencing, the development of simple sequence repeat (SSR) markers, and analysis of genetic diversity. De novo assembly produced 111,018 unigenes, of which 88,164 (79.41%) unigenes were annotated. A total of 686 unigenes showed significantly different expression under grazing, including 304 and 382 that were upregulated and downregulated, respectively. These differentially expressed genes (DEGs) were significantly enriched in the “alpha-linolenic acid metabolism” and “plant-pathogen interaction” pathways. Based on transcriptome sequencing data, we developed eight SSR molecular markers and investigated the genetic diversity of S. breviflora in grazed and ungrazed sites. We found that a relatively high level of S. breviflora genetic diversity occurred under grazing. The findings of genes that improve resistance to grazing are helpful for the restoration, conservation, and management of desert steppe.

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          Basic local alignment search tool.

          Stephen F Altschul, Warren Gish, Webb Miller … (1990)
          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

            Bo Li, Colin Dewey (2011)
            Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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              Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

              Manfred Grabherr, Brian Haas, Moran Yassour … (2011)
              Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
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                Author and article information

                Contributors
                Dongqing Yan: Role: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – original draft
                Jing Ren: Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: Software
                Jiamei Liu: Role: MethodologyRole: SoftwareRole: Visualization
                Yu Ding: Role: InvestigationRole: MethodologyRole: Software
                Jianming Niu: Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Branislav T. Šiler: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 22 December 2020
                Publication date Collection: 2020
                Volume: 15
                Issue: 12
                Electronic Location Identifier: e0244222
                Affiliations
                [1 ] School of Ecology and Environment, Inner Mongolia University, Hohhot, People’s Republic of China
                [2 ] Inner Mongolia Key Laboratory of Grassland Ecology and the Candidate State Key Laboratory of Ministry of Science and Technology, Hohhot, People’s Republic of China
                [3 ] Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Hohhot, People’s Republic of China
                Institute for Biological Research "S. Stanković", University of Belgrade, SERBIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                Publisher ID: PONE-D-20-16248
                DOI: 10.1371/journal.pone.0244222
                PMC ID: 7755183
                PubMed ID: 33351838
                SO-VID: 0eafb916-87a0-4d94-b88d-bdc088825e7d
                Copyright © © 2020 Yan et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 29 May 2020
                Date accepted : 4 December 2020
                Page count
                Figures: 3, Tables: 9, Pages: 16
                Funding
                Funded by: Natural Science Foundation of China (NSFC)
                Award ID: 31460154
                Award Recipient :
                Funded by: Natural Science Foundation of China (NSFC)
                Award ID: 31860106
                Award Recipient :
                Funded by: Major Science and Technology Projects of Inner Mongolia Autonomous Region
                Award ID: 2019ZD008
                Award Recipient :
                This study was supported by Natural Science Foundation of China (NSFC) (31460154, 31860106), and Major Science and Technology Projects of Inner Mongolia Autonomous Region (2019ZD008). JN received the above three awards. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Psychology
                Subject: Behavior
                Subject: Animal Behavior
                Subject: Grazing
                Subject: Social Sciences
                Subject: Psychology
                Subject: Behavior
                Subject: Animal Behavior
                Subject: Grazing
                Subject: Biology and Life Sciences
                Subject: Zoology
                Subject: Animal Behavior
                Subject: Grazing
                Subject: Biology and Life Sciences
                Subject: Evolutionary Biology
                Subject: Population Genetics
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Population Genetics
                Subject: Biology and Life Sciences
                Subject: Population Biology
                Subject: Population Genetics
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Gene Types
                Subject: Microsatellite Loci
                Subject: Biology and Life Sciences
                Subject: Computational Biology
                Subject: Genome Analysis
                Subject: Transcriptome Analysis
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Genomics
                Subject: Genome Analysis
                Subject: Transcriptome Analysis
                Subject: Biology and Life Sciences
                Subject: Ecology
                Subject: Ecological Metrics
                Subject: Species Diversity
                Subject: Shannon Index
                Subject: Ecology and Environmental Sciences
                Subject: Ecology
                Subject: Ecological Metrics
                Subject: Species Diversity
                Subject: Shannon Index
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Plant Genetics
                Subject: Biology and Life Sciences
                Subject: Plant Science
                Subject: Plant Genetics
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Heredity
                Subject: Heterozygosity
                Subject: Biology and Life Sciences
                Subject: Ecology
                Subject: Ecological Metrics
                Subject: Species Diversity
                Subject: Ecology and Environmental Sciences
                Subject: Ecology
                Subject: Ecological Metrics
                Subject: Species Diversity
                Custom metadata
                Data Availability All transcriptome data have been deposited in the NCBI Sequence Read Archive (SRA) with accession number as SRR9903413, SRR9903414, SRR9903415, SRR9903416, SRR9903417, and SRR9903418.

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

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