Suggestions(2)
Exact(3)
Furthermore, the filtering removed transcripts with speculative coding capacity.
From the filtered transcripts, we removed transcripts with overlapping Swiss-Prot protein sequences (http://www.uniprot.org/).org/
We removed transcripts with lengths less than 150 bp, and the remaining 50,770 transcripts (98.38% of all transcripts), corresponding to 37,093 genes, were used for functional annotation and expression profiling in each sample.
Similar(57)
The normalized dataset was further filtered by removing transcripts with low intensity values and low across-sample variances; thus, 29892 transcripts were reduced to 26903.
After removing transcripts with expression levels below background, we included 12,596 transcripts in the analysis.
These extracted protein sequences were input to CD-HIT with 100% identity threshold to remove transcripts with identical protein translations.
A matrix of summed tag counts for each cell line/tissue and for each transcript was then compiled, removing transcripts with no tags in any cell type.
Then NOISeq count filter was used to remove transcripts with Counts per Million (CPM) < 1 in both samples which avoids noise from lowly expressed transcripts.
After removing transcripts with expression levels below background, 11,746 and 12,492 transcripts for midgut and fat body tissues were retained for further analysis.
Outliers were filtered out by removing transcripts with tag counts that were more than 1% of the total library in at least 3 sequenced libraries.
In the first step, differentially expressed transcripts were identified by established methods employing significance analysis of microarrays (SAM) and removing transcripts with low signal intensities (Table 2, see Materials and Methods).
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