<p>We see in single-cell RNA-seq sometimes up to 50% unmapped reads while data are of pristine quality in all relevant metrics during downstream analysis. Ask yourself whether spending time on investigating this makes any difference. Probably it won't. Maybe it's really some bacterial contamination, or just some random hits. After all, there are billions of bacterial sequences known and with four nucleotides to sample from some random hits are probably expected just by chance. In any case, it won't make an impact if your data that are mapped are fine. I would proceed with downstream analysis and only get back to this if there is any relevant issue downstream.</p>

CC BY-SA 4.0
2024-04-19T07:21:20.543Z
The technique is supposed to capture only polyadenalated RNA, so curious how the bacterial fragments got in there. I thought the same as you, however in my rush to write the post I forgot to mention that I am seeing this behaviour in negative controls i.e. RNA-free water as well. The water samples have good amount of reads which align to human genome and a decent chunk of unmapped reads. The analysis I performed was from this subset. Should I still be concerned or just carry on with my analysis?
Karthik Nair
CC BY-SA 4.0
2024-04-19T09:56:59.933Z
No method is bias-free. Continue and see if mapped data are fine.
ATpoint
CC BY-SA 4.0
2024-04-19T11:37:20.540Z
Only 25% of the reads are aligning to exon or intronic region, 25% are aligning to intergenic region and 50% are unmapped. Would this number appear concerning to you?
Karthik Nair
CC BY-SA 4.0
2024-04-19T13:17:59.270Z
Sounds indeed like a bit much, but again, see whether data are fine downstream and only come back to this if not.
ATpoint
CC BY-SA 4.0
2024-04-19T07:04:14.220Z
2024-04-19T07:04:14.220Z
ATpoint
<p>Pretty much the title. We have performed RNA-seq using SmartSeq3 and processed it with zUMIs using human genome (GRCh38) as reference and gencode 44 for annotation. We noticed plenty of unmapped reads, and importantly there were plenty of reads in the negative control (RNA-free water). To investigate the matter, I extracted the unmapped reads and used SPADEs (in <code>--rna</code> mode) to assemble them. I got around 3k contigs, and when I blasted the top 3 longest genes (sizing 2.2k, 1.7k and 1.4k in length) in NCBI, the hits for 2.2k and 1.4k sequence was Cutibacterium acnes SZ2 DNA and 1.7k matched with Eukaryotic synthetic construct chromosome 16</p>
<p>Could this indicate? Are the unmapped reads due to bacterial contamination? Technically there should shouldn't be many reads in the water sample, where could they come from?</p>

CC BY-SA 4.0
2024-04-18T14:06:05.097Z
`plenty of unmapped reads` => unmapped to what? Human transcriptome? hg19 genomic sequence? To be more certain that your RNA-Seq reads are from other species you may try to use T2T genome assembly and the best annotation you can find to map the original/whole set of your RNA-seq reads.
darked89
CC BY-SA 4.0
2024-04-19T06:17:27.940Z
Unmapped to GRCh38, human transcriptome, with gencode 44. Sorry I missed this detail.
Karthik Nair
<rna-seq>
Plenty of unmapped reads in human RNA-seq data, why does the largest contig from SPADES alignes to human 18sRNA?
# Plenty of unmapped reads in human RNA-seq data, why does the largest contig from SPADES alignes to human 18sRNA?

(Asked by: username_0 on 2024-04-18)

Pretty much the title. We have performed RNA-seq using SmartSeq3 and processed it with zUMIs using human genome (GRCh38) as reference and gencode 44 for annotation. We noticed plenty of unmapped reads, and importantly there were plenty of reads in the negative control (RNA-free water). To investigate the matter, I extracted the unmapped reads and used SPADEs (in `--rna` mode) to assemble them. I got around 3k contigs, and when I blasted the top 3 longest genes (sizing 2.2k, 1.7k and 1.4k in length) in NCBI, the hits for 2.2k and 1.4k sequence was Cutibacterium acnes SZ2 DNA and 1.7k matched with Eukaryotic synthetic construct chromosome 16

Could this indicate? Are the unmapped reads due to bacterial contamination? Technically there should shouldn't be many reads in the water sample, where could they come from?

**Tags:** rna-seq
> **Comments:**
> **username_1**: `plenty of unmapped reads` => unmapped to what? Human transcriptome? hg19 genomic sequence? To be more certain that your RNA-Seq reads are from other species you may try to use T2T genome assembly and the best annotation you can find to map the original/whole set of your RNA-seq reads.  
> **username_0**: Unmapped to GRCh38, human transcriptome, with gencode 44. Sorry I missed this detail.  

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## 1 answers

### Answer by username_2 on 2024-04-19. Score: 1

We see in single-cell RNA-seq sometimes up to 50% unmapped reads while data are of pristine quality in all relevant metrics during downstream analysis. Ask yourself whether spending time on investigating this makes any difference. Probably it won't. Maybe it's really some bacterial contamination, or just some random hits. After all, there are billions of bacterial sequences known and with four nucleotides to sample from some random hits are probably expected just by chance. In any case, it won't make an impact if your data that are mapped are fine. I would proceed with downstream analysis and only get back to this if there is any relevant issue downstream.

> **Comments:**
> **username_0**: The technique is supposed to capture only polyadenalated RNA, so curious how the bacterial fragments got in there. I thought the same as you, however in my rush to write the post I forgot to mention that I am seeing this behaviour in negative controls i.e. RNA-free water as well. The water samples have good amount of reads which align to human genome and a decent chunk of unmapped reads. The analysis I performed was from this subset. Should I still be concerned or just carry on with my analysis?  
> **username_2**: No method is bias-free. Continue and see if mapped data are fine.  
> **username_0**: Only 25% of the reads are aligning to exon or intronic region, 25% are aligning to intergenic region and 50% are unmapped. Would this number appear concerning to you?  
> **username_2**: Sounds indeed like a bit much, but again, see whether data are fine downstream and only come back to this if not.  

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