Goal: Learn how to use various tool to extract information from RNAseq reads. 4.1 Mapping RNAseq Reads to a Genome Assembly

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1 ESSENTIALS OF NEXT GENERATION SEQUENCING WORKSHOP 2014 UNIVERSITY OF KENTUCKY AGTC Class 4 RNAseq Goal: Learn how to use various tool to extract information from RNAseq reads. Input(s): magnaporthe_oryzae_70-15_8_supercontigs.fasta Moryzae_70-15_*_RNA_sample_{1-2}.fastq magnaporthe_oryzae-70-15_8_transcripts.gtf Output(s): 70-15_RNA_sample_{1-3}_thout directory 70-15_RNA_sample_{1-3}_clout directory merged.gtf file gene_exp.diff file 4.1 Mapping RNAseq Reads to a Genome Assembly We will use TopHat2 to align RNAseq reads to a genome assembly of the fungal strain from which they were derived (strain 70-15). Trapnell et al. (2009) TopHat: discovering splice junctions with RNAseq. Bioinformatics 25: TopHat2 uses the Bowtie alignment engine to map RNA seq reads to the genome assembly. Bowtie utilizes an indexed transformation of the genome assembly to perform its alignment, so the first step is to create these indexes. Usage: bowtie2-build [options] -f <reference_genome> <index_prefix> Where <reference genome> is the path to the genome multifasta file and <index_prefix> is the name to be given to the index. Change to the RNAseq directory. Remember, there is no need to leave this directory. All operations, such as listing of subdirectories, etc. can be performed from this location. Essentials of Next Generation Sequencing 2014 Page 1 of 5

2 Generate the bowtie index: bowtie2-build f magnaporthe_oryzae_70-15_8_supercontigs.fasta \ Moryzae -f specifies the name of a multifasta file, or a directory containing multiple fasta files Create a new directory called index and place the resulting index files inside it (note: the relevant files will have a.bt2 suffix). Use Tophat2 to map each set of RNAseq reads to the bowtie index: Usage: tophat2 [options] o <output_dir> <path-to-indexes> <input-file(s)> tophat2 -p 2 -o 70-15_mycelial_RNA_sample_1_thout index/moryzae \ Moryzae_70-15_mycelial_RNA_sample_1.fastq -p number of processors to use (select 2) -o name of output directory TopHat2 invoked with the above command will produce an output folder (70-15_mycelial_RNA_sample1_thout) containing several files and a subdirectory containing log files: accepted_hits.bam: contains alignment information for all of the reads that were successfully mapped to the genome. left_kept_reads_info: minimum read length, maximum read length; total reads; successfully mapped read. insertions.bed: lists nucleotide insertions in the input sequences deletions.bed: lists nucleotide deletions in the input sequences junctions.bed: lists splice junctions Use a command line function to take a look at the results in the accepted_hits.bam file. Hint: to view the file, you will either need to change into the output directory created by TopHat, or specific the complete path to the file you wish to view. Does the output make any sense? No? Let s use samtools to convert the.bam file into the humanreadable.sam format: samtools view 70-15_mycelial_RNA_sample_1_thout/accepted_hits.bam Whoa! Did you catch all that? Try piping the results through the more command line function. Next use redirection to write the output to a file. Essentials of Next Generation Sequencing 2014 Page 2 of 5

3 Repeat the mapping process for the remaining sequence files (remember that you need to be in the RNAseq directory): Moryzae_70-15_mycelial_RNA_sample_2.fastq Moryzae_70-15_spore_RNA_sample_1.fastq Hint: you can use the up arrow key to copy the previous command to the current command line buffer. However, you must remember to change the input and output names to prevent overwriting of previous results. 4.2 Assembling Transcripts From RNAseq Data We will use cufflinks to build transcripts from RNAseq reads and compare expression profiles between different RNA samples: Trapnell et al. (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology 28: The first step in differential gene expression analysis is to identify the gene from which each sequence read is derived. Cufflinks examines the raw RNAseq mapping results and attempts to reconstruct complete transcripts and identify transcript isoforms. Usage: cufflinks [options] o <output_dir> <path/to/accepted_hits.bam> Make sure you are in the RNAseq directory. Run cufflinks, providing a reference transcriptome in the form of a.gtf file. All one line: cufflinks p 1 g magnaporthe_oryzae_70-15_8_transcripts.gtf \ o 70-15_mycelial_RNA_sample_1_clout \ 70-15_mycelial_RNA_sample_1_thout/accepted_hits.bam -o name of output directory p number of processors to use -g/--gtf-guide tells cufflinks to use the provided reference annotation to guide transcript assembly but also to report novel transcripts/isoforms Notes: A) Omitting the g option (and accompanying.gtf file specification) from the above command would tell the program to generate a de novo transcript assembly. Alternatively, once can use -G/--GTF which will tell the program to to assemble only those reads that correspond to previously identified genes/transcripts. B) The developers recommend that you assemble your replicates individually, i) to speed computation; and ii) to simplify junction identification. Therefore, you will need to run cufflinks separately for each Essentials of Next Generation Sequencing 2014 Page 3 of 5

4 of your.bam files. With the above example, the results will be saved in a directory named 70-15_mycelial_RNA_sample_1_clout Rerun cufflinks on each of your accepted_hits.bam files, remembering to change sample_1 in both the input and output folder names. Examine one of the.gtf files produced by cufflinks. See if you can determine what information is contained in the various columns. 4.3 Merging Transcript Assemblies We will use cuffmerge to generate a super-assembly of transcripts based on the mapping information from all three RNAseq datasets. Usage: cuffmerge [options] <list_of_gtf_files> Make sure you are in the RNAseq directory Open a text editor and create a list of the.gtf files that will be incorporated into the super-assembly. The list should have the following format:./70-15_mycelial_rna_sample_1_clout/transcripts.gtf./70-15_mycelial_rna_sample_2_clout/transcripts.gtf./70-15_spore_rna_sample_1_clout/transcripts.gtf etc. Include the.gtf files for all datasets and save the file using the name assemblies.txt. Run cuffmerge (changing filenames as necessary): cuffmerge p 1 s magnaporthe_oryzae_70-15_8_supercontigs.fasta \ -g magnaporthe_oryzae-70-15_8_transcripts.gtf assemblies.txt -s points to the genome sequence which is used in the classification of transfrags that do not correspond to known genes -p number of processors to use Examine the merged.gtf file produced by cuffmerge inside of merged_asm. Use command line tools to interrogate the file to identify novel transcripts that have not been previously identified. Note: these will lack MGG identifiers. 4.4 Differential Gene Expression Analysis We will use cuffdiff to determine if any genes are differentially expressed in one of the RNAseq datasets. To compare gene expression levels, it is necessary to have a set of genes to start off with. Cuffdiff utilizes the merged.gtf file produced by cuffmerge, which combines existing gene annotations (if available) with new information (novel transcripts, isoforms, etc.) generated from the RNAseq data. It then uses the alignment data (in the.bam files) to calculate and compare abundances. Essentials of Next Generation Sequencing 2014 Page 4 of 5

5 Usage: cuffdiff [options] <transcripts.gtf> <sample1.replicate1.bam,sample1.replicate2.bam > <sample2.replicate1.bam,sample2.replicate2.bam > Note: experimental replicates are separated with commas; datasets being compared are separated by a space (i.e.: Set1_rep1,Set1_rep2 Set2_rep1,Set2_rep2) For our experiment, we will compare transcript abundance in spores versus two replicates of mycelium Run cuffdiff as follows: cuffdiff -o diff_out p 2 L mycelium,spores \ u merged_asm/merged.gtf \./70-15_mycelial_RNA_sample_1_thout/accepted_hits.bam,\./70-15_mycelial_RNA_sample_2_thout/accepted_hits.bam \./70-15_spore_RNA_sample_1_thout/accepted_hits.bam -o output directory where results will be deposited -p number of processors to use -L Labels to use for the three samples being compared. These labels will appear at the top of the relevant columns in the various output files. -u Tells cufflinks to do an initial estimation procedure to more accurately weight reads mapping to multiple locations in the genome Be sure not to put spaces around the comma! By default cuffdiff writes results to a file named gene_exp.diff, inside of your defined output folder. The gene expression differences are written to the file named gene_exp.diff. View the header of this file and see if you can determine what information is contained in the various columns. If necessary, go online and look at the cuffdiff manual (cufflinks.cbcb.umd.edu/manual.html ) Produce a list that contains the identities of the genes that show significant differences in their expression levels (only the names of the genes and nothing else) Hint: You will need to use awk. Essentials of Next Generation Sequencing 2014 Page 5 of 5