A chromosome-level genome assembly of the oriental river prawn,  Macrobrachium nipponense

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Abstract

Background

The oriental river prawn, Macrobrachium nipponense, is an economically important shrimp in China. Male prawns have higher commercial value than females because the former grow faster and reach larger sizes. It is therefore important to reveal sex-differentiation and development mechanisms of the oriental river prawn to enable genetic improvement.

Results

We sequenced 293.3 Gb of raw Illumina short reads and 405.7 Gb of Pacific Biosciences long reads. The final whole-genome assembly of the Oriental river prawn was ∼4.5 Gb in size, with predictions of 44,086 protein-coding genes. A total of 49 chromosomes were determined, with an anchor ratio of 94.7% and a scaffold N50 of 86.8 Mb. A whole-genome duplication event was deduced to have happened 109.8 million years ago. By integration of genome and transcriptome data, 21 genes were predicted as sex-related candidate genes.

Conclusion

The first high-quality chromosome-level genome assembly of the oriental river prawn was obtained. These genomic data, along with transcriptome sequences, are essential for understanding sex-differentiation and development mechanisms in the oriental river prawn, as well as providing genetic resources for in-depth studies on developmental and evolutionary biology in arthropods.

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Most cited references 60

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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au

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MUSCLE: multiple sequence alignment with high accuracy and high throughput.

R. C. Edgar (2004)

We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

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HISAT: a fast spliced aligner with low memory requirements.

Daehwan Kim, Ben Langmead, Steven L Salzberg (2018)

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

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Author and article information

Contributors

Shubo Jin:

ORCID: https://orcid.org/0000-0002-6550-9268

Chao Bian:

ORCID: https://orcid.org/0000-0001-9904-721X

Sufei Jiang:

ORCID: https://orcid.org/0000-0002-4745-8639

Kai Han

Yiwei Xiong

Wenyi Zhang

Chengcheng Shi

Hui Qiao

Zijian Gao

Ruihan Li

Yu Huang

Yongsheng Gong

Xinxin You

Guangyi Fan

Qiong Shi:

ORCID: https://orcid.org/0000-0002-6358-976X

Hongtuo Fu:

ORCID: https://orcid.org/0000-0003-2974-9464

Journal

Journal ID (nlm-ta): Gigascience

Journal ID (iso-abbrev): Gigascience

Journal ID (publisher-id): gigascience

Title: GigaScience

Publisher: Oxford University Press

ISSN (Electronic): 2047-217X

Publication date (Electronic): 18 January 2021

Publication date Collection: January 2021

Publication date PMC-release: 18 January 2021

Volume: 10

Issue: 1

Electronic Location Identifier: giaa160