CoChat
Sign Up
Cited by 1,597

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Repetitive DNA and next-generation sequencing: computational challenges and solutions

Treangen, Todd J., Salzberg, Steven L.·2012·Nature Reviews Genetics
DOI 10.1038/nrg3117PMC PMC3324860
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/nrg3117

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 66

  1. Weigel, D. & Mott, R. The 1001 genomes project for Arabidopsis thaliana. Genome Biol. 10, 107 (2009). · DOI
  2. Genome 10K Community of Scientists. Genome 10K: a proposal to obtain whole-genome sequence for 10,000 vertebrate species. J. Hered. 100, 659–674 (2009). · DOI
  3. Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008). · DOI · Paper page
  4. Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008). · DOI
  5. Park, P. J. ChIP–seq: advantages and challenges of a maturing technology. Nature Rev. Genet. 10, 669–680 (2009). · DOI · Paper page
  6. Schmidt, D. et al. Five-vertebrate ChIP–seq reveals the evolutionary dynamics of transcription factor binding. Science 328, 1036–1040 (2010). · DOI · Paper page
  7. Johnson, D. S., Mortazavi, A., Myers, R. M. & Wold, B. Genome-wide mapping of in vivo protein–DNA interactions. Science 316, 1497–1502 (2007). · DOI · Paper page
  8. Garber, M., Grabherr, M. G., Guttman, M. & Trapnell, C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nature Methods 8, 469–477 (2011). · DOI · Paper page
  9. Brunner, A. L. et al. Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res. 19, 1044–1056 (2009). · DOI
  10. Hormozdiari, F., Alkan, C., Eichler, E. E. & Sahinalp, S. C. Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes. Genome Res. 19, 1270–1278 (2009). · DOI
  11. Meyerson, M., Gabriel, S. & Getz, G. Advances in understanding cancer genomes through second-generation sequencing. Nature Rev. Genet. 11, 685–696 (2010). · DOI
  12. Medvedev, P., Stanciu, M. & Brudno, M. Computational methods for discovering structural variation with next-generation sequencing. Nature Methods 6, S13–S20 (2009). · DOI
  13. Li, Y., Hu, Y., Bolund, L. & Wang, J. State of the art de novo assembly of human genomes from massively parallel sequencing data. Hum. Genomics 4, 271–277 (2010). · DOI
  14. Roberts, A., Pimentel, H., Trapnell, C. & Pachter, L. Identification of novel transcripts in annotated genomes using RNA-seq. Bioinformatics 27, 2325–2329 (2011). · DOI
  15. Gnerre, S. et al. High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc. Natl Acad. Sci. USA 108, 1513–1518 (2011). This paper presents a highly effective NGS genome assembler that integrates several effective strategies for handling repeats. · DOI
  16. Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nature Biotech. 29, 644–652 (2011). · DOI
  17. Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936–939 (2011). · DOI · Paper page
  18. Schmid, C. W. & Deininger, P. L. Sequence organization of the human genome. Cell 6, 345–358 (1975). · DOI
  19. Batzer, M. A. & Deininger, P. L. Alu repeats and human genomic diversity. Nature Rev. Genet. 3, 370–379 (2002). · DOI
  20. Jurka, J., Kapitonov, V. V., Kohany, O. & Jurka, M. V. Repetitive sequences in complex genomes: structure and evolution. Annu. Rev. Genomics Hum. Genet. 8, 241–259 (2007). · DOI · Paper page
  21. Britten, R. J. Transposable element insertions have strongly affected human evolution. Proc. Natl Acad. Sci. USA 107, 19945–19948 (2010). · DOI
  22. Hua-Van, A., Le Rouzic, A., Boutin, T. S., Filee, J. & Capy, P. The struggle for life of the genome's selfish architects. Biol. Direct 6, 19 (2011). · DOI
  23. Kim, P. M. et al. Analysis of copy number variants and segmental duplications in the human genome: evidence for a change in the process of formation in recent evolutionary history. Genome Res. 18, 1865–1874 (2008). · DOI
  24. Zhang, L., Lu, H. H., Chung, W. Y., Yang, J. & Li, W. H. Patterns of segmental duplication in the human genome. Mol. Biol. Evol. 22, 135–141 (2005). · DOI
  25. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796–815 (2000). · DOI · Paper page
  26. Schnable, P. S. et al. The B73 maize genome: complexity, diversity, and dynamics. Science 326, 1112–1115 (2009). · DOI
  27. Reichwald, K. et al. High tandem repeat content in the genome of the short-lived annual fish Nothobranchius furzeri: a new vertebrate model for aging research. Genome Biology 10, R16 (2009). · DOI
  28. Cho, N. H. et al. The Orientia tsutsugamushi genome reveals massive proliferation of conjugative type IV secretion system and host-cell interaction genes. Proc. Natl Acad. Sci. USA 104, 7981–7986 (2007). · DOI
  29. Shen, Y. et al. A SNP discovery method to assess variant allele probability from next-generation resequencing data. Genome Res. 20, 273–280 (2010). · DOI
  30. Mu, X. J., Lu, Z. J., Kong, Y., Lam, H. Y. & Gerstein, M. B. Analysis of genomic variation in non-coding elements using population-scale sequencing data from the 1000 Genomes Project. Nucleic Acids Res. 39, 7058–7076 (2011). · DOI
  31. Gravel, S. et al. Demographic history and rare allele sharing among human populations. Proc. Natl Acad. Sci. USA 108, 11983–11988 (2011). · DOI
  32. Simola, D. F. & Kim, J. Sniper: improved SNP discovery by multiply mapping deep sequenced reads. Genome Biol. 12, R55 (2011). · DOI
  33. Tucker, B. A. et al. Exome sequencing and analysis of induced pluripotent stem cells identify the cilia-related gene male germ cell-associated kinase (MAK) as a cause of retinitis pigmentosa. Proc. Natl Acad. Sci. USA 108, E569–E576 (2011). This study shows a striking example of why multi-reads should not be discarded. · DOI
  34. Robinson, J. T. et al. Integrative genomics viewer. Nature Biotech. 29, 24–26 (2011). · DOI · Paper page
  35. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genet. 43, 491–498 (2011). · DOI
  36. Li, H., Ruan, J. & Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 18, 1851–1858 (2008). · DOI · Paper page
  37. Li, R. et al. SNP detection for massively parallel whole-genome resequencing. Genome Res. 19, 1124–1132 (2009). · DOI · Paper page
  38. Koboldt, D. C. et al. VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25, 2283–2285 (2009). · DOI
  39. Hormozdiari, F. et al. Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery. Bioinformatics 26, i350–i357 (2010). The authors of this paper present variation detection software that explicitly searches for repetitive transposon sequences. · DOI
  40. He, D., Hormozdiari, F., Furlotte, N. & Eskin, E. Efficient algorithms for tandem copy number variation reconstruction in repeat-rich regions. Bioinformatics 27, 1513–1520 (2011). · DOI
  41. Ye, L. et al. A vertebrate case study of the quality of assemblies derived from next-generation sequences. Genome Biol. 12, R31 (2011). · DOI
  42. Schatz, M. C., Delcher, A. L. & Salzberg, S. L. Assembly of large genomes using second-generation sequencing. Genome Res. 20, 1165–1173 (2010). · DOI
  43. Pop, M. & Salzberg, S. L. Bioinformatics challenges of new sequencing technology. Trends Genet. 24, 142–149 (2008). · DOI
  44. Phillippy, A. M., Schatz, M. C. & Pop, M. Genome assembly forensics: finding the elusive mis-assembly. Genome Biol. 9, R55 (2008). · DOI
  45. Alkan, C., Sajjadian, S. & Eichler, E. E. Limitations of next-generation genome sequence assembly. Nature Methods 8, 61–65 (2011). This is an excellent review that highlights the difficulties repeats pose for NGS assemblers. · DOI
  46. Read, T. D. et al. Comparative genome sequencing for discovery of novel polymorphisms in Bacillus anthracis. Science 296, 2028–2033 (2002). · DOI
  47. Rasko, D. A. et al. Bacillus anthracis comparative genome analysis in support of the Amerithrax investigation. Proc. Natl Acad. Sci. USA 108, 5027–5032 (2011). This paper provides a description of how scientists used DNA sequencing to discover a few rare variants in the anthrax-causing bacterium, which led US Federal Bureau of Investigation (FBI) investigators to the original source of the mailed anthrax from the 2001 attacks. · DOI
  48. Pevzner, P. A., Tang, H. & Waterman, M. S. An Eulerian path approach to DNA fragment assembly. Proc. Natl Acad. Sci. USA 98, 9748–9753 (2001). · DOI · Paper page
  49. Wetzel, J., Kingsford, C. & Pop, M. Assessing the benefits of using mate-pairs to resolve repeats in de novo short-read prokaryotic assemblies. BMC Bioinformatics 12, 95 (2011). · DOI
  50. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009). · DOI
  51. Lesniewska, A. & Okoniewski, M. J. rnaSeqMap: a Bioconductor package for RNA sequencing data exploration. BMC Bioinformatics 12, 200 (2011). · DOI
  52. Grant, G. R. et al. Comparative analysis of RNA-seq alignment algorithms and the RNA-seq unified mapper (RUM). Bioinformatics 27, 2518–2528 (2011). · DOI
  53. 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 Res. 38, 4570–4578 (2010). · DOI
  54. Kim, D. & Salzberg, S. L. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 12, R72 (2011). · DOI
  55. Sboner, A. et al. FusionSeq: a modular framework for finding gene fusions by analysing paired-end RNA-sequencing data. Genome Biol. 11, R104 (2010). · DOI
  56. Kinsella, M., Harismendy, O., Nakano, M., Frazer, K. A. & Bafna, V. Sensitive gene fusion detection using ambiguously mapping RNA-seq read pairs. Bioinformatics 27, 1068–1075 (2011). · DOI
  57. Jiang, H. & Wong, W. H. Statistical inferences for isoform expression in RNA-seq. Bioinformatics 25, 1026–1032 (2009). · DOI
  58. Chung, D. et al. Discovering transcription factor binding sites in highly repetitive regions of genomes with multi-read analysis of ChIP–seq data. PLoS Comput. Biol. 7, e1002111 (2011). · DOI
  59. Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A. & Dewey, C. N. RNA-seq gene expression estimation with read mapping uncertainty. Bioinformatics 26, 493–500 (2010). · DOI · Paper page
  60. Homer, N., Merriman, B. & Nelson, S. F. BFAST: an alignment tool for large scale genome resequencing. PLoS ONE 4, e7767 (2009). · DOI
  61. Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009). · DOI
  62. Miller, J. R. et al. Aggressive assembly of pyrosequencing reads with mates. Bioinformatics 24, 2818–2824 (2008). · DOI
  63. Zerbino, D. R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008). · DOI
  64. Zerbino, D. R., McEwen, G. K., Margulies, E. H. & Birney, E. Pebble and rock band: heuristic resolution of repeats and scaffolding in the velvet short-read de novo assembler. PLoS ONE 4, e8407 (2009). · DOI
  65. Robertson, G. et al. De novo assembly and analysis of RNA-seq data. Nature Methods 7, 909–912 (2010). · DOI
  66. Garg, R., Patel, R. K., Tyagi, A. K. & Jain, M. De novo assembly of chickpea transcriptome using short reads for gene discovery and marker identification. DNA Res. 18, 53–63 (2011). · DOI · Paper page

Cited by

Sorted by citations

See all 1,597

Metadata

Authors
Treangen, T. J., & Salzberg, S. L.
Venue
Nature Reviews Genetics
Publisher
Springer Science and Business Media LLC
Published
2011-11-29
Volume
13
Issue
1
Pages
36-46
ISSN
  • 1471-0056
  • 1471-0064
DOI
10.1038/nrg3117
PMC
PMC3324860
Cited by
1,597
References
11
Language
en
Update notices
erratum (2012-01-17)
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review