data/project_data/fastq/cleanreads/$myRight > $myShort"_bwaaln.sam" data/project_data/fastq/cleanreads/$myLeft \ P /data/project_data/assembly/longest_orfs.cds $myLeft".sai" $myRight".sai" \ # bwa index /data/project_data/assembly/longest_orfs.cds # This only needs to be done once on the referenceīwa aln /data/project_data/assembly/longest_orfs.cds /data/project_data/fastq/cleanreads/$myLeft > $myLeft".sai"īwa aln /data/project_data/assembly/longest_orfs.cds /data/project_data/fastq/cleanreads/$myRight > $myRight".sai" # To run from present directory and save output. Step through the script to make sure you understand each command.You need to enter your “left” reads file name (for those cleaned and paired).copy cp to your home directory ~/scritps/ and.Navigate to the /data/scripts/ directory to find a script called bwaaln.sh that you can.Map reads from individual samples to reference transcriptome We can evaluate based on the percentage of genes that have good blastp hits and the percentage of single copy orthologs included in the reference (for example using the new program BUSCO. Options for improving this assembly include: (1) using more reads from other individuals or trying a different individual, (2) changing the cleaning and assembly parameters. But for now we will map to the 5,693 “genes” based on the longest ORFs. These transcriptome assembly processes are ongoing. Makeblastdb -in uniprot_sprot.pep -dbtype prot -out uniprot_sprotīlastp -query /data/project_data/assembly/_dir/longest_orfs.pep -db /data/popgen/databases/uniprot_sprot -max_target_seqs 1 -outfmt 6 -evalue 1e-5 -num_threads 10 > blastp.outfmt6 TransDecoder.Predict -t target_transcripts.fasta -retain_blastp_hits blastp.outfmt6 data/popgen/Trinotate-3.0.1/admin/Build_Trinotate_Boilerplate_SQLite_db.pl Trinotate #!/bin/bash/ # Run the script to download the relevant databases. We can also evaluate this assembly by using blastp to compare it to the uniprot_swissprot database. # Stats based on ONLY LONGEST ISOFORM per 'GENE': $ /data/popgen/trinityrnaseq-Trinity-v2.3.2/util/ longest_orfs.cds Then, run TransDecoder.Predict for your final coding region predictions.Įvaluate the “longest_orfs.cds” assembly after running Transdecoder. Use file: _dir/longest_orfs.pep for Pfam and/or BlastP searches to enable homology-based coding region identification. first extracting base frequencies, we'll need them later.ĬMD: /data/popgen/TransDecoder-3.0.1/util/compute_base_ Trinity.fasta 0 > _dir/base_freqs.datĬMD: touch _dir/base_ $ /data/popgen/TransDecoder-3.0.1/TransDecoder.LongOrfs -t Trinity.fasta To do this we can use the program TransDecoder.ĭownload TransDecoder to use to predict longest Open Reading Frames (ORFs) wget $ cd /data/project_data/assembly/ A quick way for us to move forward with our data analyses for this course, however, is to predict open reading frames (ORFs - include start and stop codons) and keep only transcripts that are at least 100 amino acids long. Settling on a high quality reference transcriptome is an iterative process that requires testing different assembly parameters and inputs and evaluating the quality several ways. With these two types of data, we can go on to differential gene expression analyses and population genomics. sam files)įrom these sequence alignment files, we can extract two types of information: (a) read counts - the number of reads that uniqely map to each “gene” and (b) single nucleotide polymorphisms between a sample and the reference. Map cleaned reads to the transcriptome assembly (makes. Make and evaluate a transcriptome assembly (.fasta) Recall that the general RNAseq data processing work flow is to: Making a reference transcriptome and Mapping reads to the reference transcriptome
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