Computational protocol: Transcriptomic and Immunohistochemical Profiling of SLC6A14 in Pancreatic Ductal Adenocarcinoma

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Protocol publication

[…] The Mayo Analysis Pipeline for RNA Sequencing (MAP-RSeq), developed by the Bioinformatics Core at Mayo Clinic, was used to perform the thorough processing of the paired-end RNASeq data. MAP-RSeq integrates a suite of open source bioinformatics tools with in-house developed methods to analyze paired-end RNASeq data. The application processes the reads produced by the sequencer in the following manner: quality control, genomic alignments, reference and novel transcriptomic junction alignments, alignment cleanup, identification of genomic features per sample, and summary of data across samples. Postanalysis quality control measures were taken computationally and manually to verify that the analysis was successful. Such computational measures included estimating the distance between paired-end reads, sequencing depths at alternate splicing sites, the rate of duplicate reads, and evaluating coverage across genes. These and other metrics were measured using RSeQC software []. Paired-end reads were aligned by TopHat 2.0.6 [] against the hg19 genome build with the Bowtie1 aligner [] as a backbone. Gene counts for evaluating RNA fold change within and across samples were generated using HTseq software (http://www.huber.embl.de/users/anders/HTSeq/doc/overview.html) and the gene annotation files used were obtained from Illumina (http://cole-trapnell-lab.github.io/cufflinks/). […]

Pipeline specifications

Software tools MAP-RSEQ, RSeQC, TopHat, Bowtie, HTSeq, Cufflinks
Application RNA-seq analysis
Organisms Homo sapiens
Diseases Neoplasms, Carcinoma, Pancreatic Ductal
Chemicals Amino Acids