DOI: 10.5061/dryad.34tmpg4qp
title: The genome assemble of Bemisia tabaci MED

description: Aim: The sweet potato whitefly, Bemisia tabaci MED is a globally invasive
 species that causes serious economic damage to agroecosystems. Despite the
 significant threat it poses to agricultural and economic crops worldwide,
 the global perspective of the invasion patterns and genetic mechanism
 contributing to the success of this notorious pest is still poorly
 understood. The objective of this research was to enhance genome and
 population genetic analyses to better understand the intricate invasion
 patterns of B. tabaci MED. Location: Samples were collected in native
 (Spain, Croatia, Bosnia and Herzegovina, Cyprus, and Israel) and invaded
 regions (China, South Korea and North America). Methods: We first
 assembled a chromosome-scale reference genome of B. tabaci MED, and then
 employed the restriction site‐associated 2b‐RAD method to genotype over
 20, 000 high‐quality single nucleotide polymorphisms from 29 geographical
 populations. Results: A reference genome of B. tabaci MED, with a size of
 637.47 Mb, was available. The majority of the assembled sequences (99%)
 were anchored onto ten linkage groups, with an N50 size of 58.76 Mb,
 representing a significant improvement over previous whitefly genome
 assemblies. We identified rapidly expanded gene families and positively
 selected genes, probably contributing to successful invasion and rapid
 adaptation to the new environment. Population genomics analysis showed
 that three highly differentiated genetic groups were formed, and complex
 and extensive gene flow occurred across the Mediterranean populations. The
 genetic admixture patterns in East Asia populations were distinct from
 those in North America, indicating that they had different source
 populations. Conclusions: The high-quality, chromosome-scale genome of B.
 tabaci MED offered opportunities for more comprehensive genome-wide
 studies, and provided a solid foundation for the complex introduction
 events and the differential invasiveness of B. tabaci MED worldwide.
description: Genome survey and
 sequencing To evaluate the genome size of <em>B.
 tabaci </em>MED, an Illumina high-throughput sequencing library with
 an insert size of 350 bp was constructed from genomic DNA and paired-end
 sequenced on the Illumina NovaSeq-6000 platform. We performed the k-mer analysis to determine the
 genome size using Jellyfish v2.1.3 software with the k-mer value of 17
 (Figure S1) (Marcais and Kingsford, 2011). Afterwards, the repeat content
 and heterozygosity rate were calculated with GenomeScope 2.0 software
 following the k-mer frequency distribution (Ranallo-Benavidez et al.,
 2020). A short paired-end libraries (350-bp insert
 size) were generated and sequenced on the Illumina NovaSeq-6000 platform
 for Illumina sequencing. A SMRT bell library (20-kb insert size) was
 constructed and run on the PacBio Sequel II platform for Pacific
 Biosciences (PacBio) sequencing. We filtered the Illumina raw sequencing
 data by trimming the adapters, over 10% of nucleotides (N) designated as
 unknown, and 50% of low quality bases (Q-value &lt;= 10), generating a
 total of 105.92Gb clean and high-quality reads. For the raw data from PacBio, short subreads
 (&lt;5 kb) were removed and only a single representative subread was
 kept for each PacBio read. Approximately 300 pairs of fresh<em> B.
 tabaci</em> MED adult samples were prepared for Hi-C library
 construction with a slight modification of the procedure described by
 Putnam et al., 2016. The procedure of cross-linking involved subjecting
 the cells to a 2% formaldehyde solution, which was terminated by the
 addition of 2.5M glycine. Afterwards, the cells were quickly frozen in
 liquid nitrogen and preserved at a temperature of -80℃. Then, the
 chromatin underwent digestion utilizing the Hind III restriction enzyme.
 The preparation of the Hi-C library involved incubating it with biotin,
 followed by the addition of T4 DBA ligase to facilitate DNA ligation.
 After adding the Proteinase K for reverse cross-linking, the ligated DNA
 was then cut into fragments that varied in size from 300 to 700
 bp. It was repaired by blunt-end ligation and
 A-tailing, after which it was purified using biotin-streptavidin
 pull-down. They were sequenced using the Illumina NovaSeq6000 platform
 after quality assessment. Genome assembly
 We constructed the draft
 genome by utilizing the raw reads produced by both the Illumina and PacBio
 sequencing platforms. PacBio long reads established the genome framework
 and Illumina short reads improved the genome assembly. The correction of
 PacBio long reads was carried out first using the CANU program v1.7,
 followed by assembling the corrected reads into contigs using SmartDeNovo
 (available at https://github.com/ruanjue/smartdenovo) (Koren et al., 2017). The PacBio reads were used to improve the primary contigs with Quiver software (SMRT Link v5.0.1) and the gaps were filled with PBJelly software (English et al., 2012). Finally, the Illumina short reads were used to correct the genome assembly errors with Pilon v1.22 software (Walker et al., 2014).  Th Hi-C reads were aligned to the <em>B. tabaci</em> MED draft genome and only the data that was uniquely mapped were used for assembly with the LACHESIS software (Langmead et al., 2009; Burton et al., 2013). After removing the duplicate data, the HiC-Pro (version 2.8.1) was applied to assess the quality (Servant et al., 2015). Afterwards, the corrected scaffolds were put together using LACHESIS and the interaction matrix of all chromosomes was generated along with heatmaps to provide a visual representation. The quality and completeness were assessed by conducting Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis using busco version 3.0 (Li, 2013; Simão et al., 2015) and Core Eukaryotic Genes Mapping Approach (CEGMA) analysis using cegma v2.5 (Parra et al., 2007). Genome annotation We first constructed a de novo library using RepeatModeler (http://www.repeatmasker.org/RepeatModeler/), LTR_Finder and RepeatScout (Price et al., 2005; Xu et al., 2007; Hoede et al., 2014). Then, the de novo libraries of repeat sequences merging with the Repbase database (Jurka et al., 2005) were used to search against <em>B. tabaci </em>MED genome for the discovery of repeat sequences using RepeatMasker (http://www.repeatmasker.org/) (Tarailo-Graovac & Chen, 2009). Tandem repeats in the genome were identified using Tandem Repeat Finder v4.07b. With these procedures, we successfully annotated the repeat sequences of the <em>B. tabaci</em> MED genome. We used multiple methods to identify the conventional small non-coding RNAs, such as small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), transfer RNAs (tRNAs), and ribosomal RNAs (rRNAs). In detail, rRNAs were located through a BLAST search against a database of invertebrate rRNA, with a threshold E-value of E-value of 1e-10.The tRNAscan-SE software was used for the identification of tRNAs (Lowe & Eddy, 1997). The identification of miRNAs, snRNAs, and snoRNAs was performed using INFERNAL (v1.1rc4) and searching against the Rfam database (Nawrocki & Eddy, 2013). We applied three different gene annotation methods including transcriptome-based annotation, homology-based annotation, and ab initio prediction for the gene annotation. Specifically, the homolog‐based approach employed in GeMoMa v1.3.1 software by referring the protein sequences of <em>Drosophila_melanogaster</em>, <em>Halyomorpha_halys</em>, <em>Nilaparvata_lugens</em> from GenBank and those of <em>Bemisia_tabaci</em> from the GigaScience database (Chen et al., 2016, 2019; Keilwagen et al., 2016; Xie et al., 2017). The functions of the genes were determined through alignment with several widely used databases, including eukaryotic orthologous groups of proteins (KOG), National Center for Biotechnology Information (NCBI), non-redundant protein database (NR), Trembl, Kyoto Encyclopedia of Genes and Genomes (KEGG), and swiss‐prot. The alignment was performed using blast v2.2.31 with a 1e‐5 threshold (Altschul et al., 1990). The Gene Ontology (GO) database was used for gene annotation via the blast2go software following the aligned results from the NR database (Conesa et al., 2005; Marchler-Bauer et al., 2010).