# Transcriptome Landscape Variation in the Genus _Thymus_
## 2 Material and Methods
### Data Processing, Transcriptome Assembly and Annotation  
Prior to analysis of data, raw reads were processed to filter out low quality nucleotides (<Q30), contaminant sequence, adapters, and short reads (<50 bp). Pre-processing quality was performed by using the Trimmomatic v0.32 program [21] with ILLUMINACLIP:2:30:10, LEADING:5, TRAILING:5, SLIDINGWINDOW:4:30, and MINLEN:50 parameters. Then, all RNA-Seq data were deposited in the NCBI SRA database under the project PRJNA481444. Only clean reads were used in downstream analysis. De novo transcriptome assemblies were constructed and evaluated with the four following tools: (1) Trinity v2.4.0, using a k-mer size of 25 [22]. (2) Velvet v1.2.10 [23] followed by Oases v0.2.09 [24] with a series of k-mer sizes from 21 to 61 (step size 2). The parameters used with Velvet were \"-shortPaired and -read_trkg\" and Oases \"-min_trans_1 gth 200\" to set the minimum length for a transcript in the output file to 200 bp. (3) BinPacker v1.0 [25]. (4) Bridger [26]. Similar to Trinity, BinPacker and Bridger programs were used with a k-mer size of 25, because this k-mer size works well for most organisms. All de novo assemblies were performed with default settings. To evaluate the quality of transcriptome assemblies (assembly completeness), the number of full-length or nearly full-length transcripts was counted. In this case, a sequence homology search was run on all de novo assemblieswith BLASTX and the Swiss-Prot/UniProt database [27]. Then, the BLASTX hits were filtered from annotated transcriptomes with an e-value \(>1\times 10^{-20}\). Next, the length coverage percentage for the top matching database entries was examined using analyze_blastPlus_topHit_coverage.pl script from the Trinity package [22]. The detonate score was calculated by DETONATE's RSEM-EVAL [27] as an additional criterion to evaluate the quality of different assemblies. TransDecoder and Trinotate software suites were used for functional annotation of each assembly following the method outlined at [[http://trinotate.github.io/](http://trinotate.github.io/)]([http://trinotate.github.io/](http://trinotate.github.io/)). TransDecoder v2.0.1 (available at [[http://transdecoder.sourceforge.net/](http://transdecoder.sourceforge.net/)]([http://transdecoder.sourceforge.net/](http://transdecoder.sourceforge.net/))) was used to predict open reading frames (ORFs) of at least 100 amino acids in length. To further maximize sensitivity for capturing ORFs, all ORFs were scanned for homology to known proteins (including a BLASTP search against UniProtKB/Swiss-Prot, and a search of the peptides for protein domains using Pfam). After extraction of ORFs from the assembly, the Trinotate v2.0.2 (available at [[http://trinotate.github.io](http://trinotate.github.io)]([http://trinotate.github.io](http://trinotate.github.io))) was preferred for assigning functions, as the package uses multiple databases for annotation. The Trinotate annotation pipeline comprises several software packages such as BLASTX, BLASTP, Pfam search, SignalP, and RNAmmer. Initially, transcripts were searched against the Swiss-Prot protein database by running BLASTX, allowing one hit and with the output in tabular format. Then, the expected protein translations obtained using TransDecoder were searched against the Swiss-Prot/UniProt databases. Functional domains were identified using HMMER v.3.1b2 tools and the Pfam domain database [28; 29]. Potential signal peptides were predicted using SignalP v4.1 [30]. TMHMM v.2.0c was used for prediction of transmembrane helices in proteins [31] and RNAmmer v.1.2 was applied to predict ribosomal RNA [28]. All results were deposited into the Trinotate-provided SQLite database, and a spreadsheet summary report was generated. WEGO software [32] was used for Gene ontology (GO) functional classification using the Trinotate-integrated UniProtKB GO annotations.