title: RNA-Seq of Isochrysis galbana
design: 1 Sample collection and preparation1.1 RNA quantification and qualificationThe purity, concentration and integrity of RNA samples are tested using advanced molecular biology equipment to ensure the use of qualified samples for transcriptome sequencing.1.2 Library preparation for Transcriptome sequencingA total amount of 1 g RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNextUltra RNA Library Prep Kit for Illumina(NEB, USA) following manufacturers recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer5X. First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase. Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H . Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3 ends of DNA fragments, NEBNext Adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 240 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 l USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37C for 15 min followed by 5 min at 95C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.1.3 Clustering and sequencingThe clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturers instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq 2000 platform and paired-end reads were generated.2 Data analysis2.1 Quality controlRaw data (raw reads) of fastq format were firstly processed through in-house perl scripts. In this step, clean data(clean reads) were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. At the same time, Q20, Q30, GC-content and sequence duplication level of the clean data were calculated. All the downstream analyses were based on clean data with high quality.2.2 Transcriptome assemblyThe left files (read1 files) from all libraries/samples were pooled into one big left.fq file, and right files (read2 files) into one big right.fq file. Transcriptome assembly was accomplished based on the left.fq and right.fq using Trinity (Grabherr et al, 2011) with min_kmer_cov set to 2 by default and all other parameters set default.2.3 Gene functional annotationGene function was annotated based on the following databases: NR (NCBI non-redundant protein sequences)Pfam (Protein family)KOG/COG/eggNOG (Clusters of Orthologous Groups of proteins)Swiss-Prot (A manually annotated and reviewed protein sequence database)KEGG (Kyoto Encyclopedia of Genes and Genomes)GO (Gene Ontology).2.4 SNP callingPicard - tools v1.41 and samtools v0.1.18 were used to sort, remove duplicated reads and merge the bam alignment results of each sample. GATK2 software was used to perform SNP calling. Raw vcffiles were filtered with GATK standard filter method and other parameters ( clusterWindowSize: 35; MQ0 >= 4 and (MQ0/(1.0*DP)) > 0.1; QUAL < 10; QUAL < 30.0 or QD < 5.0 or HRun > 5), and only SNPs with distance > 5 were retained.2.5 SSR detection and primer designSSR of the transcriptome were identified using MISA (http://pgrc.ipk-gatersleben.de/misa/misa.html), and primer for each SSR was designed using Primer3 (http://primer3.sourceforge.net/releases.php).2.6 Quantification of gene expression levelsGene expression levels were estimated by RSEM (Li et al, 2011) for each sample:1. Clean data were mapped back onto the assembled transcriptome2. Readcount for each gene was obtained from the mapping results2.7 Differential expression analysisFor the samples with biological replicates:Differential expression analysis of two conditions/groups was performed using the DESeq R package (1.10.1). DESeq provide statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P values were adjusted using the Benjamini and Hochbergs approach for controlling the false discovery rate. Genes with an adjusted P-value <0.05 found by DESeq were assigned as differentially expressed.For the samples without biological replicates:Prior to differential gene expression analysis, for each sequenced library, the read counts were adjusted by EBSeq program package through empirical Bayesian approach . Differential expression analysis of two samples was performed using the EBSeq R package. Pvalue was adjusted using q value (Storey et al, 2003). qvalue<0.005 & |log2 foldchange |>1 was set as the threshold for significantly differential expression.2.8 GO enrichment analysisGene Ontology (GO) enrichment analysis of the differentially expressed genes (DEGs) was implemented by the topGO R packages based KolmogorovSmirnov test.2.9 KEGG pathway enrichment analysisKEGG (Kanehisa et al., 2008) is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies (http://www.genome.jp/kegg/). We used KOBAS (Mao et al., 2005) software to test the statistical enrichment of differential expression genes in KEGG pathways.3.0 PPI (Protein Protein Interaction)The sequences of the DEGs was blast (blastx) to the genome of a related species (the protein protein interaction of which exists in the STRING database: http://string-db.org/) to get the predicted PPI of these DEGs. Then the PPI of these DEGs were visualized in Cytoscape (Shannon et al, 2003).