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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.

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.

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Cuffmerge maximizes assembly quality by removing transcripts that are artifacts and merging novel isoforms with known isoforms across all Cufflinks assemblies.

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).

After removing transcripts with expression levels below background, we included 12,596 transcripts in the analysis.

Lastly, we reduced the effect of inaccurate annotations by removing transcript clusters with more than one annotated Entrez Gene identifiers as well as multiple transcript clusters annotated by the same Entrez Gene identifier.

We then sorted the transcripts by minimum exonic density and picked the top half in order to remove transcripts with low read counts in exons that would result in imprecise calculations.

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/

A subset of 9,613 high-confidence transcripts (from the total set of 107,717) was selected by removing all transcripts shorter than 225 bp or with an average read coverage below 30×.

By default, the application removes transcripts less than 900 bp.

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