De novo assembly and annotation of Hellula undalis (Fabr.) (Lepidoptera: Pyralidae) transcriptome

doi:10.21203/rs.3.rs-3136152/v1

Download PDF

Research Article

De novo assembly and annotation of Hellula undalis (Fabr.) (Lepidoptera: Pyralidae) transcriptome

https://doi.org/10.21203/rs.3.rs-3136152/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

The cabbage webworm, Hellula undalis (Fabricius) (Lepidoptera: Pyralidae), is a significant pest of brassicas and other cruciferous plants in warm regions worldwide. Transcriptome analysis is valuable for investigation of molecular mechanisms underlying the insect development and reproduction. De novo assembly is particularly useful for acquiring complete transcriptome information of insect species when there is no reference genome available. In case of Hellula undalis, only 17 nucleotide records are currently available throughout NCBI nucleotide database. Genes associated with metabolic processes, general development, reproduction, defense and functional genomics were not previously predicted in the Hellula undalis at the genomic level.

Methods & Results

To address this issue, we constructed Hellula undalis transcriptome using Illumina NovaSeq6000 technology. Approximately 48 million 125 bp paired-end reads were obtained from sequencing. A total of 30,451 contigs were generated by de novo assembly of sample and were compared with the sequences in the NCBI non-redundant protein database (Nr). In total, 71% of contigs were matched to known proteins in public databases including Nr, Gene Ontology (GO), and Cluster Orthologous Gene Database (COG), and then, contigs were mapped to 123 via functional annotation against the Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG). In addition, we compared the ortholog gene family of the Hullula undalis, transcriptome to Spodoptera frugiperda, spodotera litura and spodoptera littoralis and found that 2,749 orthologous gene families are specific to Hullula undalis.

Conclusions

This study is the first transcriptome data for Hullula undalis. Additionally, it serves as a valuable resource for identifying target genes and developing effective and environmentally friendly strategies for pest control.

Hellula undalis

Transcriptome

Annotation

Immune-related genes

Orthologous gene

The cabbage webworm, Hellula undalis (Fabricius) (Lepidoptera: Pyralidae), is a significant pest of brassicas and other cruciferous plants in warm regions worldwide [1]. It originated in Europe and has caused severe outbreaks in various Asian countries, such as India, Malaysia and Vietnam [2, 3]. The larvae of H. undalis initially bore into the stems of growing shoots, while mature larvae mine the leaves and leaf stems. They create a silk web around their feeding area, which becomes filled with their waste material. Even a single larva's feeding damage on a cabbage plant during the pre-heading stage can lead to plant death or the production of multiple non-marketable heads [4].

RNA-seq revolutionizes transcriptome analysis by providing significant advantages over traditional methods. It facilitates genomic research in non-model species, offers high-resolution sequence variation detection, quantifies gene expression with exceptional sensitivity, elucidates gene architecture, and enables the large-scale development of molecular markers. These capabilities make RNA-seq a powerful tool for diverse biological investigations and contribute to our understanding of gene expression, regulation, and genetic diversity [5, 6]. In the case of Hellula undalis, only 17 nucleotide records are currently available through the NCBI nucleotide database.

This study focuses on the sequencing and assembly of Hellula undalis transcriptome using Illumina NovaSeq6000 technology. Genes associated with metabolic processes, general development, reproduction were identified and functionally annotated. The transcriptome analysis is valuable for investigating the molecular mechanisms underlying the insect's development and reproduction. Additionally, it serves as a valuable resource for identifying target genes and developing effective and environmentally friendly strategies for pest control.

Insect collection, cDNA library preparation and sequencing

The cabbage plants infected with cabbage webworm (Hellula undalis) were collected from the Horticulture Experimental Field of Sardar Vallabhbhai Patel University of Agriculture and Technology in Meerut, Uttar Pradesh, India. Approximately twenty larvae at different instar were collected and preserved in RNA Later® at -80°C until further use.

To extract total RNA from Hellula undalis, the Quick-RNA Tissue/Insect Kit from Zymo Research was employed following the instructions provided by the manufacturer. The RNA Integrity Number (RIN) was determined using the RNA Nano 6000 Assay Kit from Agilent Technologies and the Agilent 2100 Bioanalyzer. For library preparation, the TruSeq stranded mRNA Sample Preparation Kit from Illumina was utilized. This involved purifying and fragmenting mRNA molecules from 1µg of total RNA using magnetic beads with oligo (dT). The fragmented mRNAs were then converted into single-stranded cDNAs using random hexamer priming, followed by second-strand synthesis to generate double-stranded cDNA. The cDNA libraries were prepared by performing end repair, A-tailing, and adapter ligation, followed by PCR amplification. The quality and quantity of the cDNA libraries were evaluated using the Agilent 2100 Bioanalyzer and the KAPA library quantification kit from Kapa Biosystems, respectively. Finally, the sequencing was conducted as paired-end (2 × 150 bp) using the Illumina NovaSeq6000 platform at Centyle Biotech Pvt., Ltd., New Delhi, India.

Data processing and de novo transcripts assembly

The sequencing data underwent quality assessment and data preprocessing using CLC Workbench 20.0.4 software. Reads with low quality, indicated by a Phred score of 30 or lower, and reads containing ambiguous bases "N" were eliminated. Adaptor sequences with low-quality bases at the 5' and 3' ends were trimmed to enhance data quality [7]. Subsequently, the filtered and clean data were subjected to de novo assembly using CLC Workbench 20.0.4 software, followed by quality control (QC) to ensure the integrity and reliability of the results.

Functional annotation and pathway assignments

The contigs assembled from Hellula undalis were used as queries in the Blastx algorithm, employing OmicsBox 3.0 software available at https://www.biobam.com/omicsbox. These queries were matched against the nonredundant (NR) databases at NCBI. A blast hit with an e-value of ≤ 1.0E − 3 was considered significant, warranting further functional annotation of the contigs. BLASTX alignments were performed between the unigenes and protein databases such as NR, Kyoto Encyclopedia Gene and Genome Database (KEGG), and Cluster Orthologous Gene Database (COG). To characterize the protein coding sequences, an InterPro domain search was conducted directly on the FASTA input file of the contigs were assigned to the identified domains. The InterPro Scan tool available via Omicsbox 3.0, which encompasses nine databases (BlastProDom, FPrintScan, HMMPIR, HMMPfam, HMMSmart HMMTigr, Profile Scan, ScanRegExp, and SuperFamily), was employed. Functional information for each contig was retrieved from the Gene Ontology (GO) database, which contains annotations for gene products from various species. Additionally, the GO database includes an evidence code qualifier that indicates the quality of the functional assignment. Omicsbox 3.0 software was used for annotation with default parameters, and Gene Ontology mapping was performed, assigning a GO annotation score to each potential GO term. The Cluster of Orthologous Groups database (COG) annotation was conducted by employing the BLASTX algorithm against the COG. Functional annotation using Gene Ontology terms was performed using the Omicsbox 3.0 program available at https://www.biobam.com/omicsbox. Lastly, pathway annotation from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was performed by comparing sequences against the KEGG database using BLASTX, with an e-value threshold of 1.0E-3 [8].

Comparative analysis of multispecies orthologous gene families and detection of SSR markers

Multispecies genome comparisons and visualization of orthologous clusters between Hullula undalis, Spodoptera frugiperda, spodotera litura and spodoptera littoralis were performed using the online web server OrthoVenn2. (https://orthovenn2.bioinfotoolkits.net/) [9]. To identify simple sequence repeat (SSR) markers, the researchers employed the MISA (microsatellites identification tool) Perl script [10]. This script was utilized to detect both perfect and compound repeats. The search focused on SSRs with motif sizes ranging from di to hexanucleotides. The parameters were set to identify perfect di, tri, tetra, penta, and hexanucleotide motifs with a minimum number of 9, 6, 5, 4, and 3 repeats, respectively. Compound repeats were identified when microsatellites were located within 10 nucleotides of each other.

High-throughput sequencing and assembly

The Hullula undalis cDNA was sequenced using the Illumina NovaSeq6000, resulting in a total of 48 million raw reads with a length of 2 x 150 base pairs. After filtering out low-quality readings, those contaminated with adapters, and those containing over 2% unknown bases (N), approximately 97% of the raw data remained. This filtering step was important to prevent false results during sequence assembly. The GC content of the raw reads was found to be 51.36% on average. The raw sequence data has been deposited in the NCBI Sequence Read Archive under the accession number SRR24041435. The processed Hullula undalis reads were then used for de novo assembly using the CLC Genomics Workbench. The resulting contig sequences were divided into two groups: scaffolded regions and regions without scaffolding. The scaffolded regions, including the contigs, were selected for further annotation and analysis related to Hullula undalis transcripts. A total of 30,451 contigs were generated, and these contigs were imported to the OmicsBox 3.0 suite for mapping and annotation.

Functional annotation of Hullula undalis transcriptome

The contigs were annotated by comparing them to sequences in the NCBI nonredundant protein (nr) database using BLASTX with a cutoff e value of 10^− 3, using the OmicsBox 3.0 suite. Out of a total of 30,451 Hullula undalis contigs, 21,806 contigs showed significant similarity to sequences in the NR db v5. Figure 1 represents the species that exhibited considerable similarity with the contig sequences of insect against NR db v5. The dominant blast hits in the NR db were observed for the groups of Spodotera litura, Spodotera littoralis, Spodoptera frugiperda, and Spodoptera exigua.

Mapping and annotation process, the most recent version of the Gene Ontology (GO) database was used to search for GO terms associated with assembled Hullula undalis contig. These GO terms are accompanied by evidence code qualifiers, which indicate the reliability of the functional assignment for each contig. The distribution of evidence codes for Hullula undalis contigs and their blast hits is visually summarized. The mapping and annotation results for the putative transcripts of Hullula undalis are presented in three categories: biological process, cellular component, and molecular function. Among these categories, the contigs associated with biological processes (30.58%) were the most prevalent, followed by contigs associated with molecular functions (35.15%) and cellular components (24.93%) (Fig. 2). Consistently, when comparing the gene numbers in the cellular component and molecular function ontologies, the categories associated with general cellular structures and ion binding processes exhibited the highest contig counts. These results suggest that most annotated contigs are belongs to metabolic processes. Furthermore, when examining the distribution of enzyme codes among different enzyme classes for the contig sequences, it was observed that the highest number of contigs encoded for hydrolases (Fig. 3).

The analysis of the Cluster of Orthologous Groups (COG) database categorized 9,646 contigs (31.67%) into 21 functional classes of COGs shown in Fig. 4. The largest proportion of unigenes fell into the "Signal transduction" category (11.47%), followed by "Post-translational modification, protein turnover, chaperone functions" (10.28%), "Transcription" (5.90%), "Carbohydrate metabolism and transport" (4.90%), "RNA processing and modification" (4.75%), "Lipid metabolism" (4.48%), "Intracellular trafficking and secretion" (4.48%) and "Translation" (4.12%) classes. These results suggest that most contigs are involves in "Signal transduction" category.

To examine the contigs associated with metabolic processes and related cellular processes, the putative transcripts were compared against the KEGG database. This analysis led to the assignment of 123 KEGG pathways to the transcripts from Hullula undalis. Further, pathways are categorized in five categories: metabolism, genetic information processing, environmental information processing, cellular processes and organismal system. Among these categories, the contigs associated with metabolism were most prevalent, followed by environmental information processing, genetic information processing, organismal systems and cellular processes (Fig. 5). Consistently, when comparing the gene numbers in the metabolism, genetic information processing and environmental information processing categories associated with global and overview maps, transcription and signal transduction exhibited the highest contig counts, respectively. These results suggest that most annotated contigs are belongs to signal transduction.

Comparative analysis of orthologous gene family

The transcriptome of Hullula undalis was compared to that of Spodoptera frugiperda, spodotera litura and spodoptera littoralis to identify orthologous groups. Orthology analysis was conducted on 7,465 unigenes from Hullula undalis, 12,796 from spodoptera frugiperda, 12,773 from spodotera litura and 11,364 from spodoptera littoralis using orthoVenn (Fig. 6). A total of 5,948 orthologous gene families are common in all selected species and out of these genes 391, 293, 593 and 167 found unique orthologous gene families. These results suggest that 391 unique orthologous gene families found in Hullula undalis transcriptome.

Molecular markers identification based on the de novo assembled transcriptome

In order to create a genomic resource to enhance the genetic enhancement of Hullula undalis, identified simple sequence repeat markers in the assembled sequences of Hullula undalis. A total of 1,913 potential SSRs was discovered in contigs. Among these, 129 SSRs were involved in forming compound structures, and 203 contigs contained more than one SSR. In total, 31 distinct sequence motifs were identified. Among these motifs, the mononucleotide repeats A and T were particularly abundant i.e., 619. In the case of di repeats, AT and TA repeats occurred most frequently i.e., 80. Additionally, tri repeats AAT and ATT were found 76, while tetra repeats ACAT/ATGT were found 10 times (Fig. 7).

The advent of next-generation sequencing has made it possible to sequence the genomes and transcriptomes of various insects. However, one of the main challenges in transcriptome analysis is the biological interpretation of these sequences [11]. For several nonmodel species without available genomic reference information, transcriptome sequencing is an effective and alternative method to gain insight into the information content of a genome. Till date, none of studies have published Hullula undalis transcriptomes by next-generation sequencing. Transcriptome assembly for various organisms, including Anthonomus grandis [12], Plutella xyllostella [13], Rhopalosiphum padi [14], Ipomoea batatas [15], Eucalyptus grandis [16], Acropora millepora [17], Bemisia tabaci [18], and Dialeurodes citri [19] has already been reported.

In this study, the cDNA of Hullula undalis was sequenced using the Illumina NovaSeq6000, resulting in a total of 48 million raw reads with a length of 2 x 150 base pairs. After filtering out low-quality and contaminated reads, as well as those containing over 2% unknown bases, approximately 97% of the raw data remained. This filtering step was crucial to ensure the accuracy of the subsequent sequence assembly. The average GC content of the raw reads was found to be 51.36%. The processed reads of Hullula undalis were then used for de novo assembly using the CLC Genomics Workbench. A total of 30,451 contigs were generated. Out of the 30,451 Hullula undalis contigs, 21,806 contigs showed significant similarity to sequences in the NR db v5. The top blast hits in the NR db with Spodotera litura

The distribution of transcripts annotated by Gene Ontology is similar to the transcriptome studies carried out in other insects [6] where cell, cell part, cellular process, and metabolic process were among the predominant GO classes. In our study, The GO mapping and annotation results contigs associated with molecular functions were the most prevalent, followed by contigs associated with biological processes and cellular components. The COG database comprises proteins obtained from the entire genetic material of bacteria, archaea, and eukaryotes [20]. COG is utilized for categorizing organisms based on their evolutionary relationships, as it assumes that each protein in the database can be traced back to a common ancestor through vertical evolution, known as the orthology concept [21]. In this study, COG database categorized 9,646 contigs (31.67%) into 21 functional classes of COGs. The largest proportion of unigenes fell into the "Signal transduction" category, followed by "post-translational modification, protein turnover, chaperone functions", "Transcription", "Carbohydrate metabolism and transport", "RNA processing and modification", "Lipid metabolism", "Intracellular trafficking and secretion" and "Translation" classes. 123 pathways from transcriptomic data associated with metabolism, genetic information processing, environmental information processing, cellular processes and organismal system. Among these categories, the contigs associated with metabolism were most prevalent, followed by environmental information processing, genetic information processing, organismal systems and cellular processes.

The assembled transcripts were analyzed using the KEGG pathway database, which is a collection of graphical representations depicting metabolic and regulatory pathways. These pathways include interactions between enzymes, proteins, and genes, enabling the understanding of complex functions through the examination of interconnected molecular networks at a systemic level [22]. This approach facilitates the interpretation of broader biological processes. In our study, the contigs associated with metabolism were most prevalent, followed by environmental information processing, genetic information processing, organismal systems and cellular processes (Fig. 5). Consistently, when comparing the gene numbers in the metabolism, genetic information processing and environmental information processing categories associated with global and overview maps, transcription and signal transduction.

There are several conventional approaches used to develop microsatellite loci, including the hybrid capture method [23], selection of loci from existing genetic/genomic data [24], and the transfer of loci from closely related species [25]. However, in comparison to these traditional methods, de novo transcriptome sequencing technology offers a faster, more cost-effective, and reliable approach for directly developing microsatellite markers from transcriptome sequences. This is particularly advantageous for non-model species [8, 26–30]. A total of 1,913 potential SSRs were detected in the contigs. Among these SSRs, 129 were classified as compound structures, indicating more complex repeats, and 203 contigs contained more than one SSR. The analysis revealed a total of 31 distinct sequence motifs within the identified SSRs. Notably, the mononucleotide repeats A and T were the most abundant, occurring 619 times. Among the di-nucleotide repeats, the AT and TA repeats were the most frequently observed, appearing 80 times. Furthermore, tri-nucleotide repeats AAT and ATT were found 76 times, while the tetra-nucleotide repeats ACAT/ATGT were identified 10 times (Fig. 7). These findings provide valuable insights into the repetitive DNA sequences present in Hullula undalis, which can be utilized as genetic markers for further genetic enhancement studies.

This study utilized high-throughput sequencing and assembly to explore the transcriptome of Hullula undalis. The results yielded valuable insights into its genetic makeup and functionality. Various databases were employed for annotation, including NR, GO, COG, and KEGG. The analysis revealed significant contigs associated with biological processes, molecular functions, and cellular components. Prominent functional categories included metabolic processes, signal transduction, and post-translational modification. Comparisons with other insect species unveiled shared and unique genetic elements, shedding light on evolutionary relationships and functional divergence. Categorization into COGs provided insights into gene roles, particularly in signal transduction, transcription, and metabolism. KEGG pathway assignment enriched understanding of biological pathways, particularly those related to metabolism. This study enhances knowledge of the Hullula undalis transcriptome, offering a foundation for future research on its biology, genetics, and functionality. It paves the way for investigating molecular mechanisms underlying important processes and developing pest control strategies.

Acknowledgements

The authors acknowledge the Vice Chancellor, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut-250110, Uttar Pradesh, CoE in Agri Biotech., Council of Science & Technology, Uttar Pradesh and Bioinformatics facility, Department of Biotechnology, India for providing the facilities to carry out this research work.

Funding: No funding source available.

Data Availability Statement: The SRA of Hullula undalis has been deposited in NCBI under accession number SRR24455998.

Author Contributions: Conceptualization, P.K., J.S. and R.S.; methodology, M.R.P.; software, M.R.P.; formal analysis, M.R.P., R.S. and R.P.S.; investigation, M.R.P; resources, P.K. and R.S.; data curation, J.S.; writing—original draft preparation, M.R.P.; writing—review and editing, M.K.B., J.S., N.K., L.K.G., S.S.G., R.D. and S.P.; supervision, P.K. and J.S.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving animal participants

No animal experimental procedures were used in this study.

Informed consent
Informed consent was obtained from all individual participants included in the study.

Labou B, Brévault T, Sylla S, Diatte M, Bordat D, Diarra K (2017) Spatial and Temporal Incidence of Insect Pests in Farmers’ Cabbage Fields in Senegal. Int J Trop Insect Sci 37(4):225–233. 10.1017/S1742758417000200
Kunjwal N, Srivastava RM (2018) Insect Pests of Vegetables. Pests Manag 163–221
Tran TT, Le VV, Nguyen LH (2018) Study on Damage Situation of the Cabbage Webworm, Hellula Undalis Fabricius (Lepidoptera: Crambidae) on Green Mustards at the Mekong Delta. Can Tho Univ J Sci 54:115–124
Sivapragasan A (2005) Development of Cabbage Webworm, Hellula undalis (Fabr.) on Head Cabbage, Brassica oleracea var. capitata. J Trop Agric Food Sci 33:321–331
Wang Z, Gerstein M, Snyder M, RNA-Seq: (2009) A Revolutionary Tool for Transcriptomics. Nat Rev Genet 10(1):57–63. 10.1038/nrg2484
Chang JC, Ramasamy S (2018) Transcriptome Analysis in the Beet Webworm, Spoladea Recurvalis (Lepidoptera: Crambidae). Insect Sci 25(1):33–44. 10.1111/1744-7917.12375
Prajapati MR, Manav A, Singh J, Kumar P, Kumar A, Kumar R, Prakash S, Baranwal VK (2022) Identification and Characterization of a Garlic Virus E Genome in Garlic (Allium sativum L.) Using High-Throughput Sequencing from India. Plants (Basel) 11(2):224. 10.3390/plants11020224
Prajapati MR, Singh J, Kumar P, Dixit R (2023) De Novo Transcriptome Analysis and Identification of Defensive Genes in Garlic (Allium sativum L.) Using High-Throughput Sequencing. J Genet Eng Biotechnol 21(1):56. 10.1186/s43141-023-00499-5
Xu L, Dong Z, Fang L, Luo Y, Wei Z, Guo H, Zhang G, Gu YQ, Coleman-Derr D, Xia Q, Wang Y (2019) OrthoVenn2: A Web Server for Whole-Genome Comparison and Annotation of Orthologous Clusters Across Multiple Species. Nucleic Acids Res 47(W1):W52–W58. 10.1093/nar/gkz333
Beier S, Thiel T, Münch T, Scholz U, Mascher M (2017) MISA-Web: A Web Server for Microsatellite Prediction. Bioinformatics 33(16):2583–2585. 10.1093/bioinformatics/btx198
Crawford JE, Guelbeogo WM, Sanou A, Traoré A, Vernick KD, Sagnon NF, Lazzaro BP (2010) De Novo Transcriptome Sequencing in Anopheles Funestus Using Illumina RNA-Seq Technology. PLoS ONE 5(12):e14202. 10.1371/journal.pone.0014202
Firmino AAP, Fonseca FCDA, de Macedo LLP, Coelho RR, de Antonino S Jr, Togawa JD, Silva-Junior RC, Pappas OB, da Silva GJ, Engler MC (2013) Grossi-de-Sa, M. F. Transcriptome Analysis in Cotton Boll Weevil (Anthonomus grandis) and RNA interference in Insect Pests. PLoS ONE 8(12):e85079. 10.1371/journal.pone.0085079
Yang S, Cao D, Wang G, Liu Y (2017) Identification of Genes Involved in Chemoreception in Plutella Xyllostella by Antennal Transcriptome Analysis. Sci Rep 7(1):11941. 10.1038/s41598-017-11646-7
Duan X, Wang K, Su S, Tian R, Li Y, Chen M (2017) De Novo Transcriptome Analysis and Microsatellite Marker Development for Population Genetic Study of a Serious Insect Pest, Rhopalosiphum Padi (L.)(Hemiptera: Aphididae). PLoS ONE 12(2):e0172513. 10.1371/journal.pone.0172513
Wang Z, Fang B, Chen J, Zhang X, Luo Z, Huang L, Li Y (2010) De Novo Assembly and Characterization of Root Transcriptome Using Illumina Paired-End Sequencing and Development of cSSR Markers in Sweet Potato (Ipomoea batatas). B M C Genomics 11(Suppl 3):1–14
Novaes E, Drost DR, Farmerie WG, Pappas GJ, Grattapaglia D, Sederoff RR, Kirst M (2008) High-Throughput Gene and SNP Discovery in Eucalyptus grandis, an Uncharacterized Genome. B M C Genomics 9(1):312. 10.1186/1471-2164-9-312
Meyer E, Aglyamova GV, Wang S, Buchanan-Carter J, Abrego D, Colbourne JK, Willis BL, Matz MV (2009) Sequencing and de Novo Analysis of a Coral Larval Transcriptome Using 454 GSFlx. B M C Genomics 10(1):219. 10.1186/1471-2164-10-219
Wang XW, Luan JB, Li JM, Bao YY, Zhang CX, Liu SS (2010) De Novo Characterization of a Whitefly Transcriptome and Analysis of Its Gene Expression During Development. B M C Genomics 11:400. 10.1186/1471-2164-11-400
Chen EH, Wei DD, Shen GM, Yuan GR, Bai PP, Wang JJ (2014) De Novo Characterization of the Dialeurodes citri Transcriptome: Mining Genes Involved in Stress Resistance and Simple Sequence Repeats (SSRs) Discovery. Insect Mol Biol 23(1):52–66. 10.1111/imb.12060
Tatusov RL, Koonin EV, Lipman DJ (1997) A Genomic Perspective on Protein Families. Science 278(5338):631–637. 10.1126/science.278.5338.631
Fitch WM (1970) Distinguishing Homologous from Analogous Proteins. Syst Zool 19(2):99–113. 10.2307/2412448
Kanehisa M, Goto SKEGG (2000) Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28(1):27–30. 10.1093/nar/28.1.27
Ostrander EA, Jong PM, Rine J, Duyk G (1992) Construction of Small-Insert Genomic DNA Libraries Highly Enriched for Microsatellite Repeat Sequences. Proc. Natl. Acad. Sci. U. S. A. 89 (8), 3419–3423. DOI: 10.1073/pnas.89.8.3419
Blair MW, Pedraza F, Buendia HF, Gaitán-Solís E, Beebe SE, Gepts P, Tohme J (2003) Development of a Genome-Wide Anchored Microsatellite Map for Common Bean (Phaseolus vulgaris L). Theor Appl Genet 107(8):1362–1374. 10.1007/s00122-003-1398-6
Karaiskou N, Buggiotti L, Leder E, Primmer CR (2008) High Degree of Transferability of 86 Newly Developed Zebra Finch EST-Linked Microsatellite Markers in 8 Bird Species. J Hered 99(6):688–693. 10.1093/jhered/esn052
Artigaud S, Thorne MA, Richard J, Lavaud R, Jean F, Flye-Sainte-Marie J, Peck LS, Pichereau V, Clark MS (2014) Deep Sequencing of the Mantle Transcriptome of the Great Scallop Pecten maximus. Mar Genomics 15:3–4. 10.1016/j.margen.2014.03.006
Uliano-Silva M, Americo JA, Brindeiro R, Dondero F, Prosdocimi F, Rebelo M (2014) Gene Discovery Through Transcriptome Sequencing for the Invasive Mussel Limnoperna fortunei. PLoS ONE 9(7):e102973. 10.1371/journal.pone.0102973
Cerqueira-Silva CB, Santos ES, Vieira JG, Mori GM, Jesus ON, Corrêa RX, Souza AP (2014) New Microsatellite Markers for Wild and Commercial Species of Passiflora (Passifloraceae) and Cross‐Amplification. Appl Plant Sci 2(2):1300061. 10.3732/apps.1300061
Huang D, Zhang Y, Jin M, Li H, Song Z, Wang Y, Chen J (2014) Characterization and High Cross-Species Transferability of Microsatellite Markers from the Floral Transcriptome of Aspidistra saxicola (Asparagaceae). Mol Ecol Resour 14(3):569–577. 10.1111/1755-0998.12197
Li J, Zhu L, Hull JJ, Liang S, Daniell H, Jin S, Zhang X (2016) Transcriptome Analysis Reveals a Comprehensive Insect Resistance Response Mechanism in Cotton to Infestation by the Phloem Feeding Insect Bemisia tabaci (Whitefly). Plant Biotechnol J 14(10):1956–1975. 10.1111/pbi.12554

Download PDF

Editorial decision: Major Revisions Needed
31 Jul, 2023
Reviewers agreed at journal
12 Jul, 2023
Reviewers invited by journal
12 Jul, 2023
Editor assigned by journal
10 Jul, 2023
First submitted to journal
08 Jul, 2023

You are reading this latest preprint version

De novo assembly and annotation of Hellula undalis (Fabr.) (Lepidoptera: Pyralidae) transcriptome

Status:

Version 1

Abstract

Background

Methods & Results

Conclusions

Figures

Introduction

Materials and methods

Insect collection, cDNA library preparation and sequencing

Data processing and de novo transcripts assembly

Functional annotation and pathway assignments

Comparative analysis of multispecies orthologous gene families and detection of SSR markers

Results

High-throughput sequencing and assembly

Functional annotation of Hullula undalis transcriptome

Comparative analysis of orthologous gene family

Molecular markers identification based on the de novo assembled transcriptome

Discussion

Conclusion

Declarations

References

Status:

Version 1