[super0925]

I think cutadapt can do quality trimming to, but if not then trim_galore can do both (it's a wrapper around cutadapt, in fact). There's also trimmomatic, which is relatively popular. I haven't used ion proton reads myself so I can't make any specific recommendations there.

Thank you. Sorry what I mean is that is same adatptor I need to remove "GGCCAAGGCG " like I did in Proton data?

Besides, I think cutadaptor could to Qualitytrimming, pls see help

Additional modifications to the reads:
-q CUTOFF, --quality-cutoff=CUTOFF
Trim low-quality ends from reads before adapter
removal. The algorithm is the same as the one used by
BWA (Subtract CUTOFF from all qualities; compute
partial sums from all indices to the end of the
sequence; cut sequence at the index at which the sum
is minimal) (default: 0)
--quality-base=QUALITY_BASE
Assume that quality values are encoded as
ascii(quality + QUALITY_BASE). The default (33) is
usually correct, except for reads produced by some
versions of the Illumina pipeline, where this should
be set to 64. (default: 33)
-x PREFIX, --prefix=PREFIX
Add this prefix to read names
-y SUFFIX, --suffix=SUFFIX
Add this suffix to read names
--strip-suffix=STRIP_SUFFIX
Remove this suffix from read names if present. Can be
given multiple times.
-c, --colorspace Colorspace mode: Also trim the color that is adjacent
to the found adapter.
-d, --double-encode
When in color space, double-encode colors (map
0,1,2,3,4 to A,C,G,T,N).
-t, --trim-primer When in color space, trim primer base and the first
color (which is the transition to the first
nucleotide)
--strip-f3 For color space: Strip the _F3 suffix of read names
--maq, --bwa MAQ- and BWA-compatible color space output. This
enables -c, -d, -t, --strip-f3, -y '/1' and -z.
--length-tag=TAG Search for TAG followed by a decimal number in the
name of the read (description/comment field of the
FASTA or FASTQ file). Replace the decimal number with
the correct length of the trimmed read. For example,
use --length-tag 'length=' to correct fields like
'length=123'.
-z, --zero-cap Change negative quality values to zero (workaround to
avoid segmentation faults in old BWA versions)

So is that -q set to 5?

[dpryan]

Ah no, the adapter is different. The quality thresholding works the same though.

[super0925]

Ah no, the adapter is different. The quality thresholding works the same though.

But which adapter do I need to remove?
Sorry I haven't got the raw data from Illumina.
How could I find it?
The adapter of proton's data 'GC....' is recommended from Ion Community.

[dpryan]

Usually it's something like AGATCGGAAGAGC, which is the invariant part (illumina uses Y-shaped adapters). You can always ask whomever is doing the sequencing for you if this leads to problems. BTW, you should also run fastQC on things after trimming as that'll tell you if you missed something obvious or trimmed off the wrong thing.

[super0925]

Usually it's something like AGATCGGAAGAGC, which is the invariant part (illumina uses Y-shaped adapters). You can always ask whomever is doing the sequencing for you if this leads to problems. BTW, you should also run fastQC on things after trimming as that'll tell you if you missed something obvious or trimmed off the wrong thing.

Thank　you. I also have found it on trim_galore --help.
For you said (I trim off adapters and bases with a phred score of 5 or below)
Is this command line work fine?
trim_galore --length 100 --quality 20 --stringency 5 SampleSeq1.fastq

[dpryan]

You'll want something like:

trim_galore --length 20 --quality 5 -s 5 sample.fastq

[super0925]

You'll want something like:

trim_galore --length 20 --quality 5 -s 5 sample.fastq

Thank you! That is so-called 'gentle trimmed'

[super0925]

Usually it's something like AGATCGGAAGAGC, which is the invariant part (illumina uses Y-shaped adapters). You can always ask whomever is doing the sequencing for you if this leads to problems. BTW, you should also run fastQC on things after trimming as that'll tell you if you missed something obvious or trimmed off the wrong thing.

If I find my mapping rate is by Tophat is ~80-85%, is it high, normal, or low?
In the Ion-proton data, the Community recommended me to follow that procedure (the Tophat mapping rate is ~50% and if we use bowtie2 to mapping the unmapped reads and merge , the rate will be increased to ~90%)
Do I need to re-aligned the unmapped reads and merged them together in Illumina as well? Or just leave it and go to Cufflinks and DE analysis?

[dpryan]

Depending on the species that's not unreasonable. You might also try STAR, though that requires more memory. Tophat2 can use bowtie2 already, so just don't give it the --no-mixed option and it will try to map unmapped paired-end reads as single-end for you.

[super0925]

Depending on the species that's not unreasonable. You might also try STAR, though that requires more memory. Tophat2 can use bowtie2 already, so just don't give it the --no-mixed option and it will try to map unmapped paired-end reads as single-end for you.

Hi I am talking about Bovine cell and sing-ended reads,my mapping rate is by Tophat is ~80-85%.
Thank you!
MY question is the mapping the unmapped reads is meaningful or essential in mapping?

[dpryan]

Ah, then you're unlikely to gain much by remapping unmapped reads with bowtie2 (while it does allow more mismatches by default, if you're getting up to 85% alignment already then we're looking at seriously diminishing returns). You could try on one sample and see how much of a difference it makes.

[super0925]

Ah, then you're unlikely to gain much by remapping unmapped reads with bowtie2 (while it does allow more mismatches by default, if you're getting up to 85% alignment already then we're looking at seriously diminishing returns). You could try on one sample and see how much of a difference it makes.

Ok probably I will leave the 85%.
i think it is high enough

[dpryan]

Don't let the perfect be the enemy of the "good-enough, let's finish analysing the data"

[super0925]

Don't let the perfect be the enemy of the "good-enough, let's finish analysing the data"

Have you used STAR before? Or is it better than Tophat? I read some blogs talking about the mapping rate of STAR is higher than Tophat. But I don't know is it (higher or not) is really key to downstream analysis.

[dpryan]

Have a look at my answer over on biostars to a similar question. That should tell you most of what you want to know (particularly given the included links and replies from others).