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Here, we mapped the transcripts produced in an S. cerevisiae strain lacking Set2, and applied rigorous statistical methods to identify sites of cryptic transcription at high resolution.
We mapped the transcripts with BLAST and GeneSequer.
To provide positional candidates for retinal disease genes, we have mapped the transcripts representing the reference retinome to the minimal regions defined for 42 retinal disease loci with as yet undefined gene mutations.
We then randomly selected 5,000 ESTs from both groups, mapped the transcripts to the Arabidopsis cDNA dataset using BLASTn, and generated start site distribution based on cDNA length.
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The UCSC Genome Bioinformatics mapped the transcript sequences back to genomes, defining the exact genomic boundaries of transcript exons.
The E. fischeriana root transcriptome was further annotated by mapping the transcripts onto pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG).
The protein sequences of the resulting transcripts were predicted using Trapid [ 112] and then manually curated by mapping the transcripts to an in-house draft genome of S. robusta (Vandepoele, De Veylder & Vyverman, in preparation).
Gene expression data were obtained by re-mapping the transcript reads to the extracted transcripts using RSEM and calculating the expected counts at the gene level (Li and Dewey, 2011).
Twenty-two individuals from a segregating F1 population between P. squamulatum and P. glaucum were used for mapping the transcript fragments to the ASGR.
Out of 118 M total reads, 92.2% of the reads mapped to the transcripts in the total transcript set and 91.2% mapped to the reduced set.
Clean DEG sequence reads were mapped to the transcripts by RSEM (Li and Dewey 2011) and were used to obtain the read count for each sample that mapped to each gene.
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