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Accuracy and quality of massively parallel DNA pyrosequencing
© Huse et al.; licensee BioMed Central Ltd. 2007
Received: 1 March 2007
Accepted: 20 July 2007
Published: 20 July 2007
Massively parallel pyrosequencing systems have increased the efficiency of DNA sequencing, although the published per-base accuracy of a Roche GS20 is only 96%. In genome projects, highly redundant consensus assemblies can compensate for sequencing errors. In contrast, studies of microbial diversity that catalogue differences between PCR amplicons of ribosomal RNA genes (rDNA) or other conserved gene families cannot take advantage of consensus assemblies to detect and minimize incorrect base calls.
We performed an empirical study of the per-base error rate for the Roche GS20 system using sequences of the V6 hypervariable region from cloned microbial ribosomal DNA (tag sequencing). We calculated a 99.5% accuracy rate in unassembled sequences, and identified several factors that can be used to remove a small percentage of low-quality reads, improving the accuracy to 99.75% or better.
By using objective criteria to eliminate low quality data, the quality of individual GS20 sequence reads in molecular ecological applications can surpass the accuracy of traditional capillary methods.
Direct interrogation of microbial genomes based upon comparisons of orthologous gene sequences or metagenomic surveys provides a means to assess the diversity of microbial communities without requiring the cultivation of microbes in the laboratory. Since the cost of cloning DNA templates and capillary-based DNA sequencing constrains the number of sequences included in most of these investigations, the detection of low abundance taxa demands surveys that are many orders of magnitude larger than those reported in the literature. Massively parallel pyrosequencing on the Roche GS20 system developed by 454 Life Sciences offers a means to more extensively sample molecular diversity in microbial populations. It is now possible to generate hundreds of thousands of short (100-200 nucleotide) DNA sequence reads in a few hours without requiring the preparation of sequence templates by conventional cloning. In the near future, technical advances will likely increase the number and length of sequence reads.
Pyrosequencing technology relies upon enzyme cascades and CCD luminescence detection capabilities to measure the release of inorganic pyrophosphate with every nucleotide incorporation . The GS20 system takes advantage of DNA capture beads that contain, on average, one single-stranded template, which is amplified to millions of copies in an oil emulsion PCR (emPCR). The beads are then distributed on a solid-phase sequencing substrate (a PicoTiterPlate™) with 1.6 million wells that can each hold a bead and additional reagents, including polymerase, luciferase, and ATP sulfurylase. Microfluidics cycle each of the four nucleotide triphosphates over the PicoTiterPlate™, and incorporation of a nucleotide releases pyrophosphate, the substrate for a luminescence reaction, which is recorded with a cooled CCD camera. The record of intensity of each flow of a nucleotide is a flowgram, analogous to a chromatogram that reports the order of A, C, G and T residues from a DNA sequencing template. Flowgram values correspond to the homopolymer length for that base. The average number of wells with detectable sequencing templates is about 450,000, which produces about 200,000 usable reads. This new methodology brings with it different sources of error to traditional dideoxy capillary sequencing. Since the nucleotide triphosphates are flowed one at a time, substitutions are less likely than with traditional methods. However, it is sometimes difficult to resolve the intensity of luminescence produced when a homopolymer is encountered. The result can be ambiguity of homopolymer length, particularly for longer homopolymers. In addition, insufficient flushing between flows can cause single base insertions (carry forward events) usually near but not adjacent to homopolymers. Insufficient nucleotides within a flow can cause incomplete extension within homopolymers. Generally, an excess of intermediate flowgram values indicates a poor quality read . The GS20 software makes corrections for carry forward and incomplete extensions (CAFIE); it shortens reads from the 3' end until fewer than 3% of the remaining flowgram values are of intermediate value, and it removes reads if the trimming falls below a threshold length. The software identifies as ambiguous flow cycles in which no flowgram value was greater than 0.5. If 5% or more of the flow cycles for a read are ambiguous, the read is removed.
The assembly of many overlapping pyrosequencing reads can produce highly accurate consensus sequences [3, 4]. Wicker et al.  compared assemblies of the barley genome produced by reads from GS20 pyrosequencing and from ABI dideoxy sequencing. Both methods produced consensus sequences with error rates of approximately 0.07% at each consensus position. Gharizadeh et al.  compared pyrosequences with Sanger dideoxy methods for 4,747 templates. Comparisons of the traditional capillary sequences with the 25-30 nucleotide pyrosequence reads demonstrated similar levels of read accuracy. Assemblies of massively parallel pyrosequencing reads of plastid genomes from Nandina and Platanus exhibited overall error rates of 0.043% and 0.031%, respectively, in the consensus sequence . The generation of consensus sequences to improve accuracy, however, is generally not appropriate for studies that seek information about natural variation from every read. For example, in metagenomic  or PCR amplicon  libraries from environmental DNA samples, each sequence read can theoretically represent DNA from a distinct gene from a complex mixture of microbial genes.
A viable but imperfect alternative to building consensus sequences for metagenomic and diversity investigations is to identify and remove pyrosequencing reads that are likely to be incorrect. For example, Gilbert et al. , in a study of ancient woolly mammoth mitochondrial DNA, removed pyrosequencing reads that were not 98% identical to previously sequenced mammoth mitochondrial DNA sequences, assuming that they must be poor quality. Dostie et al.  sequenced an amplicon library and discarded reads in which the PCR primer was not recognized by BLAST. These studies removed 15% and 7% of their reads, respectively, but it is not clear that these statistics improved the quality of the remaining data.
To explore error modalities, we used the GS20 system to generate more than 340,000 reads from a PCR amplicon library that was prepared from a collection of 43 reference templates of known sequence. Each reference template contains a distinct ribosomal RNA gene (rDNA), including the V6 hypervariable region from a collection of 43 divergent bacteria . Differences between pyrosequences and their cognate reference sequences identified signatures of low quality data.
Types of error
Number of occurrences
Percent of errors
Homopolymer extension and CAFIE
Not associated with homopolymers
Incomplete homopolymer extension
Not associated with homopolymers
Homopolymer extension and CAFIE
Not associated with homopolymers
Ambiguous base calls (N)
Number of occurrences
Cumulative percent of reads
Percent of reads
Reads with no errors (perfect match)
Reads with no more than one error
Reads with no more than two errors
Reads with more than two errors
Two transition mismatches, A to G and T to C, were more frequent than other mismatches, but the reverse transitions, G to A and C to T, were not. Nearly 70% of the homopolymer extensions were A/T. After correcting for the relative number of homopolymers of each type (A/T versus C/G) in the reference sequences (45% and 55%, respectively), the prevalence of A/T extensions was 24% higher than expected, and C/G was concomitantly lower. Although substantially less common, the carry-forward errors, where a base is inserted or substituted ahead of a homopolymer run of the same base (for example, GACTGGG could become GACGTGGG with a carry forward insertion of a G) reflected the same relative increase of A/T over C/G.
We compared error rates in the proximal primer region with error rates in the sequence following the primer. We found a positive, non-linear correlation between the two error rates (not shown). Primers with three or more errors are indicative of low-quality sequences downstream of the primer, but primers with fewer errors are not a good predictor of the overall error of the read. In our experiment, 3% of the reads contained inexact matches to the proximal primer, but removing them only reduced the error rate from 0.49% to 0.45%: most of this improvement reflects elimination of incorrect base calls in the primer sequence. When all reads with Ns, primer errors, and long or short read lengths were removed the total error rate was reduced to 0.16%. The remaining errors correspond to insertions (51%), deletions (33%) and substitutions (16%).
We looked for spatial patterns of read errors using the xy location of each read on the plate (available in the GS20 output files). There can be some minor edge effects but these are not consistent across runs and do not represent a large enough portion of the reads under normal conditions to be a useful tool. However, we note that the total number of errors due to edge effects will be greater in experiments that use 4 or 16 lane gaskets, as the total number of edges will be proportionally larger.
The massively parallel pyrosequencing of emulsification PCR-based templates holds great promise for revolutionizing high-throughput sequencing. However, there is concern over the potentially high degree of error, particularly for applications that cannot rely on consensus of large assemblies. Consensus-based projects would also benefit from a lower error rate, as fewer sequences would be required to build a reliable consensus. The original description of the 454 Life Sciences system reported an error rate for shotgun library reads of four bases per hundred nucleotide positions. The test fragment data in that publication had much lower error rates despite the fact that the test fragments have extensive homopolymers and are designed to be difficult to read correctly. This discrepancy indicates that the basic method of pyrosequencing, luminescence detection and flow intensity resolution, is sound, and suggests that the higher error with experimental data may come from the experimental manipulation of the sequences prior to pyrosequencing. Margulies et al.  suggested that this may be due to multiple sequences binding to an individual bead prior to the emPCR amplification, resulting in a heterogeneous amplification pool. The GS20 quality filters will eliminate sequences from beads that contain two highly divergent DNA templates but the software will attempt to interpret flowgrams from a single bead that contains two similar but non-identical sequences. Unlike shotgun genomic data, V6-tag data may have large numbers of highly similar sequences. It is, therefore, even more important in V6-tag and metagenomic sequencing to remove reads that may result from multi-templated beads.
We conducted an in-depth analysis of experimentally generated GS20 reads by sequencing an amplicon library made from a set of clones of known sequence. We found an error rate (incorrect bases/total number of expected nucleotides) of 0.49%, considerably lower than that reported by Marguiles et al.  but still higher than they or we found for test fragment data. Significantly, we found that the errors in our experimental reads were not randomly distributed across all reads: 86% of the reads contain no errors, while reads that differ from the reference sequence by more than 4% contained nearly 50% of the errors (Figure 1). In contrast, errors were much more randomly distributed in our test fragments, where 50% of the errors were from those fragments that differ by less than 1% from the reference. A multi-templated bead would frequently have multiple bases at a position, which could cause indeterminate flows - neither base having ample luminescence to clearly register. The convergence of the error rates of the two separate sequencing runs when the reads containing Ns were removed is consistent with the multi-templated bases as the primary source of error. The error distribution across reads and the similarity of error rates for reads with no Ns are consistent with a high general accuracy of the pyrosequencing method and poor resolution of a small number of beads with a heterogeneous amplification population.
If heterogeneous templates on a single bead represented a major contribution to observed errors in a low quality read, we anticipate a disproportionate number of errors would occur in sequences that correspond to low abundance templates in the original emPCR reactions; if a low-frequency strand shares a bead with another sequence, the other sequence is likely to be different. In contrast, high-frequency sequences are more likely to be contaminated by an identical sequence. Our data match this pattern. The removal of the bulk of the errors via the removal of reads with ambiguous bases is also consistent with multi-templated beads. All of our reads shared the same proximal primer, and would, therefore, sequence with few errors in the primer, even on multi-templated beads. Other experiments with heterogeneous primers may find primer fidelity to be more useful at identifying low-quality reads.
A significant decrease in the heterogeneous amplification population (HAP) between the original reported experiment and ours is likely given the improvements in the protocol developed by 454 Life Sciences. Unfortunately, it suggests that the highest single source of error in an emPCR-based pyrosequencing experiment may vary from experiment to experiment. The evidence from our two separate sequencing runs, however, is that the removal of reads with Ns is a good surrogate for the removal of reads from multi-templated beads. Nonsynchronized extension of fragments will also produce Ns. These will also be culled when reads with Ns are removed. Advances in pyrosequencing that would reduce the occurrence of multi-templated beads, reduce nonsynchronized extension, or better identify these errors in the base-calling software could significantly improve the overall accuracy of the technology.
Our analysis of the distribution of the types of error in pyrosequencing of emPCR libraries suggests ways of identifying and removing these HAP-hazards: reads with a disproportionately large number of errors are disproportionately likely to contain ambiguous bases (Ns) and to be aberrantly long or short. Short reads arise from short fragments on emPCR beads, but also, and perhaps more likely, from sequential deletion of a read by the software in the GS20 machine, which successively trims bases presumed to be in error from the end of reads. The more bases trimmed, the more likely the entire read is of dubious quality. These errors may be multi-templated beads of similar sequence, or nonsynchronized extension of the templates. Once fragments lose synchrony, they will have successively more errors as the read extends. A reduced threshold for removing reads that have bases trimmed from the end might remove many of these poor-quality short reads. Many sequencing projects cannot judge an appropriate read length, although it is always possible to detect and remove short reads. Reads of aberrant length represented only a small fraction, approximately 1%, of our data, and most of these reads, >60%, also contain Ns.
Our analysis of the GS20 sequencing error rate of the V6 region of bacterial rRNA genes shows a marked improvement over the original error rates published by Margulies et al . The largest source of errors may be due to multi-templated beads, and enhancements to both the chemistry protocol for the GS20 and the built-in bioinformatics software may account for the change in error rates. Our results highlight that a small proportion of low quality reads, presumably from multi-templated beads, are responsible for the majority of sequencing errors. The ability to identify and remove these reads is the best way to improve the accuracy of the entire dataset. It is not a replacement for assigning quality scores to detect the position of miscalled bases. The interpretation of chromatograms by programs such as PHRED  employs quality scores that reflect the probability of any type of base call error. Although it uses the same scale, the GS20 software generates quality values based on the probability of homopolymer extension rather than probability of a correct base call.
Identifying low-quality reads and their contribution to the error rate
Percent of reads
Reads with no Ns
Reads with one or more Ns
Reads with length ≥81 and ≤108
Reads with length <81 or >108
Reads with no Ns and length ≥81 and ≤108
Reads with no proximal errors
Reads with fewer than three proximal errors
Reads with more than three proximal errors
Reads with no Ns and length ≥81 and ≤108 and no proximal errors
Our strategy for detecting low quality reads circumvents the need to generate consensus sequences for improving data quality in massively parallel pyrosequencing experiments of environmental DNA. Our criteria for detecting reads with errors allows for the acquisition of pyrosequencing data in the context of molecular ecology that can surpass the accuracy of traditional capillary methods.
Materials and methods
Generation of 1 kb clone library and selection of clones for pyrosequencing
DNA was extracted according to Huber et al.  from diffuse flow hydrothermal vent samples as described in Sogin et al. . PCR primers were designed using ARB software  to target the bacterial 16S rDNA. The primers used were 337 F (5' CAN CCT ACG GGN GGC NGC) and 1391R (5' GAC GGG CGG TGW GTN CA). The amplification mix contained 5 units Pfu Turbo polymerase (Stratagene, La Jolla, CA, USA), 1× Pfu reaction buffer, 200 μM dNTPs (Pierce Nucleic Acid Technologies, Milwaukee, WI, USA), and 0.2 μM each primer in a volume of 100 μl. Environmental DNA (3-10 ng) was added to 3 separate 30 μl amplification mixes. A positive control (Marinobacter aquaeolei genomic DNA) and two negative controls (no DNA and genomic DNA from the archaeon Methanococcus jannaschii) were also run. An initial denaturation step of 3 min at 94°C was followed by 30 cycles of 94°C for 30 s, 55°C for 45 s, and 72°C for 2 minutes. The final extension step was 72°C for 2 minutes. Following PCR, three reactions for each sample were combined, purified, and concentrated using the MinElute PCR Purification Kit (Qiagen, Valencia, CA, USA) according to the manufacturer's instructions. PCR product quality was assessed on a 0.8% agarose gel stained with ethidium bromide and ligated with pCR4-TOPO vector for 20 minutes at room temperature and transformed with TOP10 electrocompetent cells according to the manufacturer's instructions (Invitrogen, Carlsbad, CA, USA). Colonies for each library were randomly selected and grown in SuperBroth with 50 mg/ml kanamycin in 96 deep-well blocks overnight. Alkaline lysis template preparation was carried out on cell pellets using the RevPrep Orbit II (Genomic Solutions, Ann Arbor, MI, USA) or the Biotech RoboPrep2500 (MWG Biotech, Ebersberg, Germany). The 1,000 base-pair amplicons were sequenced bidirectionly using primers T3 (5'-ATT AAC CCT CAC TAA AGG GA) and T7 (5'-TAA TAC GAC TCA CTA TAG GG), and on an ABI 3730 × l genetic analyzer. Sequences were aligned with MUSCLE (with parameters -diags and -maxiters 10)  and manually manipulated in the BioEdit 7.0.1 program . Distance matrixes were calculated using quickdist , and taxonomic identities determined using RDP-II Classifier . Sequences were trimmed to include only the V6 region of the gene, the distance matrix re-calculated, and from this analysis, 43 divergent sequences were chosen for further experimentation. The average length of the V6 region for these clones was 101 bases, ranging from 95 to 109, with one longer reference of 183 bases. The 16S rDNA sequences are deposited at GenBank under accession numbers DQ909092, DQ909128DQ909132, DQ909133, DQ909142, DQ909144, DQ909158, DQ909184, DQ909202, DQ909204, DQ909218, DQ909223, DQ909224, DQ909248, DQ909251, DQ909253, DQ909266, DQ909274, DQ909337, DQ909368. DQ909392, DQ909396, DQ909400, DQ909414, DQ909423, DQ909438, DQ909440, DQ909465, DQ909474, DQ909498, DQ909513, DQ909519, DQ909538, DQ909603, DQ909618, DQ909631, DQ909662, DQ909688, DQ909702, DQ909706, DQ909719. DQ909727, DQ909753.
Generation of known V6 amplicon library
We treated each plasmid with plasmid-safe DNAase (Epicentre, Madison, WI, USA) to remove Escherichia coli genomic DNA and confirmed that each plasmid produced an amplification product of the expected size with primers targeting the V6 region of the bacterial rDNA according to Sogin et al. . We then pooled the individual plasmids and amplified with the primers that flank the V6 region of rRNA genes according to Sogin et al. . We assessed the product quality using a BioAnalyzer Agilent DNA 1000 LabChip following the manufacturer's instructions. Three reactions were combined, purified, and concentrated using the MinElute PCR Purification Kit (Qiagen). The final amplicon library was sequenced independently by 454 Life Sciences and our own lab. Both labs used the Roche Genome Sequencer 20 (GS20) according to the manufacturer's specifications . The original GS20 output files as text are available in Additional data files 3-5.
Error rate calculations
We combined the data from both sequencing runs for a total of 340,150 reads (226,150 and 114,000), with an average read length of 94.5 nucleotides and a total of 32,816,656 bases. These sequences are available in fasta format in Additional data file 2. To determine the reference sequence source of each pyrosequencing read, we ran a separate multiple sequence alignment of each individual read against the 43 reference sequences using MUSCLE  (default options plus maxiters 2, diags). We calculated the number of sequence differences between each read and the reference sequences to determine the reference sequence to which each read mapped most closely. All subsequent error calculations are based on comparing reads to their assigned reference sequence.
The overall error rate is the number of errors in a read divided by the length of sequence. Specifically, we calculated errors in several ways. In all methods, each base mismatch or N in the test sequence counts as an error, and a terminal gap caused by a GS20 read terminating before the end of the reference does not count as an error. In the first and second methods each base of an insertion or deletion counts as one error. In the third method, insertions or deletions are counted by homopolymer runs. If a TAAA is inserted, it is counted as two insertions, one single-T and one multi-A insertion. The denominator for the first method was the read length. The denominator for the second and third methods was the length of reference sequence minus any discounted terminal gaps. All error rate calculations produced essentially the same results. We report error rates using all base errors (not by homopolymer run) divided by the expected length (reference sequence length minus terminal gaps).
The error rates were calculated for each sequence in a pairwise comparison of the pyrosequencing read and the reference sequence to which it was assigned. We used the Needleman-Wunsch algorithm  for these pair-wise alignments because it selects for the best possible alignment given its run parameters. Using a set of 100 sequences and a matrix of Needleman-Wunsch run parameter combinations, we found that a gap opening penalty of 5.75 and a gap extension penalty of 2.75 minimized the calculated error rate. Error rates, reference sequences and read sequences were imported into a MySQL database for storage and analysis.
Additional data files
The following additional data are available with the online version of this paper. Additional data file 1 is a fasta file of the 43 known sequences used. Additional data file 2 is a gzip-compressed fasta file of the sequences output by the GS20. These sequences correspond to those included in Additional data files 3, 4, 5 but include only the final sequence information. Additional data files 3, 4, 5 are three compressed text files representing the text translations of the original GS20 binary output (sff) files for all of the sequencing used in the analysis, including sequence, flowgram and other run information. GS20 data are reported by region of the PicoTiterPlate™; we sequenced three plate regions.
We thank Richard Fox and Jonathan Neff for computing facility support. This work was supported by National Aeronautics and Space Administration Astrobiology Institute Cooperative Agreement NNA04CC04A (to MLS), subcontracts from the Woods Hole Center for Oceans and Human Health from the National Institutes of Health and National Science Foundation (NIH/NIEHS 1 P50 ES012742-01 and NSF/OCE 0430724-J Stegeman PI to HGM and MLS), grants from the WM Keck Foundation and the G Unger Vetlesen Foundation (to MLS), and a National Research Council Research Associateship Award (to JAH).
- Ronaghi M, Uhlén M, Nyrén P: DNA sequencing: a sequencing method based on real-time pyrophosphate. Science. 1998, 281: 363-365. 10.1126/science.281.5375.363.PubMedView ArticleGoogle Scholar
- Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z, et al: Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005, 437: 376-380.PubMedPubMed CentralGoogle Scholar
- Goldberg SMD, Johnson J, Busam D, Feldblyum T, Ferriera S, Friedman R, Halpern A, Khouri H, Kravitz SA, Lauro FM, et al: A Sanger/pyrosequencing hybrid approach for the generation of high-quality draft assemblies of marine microbial genomes. Proc Natl Acad Sci USA. 2006, 103: 11240-11245. 10.1073/pnas.0604351103.PubMedPubMed CentralView ArticleGoogle Scholar
- Moore MJ, Dhingra A, Soltis PS, Shaw R, Farmerie WG, Folta KM, Soltis DE: Rapid and accurate pyrosequencing of angiosperm plastid genomes. BMC Plant Biol. 2006, 6: 17-10.1186/1471-2229-6-17.PubMedPubMed CentralView ArticleGoogle Scholar
- Wicker T, Schlagenhauf E, Graner A, Close T, Keller B, Stein N: 454 sequencing put to the test using the complex genome of barley. BMC Genomics. 2006, 7: 275-10.1186/1471-2164-7-275.PubMedPubMed CentralView ArticleGoogle Scholar
- Gharizadeh B, Herman ZS, Eason RG, Jejelowo O, Pourmand N: Large-scale pyrosequencing of synthetic DNA: A comparison with results from Sanger dideoxy sequencing. Electorphoresis. 2006, 27: 3042-3047. 10.1002/elps.200500834.View ArticleGoogle Scholar
- Edwards RA, Rodriguez-Brito B, Wegley L, Haynes M, Breitbart M, Peterson DM: Using pyrosequencing to shed light on deep mine microbial ecology. BMC Genomics. 2006, 7: 57-10.1186/1471-2164-7-57.PubMedPubMed CentralView ArticleGoogle Scholar
- Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, Arrieta JM, Herndl GJ: Microbial diversity in the deep sea and the underexplored "rare biosphere". Proc Natl Acad Sci USA. 2006, 103: 12115-12120. 10.1073/pnas.0605127103.PubMedPubMed CentralView ArticleGoogle Scholar
- Gilbert MTP, Binladen J, Miller W, Wiuf C, Willerslev E, Poinar H, Carlson JE, Leebens-Mack JH, Schuster SC: Recharacterization of ancient DNA miscoding lesions: insights in the era of sequencing-by-synthesis. Nucleic Acids Res. 2007, 35: 1-10. 10.1093/nar/gkl483.PubMedPubMed CentralView ArticleGoogle Scholar
- Dostie J, Richmond TA, Arnaout RA, Selzer RR, Lee WL, Honan TA, Rubio ED, Krumm A, Lamb J, Nusbaum C, et al: Chromosome Conformation Capture Carbon Copy (5C): A massively parallel solution for mapping interactions between genomic elements. Genome Res. 2006, 16: 1299-1309. 10.1101/gr.5571506.PubMedPubMed CentralView ArticleGoogle Scholar
- Kysela DT, Palacios C, Sogin ML: Serial Analysis of V6-ribosomal sequence tags (SARST-V6): A method for efficient, high-throughput analysis of microbial community composition. Environmental Microbiol. 2005, 7: 356-364. 10.1111/j.1462-2920.2004.00712.x.View ArticleGoogle Scholar
- Roche Applied Sciences: GS20 Data Processing Software Manual. 2006, Penzberg: Roche Diagnostics GmbHGoogle Scholar
- Ewing B, Green P: Base-calling of automated sequencer traces using Phred. II. Error probabilities. Genome Res. 1998, 8: 186-194.PubMedView ArticleGoogle Scholar
- Huber JA, Butterfield DA, Baross JA: Temporal changes in archaeal diversity and chemistry in a mid-ocean ridge subseafloor habitat. Applied Environmental Microbiol. 2002, 68: 1585-1594. 10.1128/AEM.68.4.1585-1594.2002.View ArticleGoogle Scholar
- Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar , Buchner A, Lai T, Steppi S, Jobb G, et al: ARB: a software environment for sequence data. Nucleic Acids Res. 2004, 32: 1363-1371. 10.1093/nar/gkh293.PubMedPubMed CentralView ArticleGoogle Scholar
- Edgar RC: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32: 1792-1797. 10.1093/nar/gkh340.PubMedPubMed CentralView ArticleGoogle Scholar
- Hall TA: BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp Series. 1999, 41: 95-98.Google Scholar
- Cole JR, Chai B, Farris RJ, Wang Q, Kulam SA, McGarrell DM, Garrity GM, Tiedje JM: The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis. Nucleic Acids Res. 2005, 33: D294-296. 10.1093/nar/gki038.PubMedPubMed CentralView ArticleGoogle Scholar
- Needleman SB, Wunsch CD: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol. 1970, 48: 443-453. 10.1016/0022-2836(70)90057-4.PubMedView ArticleGoogle Scholar
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