32 votes
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How to compute RPKM in R?

First off, Don’t use RPKMs. They are truly deprecated because they’re confusing once it comes to paired-end reads. If anything, use FPKMs, which are mathematically the same but use a more correct ...
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19 votes
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What is the difference between a transcriptome and a genome?

In brief, the “genome” is the collection of all DNA present in the nucleus and the mitochondria of a somatic cell. The initial product of genome expression is the “transcriptome”, a ...
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  • 428
11 votes
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Alignment based vs reference-free (transcriptome analysis)?

First of all, I would emphasize that "alignment-free" quantification tools like Salmon and Kallisto are not reference-free. The basic difference between them and more traditional aligners is that they ...
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9 votes

How to compute RPKM in R?

RPKM is defined as: RPKM = numberOfReads / ( geneLength/1000 * totalNumReads/1,000,000 ) As you can see, you need to have gene lengths for every gene. Let's say ...
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9 votes
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Normalization methods with RNA-Seq ERCC spike in?

You may consider using RUVSeq. Here is an excerpt from the 2013 Nature Biotechnology publication: We evaluate the performance of the External RNA Control Consortium (ERCC) spike-in controls and ...
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7 votes

Alignment based vs reference-free (transcriptome analysis)?

I wouldn't say Kallisto (or Salmon) are reference-free. They use a transcriptome as reference anda concept called pseudo-alignment which greatly speed up the process of assigning your reads to a ...
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6 votes

What is the difference between a transcriptome and a genome?

They are two very different things. Your genome is a large section of about 3 billion DNA nucleotide bases. It has no concept of exon and introns. Transcriptome is a study of transcriptions. You have ...
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  • 2,649
6 votes
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Coverage calculation: long reads (RNA-seq)

The read length is irrelevant when calculating the mean coverage statistic. It's simply the total number of bases sequenced divided by the target Xome length. In the example provided in the question, ...
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  • 11.7k
6 votes
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tools to reconcile experimental transcripts with reference annotation

I've never tried this myself, so I don't know how easy this is... One option would be to start with GMAP, which is meant to align whole transcripts against the genome. The really nice thing about ...
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  • 19.2k
6 votes

What is "Possible polymerase run-on fragment"?

On most coding genes (with the exception of replication dependent histone genes), Transcripts (as opposed to transcription) are terminated by cleavage polyadenylation sites, where the growing ...
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  • 3,211
6 votes
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gene-level versus transcript-level analysis

There are a variety of reasons people use gene-level quantitations. Transcript-level differences are difficult to biologically interpret. Let's be honest, few groups are likely to put in the work ...
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5 votes
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RapMap: reference transcriptome for simulated reads

Thanks for your interest in RapMap. At that time we were using flux simulator for simulating read sequence data. We used the genome and gtf file together as an input to flux. I dug into the scripts ...
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5 votes
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What is "Possible polymerase run-on fragment"?

The general idea behind a run-on fragment is that it's background noise. This derives from an open area of the genome that's next to an area that's actually being transcribed. Thus, all of the ...
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  • 19.2k
5 votes

tools to reconcile experimental transcripts with reference annotation

As per my answer to @_julien_roux on twitter: Trying to find novel transcripts within the context of an existing annotation is much less straightforward. You probably need to do a "genome-guided ...
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  • 356
5 votes
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PacBio long-reads impact in transcriptome de novo assembly?

A few comments: Never use N50 as a metric especially for transcriptomes. It has some semblance of relevance for genome assembly, but all that is void for a transcriptome with inherently dynamic ...
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5 votes
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Calculating most abundant transcript from RNA-Seq data

Transcript abundance quantification is a tricky topic since a read often could belong to several transcripts, so any "count" is a best guess as to which transcript it actually originates ...
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4 votes
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Linear models of complex diseases

There are two potential sources of bias in this design. We cannot distinguish correlation from causation. Imagine two cases. In the first, the disease progression is inducing immune response. Later ...
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4 votes

Normalization methods with RNA-Seq ERCC spike in?

We have added ERCC spike-ins to all our RNASeq data, just in case other people might find it useful in the future. However, I have never used it in my own analyses because I can't think of a ...
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4 votes

Analysis of differential transcript usage (DTU)

I will take the liberty of giving one possible answers to my own question – but I’m very interested in other answers. One analysis type that such data enables is the analysis of transcript switches ...
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4 votes

What process and input data is required for a cellranger reference transcriptome?

Your problem is caused by using the transcriptome fasta file rather than the genome fasta file. You've already given it transcriptome information with ...
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  • 19.2k
4 votes

gene-level versus transcript-level analysis

To add to the list that Devon Ryan outlined (or perhaps to elaborate on point 2?): Although Salmon/Kallisto/RSEM are the more accurate in their transcript quantification than the methods they ...
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  • 3,211
4 votes
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How to find novel transcripts using GFFcompare?

The output of gffcompare includes several files per run (just like cuffcompare). Example for a run: ...
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  • 2,644
3 votes
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Hierarchical models with limma?

Yes, you can use limma for this mixed model approach. Like you suggest, the random effect (persons) can be put in duplicateCorrelation(). Here is a similar example with RNAseq data, on bioconductor ...
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3 votes
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Command not found error on HISAT2

tldr: Remove the $ from the command. I imagine you're literally typing $hisat2, where you mean to instead type ...
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  • 19.2k
3 votes

Difference between de novo transcriptome assembly methods

It's really a misnomer to call StringTie's non-reference based mode 'de-novo.' It's still using the reference genome sequence to guide the transcript assembly, it's just not using the reference ...
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  • 1,806
3 votes

Difference between de novo transcriptome assembly methods

Your intuition is correct. stringTie is just looking at clumps of alignments and how they might relate to each other (either due to spliced alignments or proximity). Trinity is doing the more ...
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  • 19.2k
3 votes

How to compare transcriptomic profiles of two cell types (single cell RNA-seq)?

You are looking for a way to compare expression profiles between cell types, and your data is counts of genes expressed per cell. Your issue is that the data is highly dimensional. It has as many ...
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  • 1,119
3 votes

Analysis of differential transcript usage (DTU)

I am not sure if this has been done, is common, or are research lines, but here is what I think can be done with transcripts' differences (aside from comparing the change of the expression of each ...
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  • 4,602
3 votes
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Reduce number of transcripts in a highly variable de novo transcriptome assembly

As you've suggested, CD-HIT works for reducing transcript numbers. We used a mixture of expression-based filtering and CD-HIT for reducing transcript counts for our genome-guided transcriptome ...
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  • 11.7k
3 votes
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At what stage of a transcriptome assembly is it better to perform read contaminant filter?

First, to answer your question about mapping to a low-quality reference: 1. Mapping For mapping, low genome contiguity (low N50) doesn't really matter. You will be using a spliced aligner and short ...
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