I have been working with genomes of photosynthetic in a number of different projects from the start of my graduate studies. We those few of us who are interested and invested in plant genomics and bioinformatics know what a valuable resource Phytozome is. Phytozome is a resource maintained by the JGI. It contains data from 47 different species and 50 genome assemblies (Some species have more than one genome assembly). For some of the species such as Chlamydomonas, genome assemblies are exclusively released through Phytozome.
When I was working on a project withChlamydomonas reinhardtii genome, I needed to align some RNA-seq data to the genome and count the number of reads that were aligned to each transcript. The newer version of Phytozome (v10.1), is especially great and fast for downloading the essential files for RNA-seq analysis.
- The genome sequence to align the reads to
- The GFF3 file which allows us to identify the transcripts and their components
I downloaded the genome sequence and aligned the reads use GSNAP, which is an aligner of personal preference, because of previous experience in using it and optimizing it for different projects. This part was quick and painless.
The next step was to count the number of reads that are aligned to each transcript. This is where things get a little tricky. There are a couple of programs out there to count the number of reads aligned to each transcript. HTSeq which is based on Python, and featureCounts which is part of the subread aligner package. There might be more, but I have not used them. I have used HTSeq for a couple of past projects, and it works well, but it is relatively slow. The large number of files I had for this project necessitated that I use a program that is fast, an2d featureCounts package fit the bill for that. featureCounts is written in C and also has the capability to multithread read counting. It can also summarize multiple bam files at the same time.
When I tried to use the GFF3 file I downloaded from Phytozome, I ran into some issues. Both the read counting programs use a very simple aggregation method to count the number of reads mapped to a gene. A small example of the contents of a GFF3 file is given below.
##gff-version 3 ##annot-version v5.52 chr_1 phytozomev10 gene 18766 20237 0 + 0 ID=Cre01.g000017.v5.5;Name=Cre01.g000017 chr_1 phytozomev10 mRNA 18766 20237 0 + 0 ID=Cre01.g000017.t1.1.v5.5;Name=Cre01.g000017.t1.1;pacid=30789166;longest=1;Parent=Cre01.g000017.v5.5 chr_1 phytozomev10 five_prime_UTR 18766 19162 0 + 0 ID=Cre01.g000017.t1.1.v5.5.five_prime_UTR.1;Parent=Cre01.g000017.t1.1.v5.5;pacid=30789166 chr_1 phytozomev10 CDS 19163 19178 0 + 0 ID=Cre01.g000017.t1.1.v5.5.CDS.1;Parent=Cre01.g000017.t1.1.v5.5;pacid=30789166 chr_1 phytozomev10 CDS 19329 19948 0 + 2 ID=Cre01.g000017.t1.1.v5.5.CDS.2;Parent=Cre01.g000017.t1.1.v5.5;pacid=30789166 chr_1 phytozomev10 three_prime_UTR 19949 20237 0 + 0 ID=Cre01.g000017.t1.1.v5.5.three_prime_UTR.1;Parent=Cre01.g000017.t1.1.v5.5;pacid=30789166
For each gene there are multiple lines indicating the presence of the following features. GFF3 allows only one feature per line.
- transcripts (mRNA)
- Untranslated regions (five_prime_UTR,three_prime_UTR)
- Coding Sequences (CDS)
# gffread chlamy_v5-5.gff3 -o chlamy_v5-5.fc.gff3 -O ##gff-version 3 chr_1 phytozomev10 gene 18766 20237 . + . ID=Cre01.g000017.v5.5;geneID=Cre01.g000017.v5.5;gene_name=Cre01.g000017 chr_1 phytozomev10 mRNA 18766 20237 . + . ID=Cre01.g000017.t1.1.v5.5;Parent=Cre01.g000017.v5.5;geneID=Cre01.g000017.v5.5;gene_name=Cre01.g000017 chr_1 phytozomev10 exon 18766 19178 . + . Parent=Cre01.g000017.t1.1.v5.5 chr_1 phytozomev10 exon 19329 20237 . + . Parent=Cre01.g000017.t1.1.v5.5 chr_1 phytozomev10 CDS 19163 19178 . + 0 Parent=Cre01.g000017.t1.1.v5.5 chr_1 phytozomev10 CDS 19329 19948 . + 2 Parent=Cre01.g000017.t1.1.v5.5
Here we can see that exon attributes have been added and these have coordinates which overlap the closest UTRs and CDS regions together. This makes it easier to count the number of reads using HTSeq and featureCounts. The commands are given below.
I have found this to be slightly incorrect when working with multiple transcripts for a gene which have overlapping introns. Then featureCounts does not count the reads correctly and assigns exons shared by multiple transcripts as ambiguous. You can use the -O option to allow fragements/reads aligned to overlapping meta-features to be counted correctly.
#count the reads aligned to each transcript "-g Parent" parameter is essential to aggregate the read count per transcript otherwise program will stop with an error featureCounts -a sample.gff3 -O -o sample.counts -g Parent sample.bam
The gff3 output (e.g. sample.fc.gff3) can be used directly by either program mentioned above for read counting. The flip side is that the read count will be done per transcript instead of counting the reads per gene. The proportion of genes which have more than one transcript vary from species to species. For example this number in latest Chlamydomonas genome release is ~8 %. Depending on the level of accuracy and the question asked there can be multiple ways to summarize the counts per gene. We will explore these different avenues in forthcoming posts.