You can see how many features were counted on the basis of information you provided in SAF table. fc_SE <- featureCounts("alignResults.BAM",annot.ext=ann) BAM file that we created by aligning to Senecavirus A genome have accession number liked to each reads. Here you have to use Genebank accession number of virus genome as Chr. ![]() So, lets use follwing code to create one SAF file for this analysis. Rsubread package allows you to create such files in tabular format which they call it SAF format. ![]() For viruses, in most of the cases you don’t find well-annotated GTF or GFF files. We need a annotated file in GTF or GFF format to count the features or genes aligned. I saw that all the reads that were in meta.fastq belonging to Senecavirus A were aligned while, all the Zika virus reads were ignored.The output will be in. align(index="seneca",readfile1=”meta.fastq”,output_file="alignResults.BAM") Now, I can align reads in meta.fastq to the index file which I created above. In my case it would be buildindex(basename= “seneca”, reference= “sva.fasta”) #syntaxīuildindex(basename="reference_index",reference=ref) ![]() Use the same Senecavirus A whole genome file which you used to extract reads in the above example. First, we need to build index of our reference files. #meta.fasta is my input file, meta.fastq is the output file and we are assigning quality score of 34 to all the basepairs. reformat.sh in= meta.fasta out=meta.fastq qfake=35 You can find details about bbmap and reformat.sh script elsewhere. I used reformat.sh script which is a part of bbmap. There are many tools available to convert fasta file to fastq format. This fasta file needs to be changed into fastq format. TGCCGTACCGAGTCACGAGTACCTGCAGGCAAGATGGAGGGCCTTGTTCGACTGACCTGGATAGCCCAACGCGCTTCGGTGCTGCCGGCGATTCTGGGAGAACTCAGTCGGAĪGTTGTTGATCTGTGTGAATCAGACTGCGACAGTTCGAGTTTGAAGCGAAAGCTAGCAACAGTATCAACAĪAAGCTAGCAACAGTATCAACAGGTTTTATTTTGGATTTGGAAACGAGAGTTTCTGGTCATGAAAAACCCA GTGTGGTCTGCGAGTTCTAGCCTACTCGTTTCTCCCCTACTCACTCATTCACACACAAAAAĬTACAAGATTTGGCCCTCGCACGGGATGTGCGATAACCGCAAGATTGACTCAAGCGCGGAAAGCGCTGTAACC GTTTCTCCCCTACTCACTCATTCACACACAAAAACTGTGTTGTAACTACAAGATTTGGCCCTCGCACGGGĪTGTGCGATAACCGCAAGATTGACTCAAGCGCGGAAAGCGCTGTAACCACATGCTGTTAGTCCCTTTATGĬGGGGGGTAAACCGGCTGTGTTTGCTAGAGGCACAGAGGAGCAACATCCAACCTGCTTTTGTĬGGCTCCAATTCCTGCGTCGCCAAAGGTGTTAGCGCACCCAA ![]() So, Zika virus reads should not be counted by Rsubread while aligning.ĪAGGAAGGACTGGGCATGAGGGCCCAGTCCTTCCTTTCCCCTTCCGGGGGGTAAACCGGCTGTGTTTGCTĪGAGGCACAGAGGAGCAACATCCAACCTGCTTTTGTGGGGAACGGTGCGGCTCCAATTCCTGCGTCGCCAĪAGGTGTTAGCGCACCCAAACGGCGCATCTACCAATGCTATTGGTGTGGTCTGCGAGTTCTAGCCTACTC I will be aligning my reads to Senecavirus A genome. And I also pulled some sequences from the Zika virus which are names as Zika1 and Zika2. I created a fasta file with a few contigs each containing about 70-100 basepairs, and named each contig as read 1, read 2 and so on. fasta file by pulling some of the sequences from the Senecavirus A genome. source("")įor this simulation I created a small. Another important aspect of learning RNA-Seq analysis is understanding the algorithms behind the analysis.To this end, I decided to run a small simulation to understand how RNA-Seq analysis algorithms work.It is amazing how a single R package can do things like read aligning, read mapping and read counts in few lines of codes. Softwares with graphical user interface like CLC Workbench, have made RNA-Seq data analysis quite easier.However, they are expensive and in most of the cases you might not be able to tweak your analysis in the exact way you want. RNA-Seq data analysis can be complicated.
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