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The Brain Transcriptome of the Wolf Spider, Schizocosa Ocreata Daniel Stribling1,5† , Peter L

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The Brain Transcriptome of the Wolf Spider, Schizocosa Ocreata Daniel Stribling1,5† , Peter L Stribling et al. BMC Res Notes (2021) 14:236 https://doi.org/10.1186/s13104-021-05648-y BMC Research Notes DATA NOTE Open Access The brain transcriptome of the wolf spider, Schizocosa ocreata Daniel Stribling1,5† , Peter L. Chang2† , Justin E. Dalton1, Christopher A. Conow2, Malcolm Rosenthal3 , Eileen Hebets3, Rita M. Graze4 and Michelle N. Arbeitman1* Abstract Objectives: Arachnids have fascinating and unique biology, particularly for questions on sex diferences and behav- ior, creating the potential for development of powerful emerging models in this group. Recent advances in genomic techniques have paved the way for a signifcant increase in the breadth of genomic studies in non-model organisms. One growing area of research is comparative transcriptomics. When phylogenetic relationships to model organisms are known, comparative genomic studies provide context for analysis of homologous genes and pathways. The goal of this study was to lay the groundwork for comparative transcriptomics of sex diferences in the brain of wolf spiders, a non-model organism of the pyhlum Euarthropoda, by generating transcriptomes and analyzing gene expression. Data description: To examine sex-diferential gene expression, short read transcript sequencing and de novo tran- scriptome assembly were performed. Messenger RNA was isolated from brain tissue of male and female subadult and mature wolf spiders (Schizocosa ocreata). The raw data consist of sequences for the two diferent life stages in each sex. Computational analyses on these data include de novo transcriptome assembly and diferential expression analy- ses. Sample-specifc and combined transcriptomes, gene annotations, and diferential expression results are described in this data note and are available from publicly-available databases. Keywords: Schizocosa ocreata, Wolf spiders, Gene expression, Transcriptomes, Sexual dimorphism, Sex-diferential gene expression, Sex biased expression, Brain and central nervous system, De novo transcriptome assembly Objectives a broader set of inferences that goes beyond what has Arachnids, including spiders, have diverse and unique been learned from model organisms. For example, copu- reproductive behavior, including sexual cannibalism and latory wounding, male leg ornamentation, and elaborate female aggression, copulatory wounding, and elaborate courtship are well-studied in Drosophila, and arachnid courtship with sexual dimorphism in morphology and genomic comparative studies can reveal parallel or diver- coloration [1–5]. Te development of genomic resources gent mechanisms [6–15]. in arachnids will allow for key comparisons not only in Several arachnid genomes and transcriptomes, includ- genome biology, but also in evolution and in the biol- ing those of spiders, mites and scorpions, have recently ogy of sex. Comparative studies between arachnids and become available [16–18]. Given that spiders have unique model organisms in other arthropod classes can provide sex-specifc behaviors and that progress is ongoing in developing arachnid genomics, our goal was to generate transcriptomes and gene expression data using mRNA *Correspondence: [email protected] from brains of males and females of the wolf spider, † Daniel Stribling and Peter L. Chang contributed equally to this work Schizocosa ocreata. Studies of wolf spider sexual dimor- 1 Biomedical Sciences Department, College of Medicine, Florida State University, Tallahassee, FL 32306, USA phism in morphology and behavior have revealed intrigu- Full list of author information is available at the end of the article ing parallels to textbook examples of sex dimorphism © The Author(s) 2021. 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:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Stribling et al. BMC Res Notes (2021) 14:236 Page 2 of 5 Table 1 Overview of data fles/data sets Label Name of data fle/data set File types (fle extension) Data repository and identifer (DOI or accession number) Data set 1 Raw illumina data FASTQ fles (.fq) NCBI SRA: https:// ident ifers. org/ ncbi/ insdc. sra: SRP30 2932 [45] Data fle 1 Trimmed-read FastQC statistics PDF fle (.pdf) NCBI GEO: https:// ident ifers. org/ geo: GSE16 8766 [46] Data set 2 Schizocosa ocreata immature male 1, de novo tran- FASTA fles (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZN0 scriptome assembly 00000 00 [47] Data set 3 Schizocosa ocreata immature male 2, de novo tran- FASTA fles (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZS0 scriptome assembly 00000 00 [48] Data set 4 Schizocosa ocreata immature male 3, de novo tran- FASTA fles (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZT0 scriptome assembly 00000 00 [49] Data set 5 Schizocosa ocreata immature female 2, de novo FASTA fles (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZM0 transcriptome assembly 00000 00 [50] Data set 6 Schizocosa ocreata immature female 3, de novo FASTA fles (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZR0 transcriptome assembly 00000 00 [51] Data set 7 Schizocosa ocreata mature male 2, de novo transcrip- FASTA fles (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZP0 tome assembly 00000 00 [52] Data set 8 Schizocosa ocreata mature male 3, de novo transcrip- FASTA fles (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZW0 tome assembly 00000 00 [53] Data set 9 Schizocosa ocreata mature female 1, de novo tran- FASTA fles (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZO0 scriptome assembly 00000 00 [54] Data set 10 Schizocosa ocreata mature female 2, de novo tran- FASTA fles (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZU0 scriptome assembly 00000 00 [55] Data set 11 Schizocosa ocreata mature female 3, de novo tran- FASTA fles (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZV0 scriptome assembly 00000 00 [56] Data set 12 Schizocosa ocreata consensus coding sequences FASTA fle (.fa) NCBI TSA: https:// ident ifers. org/ ncbi/ insdc: GIZQ0 00000 00 [57] Data fle 2 Transcriptome assembly statistics PDF fle (.pdf) NCBI GEO https:// ident ifers. org/ geo: GSE16 8766 [46] Data fle 3 Gene annotations Tabular text fle (.txt) NCBI GEO https:// ident ifers. org/ geo: GSE16 8766 [46] Data fle 4 Gene expression values Tabular text fle (.txt) NCBI GEO https:// ident ifers. org/ geo: GSE16 8766 [46] Data fle 5 Diferential expression values MS excel fle (.xlsx) NCBI GEO https:// ident ifers. org/ geo: GSE16 8766 [46] Data fle 6 Transcriptome fow (TFLOW): de novo transcriptome Zip archive (.zip) Zenodo https:// doi. org/ 10. 5281/ zenodo. 38174 74 [58] analysis pipeline Data fle 7 Gene clustering analysis script Python script (.py) Zenodo https:// doi. org/ 10. 5281/ zenodo. 43307 38 [59] Data fle 8 Diferential expression analysis script R script (.R) Zenodo https:// doi. org/ 10. 5281/ zenodo. 43307 38 [59] in well-studied model organisms [19–25]. Te data pre- sequences; quality assessments are provided (Data fle 1; sented here are valuable in laying necessary groundwork Table 1; GEO GSE168766). for broad comparative functional genomics of sex difer- Transcriptome assembly was performed for each sam- ences in brain and behavior across arthropods. ple using Trinity followed by CAP3 [26, 27]. A consensus transcriptome was assembled combining all individual Data description assemblies using CAP3. Transcriptome quality was eval- mRNA was isolated from brain samples of immature uated on individual sample and consensus transcriptome (Imm; subadult) and mature (Mat; adult) male and assemblies based on the number of conserved protein female Schizocosa ocreata, collected in Lancaster County, coding genes identifed from the Core Eukaryotic Genes Nebraska (see detailed methods: NCBI Gene Expression Mapping Approach (CEGMA) and Benchmarking sets of Omnibus [GEO] Series accession number GSE168766). Universal Single-Copy Orthologs (BUSCO) Arthropod Illumina paired-end sequencing was performed with databases (Data fle 2; GEO GSE168766) [28–30]. Both libraries generated from mRNA derived from indi- CEGMA and BUSCO alignments used the Basic Local vidual brain samples, with three replicates for each sex/ Alignment Search (BLAST) utility with default threshold stage (Data set 1; NCBI SRA: SRP302932). Sequence E-value of 1e−20 [31]. In the consensus assembly, 99% reads were processed to remove index and low-quality of CEGMA and 95% of BUSCO genes were identifed, Stribling et al. BMC Res Notes (2021) 14:236 Page 3 of 5 demonstrating a high assembly quality. To facilitate this the completion of this study, the CEGMA annotation workfow, the Transcriptome-Flow (TFLOW) pipeline database has been discontinued. Te TFLOW software was developed (Python 2.7; Zenodo; Data fle 6). Each package was developed in the Python2.7 programming individual assembly was fltered to remove contaminant language which is no longer actively supported. Archival sequences and uploaded to the NCBI Transcriptome versions of Python2.7 may be utilized to execute TFLOW, Shotgun Assembly (TSA) database (Data sets 2–11; or conversion of this software to a currently-supported Table 1). version of Python can be performed using a python ver- Putative protein coding sequences from the consensus sion-update package.
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