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I'm wondering if someone can check my work using STAR to map and StringTie for assembly and quantification. My data is from RNAseq on 29 timecourse samples, 60M PE 100bp reads/sample. My initial goal is to discover novel isoforms that are present in my timecourse compared to the genome annotation file: Mus_musculus.GRCm38.100.gtf
Can someone please confirm that my merged.stats make sense and there aren't any red flags in the results (or glaring problems with the pipeline)? Also, help to determine how many novel transcripts are present in my samples and not in the reference? I see novel introns and novel exons and novel loci, but not sure how to calculate how many novel transcripts are there. Maybe this is a dumb question.....Help!
Hi,Since you have 100bp PE reads (that too in good lib. size), you should use additional confidence measures for alignment in STAR. In the STAR manual, look at the ENCODE options section. Specifically --outFilterType and --alignSJoverhangMin are worthwhile in keeping false +ve rate low if StringTie (or any other transcriptome assembler) is to be used downstream. Also, you are using --outFilterMultimapNmax 1, which means any read can map to max. 1 loc. only. Mammalian genomes have loads of repetitive/ transposable element content and sometimes these are biologically relevant. You might be loosing out genuine alignments where only one of the read has multi-hits, but the other in the pair has a unique hit. The default value of 10 is sensible and not too lenient.
Now to StringTie; I think that you are missing one step after stringtie --merge. The GTF output created from this step has all transcript models detected in any of your samples. The next step, before going to gffcompare, is to run StringTie again for each of the samples using the output GTF from stringtie --merge. Remember that for this second run of StringTie, the -e param should be activated, so that only transcript models present in the input GTF (i.e. adipo_stringtie_merged.gtf) are used. We do not want any more novel isoform detection in this second run.
And I apologize. I should've been more specific as to what step I am on in the analysis. I paused after I compared the merged.gtf file to the genome annotation file so that I could see how many novel transcripts were found in the entire time-course. After this step I will run the below command for all my samples using the merged.gtf file and then look at novel transcripts in each of the samples using gffcompare.
While I have you- I actually did try running this command on one sample and then looked at the "WT2_gene_abund.txt" output file and saw that only the MSTRG transcripts had counts. All other transcripts (those annotated with gene names) had all "0.0" so something obviously went wrong... any idea why this is??
You are right, you do not need to have provided anything additional to STAR for strand-sensitivity. But for StringTie, you need to specify either the --rf or --fr param to indicate which strand is being sequenced. If you are not sure which strand has been sequenced, then you can identify that empirically by running STAR with --quantMode GeneCounts param, which generates a count file. If you then look into that count file and see something like this (for e.g.) -
Coming to StringTie run with the merged GTF; I am not sure why you would find only MSTRG loci with counts.Maybe you should check from STAR stage if things are going alright. Look into Log.final.out created by STAR run, and within there what is the value for Uniquely mapped reads % ?A decent dataset would have anything upwards of 70%If this value is not upto the mark then that might indicate something wrong. Also, before the alignment itself, you would have run fastQC on the raw reads. That should have told if the fastq reads are more or less ok.Finally, you could look into the GTF produced during the first run of StringTie. Did you have FPKM or TPM values present for known genes? Pick up some positive controls (genes which you know would have expression value) and grep their symbol (or their ENSMUSG ID) in the GTF file (from 1st run). If you still are not seeing values for known markers then open your STAR BAM within IGV and look at some of those genes. Do you see alignment with coverage rising above exons, and "blue bridges" spanning introns (visual representation in IGV for splice-aware alignment).As an e.g., here is a IGV snapshot for a human RNA-seq data.
I looked at the WT2.gtf file (the one created during the first run of StringTie and strangely it includes a very small number of known genes (i.e. mostly STRNG identifiers). I searched for well known genes and could not find them, so that explains why they are not being counted in the second run of StringTie. I'm not sure why this is.... I will look at the STAR output in IGV. Was the appropriate STAR .bam file "Aligned.sortedByCoord.out.bam" or should In have used "Aligned.toTranscriptome.out.bam" for the first run of StringTie??
Yes, "Aligned.sortedByCoord.out.bam" is the file to look at using IGV.Why should you not see blue bridges.. if the read quality was ok (as per fastQC) and STAR says 90% unique alignment. That is quite puzzling, to be honest.If there was a chance of contamination in the reads (some other organism.. just wondering) then your STAR unique algn. % should have been much lower.You can do some more debugging -1) Look further into the Log.final.out from STAR and look at the values for the -
I literally have no idea what is wrong. When I look at the STAR Aligned.sortedByCoord.out.bam file in IGV, it's showing that reads mapped to annotated genes, but then StringTie just isn't assigning the counts correctly. It's really weird.
1) If the gene count data from STAR seem alright then the issue might be somewhere in StringTie.2) If quantification of alternative transcripts satisfies your question then you could skip all this alignment+ assembly and go to tools like Kallisto. It would (once you have the index ready) give you isoform-level (NOTE - known isoforms only) quantification in 10-15min per sample.3) Finally, if novel events (novel exons, novel exon junction boundaries, retained intron) are your interest and StringTie is fails to work, there are other tools as well. Check rMATS, MAJIQ, LeafCutter etc.
Looking at the gene counts file, I see the following. There are only 90 gene IDs listed. There should be 55,000 genes. Should they all be listed in this file, or is this just a subset? I should say that I checked a few of these and they are found in the merged.gtf that Stringtie gives me (sorry for jumbled formatting...not sure how to make it look like it does in the file I'm coping from)
You could further check by going into your index dir. and looking for geneInfo.tab file. If you do head command on it, then the 1st line tells you the number of genes in the index. You could also wc -l that file for the same outcome.You could also check the log file created while the index was made.
I re-created the index a bunch of times using different genome and annotation files and the same thing kept happening, BUT I had a star index I did a few months ago on a different drive and that one looks fine. The arguments for the one that worked (from GenomeParameters.txt) was-
I actually tried running the indexing step on a different machine and it worked with the same exact code that failed above (looked at the geneInfo.tab file and everything looked good), so it's something funky behind the scenes on my computer. No idea. You've been a ton of help! I appreciate it.
I did go through and run the pipeline again (for only 4 samples tol see if the results make sense) and am looking at the merged.stats file and the numbers for novel stuff is REALLY low, which makes me think I need to change my run parameters somewhere to be less stringent somehow. Here is all the code I ran and the contents of my merged stats file:
Hi,This STAR+StringTie workflow is able to detect novel transcripts as far as I can tell from my own experience with multiple datatypes (splicing defects, CRISPR knockouts etc.). But to be fair, I knew which gene to look at. So, I do not have an idea of how much novel stuff you can detect on average in human/ mouse RNA-seq.
In any case, 435 novel loci is not too bad. There could be two conditions I think, broadly which would lead to detecting novel stuff -a) A transcript/ gene which is expressed in that tissue, but not known in reference annotationb) A transcript/ gene arising due to aberrant splicing. This could be happening due to say a splice-site altering mutation. Or it could also happen if splicing complex proteins are compromised (some known cases in human data include SF3B1, U1 snRNA etc.)
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Caitlin Mullen earned a BA in English and creative writing from Colgate University, an MA in English from NYU, and an MFA in fiction from Stony Brook University. While at Stony Brook, she taught undergraduate creative writing, served as an editor and contributing writer for The Southampton Review, and worked as a bookseller at WORD in Greenpoint. She grew up in upstate New York and the Jersey Shore and currently lives in Brooklyn. Please See Us is her debut novel.
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