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Series GSE26248 Query DataSets for GSE26248
Status Public on Aug 03, 2011
Title Comparative Analysis of RNA-Seq Alignment Algorithms and the RNA-Seq Unified Mapper (RUM).
Organism Mus musculus
Experiment type Expression profiling by high throughput sequencing
Summary A critical task in high throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data is discrete in nature; therefore with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. The exercise to rigorously compare all viable published RNA-Seq algorithms has not previously been performed.

We developed an RNA-Seq simulator that models the main impediments to RNA alignment, including alternative splicing, insertions, deletions, substitutions, sequencing errors, and intron signal. We used this simulator to measure the accuracy and robustness of available algorithms at the base and junction levels. Additionally, we used RT-PCR and Sanger sequencing to validate the ability of the algorithms to detect novel transcript features such as novel exons and alternative splicing in RNA-Seq data from mouse retina. A pipeline based on BLAT was developed to explore the performance of established tools for this problem, and to compare it to the recently developed methods. This pipeline, the RNA-Seq Unified Mapper (RUM) performs comparably to the best current aligners and provides an advantageous combination of accuracy, speed and usability.
Overall design RNA-Seq of mouse retinal RNA, as described.
Contributor(s) Farkas MH, Grant GR, Pierce EA
Citation(s) 21775302
Submission date Dec 21, 2010
Last update date May 15, 2019
Contact name Eric Pierce
Organization name Massachusetts Eye and Ear Infirmary
Department Ophthalmology
Lab Ocular Genomics Institute
Street address 243 Charles Street
City Boston
State/province MA
ZIP/Postal code 02114
Country USA
Platforms (1)
GPL11002 Illumina Genome Analyzer IIx (Mus musculus)
Samples (4)
GSM663550 retina_2 months_lane_1
GSM663551 retina_2 months_lane_2
GSM663552 retina_2 months_lane_3
SRA SRP007655
BioProject PRJNA135077

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE26248_RAW.tar 12.4 Gb (http)(custom) TAR (of SAM, TXT)
SRA Run SelectorHelp
Raw data are available in SRA
Processed data provided as supplementary file

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