I would suggest use RALEE—RNALignment Editor in Emacs. It can get for you the consensus secondary structure, you can move left/right sequences and their secondary structures (you can't do it in JalView!), and more.
It's an Emacs mode, so could be a bit hard to start off, but just try, you don't have to use all Emacs features to edit your alignments!
It's unclear if your paired-end reads are actually in fasta format, I'll presume that they're in fastq instead.
The easiest tool to use is salmon, which nicely deals with things like multimapping. If you're trying to judge the quality of an assembly, then I recommend having a look at transrate, which uses some related methodology for assessing contig ...
There's nothing really special about RNA alignments, you can use any alignment editor, including whichever one you use for protein. That said, a classic and very useful tool for this sort of thing is JalView. It can be installed locally or run as a Java webapp from your browser.
Jalview has built in DNA, RNA and protein sequence and structure ...
I expect there was a sequencing problem during the last base, where some of the reagents were running low on the sequencer. This won't pose any real problem, RNAseq aligners like STAR will just soft-clip the last base or two if they're mismatches.
It's common to see a bit of bias toward the 5' or 3' ends in RNAseq, mostly due to whether poly-A selection was ...
split reads - These are read that have two or more alignments to the reference from unique region of the read. In this example a 150bp read sequenced from RNA could have base 1-75 aligning to the 3' end of exon2 and bases 76-150 aligning to the 5' end of exon3. This would be a split read because it have two alignments (exon2 and exon3) and those alignment ...
I've had great results using minimap2, particularly when combined with a pre-treatment of Canu for error correction (using minimap2 for the read-to-read mapping):
# correct reads
~/install/canu/canu-1.6/Linux-amd64/bin/canu overlapper=minimap \
genomeSize=100M minReadLength=100 minOverlapLength=30 -correct \
-p 4T1_BC06 -d 4T1_BC06 \
Spliced aligners just make many small matches along the read pair or single read to determine splice junctions and output an alignment for the whole read, as long as it matches somewhere in the genome.
mRNA and translation
In the cell, after transcription, introns are spliced out of RNAs to become mature RNA. The protein-coding sequence does have ...
Try reducing the number of threads.
When multithreading the index creation (and other memory bound tasks), the memory usage increases linearly with the number of threads. If you required a 10 GiB working space work with a single thread, then the requirement for 10 threads would be at 100 GiB.
STAR is particularly memory hungry for the index creation to ...
STAR versions 2.6.0b and 2.6.0a are unstable, as written Issues performing variant calling with GATK you are using version 2.6.0b. You should switch to version 2.6.0c which is stable. Also avoid to perform multiple alignments in the same directory.
I don't think you can use the --quantMode GeneCounts option with no annotations. I think the error is trying to look for an exon file generated from the annotations to do the quantitation on. Remove that and I think it should work, as the manual specifically states that annotations are optional but highly recommended.
If you only have one gene and you only need to do this once then the simplest possible workflow is to generate the aliment using STAR (optimally with the two pass method) and open the two resultant .bam files in IGV, the coverage column at the stop above your alignment track should clearly show the counts of reference and non-reference supporting reads. The ...
For counting reads I use mpileup, e.g. samtools mpileup --reference hg38.fa -r Chr10:18000-45500 input.bam, which will give base-resolution coverage for a BAM file.
I've written my own script to process mpileup output and make it easier to understand. By default it reports read coverage as a proportion of total coverage, but this can be modified by using ...
Just use minimap2 in split alignment mode to realign the reads.
If that is not an option, then you could try using pysam to modify the CIGAR strings. I do not recommend this, as there are many opportunities for subtle bugs because the SAM spec is complex. You would need to:
Sort the BAM on read ID so that you can efficiently retrieve reads that you want to ...
It's not possible to compute absolute expression from RNASeq reads if they are processed in the usual way, where a sequencer produces the same number of reads regardless of the input RNA amount. At best, RNASeq will give you an indication of proportional expression within a single sample. For this reason, relative expression (i.e. that used by differential ...
In addition to the other answer (to reduce the number of threads), you might also need to add a --genomeChrBinNbits argument. Your reference has more than 5000 scaffolds, so you need to adjust the --genomeChrBinNbits argument to reduce RAM consumption. Found here.
--genomeChrBinNbits} = min(18, log2(GenomeLength/NumberOfReferences))
You can back to your bam file and use cuffdiff tools it is very able to do your job follow this to liks
Ok, the answer was right here and I missed it, posting it in case anyone else will do the same mistake as me in the future.
U = unpaired
P = paired
H = hairpin
B = bulge
I = internal loop
M = multiloop
S = stem (or stack)
E = external loop
For discordant mapped reads, a position of 0 for the pair of a mapped read usually means that the pair couldn't be mapped anywhere. This would be expected for fusion events because some reads could originate from across the fusion site (i.e. not being fully contained in either "unfused" gene), leading to a sequence that doesn't match anything in ...
It's not so much that you have "intronic contamination" or "genomic contamination", rather you're not selecting explicitly for full-length mature transcripts with rRNA depletion. That is the most common cause for higher intronic read rates. There's nothing you can do about this post-hoc, just continue along.
BTW, many lncRNA's are ...
I don't see why you are trying to compare two totally different measurements.
When FASTQC says a read is unique, it means that no other read shares its sequence.
When STAR says a read is uniquely mapped, that means it maps to one and only place. That does not mean that there aren't a hundred more identical reads that map nicely to the exact same place.
I know this is an old post, but if this plot is from after trimming I would suggest a different explanation: some trimming tools remove poly-A sequences from reads. If that's the case then any read ending with A will have that removed, this leads to a 0% A base content in the final base position (and a dropping %A in the final couple of bases).
This should ...