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Realistic artificial DNA sequences as negative controls for computational genomics

Caballero, Juan, Smit, Arian F. A., Hood, Leroy, Glusman, Gustavo·2014·Nucleic Acids Research
DOI 10.1093/nar/gku356PMC PMC4081056
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Figure 1. Total number of known and predicted coding genes in Ensembl 64 for human and other species. The gene size is determined by the total length in bases of each CDS.
Figure 1. Total number of known and predicted coding genes in Ensembl 64 for human and other species. The gene size is determined by the total length in bases of each CDS.CC BY · PubMed Central
Figure 2. Overview of the algorithms. Our development consists of two stages: (1) training (left side): the fraction of the genome remaining after masking of functional and repetitive regions is analyzed for k-mer and GC content. Repeats are also modeled, separated into interspersed (IR) and simple sequence repeats (SR); (2) generation (right side): a base sequence is generated using the k-mer and GC profiles. Artificially evolved repeats are then inserted into the base sequence to create a new artificial sequence.
Figure 2. Overview of the algorithms. Our development consists of two stages: (1) training (left side): the fraction of the genome remaining after masking of functional and repetitive regions is analyzed for k-mer and GC content. Repeats are also modeled, separated into interspersed (IR) and simple sequence repeats (SR); (2) generation (right side): a base sequence is generated using the k-mer and GC profiles. Artificially evolved repeats are then inserted into the base sequence to create a new artificial sequence.CC BY · PubMed Central
Figure 3. Multi-species PCA. We compared the GC-binned tetramer composition of orthologous sequences in human (hs), chimpanzee (pt), cow (bt), dog (cf), elephant (la), guinea pig (cp), marmoset (cj), horse (ec), mouse (mm), orangutan (pa), panda (am), pig (ss), rabbit (os), rat (rn) and rhesus (rm) for intergenic (blue) and intronic (red) regions.
Figure 3. Multi-species PCA. We compared the GC-binned tetramer composition of orthologous sequences in human (hs), chimpanzee (pt), cow (bt), dog (cf), elephant (la), guinea pig (cp), marmoset (cj), horse (ec), mouse (mm), orangutan (pa), panda (am), pig (ss), rabbit (os), rat (rn) and rhesus (rm) for intergenic (blue) and intronic (red) regions.CC BY · PubMed Central
Figure 4. GESTALT comparison of artificial, real and shuffled sequences. (a) Artificial sequence, (b) Intergenic region chr4:104640972–104740972, (c) Intergenic region chr4:104640972–104740972 after dimer permutation. The elements in RepeatMasker are: LINEs in green, SINEs in red/pink, LTR, DNA transposable elements and others in brown.
Figure 4. GESTALT comparison of artificial, real and shuffled sequences. (a) Artificial sequence, (b) Intergenic region chr4:104640972–104740972, (c) Intergenic region chr4:104640972–104740972 after dimer permutation. The elements in RepeatMasker are: LINEs in green, SINEs in red/pink, LTR, DNA transposable elements and others in brown.CC BY · PubMed Central
Figure 5. Principal component analysis of composition and complexity measures. We compare 100 sequences with 100 kb each for artificial sequences (blue), selected intergenic regions (green) and their respective dimer permutation of intergenic sequences (red).
Figure 5. Principal component analysis of composition and complexity measures. We compare 100 sequences with 100 kb each for artificial sequences (blue), selected intergenic regions (green) and their respective dimer permutation of intergenic sequences (red).CC BY · PubMed Central
Figure 6. Repeat identification benchmarks. (a) False positives expected in dimer-permuted sequences and synthetic sequences without repeats, (b) Alu tests, (c) MIR tests. For (b) and (c), each point represents the average sensitivity (Sn) and specificity (Sp). Colors represent the program (PC = PClouds, RM = RepeatMasker) and word size used for PClouds (8, 10, 12, 14, 16). The transparency of the color denotes the amount of repetitive sequence in each set (10%, 20%, … 90%) and the size of the point the average FPR. Each sequence set includes 10 artificial sequences of 100 kb each generated with k-mer size of 8 and window size of 1000 with a G+C content of 40–60%.
Figure 6. Repeat identification benchmarks. (a) False positives expected in dimer-permuted sequences and synthetic sequences without repeats, (b) Alu tests, (c) MIR tests. For (b) and (c), each point represents the average sensitivity (Sn) and specificity (Sp). Colors represent the program (PC = PClouds, RM = RepeatMasker) and word size used for PClouds (8, 10, 12, 14, 16). The transparency of the color denotes the amount of repetitive sequence in each set (10%, 20%, … 90%) and the size of the point the average FPR. Each sequence set includes 10 artificial sequences of 100 kb each generated with k-mer size of 8 and window size of 1000 with a G+C content of 40–60%.CC BY · PubMed Central
Figure 7. Coding and transcribed size distribution of predicted genes. Predictions in artificial (blue), intergenic (green) and dimer permuted intergenic sequences (red) are compared with known genes (gray) for (a) Augustus, (b) Genscan, (c) Twinscan and (d) FEAST.
Figure 7. Coding and transcribed size distribution of predicted genes. Predictions in artificial (blue), intergenic (green) and dimer permuted intergenic sequences (red) are compared with known genes (gray) for (a) Augustus, (b) Genscan, (c) Twinscan and (d) FEAST.CC BY · PubMed Central

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Metadata

Authors
Caballero, J., Smit, A. F. A., Hood, L., & Glusman, G.
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2014-05-06
Volume
42
Issue
12
Pages
e99-e99
ISSN
  • 0305-1048
  • 1362-4962
DOI
10.1093/nar/gku356
PMC
PMC4081056
Cited by
35
References
9
Language
en
Licence
creativecommons.org/licenses/by/3.0/ (vor, 882 d)

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Caballero, J., Smit, A. F. A., Hood, L., & Glusman, G. (2014). Realistic artificial DNA sequences as negative controls for computational genomics. Nucleic Acids Research. https://doi.org/10.1093/nar/gku356