User: CHANG
CHANG • 40
- Reputation:
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- United States
- Last seen:
- 1 year, 1 month ago
- Joined:
- 6 years, 6 months ago
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Posts by CHANG
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... any suggestion for tools on MCH Class II? ...
written 2.8 years ago by
CHANG • 40
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... I'm using gdc-client v1.2.0. I specifically sort my manifest file by patient id so I may download tumor-normal pair BAMs one after another. But in reality, BAMs were downloaded in some random order, which is not from the top to bottom of my sorted manifest file. Do other people have the same proble ...
written 3.7 years ago by
CHANG • 40
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... I followed the a paper advice (linked in my post) in removing mismatches within the first 6 bases of 5ʹ read ends due to random-hexamer priming. it cut down a good proportion of the 3' UTR mutations.
**Before removing mutations in first 6bp of 5' read**
3'UTR 1745 36.18%
Intron 570 11.82%
Mi ...
written 4.5 years ago by
CHANG • 40
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... I intersected my mutation list with regions of callable loci. The following are percentages of mutations that are callable. The percentages is high across mutation types.
3'UTR 99.37%
Missense_Mutation 98.91%
Silent 99.50%
Intron 94.8%
Can you elaborate on how to normalize the number of m ...
written 4.5 years ago by
CHANG • 40
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... Picard's rnaseqmetrics shows that majority of 43% of my reads are in coding and 27% in UTR. It doesn't seem like mapping explains the majority of the mutation.
PCT_CODING_BASES 0.436
PCT_UTR_BASES 0.276
PCT_INTRONIC_BASES 0.067
PCT_INTERGENIC_BASES 0.231
ll ...
written 4.5 years ago by
CHANG • 40
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... I noticed a high percentage of 3'UTR mutations human tumor RNAseq. Here is a [paper][1] (Fig.3) which shows < 10% of UTR mutations in their RNAseq data.
Is it unusual for such high percentage of UTR mutations? What could be some explanations? I greatly appreciate your feedback.
Here are m ...
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... Can you clarify how best splitter (gene) is chosen in a random forest? is it base on an expression threshold? if so , how is the threshold calculated?
...
written 4.9 years ago by
CHANG • 40
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... 1. In this post, https://www.biostars.org/p/106127/ It says TCGA RNAseqV2 rsem.genes.normalized_results are calculated by "For gene level estimates you divide all "raw_count" values by the 75th percentile of the column (after removing zeros) and multiply that by 1000."
What are the reasons for mul ...
written 5.0 years ago by
CHANG • 40
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... What about pooling all the raw cell files from studies and run fRMA, is there any advantage of doing that over fRMA on individual studies then run COMBAT?
...
written 5.1 years ago by
CHANG • 40
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... Thank you in advance.
...
written 5.1 years ago by
CHANG • 40
Latest awards to CHANG
Great Question
2.4 years ago,
created a question with more than 5,000 views.
For identifying miRNA in RNAseq
Popular Question
2.4 years ago,
created a question with more than 1,000 views.
For TCGA RNAseqV2 upper quartile normalization with x1000 adjustment factor
Popular Question
2.4 years ago,
created a question with more than 1,000 views.
For TCGA RNAseqV2 upper quartile normalization with x1000 adjustment factor
Popular Question
3.5 years ago,
created a question with more than 1,000 views.
For TCGA RNAseqV2 upper quartile normalization with x1000 adjustment factor
Popular Question
3.9 years ago,
created a question with more than 1,000 views.
For identifying miRNA in RNAseq
Popular Question
4.7 years ago,
created a question with more than 1,000 views.
For identifying miRNA in RNAseq
Popular Question
4.7 years ago,
created a question with more than 1,000 views.
For identifying miRNA in RNAseq
Popular Question
5.0 years ago,
created a question with more than 1,000 views.
For RNA seq: tuxedo protocol - separate non-overlapping genes of same XLOC id in cuffdiff output
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