- Research
- Open Access
VarDetect: a nucleotide sequence variation exploratory tool
https://doi.org/10.1186/1471-2105-9-S12-S9
© Ngamphiw et al; licensee BioMed Central Ltd. 2008
- Published: 12 December 2008
Abstract
Background
Single nucleotide polymorphisms (SNPs) are the most commonly studied units of genetic variation. The discovery of such variation may help to identify causative gene mutations in monogenic diseases and SNPs associated with predisposing genes in complex diseases. Accurate detection of SNPs requires software that can correctly interpret chromatogram signals to nucleotides.
Results
We present VarDetect, a stand-alone nucleotide variation exploratory tool that automatically detects nucleotide variation from fluorescence based chromatogram traces. Accurate SNP base-calling is achieved using pre-calculated peak content ratios, and is enhanced by rules which account for common sequence reading artifacts. The proposed software tool is benchmarked against four other well-known SNP discovery software tools (PolyPhred, novoSNP, Genalys and Mutation Surveyor) using fluorescence based chromatograms from 15 human genes. These chromatograms were obtained from sequencing 16 two-pooled DNA samples; a total of 32 individual DNA samples. In this comparison of automatic SNP detection tools, VarDetect achieved the highest detection efficiency.
Availability
VarDetect is compatible with most major operating systems such as Microsoft Windows, Linux, and Mac OSX. The current version of VarDetect is freely available at http://www.biotec.or.th/GI/tools/vardetect.
Keywords
- Reference Sequence
- Secondary Peak
- Primary Peak
- High Recall Rate
- Mutation Surveyor
Background
Following completion of the human genome project, detection and discovery of single nucleotide polymorphisms (SNPs) is at the forefront of genomic research. The discovery of SNPs may help to identify causative gene mutations in monogenic diseases as well as SNPs associated with predisposing genes in complex diseases [1, 2]. Most fluorescence based sequencers produce nucleotide signals (chromatograms) that must be base-called in order to detect the SNP or point mutation. The terms SNP and point mutation are considered synonymous for the algorithm described in this paper. The efficiency of nucleotide variation detection relies mainly on the accuracy of bioinformatic software used to base-call the chromatograms [3–6]. However, most base-calling tools developed for conventional sequencing may not be suitable for SNP detection because they usually misinterpret chromatogram traces at heterozygous base positions. The common sequencing artifacts, which cause most standard base-calling tools to miscall the chromatogram traces, include: 1) polymerase slipage, 2) loss of resolution, 3) contamination and 4) dye blob [7].
SNP discovery would be greatly accelerated if a reliable, automatic SNP discovery tool was available. A commercial automatic SNP detection program called Mutation Surveyor (SoftGenetics) was recently developed utilizing patented anti-correlation technology to increase the efficiency of SNP and mutation detection [8]. Non-commercial programs for SNP detection include PolyPhred (used together with Phred [6, 9], Phrap and Consed programs [10]), novoSNP and Genalys. PolyPhred was designed in conjunction with the well-known Phred and Phrap programs to base-call and assemble input chromatograms prior to SNP detection and visualize the results using the Consed program. The current version of PolyPhred is 6.11 beta at the time of writing. The novoSNP program adopts three independent cumulative scores to identify SNPs, and is able to identify more true SNPs (lower false negative rate) than PolyPhred (version 3) [11]. Genalys software attempts to minimize the number of incorrectly assigned (false positive) SNPs by using peak base ratios and surrounding peak information to identify SNPs [12]. Despite the sophistication of the mathematical models widely used in these algorithms, they still report an unacceptably high number of false negative and/or false positive SNPs.
In this study, we present VarDetect, a sequence variation exploratory software to detect SNPs efficiently from fluorescence based chromatogram data. VarDetect supersedes existing automatic SNP detection tools through utilization of rules which account for the common sequence reading artifacts, combined with pre-calculated peak content base ratios. Furthermore, SNPs can be detected by this software using sequencing data obtained from single, or two-pooled DNA samples.
Results and discussion
Illustration of VarDetect's graphical user interface. The graphical user interface comprises four panes and a quick access toolbar: a) toolbar with a wizard button located on the left-most b) graphical view of input chromatogram traces c) list of predicted SNPs d) SNP information window and e) whole-map view.
Comparison of the efficiency of VarDetect, PolyPhred, Genalys, novoSNP and Mutation Surveyor.
Gene (contigs) | Verified SNPs | VarDetect | PolyPhred | Genalys | novoSNP | Mut. Surveyor | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TP | FP | FN | TP | FP | FN | TP | FP | FN | TP | FP | FN | TP | FP | FN | ||
ACOX2 (5) | 10 | 10 | 28 | 0 | 3 | 1 | 7 | 6 | 277 | 4 | 9 | 352 | 1 | 6 | 40 | 4 |
ADM (2) | 2 | 1 | 4 | 1 | 0 | 0 | 2 | 1 | 260 | 1 | 2 | 220 | 0 | 1 | 11 | 1 |
ARRB1 (6) | 16 | 15 | 7 | 1 | 9 | 1 | 7 | 16 | 30 | 0 | 15 | 58 | 1 | 13 | 13 | 3 |
CACNA1D (11) | 26 | 23 | 9 | 3 | 12 | 4 | 14 | 20 | 363 | 6 | 26 | 361 | 0 | 22 | 60 | 4 |
CACNB3 (3) | 6 | 5 | 4 | 1 | 4 | 1 | 2 | 5 | 191 | 1 | 5 | 308 | 1 | 3 | 51 | 3 |
CCL2 (2) | 3 | 3 | 10 | 0 | 1 | 0 | 2 | 3 | 196 | 0 | 3 | 130 | 0 | 2 | 34 | 1 |
CCL3 (2) | 12 | 11 | 4 | 1 | 4 | 0 | 8 | 9 | 171 | 3 | 12 | 123 | 0 | 9 | 31 | 3 |
CCL4 (2) | 10 | 8 | 11 | 2 | 6 | 3 | 4 | 8 | 136 | 2 | 8 | 96 | 2 | 7 | 20 | 3 |
CCL5 (2) | 3 | 3 | 2 | 0 | 3 | 1 | 0 | 3 | 28 | 0 | 3 | 21 | 0 | 2 | 3 | 1 |
CCR7 (2) | 2 | 2 | 3 | 0 | 1 | 0 | 1 | 2 | 75 | 0 | 2 | 129 | 0 | 1 | 3 | 1 |
ITGAM (13) | 27 | 19 | 20 | 8 | 17 | 7 | 10 | 19 | 552 | 8 | 23 | 618 | 4 | 22 | 63 | 5 |
ITGAX (15) | 25 | 24 | 28 | 1 | 13 | 4 | 12 | 22 | 704 | 3 | 24 | 1166 | 1 | 19 | 131 | 6 |
ITGB7 (9) | 16 | 15 | 13 | 1 | 11 | 1 | 5 | 15 | 435 | 1 | 16 | 521 | 0 | 11 | 32 | 5 |
LIPG (1) | 4 | 2 | 2 | 2 | 1 | 2 | 3 | 3 | 82 | 1 | 3 | 102 | 1 | 3 | 2 | 1 |
NPY (2) | 9 | 7 | 4 | 2 | 5 | 0 | 4 | 8 | 228 | 1 | 9 | 334 | 0 | 7 | 11 | 2 |
Total 15 genes | 171 | 148 | 149 | 23 | 90 | 25 | 81 | 140 | 3728 | 31 | 160 | 4539 | 11 | 128 | 505 | 43 |
77 contigs | ||||||||||||||||
Precision (%) | 49.83 | 78.26 | 3.62 | 3.40 | 20.22 | |||||||||||
Recall (%) | 86.55 | 52.63 | 81.87 | 93.57 | 74.85 | |||||||||||
F-score (%) | 63.25 | 62.94 | 6.93 | 6.56 | 31.84 |
Of the five tools, VarDetect and novoSNP yielded the lowest false negative counts (23 and 11 respectively), which is of paramount importance in most SNP/mutation discovery projects. PolyPhred reported the fewest false positives (25), while novoSNP reported the most false positives (4539). VarDetect had the second lowest false positive count (149). The chromatograms and analysis results of this experiment can be obtained and visualized online in scalable vector graphics (SVG) format from the above VarDetect website. Finally, we measured the software precision and recall ratios in order to compare them using F-score [13]. These scores are presented in the last row of Table 1 where VarDetect had the highest F-score (63.25%), slightly greater than PolyPhred (62.94%). Despite the very similar efficiency, VarDetect reported considerably fewer FNs (about 3.5 times lower) than PolyPhred; hence, VarDetect is preferable for SNP discovery. This implies higher recall rate (higher sensitivity) which is the ability to detect SNPs even from low or ambiguous chromatogram signals. Nonetheless, higher recall rate has a tradeoff that is having lower precision. In other words, a lot more FPs would be predicted as the program sensitivity is improved. Mutation Surveyor, Genalys and novoSNP had low efficiencies due to much higher numbers of false positives (31.84%, 6.93%, and 6.56%, respectively). Overall, VarDetect is superior to other automatic SNP discovery tools because both FN and FP counts are minimized.
Chromatogram trace showing peak intensities where dashed line is the base-call position. Three peaks are detected at this position. The intensities of green, red, and blue peaks are 500, 400, and 70 units, respectively and are used in peak intensity ratio calculation.
Calculation of vicinity peak intensity ratio of the base-call position (arrowed). [2]-vicinity ratio (k = 2) is calculated by normalizing the surrounding signal intensities of two bases left and right of the observed position as described in Equation 2 as follows: = 1/4 × (1 + 0.94 + 1 + 0.97) = 0.977
Comparison of the different features between VarDetect, PolyPhred, Genalys, novoSNP and Mutation Surveyor
List of different features | VarDetect | PolyPhred | Genalys | novoSNP | Mutation Surveyor |
---|---|---|---|---|---|
Operating Systems* | All | All | Windows, Mac | Windows, Linux | Windows |
Easy installation | Yes | No | Yes | Yes | Yes |
Graphical User Interface (GUI) | Yes | w/Consed | Yes | Yes | Yes |
Command line interface (CLI) | Yes | Yes | No | No | No |
Allele frequency calculation for | Yes | No | Yes | No | No |
two-pooled DNA samples |
Conclusion
We present the framework of a novel algorithm to interpret (base-call) fluorescence based chromatograms and efficiently detect the corresponding nucleotide variations in an automatic fashion. In this framework, three main heuristic procedures are employed: 1) Partitioning and Re-sampling (PnR) algorithm that may be used to base-call the bases with ambiguous signal, 2) calculation of the observed signal intensity ratio(Q o ) and vicinity intensity ratio (Q v ) and utilizing the differences between Q v and Q o (quality difference) to check whether the heterozygous peaks are correctly called by the PnR algorithm, and 3) conversion of the chromatogram inputs to numeric code using CodeMap so that the variation can be correctly identified by computer.
The experimental results showed that VarDetect is more efficient than other existing tools, namely PolyPhred, novoSNP, Genalys, and Mutation Surveyor for detecting SNPs. VarDetect's heuristics minimize both false positive and negative errors reducing the effort needed to detect and validate SNPs, making it the tool of choice for automatic SNP detection. Furthermore, VarDetect offers the most features including the ability to detect SNPs from pooled DNA samples and the use of XML annotated reference sequence to cross check the SNP discovery results within the tool without using external applications. VarDetect is platform independent since it was implemented in Java, allowing it to run on all major operating systems without recompiling the source codes.
Methods
where is the signal intensity of a nucleotide at the ith position (the base-call position), and is the ratio of the highest signal intensity c i (b), where b ∈ {A, T, G, C}, to the sum of the signal intensities of adenine c i (A), thymine c i (T), cytosine c i (C), and guanine c i (G), respectively.
This term is the arithmetic mean of the signal intensities which flank to the left for k bases and to the right for k bases. In other words, it is the summation of observed intensities of k bases toward the left and right of divided by 2k (Figure 3). The peak content base ratio of the ith base from the above definition reflects the changes that occur when peak intensity is altered by having two or more different signals coincident at the ith position. Therefore, we can tentatively identify the heterozygous state at the ith base by observing the difference between the observed and vicinity peak content base ratios.
The peak intensity ratio approach may not correctly base-call different peak patterns. The Q o value from both boxes 1 and 2 are identical (0.551); however the black peak in box 2 is misinterpreted as a primary peak, since it clearly over-shoots from the adjacent base position.
- 1.
Reading nucleotides (base-calling) from chromatogram traces
- 2.
Alignment of input sequences to the reference sequence
- 3.
Detection of SNPs and insertions/deletions (indels)
Each step also comprises a two-fold process, namely rough and fine data processing to ensure the accuracy of the resulting data. Processed data collected from base-calling and trace-alignment are analyzed using the aforementioned intensity ratio concept.
Reading nucleotides (base-calling) from chromatogram traces
Currently, most chromatogram trace data come in two formats, namely .ab1 and .scf. Although the .ab1 extension format is proprietary, there are numerous bioinformatics tools, e.g., Phred [6, 9], PolyPhred [10], 4Peaks [16], FinchTV [17], Genalys [12], novoSNP [11], Mutation Surveyor [8] and software libraries including BioPerl [18, 19], BioJava [19], and BioPython [19] that can read this file format. We used Java and BioJava to develop VarDetect because of its high portability. From Equations 1 and 2, the intensity ratios can be pre-computed while base-calling of an input chromatogram is being processed. Each nucleotide position contains intensity values of A, T, G, and C as required by the definitions. The algorithm to calculate such ratios strictly follows these definitions.
Base-calling
Computer representation (array) of chromatogram traces.
Base-call parameter setting in VarDetect. The highest signal is determined as its primary peak, the lower signal is determined as the secondary peak. The signal contents below the noise level are ignored. The heterozygosity level in this setting roughly estimates nucleotide mixture ratio when dealing with pooled DNA.
Effect of signal intensity decay on base-calling. Correct (a) and Incorrect (b) base-calling interpretation due to signal intensity decay.
Re-sampling and calling
Improvement of base-calling by using Partitioning and Re-sampling (PnR) technique. For an observed base (shaded boxes), VarDetect divides a chromatogram peak into four equal parts (partitions) and focuses at the two middle parts (a). The two vectors and are created by connecting the points that the curve segment of the secondary peak crosses over the two partitions (b). Let ⊥ be a perpendicular vector of by rotating it 90 counter-clockwise (c). Then the secondary peak curve has a turning point if the dot product of ⊥· produces a negative value. In other words, if the angle θ between and ⊥ is obtuse, this secondary peak could be interpreted as being heterozygous peak (d).
Illustration of PnR analysis. Partitioning (a) and Re-sampling (b) of chromatogram with rising (red) peak. ⊥ is a perpendicular vector of by rotating it 90° counter-clockwise (c). The secondary peak curve has no turning point since the dot product of ⊥·. produces a positive value (d). Therefore, this peak is interpreted as a homozygous peak.
Alignment of input sequences to the reference sequence
After re-sampling and base-calling, the input sequences are then aligned against the reference sequence using a local alignment method. There are two steps in this alignment process: 1) pre-alignment and 2) alignment enhancement.
Pre-alignment
Since this tool uses the direct method to search for SNPs, alignment of input sequences to a reference sequence is required. The reference sequence in FASTA format can be obtained from the NCBI public database. VarDetect simplifies the pre-alignment task by linearly searching a local match of m contiguous bases greater than or equal to p percent. In other words, each individual sequence is aligned to the reference sequence by sliding a window of m adjacent bases (W m ) along the reference sequence until a match of p percent or greater is found. Since the noisy parts of input chromatograms can be filltered out using the intensity peak ratio concept explained previously, the candidate window W m is selected from good intensity areas of the input chromatogram. From this observation, we investigate the vicinity peak ratio such that [k] ≥ 90%, starting from the position i ≥ (0.2 × N), where N is the total number of peaks (or 20% of the total number of bases).
The 90% quality value is used to guarantee that the selected regions are readable and good enough for automatic SNP detection. The W m window is formed by inclusively extending the next m bases from the accepted observed base i. The W m window is chosen for each chromatogram trace based on this selection scheme. Each trace is aligned with the reference sequence using W m as its representative in matching the pattern whose percent similarity is greater than a given value. This algorithm, called "Quick Alignment using Sliding Window", is applied repeatedly to more than one W m window to optimize the alignment. The alignment process is performed on both forward and reverse orientations and applied iteratively to each chromatogram.
Alignment enhancement
Quick alignment using sliding window algorithm.
SNP selection
SNPs can be in two forms: homozygous and heterozygous. The homozygous form can be detected easily by comparison with the reference sequence, while the heterozygous form can be detected by observing differences between Q v and Q o . For true SNPs, there are two dominant nucleotides, which result in a low observed intensity ratio Q o (Equation 2). Therefore, significant differences between Q v and Q o are indicative of SNPs. This value δ = Q v - Q o , called the detection value, is extensively used to mask out non-SNP regions.
However, inappropriate setting of the δ value may lead to wrong SNP identification. If δ is too low, the number of false positives would be high, since a slight drop of peak height could be detected as a mutation. Conversely, if δ is too high, the number of false negatives is high. The δ value should be adjusted prior to performing automatic SNP detection since this value may differ among experimental protocols. From our empirical study results, the default (optimum) value should be set to 12.5%.
SNP detection is most accurate when analyzing sequence data obtained from individuals, since homozygous bases have a single chromatogram peak, and heterozygous bases have two peaks of similar intensities. Recently, it has been proposed that pooling of DNA samples from more than one individual can accelerate and reduce the cost for SNP discovery. However, there is the limitation that different DNA samples will be sequenced with different sequencing reaction efficiencies, owing to variable DNA quality and concentration, and variable affinity of DNA polymerase for different nucleotides [12, 14]. Despite this limitation, VarDetect can still accurately calculate allele frequencies and detect SNPs from chromatogram traces derived from pooled DNA samples.
Five possible biallelic outcomes of sequencing two-pooled DNA samples
No. | Scenarios | Peak Content | Fusion/Combination | |
---|---|---|---|---|
primary | secondary | |||
1 | XXXX | 4 | 0 | (XX)+(XX) |
2 | YYYY | 4 | 0 | (YY)+(YY) |
3 | XXYY | 2 | 2 | (XX)+(YY)-or-(XY)+(XY) |
4 | XXXY | 3 | 1 | (XX)+(XY) |
5 | YYYX | 3 | 1 | (YY)+(XY) |
CodeMap
Illustration of traces with indels and their CodeMap analysis. Noise-eliminated homozygous (a), homozygous with a C/T SNP at the 5th position (b), and T insertion at the first position (c) chromatogram traces.
Using Equation 3, CodeMap converts the chromatograms in Figures 11 to numeric arrays. The homozygous base is converted into 0 and 2 codes (Figure 11a) while heterozygous base in Figure 11b (Θ[T]) is converted to 1. VarDetect can make use of this code in conjunction with the aforementioned δ value to automatically detect SNPs.
In addition to identifying nucleotide substitutions, VarDetect also automatically detects indels through CodeMap (Figure 11c and its corresponding numeric arrays). When one base (T) is inserted, the following bases are shifted by one frame to the right. Such indels cause misinterpretation errors in most base-calling approaches. To overcome this problem, the indel chromatograms have to be manually edited by skilled operators.
In the CodeMap view, the code sequence which one would expect to often see from this phenomenon is 2(1/2), the code 2 following with either 1 or 2. With an observation on any list in base array (Θ[N]), CodeMap generates this insertion pattern by isolating the correlation between the shifted bases with each possible nucleotide type that is eventually identified by VarDetect. For the adenine array (Θ[A]) in Figure 11c, there are two patterns of "2 1" or "2 2", with four counts, "2 1", "2 1", "2 2", and "2 1". Pattern "2 1" is derived from an observed base A (represented by code 2) being shifted by one position whose base content is not an adenine; hence, it appears in the next position as a heterozygous peak (represented by code 1). Pattern "2 2" is derived from an observed base A (represented by code 2), which is shifted by one position whose content is an adenine. Thus, the next position becomes homozygous A (represented by code 2). These patterns also occur in the cytosine (Θ[C]), guanine (Θ[G]), and thymine (Θ[T]) arrays. For one base deletion, the pattern to be detected is reversed to (1/2)2, the code 1 or 2 following with code 2.
Pattern counting of numeric code shown in Figure 11c. VarDetect selects the highest frequencies (13) to determine number of indel bases.
Pattern Counts | ||||||
---|---|---|---|---|---|---|
Pattern | Θ[A] | Θ[C] | Θ[G] | Θ[T] | Total | Comment |
2(1/2) | 4 | 4 | 2 | 3 | 13 | 1 insertion |
2?[1](1/2) | 1 | 2 | 0 | 0 | 3 | 2 insertion |
2?[2](1/2) | 0 | 1 | 0 | 0 | 1 | 3 insertion |
(1/2)2 | 1 | 2 | 0 | 1 | 4 | 1 deletion |
(1/2)?[1]2 | 0 | 1 | 0 | 0 | 1 | 2 deletion |
(1/2)?[2]2 | 1 | 1 | 0 | 3 | 5 | 3 deletion |
Illustration of VNTR with ATG deletion and its CodeMap analysis. CodeMap converts chromatogram of trinucleotide repeats (r1, r2, r3) to the corresponding numeric arrays (2 0 0 2 0 0) on the right (a). When a set of trinucleotide repeats is deleted, CodeMap reveals specific numeric patterns (underlined) on the right (b), which match with (1/2)?[2]2 pattern shown in Table 4.
Declarations
Acknowledgements
VarDetect was inspired by Genalys program done by Dr. Masazumi Takahashi from the Centre National de Genotypage (CNG). The authors CN, EJ and ST thank the National Center for Genetic Engineering and Biotechnology (BIOTEC) for financial support of this project. We also acknowledge the Thailand Research Fund (TRF) through the Royal Golden Jubilee Ph.D. Program (Grant No. PHD/4.I.MU.45/C.1) for supporting AA while SK is partially supported by the Junior Science Talent Program scholarship awarded by the National Science and Technology Development Agency (NSTDA), Thailand. We thank Dr. Philip Shaw and Dr. Prasit Palittapolgarnpim for giving us valuable comments to improve this manuscript. This work would not be completed without the extensive testing from Dr. Chintana Tocharoentanaphol and Dr. Chanin Limwongse. Finally, we would like to acknowledge the Thailand SNP discovery project, which offered some of the chromatogram sequences previously published to test the efficiency of the tool against other algorithms.
This article has been published as part of BMC Bioinformatics Volume 9 Supplement 12, 2008: Asia Pacific Bioinformatics Network (APBioNet) Seventh International Conference on Bioinformatics (InCoB2008). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/9?issue=S12.
Authors’ Affiliations
References
- Uda M, Galanello R, Sanna S, Lettre G, Sankaran V, Chen W, Usala G, Busonero F, Maschio A, Albai G, et al.: Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of beta-thalassemia. Proc Natl Acad Sci USA 2008, 105: 1620–1625. 10.1073/pnas.0711566105PubMed CentralView ArticlePubMedGoogle Scholar
- Kozyrev S, Abelson A, Wojcik J, Zaghlool A, Linga Reddy M, Sanchez E, Gunnarsson I, Svenungsson E, Sturfelt G, Jonsen A, et al.: Functional variants in the B-cell gene BANK1 are associated with systemic lupus erythematosus. Nat Genet 2008, 40: 211–216. 10.1038/ng.79View ArticlePubMedGoogle Scholar
- Pandya G, Holmes M, Sunkara S, Sparks A, Bai Y, Verratti K, Saeed K, Venepally P, Jarrahi B, Fleischmann R, et al.: A bioinformatic filter for improved base-call accuracy and polymorphism detection using the Affymetrix GeneChip whole-genome resequencing platform. Nucleic Acids Res 2007, 35: e148. 10.1093/nar/gkm918PubMed CentralView ArticlePubMedGoogle Scholar
- Adzhubei A, Laerdahl J, Vlasova A: preAssemble: a tool for automatic sequencer trace data processing. BMC Bioinformatics 2006, 7: 22. 10.1186/1471-2105-7-22PubMed CentralView ArticlePubMedGoogle Scholar
- Prosdocimi F, Lopes D, Peixoto F, Mourao M, Pacifico L, Ribeiro R, Ortega J: Effects of sample re-sequencing and trimming on the quality and size of assembled consensus sequences. Genet Mol Res 2007, 6: 756–765.PubMedGoogle Scholar
- Ewing B, Green P: Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res 1998, 8: 186–194.View ArticlePubMedGoogle Scholar
- Common sequencing artifacts[http://seqcore.brcf.med.umich.edu/doc/dnaseq/trouble/badseq.html]
- Mutation Surveyor[http://www.softgenetics.com/ms/]
- Ewing B, Hillier L, Wendl M, Green P: Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res 1998, 8: 175–185.View ArticlePubMedGoogle Scholar
- Nickerson D, Tobe V, Taylor S: PolyPhred: automating the detection and genotyping of single nucleotide substitutions using fluorescence-based resequencing. Nucleic Acids Res 1997, 25: 2745–2751. 10.1093/nar/25.14.2745PubMed CentralView ArticlePubMedGoogle Scholar
- Weckx S, Del-Favero J, Rademakers R, Claes L, Cruts M, De Jonghe P, Van Broeckhoven C, De Rijk P: novoSNP, a novel computational tool for sequence variation discovery. Genome Res 2005, 15: 436–442. 10.1101/gr.2754005PubMed CentralView ArticlePubMedGoogle Scholar
- Takahashi M, Matsuda F, Margetic N, Lathrop M: Automated Identification of Single Nucleotide Polymorphisms from Sequencing Data. J Bioinform Comput Biol 2003, 1: 253–265. 10.1142/S021972000300006XView ArticlePubMedGoogle Scholar
- F-score calculation[http://en.wikipedia.org/wiki/F-score]
- Tocharoentanaphol C, Promso S, Zelenika D, Lowhnoo T, Tongsima S, Sura T, Chantratita W, Matsuda F, Mooney S, Sakuntabhai A: Evaluation of resequencing on number of tag SNPs of 13 atherosclerosis-related genes in Thai population. J Hum Genet 2007, 53: 74–86. 10.1007/s10038-007-0220-8View ArticlePubMedGoogle Scholar
- ThaiSNP database[http://www.biotec.or.th/thaisnp]
- 4peaks software[http://mekentosj.com/4peaks/]
- FinchTV software[http://www.geospiza.com/finchtv/]
- Stajich J, Block D, Boulez K, Brenner S, Chervitz S, Dagdigian C, Fuellen G, Gilbert J, Korf I, Lapp H, et al.: The Bioperl toolkit: Perl modules for the life sciences. Genome Res 2002, 12: 1611–1618. 10.1101/gr.361602PubMed CentralView ArticlePubMedGoogle Scholar
- Mangalam H: The Bio* toolkits – a brief overview. Brief Bioinform 2002, 3: 296–302. 10.1093/bib/3.3.296View ArticlePubMedGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.