SEQanswers - Which feature file for htseq-count for non coding elements of ribodepleted samples?

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Which feature file for htseq-count for non coding elements of ribodepleted samples?

Dear all,

Sorry if this question has been asked before, I have not found similar topics on the web, but there are probably...
Until now, I used a gtf downloaded from UCSC table (genome: human, assembly: hg19, group:genes and gene predictions, track: RefSeq genes, table: refFlat) as a feature file, with gene name, in GFF format for counting reads in genes with htseq-count.
I have one experiment with ribodepleted samples (TruSeq total RNA Stranded). I first counted the reads and performed differential expression analysis using such a gtf. In this gtf downloaded few weeks ago, there are 26688 genes, including 913 "LINC*". This number of lincRNA seems low, so I wonder if this table is comprehensive for ribodepleted experiment.
Can you please tell me which reference you use when interesting in non coding elements of ribodepleted experiments?

Thank you for your feedback,
Jane

UCSC references...kind of suck. Use Gencode/Ensembl and you'll get more complete coding and non-coding transcripts. Having said that, for lincRNAs you might want to check out lincrnadb or RNAcentral.

I used gencode. but no counts.
perhaps what column used htseq-count ?

Presumably the lack of counts is due to the difference in chromosome names.

Thank you dpryan for your answer.

Quote:

Originally Posted by dpryan (Post 193560)

UCSC references...kind of suck.

Don't you thing this refFlat reference is sufficient for experiments with polyA RNA selection?

Quote:

Originally Posted by dpryan (Post 193560)

Use Gencode/Ensembl and you'll get more complete coding and non-coding transcripts. Having said that, for lincRNAs you might want to check out lincrnadb or RNAcentral.

Since I am working with hg19 annotation, I downloaded the gtf from http://www.gencodegenes.org/releases/19.html for "ALL" regions, that is the gencode.v19.chr_patch_hapl_scaff.annotation file. I am not familiar with these annotation files. Is it the right one?

I am currently looking at what this file contains: number of genes (name, ID, status), transcripts, ...
It has not the same format like the refFlat file, so I expect some issues for read counting.

The UCSC file annotations are never that good. The file you downloaded is fine, though again the chromosome names probably differ, which is what's causing problems. I think featureCounts can handle the chromosome naming difference internally, so try using that instead (it's much faster anyway).

Quote:

Originally Posted by dpryan (Post 193582)

Thank you again. No problem yet, since I did not try to count. Looking at the file: now, I have ~56600 different gene names, compared to ~26600 with refFlat. The file contains twice more lines. Looking forward to see how the number of LINC and AS will increase.
chr1->22, chrX, chrY have the same names, but the additional chromosomes have indeed different names: chr17_ctg5_hap1, chr17_gl000205_random in refFlat and GL000191.1, GL000192.1. I start the tests right now!

Compared to how I ran htseq-count previously, I added -i option to get counts for each gene_name and not gene_ID. I could run htseq count on this new gtf without any problem :D

Code:

htseq-count --format=bam --order=name --mode=union -a 20 -i gene_name --stranded=reverse mybam.bam gencode.v19.chr_patch_hapl_scaff.annotation.gtf > mynewcounts.gencod.htseqCount

But I still have a couple of questions:
1. With the genecode annotation, we can find how many genes are attributed to each gene_type (30 gene types), like protein coding, lincRNA, pseudogene, rRNA,... Do you know where to find this information for the refFlat annotation?

2. I am a bit confused by gene_name and gene_ID:
When counting in gencode.v19.chr_patch_hapl_scaff.annotation.gtf the number of genes ("gene" in field 3), I get 63,568:

Code:

cat gencode.v19.chr_patch_hapl_scaff.annotation.gtf | cut -f3  | wc -l

63568

but the number of unique gene_name is 56,629:

Code:

cat gencode.v19.chr_patch_hapl_scaff.annotation.gtf | cut -d';' -f5 | sort | uniq | wc -l

56629

In the output file of htseq count, 56,629 are listed.

Does someone know what are those genes with same gene_name but different gene_ID?

Code:

cat gencode.v19.chr_patch_hapl_scaff.annotation.gene.gtf | cut -f9  | sort | uniq | wc -l

63568

3. Finally, I noticed that the counting changed a lot between the 2 annotations, even for well known genes as TP53, the number of reads attributed to this gene - and others - double! I am surprised that the annotation of such very well known genes changes. Is it normal?

Any clarification would be greatly appreciated.

1. Use things like "cut" and "uniq" to determine this. This isn't something you need to look up, just determine it yourself.
2. How does one define a gene? Is it a location, a sequence, something else? If you have essentially the same sequence on different chromosomes and both are expressed are they the same gene or different ones? In such cases, gencode/ensembl will give each instance a unique ID. UCSC will give each instance the same ID in such cases, which is a good way to completely break a LOT of programs.This is why one should normally quantify by gene ID. You can add gene names after everything is analysed.
3. UCSC annotations are rather minimalistic.

Quote:

Originally Posted by dpryan (Post 193617)

1. Use things like "cut" and "uniq" to determine this. This isn't something you need to look up, just determine it yourself.

Well, there is no description in the tables I downloaded from UCSC. Otherwise, I could indeed check as I did with Genecode annotation. Here are some file header:

refFlat file (RefSeq), that I used until now:

Code:

chr1        hg19_refFlat        exon        11874        12227        0.000000        +        .        gene_id "DDX11L1"; transcript_id "DDX11L1"; 

chr1        hg19_refFlat        exon        12613        12721        0.000000        +        .        gene_id "DDX11L1"; transcript_id "DDX11L1"; 

chr1        hg19_refFlat        exon        13221        14409        0.000000        +        .        gene_id "DDX11L1"; transcript_id "DDX11L1"; 

chr1        hg19_refFlat        exon        14362        14829        0.000000        -        .        gene_id "WASH7P"; transcript_id "WASH7P"; 

chr1        hg19_refFlat        exon        14970        15038        0.000000        -        .        gene_id "WASH7P"; transcript_id "WASH7P"; 

chr1        hg19_refFlat        exon        15796        15947        0.000000        -        .        gene_id "WASH7P"; transcript_id "WASH7P";

refGene file (RefSeq):

Code:

chr1        hg19_refGene        start_codon        67000042        67000044        0.000000        +        .        gene_id "NM_032291"; transcript_id "NM_032291"; 

chr1        hg19_refGene        CDS        67000042        67000051        0.000000        +        0        gene_id "NM_032291"; transcript_id "NM_032291"; 

chr1        hg19_refGene        exon        66999639        67000051        0.000000        +        .        gene_id "NM_032291"; transcript_id "NM_032291"; 

chr1        hg19_refGene        CDS        67091530        67091593        0.000000        +        2        gene_id "NM_032291"; transcript_id "NM_032291"; 

chr1        hg19_refGene        exon        67091530        67091593        0.000000        +        .        gene_id "NM_032291"; transcript_id "NM_032291"; 

chr1        hg19_refGene        CDS        67098753        67098777        0.000000        +        1        gene_id "NM_032291"; transcript_id "NM_032291"; 

chr1        hg19_refGene        exon        67098753        67098777        0.000000        +        .        gene_id "NM_032291"; transcript_id "NM_032291"; 

chr1        hg19_refGene        CDS        67101627        67101698        0.000000        +        0        gene_id "NM_032291"; transcript_id "NM_032291";

knownGenes file (UCSC):

Code:

chr1        hg19_knownGene        exon        11874        12227        0.000000        +        .        gene_id "uc010nxr.1"; transcript_id "uc010nxr.1"; 

chr1        hg19_knownGene        exon        12646        12697        0.000000        +        .        gene_id "uc010nxr.1"; transcript_id "uc010nxr.1"; 

chr1        hg19_knownGene        exon        13221        14409        0.000000        +        .        gene_id "uc010nxr.1"; transcript_id "uc010nxr.1"; 

chr1        hg19_knownGene        start_codon        12190        12192        0.000000        +        .        gene_id "uc010nxq.1"; transcript_id "uc010nxq.1"; 

chr1        hg19_knownGene        CDS        12190        12227        0.000000        +        0        gene_id "uc010nxq.1"; transcript_id "uc010nxq.1"; 

chr1        hg19_knownGene        exon        11874        12227        0.000000        +        .        gene_id "uc010nxq.1"; transcript_id "uc010nxq.1"; 

chr1        hg19_knownGene        CDS        12595        12721        0.000000        +        1        gene_id "uc010nxq.1"; transcript_id "uc010nxq.1"; 

chr1        hg19_knownGene        exon        12595        12721        0.000000        +        .        gene_id "uc010nxq.1"; transcript_id "uc010nxq.1"; 

chr1        hg19_knownGene        CDS        13403        13636        0.000000        +        0        gene_id "uc010nxq.1"; transcript_id "uc010nxq.1"; 

chr1        hg19_knownGene        stop_codon        13637        13639        0.000000        +        .        gene_id "uc010nxq.1"; transcript_id "uc010nxq.1"; 

chr1        hg19_knownGene        exon        13403        14409        0.000000        +        .        gene_id "uc010nxq.1"; transcript_id "uc010nxq.1";

There might exist one file with comprehensive description.

Quote:

Originally Posted by dpryan (Post 193617)

2. How does one define a gene? Is it a location, a sequence, something else? If you have essentially the same sequence on different chromosomes and both are expressed are they the same gene or different ones? In such cases, gencode/ensembl will give each instance a unique ID. UCSC will give each instance the same ID in such cases, which is a good way to completely break a LOT of programs.This is why one should normally quantify by gene ID. You can add gene names after everything is analysed.

Thank you for the explanation.

Quote:

Originally Posted by dpryan (Post 193617)

3. UCSC annotations are rather minimalistic.

Ok, but I am very surprised for annotation of well known genes.

Why are you not using the GTF file from UCSC instead of all those other files. You could create that from Table Browser. (iGenomes bundle does not have non-coding genes).

Edit: @Vikas Bansal has a solution to get the non-coding elements (you can choose GTF output in table browser) for hg19 here.

Quote:

Originally Posted by GenoMax (Post 193649)

Why are you not using the GTF file from UCSC instead of all those other files. You could create that from Table Browser. (iGenomes bundle does not have non-coding genes).

I was actually using only the refFlat gtf from UCSC. It looks like what I showed.
I added the refGene and knownGenes gtf files to show that these files do not contain description neither.
There seem to be some non coding elements in refFlat gtf: ~900 LINC, ...

Quote:

Originally Posted by GenoMax (Post 193649)

Edit: @Vikas Bansal has a solution to get the non-coding elements (you can choose GTF output in table browser) for hg19 here.

Thank you, I will take a look. But it is probably easier to use genecode to get everything from a single gtf file.