Observations on potential novel transcripts from RNA-Seq data
Observations on potential novel transcripts from RNA-Seq data
Chao YE, Linxi LIU, Xi WANG, Xuegong ZHANG()
Key Laboratory of Bioinformatics and Bioinformatics Division, Ministry of Education, Tsinghua National Laboratory for Information Science and Technology/Department of Automation, Tsinghua University, Beijing 100084, China
With the rapid development of next generation deep sequencing technologies, sequencing cDNA reverse-transcribed from RNA molecules (RNA-Seq) has become a key approach in studying gene expression and transcriptomes. Because RNA-Seq does not rely on annotation of known genes, it provides the opportunity of discovering transcripts that have not been annotated in current databases. Studying the distribution of RNA-Seq signals and a systematic view on the potential new transcripts revealed from the signals is an important step toward the understanding of transcriptomes.
. Observations on potential novel transcripts from RNA-Seq data[J]. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 275-282.
Chao YE, Linxi LIU, Xi WANG, Xuegong ZHANG. Observations on potential novel transcripts from RNA-Seq data. Front Elect Electr Eng Chin, 2011, 6(2): 275-282.
Mercer T R, Dinger M E, Mattick J S. Long non-coding RNAs: insights into functions. Nature Reviews Genetics , 2009, 10(3): 155-159 doi: 10.1038/nrg2521
2
van Bakel H, Hughes T R. Establishing legitimacy and function in the new transcriptome. Briefings in Functional Genomics & Proteomics , 2009, 8(6): 424-436 doi: 10.1093/bfgp/elp037
3
Schena M, Shalon D, Davis R W, Brown P O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science , 1995, 270(5235): 467-470 doi: 10.1126/science.270.5235.467
4
Shendure J, Ji H. Next-generation DNA sequencing. Nature Biotechnology , 2008, 26(10): 1135-1145 doi: 10.1038/nbt1486
5
Metzker M L. Sequencing technologies — the next generation. Nature Reviews Genetics , 2010, 11(1): 31-46 doi: 10.1038/nrg2626
6
Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics , 2009, 10(1): 57-63 doi: 10.1038/nrg2484
7
Cock P J, Fields C J, Goto N, Heier M L, Rice P M. The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Research , 2010, 38(6): 1767-1771 doi: 10.1093/nar/gkp1137
8
Mortazavi A, Williams B A, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods , 2008, 5(7): 621-628 doi: 10.1038/nmeth.1226
9
Marioni J C, Mason C E, Mane S M, Stephens M, Gilad Y. RNA-Seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Research , 2008, 18(9): 1509-1517 doi: 10.1101/gr.079558.108
10
Friedlaender M R, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N. Discovering micro-RNAs from deep sequencing data using miRDeep. Nature Biotechnology , 2008, 26(4): 407-415 doi: 10.1038/nbt1394
11
Pan Q, Shai O, Lee L J, Frey B J, Blencowe B J. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nature Genetics , 2008, 40(12): 1413-1415 doi: 10.1038/ng.259
12
Wang E T, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore S F, Schroth G P, Burge C B. Alternative isoform regulation in human tissue transcriptomes. Nature , 2008, 456(7221): 470-476 doi: 10.1038/nature07509
13
Jiang H,Wong WH. Statistical inferences for Isoform expression in RNA-Seq. Bioinformatics , 2009, 25(8): 1026-1032 doi: 10.1093/bioinformatics/btp113
14
Homer N, Merriman B, Nelson S F. BFAST: an alignment tool for large scale genome resequencing. PLoS One , 2009, 4(11): e7767 doi: 10.1371/journal.pone.0007767
15
Langmead B, Trapnel C, Pop M, Salzberg S L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology , 2009, 10(3): R25 doi: 10.1186/gb-2009-10-3-r25
16
Li H, Ruan J, Durbin R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Research , 2008, 18(11): 1851-1858 doi: 10.1101/gr.078212.108
17
Trapnell C, Pachter L, Salzberg S L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics , 2009, 25(9): 1105-1111 doi: 10.1093/bioinformatics/btp120
18
Au K F, Jiang H, Lin L, Xing Y, Wong W H. Detection of splice junctions from paired-end RNA-Seq data by SpliceMap. Nucleic Acids Research , 2010, 38(14): 4570-4578 doi: 10.1093/nar/gkq211
19
Wang K, Singh D, Zeng Z, Coleman S J, Huang Y, Savich G L, He X, Mieczkowski P, Grimm S A, Perou C M, MacLeod J N, Chiang D Y, Prins J F, Liu J. MapSplice: accurate mapping of RNA-Seq reads for splice junction discovery. Nucleic Acids Research , 2010, 38(18): e178 doi: 10.1093/nar/gkq622
20
Trapnell C, Salzberg S L. How to map billions of short reads onto genomes. Nature Biotechnology , 2009, 27(5): 455-457 doi: 10.1038/nbt0509-455
21
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/MAP format and SAMtools. Bioinformatics , 2009, 25(16): 2078-2079 doi: 10.1093/bioinformatics/btp352
22
Pruitt K D, Tatusova T, Maglott D R. NCBI reference sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Research , 2005, 33(suppl 1): D501-D504 doi: 10.1093/nar/gki025
23
Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J, Curwen V, Down T, Durbin R, Eyras E, Gilbert J, Hammond M, Huminiecki L, Kasprzyk A, Lehvaslaiho H, Lijnzaad P, Melsopp C, Mongin E, Pettett R, Pocock M, Potter S, Rust A, Schmidt E, Searle S, Slater G, Smith J, Spooner W, Stabenau A, Stalker J, Stupka E, Ureta-Vidal A, Vastrik I, Clamp M. The Ensemble genome database project. Nucleic Acids Research , 2002, 30(1): 38-41 doi: 10.1093/nar/30.1.38
24
Harrow J, Denoeud F, Frankish A, Reymond A, Chen C K, Chrast J, Lagarde J, Gilbert J G R, Storey R, Swarbreck D, Rossier C, Ucla C, Hubbard T, Antonarakis S E, Guigo R. GENCODE: producing a reference annotation for ENCODE. Genome Biology , 2006, 7(Suppl 1): S4.1-S4.9
25
Wang L K, Feng Z X, Wang X, Wang X W, Zhang X G. DEGseq: an R package for identifying differentially expressed genes from RNA-Seq data. Bioinformatics , 2010, 26 (1): 136-138 doi: 10.1093/bioinformatics/btp612
26
Kent W J, Sugnet C W, Furey T S, Roskin K M, Pringle T H, Zahler A M, Haussler D. The human genome browser at UCSC. Genome Research , 2002, 12(6): 996-1006
27
Robinson J T, Thorvaldsdóttir H, Winckler W, Guttman M, Lander E S, Getz G, Mesirov J P. Integrative genomics viewer. Nature Biotechnology , 2011, 29(1): 24-26 doi: 10.1038/nbt.1754
28
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B , 1995, 57(1): 289-300