title: Transcriptome assembly and quantification from Ion Torrent RNA-Seq data.
abstract: BACKGROUND
  High throughput RNA sequencing (RNA-Seq) can generate whole transcriptome information at the single transcript level providing a powerful tool with multiple interrelated applications including transcriptome reconstruction and quantification. The sequences of novel transcripts can be reconstructed from deep RNA-Seq data, but this is computationally challenging due to sequencing errors, uneven coverage of expressed transcripts, and the need to distinguish between highly similar transcripts produced by alternative splicing. Another challenge in transcriptomic analysis comes from the ambiguities in mapping reads to transcripts.
  
  
  RESULTS
  We present MaLTA, a method for simultaneous transcriptome assembly and quantification from Ion Torrent RNA-Seq data. Our approach explores transcriptome structure and incorporates a maximum likelihood model into the assembly and quantification procedure. A new version of the IsoEM algorithm suitable for Ion Torrent RNA-Seq reads is used to accurately estimate transcript expression levels. The MaLTA-IsoEM tool is publicly available at: http://alan.cs.gsu.edu/NGS/?q=malta
  
  
  CONCLUSIONS
  Experimental results on both synthetic and real datasets show that Ion Torrent RNA-Seq data can be successfully used for transcriptome analyses. Experimental results suggest increased transcriptome assembly and quantification accuracy of MaLTA-IsoEM solution compared to existing state-of-the-art approaches.
authors.forename: Serghei
authors.initials: S
authors.lastname: Mangul
authors.forename: Adrian
authors.initials: A
authors.lastname: Caciula
authors.forename: Sahar
authors.initials: S
authors.lastname: Al Seesi
authors.forename: Dumitru
authors.initials: D
authors.lastname: Brinza
authors.forename: Ion
authors.initials: I
authors.lastname: Mӑndoiu
authors.forename: Alex
authors.initials: A
authors.lastname: Zelikovsky
mesh_terms: D000465:Algorithms; D017398:Alternative Splicing; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D016013:Likelihood Functions; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D059467:Transcriptome
references.citation: Bioinformatics. 2011 Sep 1;27(17):2325-9
references.pmid: 21697122
references.citation: Bioinformatics. 2009 Dec 15;25(24):3207-12
references.pmid: 19808877
references.citation: In Silico Biol. 2011-2012;11(5-6):251-61
references.pmid: 23202426
references.citation: Genome Res. 2011 Aug;21(8):1260-72
references.pmid: 21712398
references.citation: Nat Methods. 2010 Oct;7(10):843-7
references.pmid: 20835245
references.citation: BMC Bioinformatics. 2011 Aug 04;12:323
references.pmid: 21816040
references.citation: J Comput Biol. 2011 Nov;18(11):1693-707
references.pmid: 21951053
references.citation: Genome Biol. 2011;12(3):R22
references.pmid: 21410973
references.citation: BMC Genomics. 2005 Nov 02;6:150
references.pmid: 16266432
references.citation: Nat Med. 2006 Jul;12(7):852-5
references.pmid: 16799556
references.citation: Nature. 2011 Jul 20;475(7356):348-52
references.pmid: 21776081
references.citation: Nat Biotechnol. 2006 Sep;24(9):1151-61
references.pmid: 16964229
references.citation: Nat Methods. 2010 Nov;7(11):909-12
references.pmid: 20935650
references.citation: Science. 2010 Aug 6;329(5992):682-5
references.pmid: 20616234
references.citation: Genome Res. 2010 Jun;20(6):816-25
references.pmid: 20354124
references.citation: Nat Methods. 2011 Jun;8(6):469-77
references.pmid: 21623353
references.citation: J Biomol Struct Dyn. 1989 Aug;7(1):63-73
references.pmid: 2684223
references.citation: Nat Biotechnol. 2010 May;28(5):511-5
references.pmid: 20436464
references.citation: BMC Bioinformatics. 2013;14 Suppl 5:S14
references.pmid: 23734605
references.citation: Genome Res. 2013 Mar;23(3):519-29
references.pmid: 23204306
references.citation: Bioinformatics. 2010 Feb 15;26(4):493-500
references.pmid: 20022975
references.citation: Algorithms Mol Biol. 2011 Apr 19;6(1):9
references.pmid: 21504602
references.citation: Nat Biotechnol. 2011 May 15;29(7):644-52
references.pmid: 21572440
references.citation: Nat Biotechnol. 2011 Nov 13;30(1):99-104
references.pmid: 22081020
references.citation: Nucleic Acids Res. 2010 Aug;38(14):4570-8
references.pmid: 20371516
references.citation: Nature. 2008 Nov 27;456(7221):470-6
references.pmid: 18978772
references.citation: BMC Bioinformatics. 2013;14 Suppl 5:S15
references.pmid: 23734627
references.citation: Nat Rev Genet. 2009 Jan;10(1):57-63
references.pmid: 19015660
references.citation: Bioinformatics. 2009 May 1;25(9):1105-11
references.pmid: 19289445
references.citation: Nat Biotechnol. 2010 May;28(5):503-10
references.pmid: 20436462
references.citation: Nature. 2008 Nov 6;456(7218):53-9
references.pmid: 18987734
references.citation: Nat Methods. 2008 Jul;5(7):621-8
references.pmid: 18516045
references.citation: Hum Mol Genet. 2010 Oct 15;19(R2):R162-8
references.pmid: 20798109
references.citation: J Comput Biol. 2011 Mar;18(3):305-21
references.pmid: 21385036
references.citation: BMC Genomics. 2012 Apr 12;13 Suppl 2:S6
references.pmid: 22537301