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Thanks....I am running TopHat now after clipping off the barcode tags. I'll report back on how it works.
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@bruss. That sample 35d-1 does indicate problems. Dips in quality near the ends, especially the 3' end, are normal. But that dip in quality at around base 18 and again around base 42 is not good. So you are going to have some reads that are not usable. Depending on your next processing steps these reads may or may not give you problems; i.e., some programs will just ignore them which will be a good thing.
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Newbie: Per Base Quality Graphs
I am a NGS newbie as well. I ran FastQC on my raw fastq files and see some that look perfect and some that have small dips in the middle. All stay mostly above 28 (box plot). Are both of these per base quality graphs acceptable? What might cause the dips at 17-19? See attached pdf.Attached Files
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Hello,
I am trying to download FASTx on a computer cluster and am running into a common problem "No package gtextutil-0.1" found. But when I try to export path to environmental path, i get errors of PKG_CONFIG_PATH: Undefined or errors of export not defined.
What am I doing wrong? I am trying to configure the fastx_toolkit-0.0.13.2.tar.bz2 and libgtextutils-0.6.1.tar.bz2 downloads.
PROBLEM SOLVED!! PLEASE ignore post.
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/usr/local/bin/fastq_quality_boxplot_graph.sh [-i INPUT.TXT] [-t TITLE] [-p] [-o OUTPUT]
Try to use the "-p" option and it will generate postscript file which can be opened with GSview.
Originally posted by son_nexg View PostThanks a lot, -Q option worked for me!!
I am now onto the next step and generating the nucleotide distribution graph by doing:
fastx_nucleotide_distribution_graph.sh \
-i /PATH/Sample_xx.fastq.stats \
-o /PATH/Sample_xx.fastq.stats_nuc.png \
-t Sample_xx
But getting the following error:
line 0: undefined variable: vertical
line 0: undefined variable: invert
gnuplot> set style histogram rowstacked
^
line 0: expecting 'data', 'function', 'line', 'fill' or 'arrow'
gnuplot> set style data histograms
^
line 0: expecting 'lines', 'points', 'linespoints', 'dots', 'impulses',
'yerrorbars', 'xerrorbars', 'xyerrorbars', 'steps', 'fsteps',
'histeps', 'filledcurves', 'boxes', 'boxerrorbars', 'boxxyerrorbars',
'vectors', 'financebars', 'candlesticks', 'errorlines', 'xerrorlines',
'yerrorlines', 'xyerrorlines', 'pm3d'
line 0: undefined function: xtic
These are the first 4 lines of my stats file:
column count min max sum mean Q1 med Q3 IQR lW rW A_Count C_Count G_Count T_Count N_Count Max_count
1 12192845 2 40 446955197 36.66 38 39 39 1 37 40 1290565 4112034 5828477 943901 17868 12192845
2 12192845 2 40 441775514 36.23 37 39 39 2 34 40 5245648 1771654 1927735 3247808 0 12192845
3 12192845 2 40 441662699 36.22 37 39 39 2 34 40 2061208 2422450 2202622 5506565 0 12192845
Any guesses what might be happening with the input format?
Cheers!!
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Hi kmcarr, Thank you for your reply regarding my questions and apologies for the delayed response. According to the bioinformaticians that also initially quality checked our data this is the reply I received when I asked which RTA version was used:Originally posted by kmcarr View PostI don't know that I can explain it but I can reassure you that what you are seeing is not out of the ordinary.
1. In a decent run you will not see significant decrease in the median Q score over only 36 cycles. In recent versions of their base caller Illumina caps the Q score at 34 and it seems the majority of its bases meet this level. (I think they're being a little generous with themselves but that's just one man's opinion.)
2. I very often see slight deviations in the base call composition over the first 2-3 cycles so you are not alone. There are two basic possibilities: the abnormal distribution is a true representation of the the DNA sequence meaning that the fragmentation or selection of fragments for library preparation is not strictly random; or the observation is due to some artifact of the sequencing or data analysis. I think the first possibility can be ruled out because I observed the bias in libraries fragmented both by nebulizer and by Covaris. It seem highly unlikely that two completely different fragmentation methods would produce the same non-random result. Also, I found that degree of bias in the first couple of cycles reduced dramatically in the shift from pipeline v1.3 to pipeline 1.4 (or maybe it was 1.4 to 1.5).
I used to remove bases from the first 2 cycles as you did but I stopped doing this after the change in the pipeline mitigated the problem. Do you know which pipeline (or RTA) version was used to call the bases?
"The RTA version used to basecall your sequence was version 1.8.70.0. Pipeline software version 1.4 was last used in October 2009."
They think that it might be due to library preparation and the fragmentase used to fragment the sample DNA. Anyhow, I trimmed those first few bases away which improved subsequent mapping, and glad to see that it is not out of the norm!
Thanks,
Laura
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Sorry I dont use FASTX for doing this step. I found FASTQC gives better pictures
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Thanks a lot, -Q option worked for me!!Originally posted by flobpf View PostSeems like you may have Sanger quality encoding (### at the end of sequence in FASTQ). Try this:
fastx_quality_stats -i /PATH/Sample_XX.fastq -Q 33 -o /PATH/Sample_xx.fastq.stats
I am now onto the next step and generating the nucleotide distribution graph by doing:
fastx_nucleotide_distribution_graph.sh \
-i /PATH/Sample_xx.fastq.stats \
-o /PATH/Sample_xx.fastq.stats_nuc.png \
-t Sample_xx
But getting the following error:
line 0: undefined variable: vertical
line 0: undefined variable: invert
gnuplot> set style histogram rowstacked
^
line 0: expecting 'data', 'function', 'line', 'fill' or 'arrow'
gnuplot> set style data histograms
^
line 0: expecting 'lines', 'points', 'linespoints', 'dots', 'impulses',
'yerrorbars', 'xerrorbars', 'xyerrorbars', 'steps', 'fsteps',
'histeps', 'filledcurves', 'boxes', 'boxerrorbars', 'boxxyerrorbars',
'vectors', 'financebars', 'candlesticks', 'errorlines', 'xerrorlines',
'yerrorlines', 'xyerrorlines', 'pm3d'
line 0: undefined function: xtic
These are the first 4 lines of my stats file:
column count min max sum mean Q1 med Q3 IQR lW rW A_Count C_Count G_Count T_Count N_Count Max_count
1 12192845 2 40 446955197 36.66 38 39 39 1 37 40 1290565 4112034 5828477 943901 17868 12192845
2 12192845 2 40 441775514 36.23 37 39 39 2 34 40 5245648 1771654 1927735 3247808 0 12192845
3 12192845 2 40 441662699 36.22 37 39 39 2 34 40 2061208 2422450 2202622 5506565 0 12192845
Any guesses what might be happening with the input format?
Cheers!!
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Seems like you may have Sanger quality encoding (### at the end of sequence in FASTQ). Try this:Originally posted by son_nexg View PostHi guys,
I want to use 'fastx_quality_stats' for my illumina data but it gives the following error:
fastx_quality_stats -i /PATH/Sample_XX.fastq -o /PATH/Sample_xx.fastq.stats
fastx_quality_stats: Invalid quality score value (char '#' ord 35 quality value -29) on line 4
I'd appreciate any help I can get with this.
Thanks a lot!!
fastx_quality_stats -i /PATH/Sample_XX.fastq -Q 33 -o /PATH/Sample_xx.fastq.stats
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This is new casava 1.8 fastq output; you should use a new version of fastqc for reading these files (There is another thread[1] where there is a link with a development snapshot of fastqc allowing to correctly reads v1.8 fastq files).Originally posted by son_nexg View PostHi guys,
I want to use 'fastx_quality_stats' for my illumina data but it gives the following error:
fastx_quality_stats -i /PATH/Sample_XX.fastq -o /PATH/Sample_xx.fastq.stats
fastx_quality_stats: Invalid quality score value (char '#' ord 35 quality value -29) on line 4
This is how my fastq file looks like:
head -4 /PATH/Sample_xx.fastq
@HWI-ST212:160:AB01NFABXX:4:1101:1355:2086 1:N:0:ATCACG
CGGGAAGTATGTACACGGGGTACGTGCCAAGCATCCTCGCGCGACCCCGAGAGCCTGGGGAGCGGGGGCTTGCCGGCCGT CGCACTCATTTACCCGGAGAC
+
HHHHHHHHHHHHHHHHHHHHHHFHHHHHHBHFHHHGHFHHHHHHHHHHHHHHHHHHHHHHEHHHHHHFEH?EEBFEDEE< >>5:ADC>CFE2CDEAD####
I'd appreciate any help I can get with this.
Thanks a lot!!
hth, Sven
[1]= http://seqanswers.com/forums/showthr...ghlight=fastqc
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Hi guys,
I want to use 'fastx_quality_stats' for my illumina data but it gives the following error:
fastx_quality_stats -i /PATH/Sample_XX.fastq -o /PATH/Sample_xx.fastq.stats
fastx_quality_stats: Invalid quality score value (char '#' ord 35 quality value -29) on line 4
This is how my fastq file looks like:
head -4 /PATH/Sample_xx.fastq
@HWI-ST212:160:AB01NFABXX:4:1101:1355:2086 1:N:0:ATCACG
CGGGAAGTATGTACACGGGGTACGTGCCAAGCATCCTCGCGCGACCCCGAGAGCCTGGGGAGCGGGGGCTTGCCGGCCGT CGCACTCATTTACCCGGAGAC
+
HHHHHHHHHHHHHHHHHHHHHHFHHHHHHBHFHHHGHFHHHHHHHHHHHHHHHHHHHHHHEHHHHHHFEH?EEBFEDEE< >>5:ADC>CFE2CDEAD####
I'd appreciate any help I can get with this.
Thanks a lot!!
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Maybe not...
Maybe this feature was not present in the old version. But atleast the one I am using gives a plot showing %A,%T,%C,%G at each position across all the reads. The image is named "per_base_sequence_content.png"Originally posted by upendra_35 View PostI have tried FastQC and it is quite good as it does most of the quality scoring stuff. The only graphic or quality checking that is missing is nucleotide distribution across each read which you can only do it either in Fastax or Galaxy.
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I have tried FastQC and it is quite good as it does most of the quality scoring stuff. The only graphic or quality checking that is missing is nucleotide distribution across each read which you can only do it either in Fastax or Galaxy.
Leave a comment:
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NGS has revolutionized clinical microbiology by offering new ways to identify and surveil pathogens without prior knowledge of the target. One popular approach is 16S rRNA and ITS amplicon sequencing, which targets specific microbial markers. But studies are increasingly incorporating shotgun metagenomics, a technique that sequences all DNA from a sample without targeting specific microbial markers. This approach allows more detailed taxonomic classification, detection...-
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Tumors contain complex mixtures of cell populations that evolve in response to treatment and host-specific factors. Tissue biopsies provide only static snapshots of a tumor’s state. But what if we could follow that evolution over time using a minimally invasive technique?
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