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      Comparative transcriptome profiling of heat stress response of the mangrove crab Scylla serrata across sites of varying climate profiles

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          Abstract

          Background

          The fishery and aquaculture of the widely distributed mangrove crab Scylla serrata is a steadily growing, high-value, global industry. Climate change poses a risk to this industry as temperature elevations are expected to threaten the mangrove crab habitat and the supply of mangrove crab juveniles from the wild. It is therefore important to understand the genomic and molecular basis of how mangrove crab populations from sites with different climate profiles respond to heat stress. Towards this, we performed RNA-seq on the gill tissue of S. serrata individuals sampled from 3 sites (Cagayan, Bicol, and Bataan) in the Philippines, under normal and heat-stressed conditions. To compare the transcriptome expression profiles, we designed a 2-factor generalized linear model containing interaction terms, which allowed us to simultaneously analyze within-site response to heat-stress and across-site differences in the response.

          Results

          We present the first ever transcriptome assembly of S. serrata obtained from a data set containing 66 Gbases of cleaned RNA-seq reads. With lowly-expressed and short contigs excluded, the assembly contains roughly 17,000 genes with an N50 length of 2,366 bp. Our assembly contains many almost full-length transcripts – 5229 shrimp and 3049 fruit fly proteins have alignments that cover >80% of their sequence lengths to a contig. Differential expression analysis found population-specific differences in heat-stress response. Within-site analysis of heat-stress response showed 177, 755, and 221 differentially expressed (DE) genes in the Cagayan, Bataan, and Bicol group, respectively. Across-site analysis showed that between Cagayan and Bataan, there were 389 genes associated with 48 signaling and stress-response pathways, for which there was an effect of site in the response to heat; and between Cagayan and Bicol, there were 101 such genes affecting 8 pathways.

          Conclusion

          In light of previous work on climate profiling and on population genetics of marine species in the Philippines, our findings suggest that the variation in thermal response among populations might be derived from acclimatory plasticity due to pre-exposure to extreme temperature variations or from population structure shaped by connectivity which leads to adaptive genetic differences among populations.

          Supplementary Information

          The online version contains supplementary material available at (10.1186/s12864-021-07891-w).

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          Most cited references54

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

              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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                Author and article information

                Contributors
                anish.shrestha@dlsu.edu.ph
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                29 July 2021
                29 July 2021
                2021
                : 22
                : 580
                Affiliations
                [1 ]GRID grid.411987.2, ISNI 0000 0001 2153 4317, Bioinformatics Lab, Advanced Research Institute for Informatics, Computing, and Networking (AdRIC), , De La Salle University, ; Manila, Philippines
                [2 ]GRID grid.411987.2, ISNI 0000 0001 2153 4317, Software Technology Department, College of Computer Studies, , De La Salle University, ; Manila, Philippines
                [3 ]GRID grid.411987.2, ISNI 0000 0001 2153 4317, Practical Genomics Laboratory, Center for Natural Science and Environment Research, , De La Salle University, ; Manila, Philippines
                [4 ]GRID grid.412775.2, ISNI 0000 0004 1937 1119, Department of Biological Sciences, College of Science, , University of Santo Tomas, ; Manila, Philippines
                [5 ]GRID grid.411987.2, ISNI 0000 0001 2153 4317, Mathematics and Statistics Department, College of Science, , De La Salle University, ; Manila, Philippines
                [6 ]GRID grid.467041.0, ISNI 0000 0004 0623 9100, Aquaculture Department, , Southeast Asian Fisheries Development Center, ; Binangoan, 1940 Rizal, Philippines
                [7 ]GRID grid.411987.2, ISNI 0000 0001 2153 4317, Biology Department, College of Science, , De La Salle University, ; Manila, Philippines
                Author information
                http://orcid.org/0000-0002-9192-9709
                Article
                7891
                10.1186/s12864-021-07891-w
                8323281
                34325654
                8377203c-3cd8-40b1-b58b-c60f1a65939f
                © The Author(s) 2021

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 24 November 2020
                : 14 July 2021
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Genetics
                mud crab,mangrove crab,scylla,rna-seq,transcriptome assembly,heat stress
                Genetics
                mud crab, mangrove crab, scylla, rna-seq, transcriptome assembly, heat stress

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