Wayne:High Throughput Sequencing Resources: Difference between revisions
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== High throughput (HT) datatypes and workflow == | == High throughput (HT) datatypes and workflow == | ||
File formats and conversions <br> | <u>Platform and read type</u><br> | ||
<ul> | |||
<li> Illumina single-end vs. paired-end | |||
<li> 454 Roche | |||
<li> SOLiD | |||
<li> MiSeq | |||
<li>Ion Torrent | |||
</ul> | |||
<u> File formats and conversions </u><br> | |||
<ul> | <ul> | ||
<li> bcl | <li> bcl | ||
| Line 9: | Line 18: | ||
</ul> | </ul> | ||
<br> | <br> | ||
Deplexing using barcoded sequence tags<br> | |||
<u> Deplexing using barcoded sequence tags </u><br> | |||
<ul> | <ul> | ||
<li> Editing (or hamming) distance | <li> Editing (or hamming) distance | ||
</ul> | </ul> | ||
<br> | <br> | ||
Quality control <br> | |||
<u> Quality control </u><br> | |||
<ul> | <ul> | ||
<li> Fastx tools | <li> Fastx tools | ||
| Line 20: | Line 31: | ||
</ul> | </ul> | ||
<br> | <br> | ||
<u> Trimming and clipping </u><br> | |||
<u> | |||
<ul> | <ul> | ||
<li> | <li> Trim based on low quality scored per nucleotide position within a read | ||
<li> | <li> Clip sequence artefacts (e.g. adapters, primers) | ||
</ul> | </ul> | ||
<br> | |||
<u>DNA sequence analysis </u><br> | <u>DNA sequence analysis </u><br> | ||
<br> | |||
<u>RNA-seq analysis</u><br> | <u>RNA-seq analysis</u><br> | ||
<ul> | <ul> | ||
Revision as of 16:51, 15 February 2013
High throughput (HT) datatypes and workflow
Platform and read type
- Illumina single-end vs. paired-end
- 454 Roche
- SOLiD
- MiSeq
- Ion Torrent
File formats and conversions
- bcl
- qseq
- fastq
Deplexing using barcoded sequence tags
- Editing (or hamming) distance
Quality control
- Fastx tools
- Using mapping as the quality control for reads
Trimming and clipping
- Trim based on low quality scored per nucleotide position within a read
- Clip sequence artefacts (e.g. adapters, primers)
DNA sequence analysis
RNA-seq analysis
- Quantifying and annotating aligned reads
- DESeq
- edgeR
A variety of additional R packages are available for normalizing RNA-Seq read count data and identifying differentially expressed genes (DEG):
- easyRNASeq (simplifies read counting per genome feature)
- DEXSeq (Inference of differential exon usage)
- DEGseq
- baySeq (also see: segmentSeq)
- Genominator (Bullard et al. 2010)
