Statistical and Computational Methods for High-Throughput Sequencing Data Analysis of Alternative Splicing

Abstract The rMAPS2 (RNA Map Analysis and Plotting Server 2) web server, freely available at http://rmaps.cecsresearch.org/, has provided the high-throughput sequencing data research community with curated tools for the identification of RNA binding protein sites. rMAPS2 analyzes differential alternative splicing or CLIP peak data obtained from high-throughput sequencing data analysis tools like MISO, rMATS, Piranha, PIPE-CLIP and PARalyzer, and then, graphically displays enriched RNA-binding protein target sites. The initial release of rMAPS focused only on the most common alternative splicing event, skipped exon or exon skipping. However, there was a high demand for the analysis of other major types of alternative splicing events, especially for retained intron events since this is the most common type of alternative splicing in plants, such as Arabidopsis thaliana. Here, we expanded the implementation of rMAPS2 to facilitate analyses for all five major types of alternative splicing events: skipped exon, mutually exclusive exons, alternative 5′ splice site, alternative 3′ splice site and retained intron. In addition, by employing multi-threading, rMAPS2 has vastly improved the user experience with significant reductions in running time, ∼3.5 min for the analysis of all five major alternative splicing types at once.

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ARSDA: A new approach for storing, transmitting and analyzing high-throughput sequencing data

10.1101/114470 ◽

2017 ◽

Cited By ~ 6

Author(s):

Xuhua Xia

Keyword(s):

Bacillus Subtilis ◽

Data Analysis ◽

High Throughput ◽

High Throughput Sequencing ◽

Sequencing Data ◽

Plain Text ◽

Link Type ◽

High Throughput Sequencing Data ◽

Fastq Format ◽

Single Entry

ABSTRACTTwo major stumbling blocks exist in high-throughput sequencing (HTS) data analysis. The first is the sheer file size typically in gigabytes when uncompressed, causing problems in storage, transmission and analysis. However, these files do not need to be so large and can be reduced without loss of information. Each HTS file, either in compressed .SRA or plain text .fastq format, contains numerous identical reads stored as separate entries. For example, among 44603541 forward reads in the SRR4011234.sra file (from aBacillus subtilistranscriptomic study) deposited at NCBI’s SRA database, one read has 497027 identical copies. Instead of storing them as separate entries, one can and should store them as a single entry with the SeqID_NumCopy format (which I dub as FASTA+ format). The second is the proper allocation reads that map equally well to paralogous genes. I illustrate in detail a new method for such allocation. I have developed ARSDA software that implement these new approaches. A number of HTS files for model species are in the process of being processed and deposited athttp://coevol.rdc.uottawa.cato demonstrate that this approach not only saves a huge amount of storage space and transmission bandwidth, but also dramatically reduces time in downstream data analysis. Instead of matching the 497027 identical reads separately against theBacillus subtilisgenome, one only needs to match it once. ARSDA includes functions to take advantage of HTS data in the new sequence format for downstream data analysis such as gene expression characterization. ARSDA can be run on Windows, Linux and Macintosh computers and is freely available athttp://dambe.bio.uottawa.ca/ARSDA/ARSDA.aspx.

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