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Can I filter mRNA from bulk RNA-seq reads?

Hi,

I have Bulk RNA-seq and microRNA-seq read data of my samples. I wanna filter the bulkRNA-seq to get only the DEG for mRNA. after that, I am interested to work on the mRNA-seq and microRNA-seq data intergration analysis.

My question is, how can I filter bulkRNA-seq to get only mRNA reads? (using bioinformatics methods)

mrna-seq bulk rna-seq
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updated by Antonio Camargo Antonio Camargo • written by maria2019 maria2019

Thank you very much. I'll try it

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written by maria2019 maria2019

I just read briefly about it. The description says that (The main application of SortMeRNA is filtering rRNA from metatranscriptomic data.). Looks like it only removes rRNA? I know that rRNA is the majority of the rna found but how about other types of RNA?

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written by maria2019 maria2019

I'm very sorry! I read your question wrong!

I don't think that there's a method that can identify if a read is from a mRNA. I can suggest two alternatives: - If the organism that you are working with has a reference annonation, you can map the reads to the mRNA sequences (using BWA or Bowtie) and then extract the reads that were aligned (Samtools). - If the organism doesn't have a reference annotation, you could assemble transcript sequences (using StringTie or Trinity, depending on whether there's a genome) and then identify mRNAs using a coding potential calculator (such as RNAsamba or FEELnc). After obtaining the coding transcripts (mRNAs) you can use the strategy I described above.

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written by Antonio Camargo Antonio Camargo

I actually have a reference annotation. Awesome! thanks

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written by maria2019 maria2019

You have two different questions, and we can't know which one you are really interested at:

  • filter the bulkRNA-seq to get only the DEG for mRNA

  • filter bulkRNA-seq to get only mRNA reads (by this you mean you want protein-coding genes?)

These are not the same thing. To filter the DE genes, you have to perform differential expression analysis and then subset the data to remove non-significant genes. To get only mRNA counts, you need a comprehensive annotation, to filter out other classes of RNA.

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written by h.mon h.mon

I want to filter mRNA and then perform 1. DEG analysis 2. integration analysis with microRNA. My question is about how I can separate mRNA. I know the steps for 1 and 2.

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written by maria2019 maria2019

Can you please clearly explain what you want to do? As far as I understand, this is what you are proposing:

  1. perform read count abundance for your bulk RNA-seq data (in which format is your bulk RNA-seq data? - FASTQ or BAM?)
  2. filter the counts matrix for protein coding mRNA
  3. perform differential expression analysis using just the protein coding mRNA
  4. ...
  5. something else with the microRNA-seq data
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written by Kevin Blighe Kevin Blighe

Hi Kevin,

I have done bulk RNA-seq DEG analysis. I want to do the following analysis next:

a) miRNA DEG analysis ( i know how to do this step)

b) mRNA and miRNA integration analysis ( for this step, I need mRNA reads)

I want to know if I can analyze just mRNA DEG ( not bulkRNA). for that, I think of filtering the reads for only mRNA from bulkRNA. I wanna know if there is a way to do that.

regarding your questions:

  1. I have fastq files.

  2. yes

  3. yes

  4. ?

  5. yes (mRNA-miRNA integration analysis)

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written by maria2019 maria2019

Yes, you can filter the bulk RNA-seq data and perform an analysis on just the 'protein coding' RNA (mRNA).

Your best option is to, first, take your raw FASTQ files and determine read count abundance over a transcriptome FASTA (search the GENCODE website for this) using Kallisto or Salmon, and, second, filter the derived raw counts to include just the protein coding RNA (MRNA).

In summary:

  1. Read count abundance: FASTQ files + Kallisto / Salmon + transcriptome FASTA = raw counts over all RNAs
  2. Filtering: filter raw counts to include 'protein coding' RNAs
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written by Kevin Blighe Kevin Blighe

Thank you very much Kevin, this was a huge help.

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written by maria2019 maria2019

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