Answers.Body: <p>Basically you want to know the VHL Gene expression in that 10x dataset. They have provided the barcode, matrix and feature files on GEO <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131685" rel="nofollow noreferrer">GSE131685</a> as Supplementary file. You could use those files and get the gene expression matrix as described on <a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/matrices" rel="nofollow noreferrer">10x website</a>.</p>
  <pre><code>library(Matrix)
  
  matrix_dir = &quot;/opt/sample345/outs/filtered_feature_bc_matrix/&quot;
  barcode.path &lt;- paste0(matrix_dir, &quot;barcodes.tsv.gz&quot;)
  features.path &lt;- paste0(matrix_dir, &quot;features.tsv.gz&quot;)
  matrix.path &lt;- paste0(matrix_dir, &quot;matrix.mtx.gz&quot;)
  
  mat &lt;- readMM(file = matrix.path)
  
  feature.names = read.delim(features.path, 
                             header = FALSE,
                             stringsAsFactors = FALSE)
  
  barcode.names = read.delim(barcode.path, 
                             header = FALSE,
                             stringsAsFactors = FALSE)
  
  colnames(mat) = barcode.names<span class="math-container">$V1
  rownames(mat) = feature.names$</span>V1
  </code></pre>
  <p>This mat can be further loaded into <a href="https://satijalab.org/seurat/" rel="nofollow noreferrer">Seurat</a> for any downstream analysis. All this can be done on a laptop.</p>
  <p>If you want to re-quantify the gene expression for whatever the reason, you need to re-run the cellRanger pipeline using GTF of your interest (it needs some <a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/tutorial_mr" rel="nofollow noreferrer">preparation</a>), which may not work on your laptop. It requires large memory and long running times, ideally to submit as job on cluster.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-07-26T05:44:20.087Z
Answers.LastActivityDate: 2020-07-26T05:44:20.087Z
Answers.UserDisplayName: geek_y
Answers.Body: <blockquote>
  <p>Should I use the original format (TenX bam, about 10 GB) or SRA
  archive data (fasta, about 3.5 GB)? Why are the files so different in
  size?</p>
  </blockquote>
  <p>I strongly recommend you hold your horses; how do you think you can analyze something if you don't even know what you are looking at?</p>
  <p>Since this is a &quot;TenX&quot; bam, you can probably do the job with samtools and grep.  Looking up 10XGenomics bam tags might be helpful.  You might need to look up SAM flags too.</p>
  <blockquote>
  <p>My objective is to quantify mRNA expression levels of the mRNA
  sequence in the transcriptome.</p>
  </blockquote>
  <p>You understand that this isn't really a thing that's possible?  With spike-ins, maybe, but you don't have that.  All you can do is work out the proportions of reads belonging to that transcript  (Or gene, really, unless you want to put in a great deal more work, beyond I believe what 10XGenomics does).  Which I guess you want to do by lumping all the cells together, and just pretending there is no QC to do?</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-07-26T05:51:28.640Z
Answers.LastActivityDate: 2020-07-26T06:03:23.530Z
Answers.LastEditDate: 2020-07-26T06:03:23.530Z
Answers.UserDisplayName: swbarnes2
Body: <p>I am a computer scientist just starting out in bioinformatics topics, and would appreciate any guidance that can be given here:</p>
  <p>I have an <a href="https://www.ncbi.nlm.nih.gov/nuccore/NM_000551.4" rel="nofollow noreferrer">mRNA sequence</a>- an isoform - whose length is about 4000 base pairs (bp) - the origin is provided in the link.</p>
  <p>I also have a <a href="https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR10377489" rel="nofollow noreferrer">bam file/fasta file</a> of the entire transcriptome of a kidney cell (see Data Access tab).</p>
  <p>My objective is to quantify mRNA expression levels of the mRNA sequence in the transcriptome.</p>
  <p>How does one get started? Should I use the original format (TenX bam, about 10 GB) or SRA archive data (fasta, about 3.5 GB)?  Why are the files so different in size?</p>
  <p>I have a MacBook with <code>samtools</code> installed and 16GB of RAM. Is this enough to process the data and what basic commands should be used to quantify the 4000 bp sequence overlaps in the transcriptome?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-26T07:31:59.743Z
Comments.Text: Hi @the_darkside this isn't what we do. The package you need IMHO is called `Seurat`, it would be recommneded that you read the manual and return with specific technical questions.
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-26T16:49:32.877Z
Comments.Text: Right.  Looking up gene counts for a single gene in a single sample, it's pretty useless. Especially on a single cell experiment, which is complicated.  Is this really a class assignment?
Comments.UserDisplayName: swbarnes2
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-26T22:32:01.760Z
Comments.Text: I know that looking at a single gene in a single cell sample is not very useful in and of itself, but the idea is to get some practice in mRNA quantification.  I hope that once I understand the process, I can repeat the process on tissue samples.  I haven't yet been able to find an available file of non-single cell mRNA sequencing of a human kidney.
Comments.UserDisplayName: the_darkside
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-27T00:04:31.453Z
Comments.Text: This is not how you do it.  Find a bulk RNASeq sample, and go through a tutorial.
Comments.UserDisplayName: swbarnes2
Tags: <sam><fasta><samtools><transcriptome>
Title: Basic question about finding mRNA sequence in transcriptome
ThreadText: # Basic question about finding mRNA sequence in transcriptome
  
  (Asked by: username_0 on 2020-07-26)
  
  I am a computer scientist just starting out in bioinformatics topics, and would appreciate any guidance that can be given here:
  
  I have an [mRNA sequence](https://www.ncbi.nlm.nih.gov/nuccore/NM_000551.4)- an isoform - whose length is about 4000 base pairs (bp) - the origin is provided in the link.
  
  I also have a [bam file/fasta file](https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR10377489) of the entire transcriptome of a kidney cell (see Data Access tab).
  
  My objective is to quantify mRNA expression levels of the mRNA sequence in the transcriptome.
  
  How does one get started? Should I use the original format (TenX bam, about 10 GB) or SRA archive data (fasta, about 3.5 GB)? Why are the files so different in size?
  
  I have a MacBook with `samtools` installed and 16GB of RAM. Is this enough to process the data and what basic commands should be used to quantify the 4000 bp sequence overlaps in the transcriptome?
  
  **Tags:** sam, fasta, samtools, transcriptome
  
  > **Comments:**
  > **username_1**: Hi @username_0 this isn't what we do. The package you need IMHO is called `Seurat`, it would be recommneded that you read the manual and return with specific technical questions.  
  > **username_2**: Right.  Looking up gene counts for a single gene in a single sample, it's pretty useless. Especially on a single cell experiment, which is complicated.  Is this really a class assignment?  
  > **username_0**: I know that looking at a single gene in a single cell sample is not very useful in and of itself, but the idea is to get some practice in mRNA quantification.  I hope that once I understand the process, I can repeat the process on tissue samples.  I haven't yet been able to find an available file of non-single cell mRNA sequencing of a human kidney.  
  > **username_2**: This is not how you do it.  Find a bulk RNASeq sample, and go through a tutorial.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2020-07-26. Score: 2
  
  Basically you want to know the VHL Gene expression in that 10x dataset. They have provided the barcode, matrix and feature files on GEO [GSE131685](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131685) as Supplementary file. You could use those files and get the gene expression matrix as described on [10x website](https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/matrices).
  
  ```
  library(Matrix)
  
  matrix_dir = "/opt/sample345/outs/filtered_feature_bc_matrix/"
  barcode.path <- paste0(matrix_dir, "barcodes.tsv.gz")
  features.path <- paste0(matrix_dir, "features.tsv.gz")
  matrix.path <- paste0(matrix_dir, "matrix.mtx.gz")
  
  mat <- readMM(file = matrix.path)
  
  feature.names = read.delim(features.path, 
                             header = FALSE,
                             stringsAsFactors = FALSE)
  
  barcode.names = read.delim(barcode.path, 
                             header = FALSE,
                             stringsAsFactors = FALSE)
  
  colnames(mat) = barcode.names$V1
  rownames(mat) = feature.names$V1
  
  ```
  
  This mat can be further loaded into [Seurat](https://satijalab.org/seurat/) for any downstream analysis. All this can be done on a laptop.
  
  If you want to re-quantify the gene expression for whatever the reason, you need to re-run the cellRanger pipeline using GTF of your interest (it needs some [preparation](https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/tutorial_mr)), which may not work on your laptop. It requires large memory and long running times, ideally to submit as job on cluster.
  
  
  ---
  
  ### Answer by username_2 on 2020-07-26. Score: 1
  
  > Should I use the original format (TenX bam, about 10 GB) or SRA
  > archive data (fasta, about 3.5 GB)? Why are the files so different in
  > size?
  
  I strongly recommend you hold your horses; how do you think you can analyze something if you don't even know what you are looking at?
  
  Since this is a "TenX" bam, you can probably do the job with samtools and grep. Looking up 10XGenomics bam tags might be helpful. You might need to look up SAM flags too.
  
  > My objective is to quantify mRNA expression levels of the mRNA
  > sequence in the transcriptome.
  
  You understand that this isn't really a thing that's possible? With spike-ins, maybe, but you don't have that. All you can do is work out the proportions of reads belonging to that transcript (Or gene, really, unless you want to put in a great deal more work, beyond I believe what 10XGenomics does). Which I guess you want to do by lumping all the cells together, and just pretending there is no QC to do?
  
  
  ---
  
  