RNA-Seq: Comparative Analysis of RNA-Seq Alignment Algorithms and the RNA-Seq Unified

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Comparative Analysis of RNA-Seq Alignment Algorithms and the RNA-Seq Unified Mapper (RUM).

Bioinformatics. 2011 Jul 19;

Authors: Grant GR, Farkas MH, Pizarro A, Lahens N, Schug J, Brunk B, Stoeckert CJ, Hogenesch JB, Pierce EA

MOTIVATION: 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. RESULTS: 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. AVAILABILITY: The RUM pipeline is distributed via the Amazon Cloud and for computing clusters using the Sun Grid Engine. http://cbil.upenn.edu/RUM. Contact Info: Gregory R Grant ([email protected]), Eric A Pierce ([email protected]) SUPPLEMENTARY INFORMATION: The RNA-Seq sequence reads described in the manuscript are deposited at GEO, accession GSE26248. (Reviewer access link: http://www.ncbi.nlm.nih.gov/geo/quer...g&acc=GSE26248).

PMID: 21775302 [PubMed - as supplied by publisher]



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[edge]

Hi Newsbot!,

Many thanks for your sharing RUM program, http://cbil.upenn.edu/RUM/userguide.php
It is wonderful and amazing program.

Regarding the following poster and published paper, http://cbil.upenn.edu/RUM/RUM_MGED.pdf
I got some question that need your advice.

Question 1:
Can I know what is the reason that they mention both genome and transcriptome mapping are both important?
I have a human RNA-seq blood sample, pair-end read with 50bp , 210 insert size right now.
Can I know more about what you mean that "genome and transcriptome mapping are both important" in the poster?
If I have a pair-end data, how do we identify when should we prefer the read aligned against genome or transcriptome?
I not really understand the reason that :
1. "Black bar shows an alignment of a read to the genome.";
2. "Black bars show the same read algined to the transcriptome.";
3. "Typically given the above information we would preference the transcriptome alignment.";
4. "However, if the reverse read maps as shown here, then we would preference the genome mapping which indicates
a retained intron."

Question 2:
For post-processing such as assembly, is it I can just used the raw read of RU_unique and RU_non_unique to run de-novo assembly?
My main purpose is used the RUM pipeline to improve mapping step and then used the result for de-novo assembly the RNA-seq data.

Many thanks for solving my doubts.

[PeteH]

Hi Newsbot!,

Many thanks for your sharing RUM program, http://cbil.upenn.edu/RUM/userguide.php
It is wonderful and amazing program.

Regarding the following poster and published paper, http://cbil.upenn.edu/RUM/RUM_MGED.pdf
I got some question that need your advice.

Question 1:
Can I know what is the reason that they mention both genome and transcriptome mapping are both important?
I have a human RNA-seq blood sample, pair-end read with 50bp , 210 insert size right now.
Can I know more about what you mean that "genome and transcriptome mapping are both important" in the poster?
If I have a pair-end data, how do we identify when should we prefer the read aligned against genome or transcriptome?
I not really understand the reason that :
1. "Black bar shows an alignment of a read to the genome.";
2. "Black bars show the same read algined to the transcriptome.";
3. "Typically given the above information we would preference the transcriptome alignment.";
4. "However, if the reverse read maps as shown here, then we would preference the genome mapping which indicates
a retained intron."

Question 2:
For post-processing such as assembly, is it I can just used the raw read of RU_unique and RU_non_unique to run de-novo assembly?
My main purpose is used the RUM pipeline to improve mapping step and then used the result for de-novo assembly the RNA-seq data.

Many thanks for solving my doubts.

Hi edge,

You would be better off contacting the authors of the paper directly as Newsbot is simply a bot that aggregates "next-gen sequencing" content from Pubmed and has no involvement in the RUM software.

[edge]

thanks for remind, PeteH