Sentence examples for mouse transcript datasets from inspiring English sources

This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.

To obtain even datasets, the human and mouse transcript sequences were also processed using ESTScan.

For human and mouse transcripts, all 3P-seq datasets for cell lines/tissues of each species were combined, after normalizing for the sequencing depth (i.e., number of uniquely mapping tags) of each dataset, to generate meta profiles.

In order to do this we took advantage of the data denoting 5' and 3' ends of mouse transcripts (the reliable cluster dataset) from the FANTOM3 consortium [ 5].

The FANTOM2 cDNA dataset represents the most complete set of mouse transcripts to date, and it was utilised by us to identify potential novel pathologs.

Mining the SwissProt protein database and the ENSEMBL collection of mouse transcripts (ECMT) for sequences similar to those present in our dataset allowed detection of transcripts from a large number of putative genes.

We used the processed annotation file and sequence features for mouse transcripts (mm10 GRCm38.p5, ANNO_FILE = SE.gold, FACTOR_FILE = mouse_factors.SE.gold).SE.gold

The Affymetrix 430 2.0 microarray that was used encompasses a complete set of mouse transcripts (45,101 transcripts).

The only exception lies in mouse transcripts with pure 5'UTRs (P = 0.216), although the median ISI of the Ga transcripts is lower than that of G0 for this dataset.

As with previous datasets shuffling the transcript dataset abrogated this signal (not shown).

ECMT - ENSEMBL Collection of Mouse Transcripts.