Please make suggestions and / or vote for the troubleshooting topic that you would like covered in the open section of the BFX workshop.
No similar topics found.
+4
Assembly using PacBio plus other data (illumina, rna-seq, etc): software options, optimal data set, etc.
We have old data from other platforms we want to integrate. Also, very interested in using RNA data to guide genome assembly.
+4
Transcript abundance quantification with IsoSeq
With IsoSeq data, the number of full-length reads matching a given transcript gives an approximate indication of the transcript's relative abundance. But the biases introduced by PCR, normalization (if done), size selection and loading add a lot of noise to the measurement.
Is there an accurate way to measure transcript abundance using IsoSeq data alone?
Alternatively, assuming you believe that Illumina RNASeq data suffers from no such biases, is there a way to combine IsoSeq and Illumina data to get at abundance?
Is there an accurate way to measure transcript abundance using IsoSeq data alone?
Alternatively, assuming you believe that Illumina RNASeq data suffers from no such biases, is there a way to combine IsoSeq and Illumina data to get at abundance?
+2
Why don't similar-looking reads cluster together in IsoSeq?
Frequently an IsoSeq run produces a large number (hundreds) of clusters, far in excess of the expected number of unique isoforms present. This population includes many clusters which are very similar or even identical, which one would expect to have clustered together.
OTOH, combining slightly different reads could cause small differences to be ignored, when they may in fact be real.
How do we decide where to draw the line? Can we control the degree of similarity required for clustering?
OTOH, combining slightly different reads could cause small differences to be ignored, when they may in fact be real.
How do we decide where to draw the line? Can we control the degree of similarity required for clustering?
+1
Troubleshooting failed RS_IsoSeq.1 runs in classify and clustering step
Find out if errors are caused by the sample, the smrtanalysis installation or a combination thereof. How can the quality of a smrt cell run be checked and what can cause problems in the classify and cluster step.
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