DOI: 10.5061/dryad.3t3n7
title: Data from: Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments.

description: Transcriptome Shotgun Sequencing (RNA-seq) has been readily embraced by
 geneticists and molecular ecologists alike. As with all high-throughput
 technologies, it is critical to understand which analytic strategies are
 best suited and which parameters may bias the interpretation of the data.
 Here we use a comprehensive simulation approach to explore how various
 features of the transcriptome (complexity, degree of polymorphism π,
 alternative splicing), technological processing (sequencing error ε,
 library normalization) and bioinformatic workflow (de novo vs. mapping
 assembly, reference genome quality) impact transcriptome quality and
 inference of differential gene expression (DE). We find that transcriptome
 assembly and gene expression profiling (edgeR vs. baySeq software) works
 well even in the absence of a reference genome, and is robust across a
 broad range of parameters. We advise against library normalization, and in
 most situations advocate mapping assemblies to an annotated genome of a
 divergent sister clade, which generally outperformed de novo assembly
 (Trans-Abyss, Trinity, SOAPdenovo-Trans). Transcriptome complexity (size,
 paralogs, alternative splicing isoforms) negatively affected the assembly
 and DE profiling, whereas the effects of sequencing error and polymorphism
 were almost negligible. Finally, we highlight the challenge of gene name
 assignment for de novo assemblies, the importance of mapping strategies,
 and raise awareness of challenges associated with the quality of reference
 genomes. Overall, our results have significant practical and
 methodological implications, and can provide guidance in the design and
 analysis of RNA-seq experiments, particularly for organisms where genomic
 background information is lacking.
description: Polishing the reference
 genomeIn the first step, Ns in
 the reference genome were substituted by one of the four bases randomly
 using perl's built in random number generator.1_referencepolish.pl.plSimulate raw sequencing data - Step 1In the second step, the reference genome is used along with expression profiles (exponential or uniform) to create fastq files using the program dwgsim version 0.1.2.2a_simulate_reads_expression.plSimulate raw sequencing data - Step 2The 2b_gnrCounts.R script generates per gene and per library expression levels for 2x10 libraries with differential expression among them. These read count files are used along with the fastq files simulated in previous step by 2c_drawreads.pl to create 2x10 libraries with differential expression among them.2b_gnrCounts.RSimulate raw sequencing data - Step 32c_drawreads.plDe novo assemblyIn the next step, De novo assemblies need to be performed as per the computational infrastructure available. We provide an example commandline used by us.3_denovo.shMapping assemblyThe mapping assembly first requires mapping the reads onto the reference using stampy read mapper. The sam/bam file obtained after the mapping step is sorted and used to call the consensus sequence. Example commandline used by us can be modified suitably.4_mapping.shCompare de novo assembliesThe quality of de novo assemblies can be evaluated using the scripts available on the GAGA website when alternative splicing is not being simulated. Step 5 consists of 7 sub-steps, each with its own script.5_compare_denovo.shCompare de novo assemblies - Steps 1 to 75_substeps.zipCompare mapping assemblies - Mapping 1The quality of the mapping assemblies obtained after the consensus step is be evaluated.6a_compare_mapping1.plCompare mapping assemblies - Mapping 2The quality of the mapping assemblies obtained after the consensus step is be evaluated.6b_compare_mapping2.plDifferential expression analysis - baySeqDifferential expression analysis across the 2x10 libraries is performed using the Bioconductor package baySeq.7a_bayseq.RDifferential expression analysis - edgeRDifferential expression analysis across the 2x10 libraries is performed using the Bioconductor packages edgeR7b_edgeR.REvaluation of differential expression analysisThis script performs some evaluation of the output from the differential expression analyses, e.g. it generates FPR's and TPR's.8_DEevaluation.RCreate data for zebra finch.Steps 1 and 2 are accomplished by running 1_2_create_datasets.sh for the zebra finch dataset.1_2_create_datasets.shZebraFinchDataZebra finch CDS downloaded from Biomart (Ensembl Version 61) with 100 bp 5' UTR and 400 bp 3' UTR per gene.DistributionsSimuated read distributions per expression category.distr.zip