Clc bio

The predicted BAC genes were analyzed with respect to their expression levels using RNA-Seq data obtained from previous work [16 (link)] in which the transcripts were obtained through fungal growth on three carbon sources: lactose (LAC), cellulose (CEL), and delignified sugarcane bagasse (DSB). The reads from the RNA-Seq library were mapped against BAC genes using CLC Genomics Workbench (CLC bio—v4.0; Finlandsgade, Dk) with the following parameters: mapping settings (minimum length fraction = 0.7, minimum similarity fraction = 0.8, and maximum number of hits for a read = 1) and paired settings (minimum distance = 180 and maximum distance = 1000, including the broken pairs counting scheme).
The expression values were expressed in RPKM (reads per kilobase of exon model per million mapped reads). The predicted BAC genes were clustered with a K-means algorithm using CLC bio. The clustering was performed applying Euclidian distance as the distance metric in 10 partitions (k = 10) for the predicted genes for each BAC according to the cluster features of the log₂-transformed expression values. K-means clustering for CAZy genes was performed with the same features, except for the number of partitions (k₌ 6). A hierarchical clustering analysis was conducted with CLC-bio using the single linkage method and Euclidian distance.

Post-sequencing genomic analyses were conducted using CLCbio and CLC Genomics Workbench version 8.5.1 (CLC Bio, Qiagen). Briefly, adaptor contamination removal and quality trimming (Phred score ≥ 20) was conducted. Reads were demultiplexed based on specific SISPA barcodes using J. Craig Venter Institute (JCVI) custom software. Duplicate reads were omitted. Reads tagged with different barcodes but belonging to the same sample were combined for mapping. Reads were de novo assembled and contigs over 500nt in length were BLASTed to identify appropriate reference genome sequences available from public sequence databases. After selecting the most appropriate reference genome, reads were mapped and non-specific matches were ignored. Partial or complete FMDV genome sequences were obtained. When required, ORFs were finished using Sanger sequencing. For Sanger sequencing, RT-PCR products were generated using SuperScript^®III One-Step RT-PCR System with Platinum^® Taq High Fidelity (Life Technologies, Carlsbad, CA), column purified, and sequenced using the di-deoxy termination method (Big dye terminator; (Life Technologies, Carlsbad, CA). Chromatograms were analyzed using Sequencher^® v5.1 (GeneCodes) and a consensus sequence generated by NGS was updated to include the Sanger derived sequence.

At least 15 million, 50 bp, SE reads were generated from each sample. Further downstream analysis of the sequenced reads from each sample was performed as per our unique in-house pipeline. Briefly, quality control checks on raw sequence data from each sample will be performed using FastQC (Babraham Bioinformatics, London, UK). Raw reads were then imported on a commercial data analysis platform CLCbio (CLCbio, MA, USA). Adapter trimming was done to remove ligated adapter from 3′ end of the sequenced reads with only one mismatch allowed; poorly aligned 3′ ends were also trimmed. Sequences shorter than 15 nucleotides length were excluded from further analysis. Trimmed Reads were then used to extract and count the small RNA which were then annotated with microRNAs in miRBase release 18 database. Samples were grouped as per their types identifiers and quantification of miRNA abundance was done. Differential expression of miRNA was calculated on the basis of their fold change (default cut-off ≥±2.0) between mapped counts observed between individual groups.