- Open Access
A multiple-alignment based primer design algorithm for genetically highly variable DNA targets
© Brodin et al.; licensee BioMed Central Ltd. 2013
- Received: 5 March 2013
- Accepted: 20 August 2013
- Published: 21 August 2013
Primer design for highly variable DNA sequences is difficult, and experimental success requires attention to many interacting constraints. The advent of next-generation sequencing methods allows the investigation of rare variants otherwise hidden deep in large populations, but requires attention to population diversity and primer localization in relatively conserved regions, in addition to recognized constraints typically considered in primer design.
Design constraints include degenerate sites to maximize population coverage, matching of melting temperatures, optimizing de novo sequence length, finding optimal bio-barcodes to allow efficient downstream analyses, and minimizing risk of dimerization. To facilitate primer design addressing these and other constraints, we created a novel computer program (PrimerDesign) that automates this complex procedure. We show its powers and limitations and give examples of successful designs for the analysis of HIV-1 populations.
PrimerDesign is useful for researchers who want to design DNA primers and probes for analyzing highly variable DNA populations. It can be used to design primers for PCR, RT-PCR, Sanger sequencing, next-generation sequencing, and other experimental protocols targeting highly variable DNA samples.
- Primer design
- DNA sequencing
- Amplicon sequencing
- Next-generation sequencing
- Primer dimer
Proper primer design is essential for projects where PCR amplification and/or DNA sequencing play an important role, and a number of algorithms and design recommendations have been proposed. Depending on the goal of a particular project, criteria for selection of primers (and probes) include simple parameters like the length of primers and products, as well as considerations regarding the information the researcher wants to extract. Because in silico design prior to starting a project may prevent later problems in experiments and analyses, many specialized primer design computer programs have been created. They focus on certain aspects of primer design, including multiplexing, degenerate sites, discriminate amplification, nested PCR, SNP protocols, and hybridization analyses such as micro-arrays and in situ hybridization, and of course DNA sequencing [1-8]. Partly because design is complicated, even for well-defined targets with little or no genetic variation, it is usually performed in discrete, non-communicating steps. This is unfortunate because limitations in one step may affect another, and similarly a problem in one step could possibly have been circumvented by an alternative in another step. Thus, when design is undertaken in a series of separate steps, it may be difficult or impossible to optimize the overall design. Therefore, there is a need for a method that can optimize primers and probes while considering all design criteria simultaneously.
Deep sequencing, i.e., re-sequencing of a known DNA sample to reveal relative frequencies of individual population variants and to detect rare mutants, has become feasible thanks to next-generation sequencing (NGS). For example, NGS has been used to investigate early immune escape variants in acute HIV-1 infection [9-12], evolution of distinct phenotypic traits such as transitions in HIV co-receptor usage , virus genetic variation during drug treatment [14-16], endogenous siRNAs , cancer gene variants , and genetic variation in gut microbiota . To accurately amplify and sequence such populations, genetic variation needs to be considered in the primer design. Primer design that captures only limited diversity relative to the sample population diversity, and favors certain variants, will bias the outcome. Thus, in addition to the advantages of designing the physical parameters of primers in parallel rather than in serial steps as discussed above, it is also valuable to consider a multiple-alignment representative of the diversity in the sample of interest in the overall design. It is important to locate primers in relatively conserved regions bounding the region of interest, and to design primers that will adequately address the level of variation that cannot be avoided.
Motivated by the scientific goals of analyzing diverse HIV-1 and SIV populations, and the fact that no general primer design software existed that included both the many steps of the design procedure, as well as the rich diversity information from a multiple-sequence alignment, we developed an algorithm that integrates all these components into a comprehensive tool. This tool was based on our experience with 454 deep sequencing, but can also be used for primer design for other NGS technologies such as IonTorrent, Illumina, SOLiD, PacBio, as well as general PCR and traditional Sanger sequencing protocols. We show examples of the use of this algorithm to successfully analyze diverse HIV-1 populations, both within and between infected patients. Note that although these viruses are among the most genetically variable organisms known [20-24], the tool is not restricted to HIV/SIV design; it can be used to design primers and probes to any aligned set of DNA sequences, regardless of diversity level and organism. To make access easy, guarantee that the latest version of the tool is used, and make the program independent of computer platform, the software is available as a web tool at the LANL HIV database.
This software was built using Perl 5 and C programming languages. The web interface was constructed using the Catalyst Model-View-Controller framework (http://www.catalystframework.org) and Moose object-oriented programming in Perl (http://moose.iinteractive.com).
The software was built in modules, most of them written in Perl 5. Modules that are computationally intensive, such as the dimerization risk estimation, were written in C. Other parts, such as the bio-barcode generation and edit distance filtering have been pre-calculated and are stored in a database for quick access during PrimerDesign optimizations. The bio-barcodes were calculated on a parallel computer, using inline C for Perl.
To illustrate PrimerDesign performance, we used sequence alignments from one of the most variable organisms known, the human immunodeficiency virus type 1 (HIV-1) . The design is based on a pre-existing multiple DNA sequence alignment.
PCR and 454 sequencing
PCR pre-amplification was performed as previously described [9, 11]. These amplifications were performed with adaptor-tag-primer constructs designed using the methods described in this paper. The pre-amplification boosts the template numbers for subsequent 454 analyses while introducing bio-barcodes for downstream bioinformatic analyses and universal adaptors for 454 emulsion PCR and sequencing.
Sequencing reactions were run on a Roche Genome Sequencer FLX system (Roche, Basel, Switzerland) as previously described [9, 11]. Briefly, all samples were diluted and pooled. For emulsion PCR, 2.25 ml of the pooled sample was added to a reaction mix containing 450,000-600,000 capture beads per reaction. The dilution corresponded to copies per bead ratios of approximately 1-10, and the yield was proportional to that ratio. The PCR products were retrieved by breaking the emulsion and enriching for beads contain amplified products. Approximately 400,000-750,000 enriched DNA beads from each reaction were deposited into one of 2 regions of a 70 × 75 mm Pico Titer Plate for sequencing.
Multiple-alignment informs primer design
User and sequencing method restrictions constrain primer design
When primers are designed for re-sequencing, including NGS-based deep sequencing, the user already has information about the ROI. Based on the alignment, the user specifies the ROI by its alignment-specific start and end coordinates. The user also can place constraints on the primers in terms of minimum and maximum length (10 ≤ L ≤ 40 bp), and maximum allowed differences in melting temperatures (T m ) in °C to match the primers to experimental conditions and desired de novo lengths. Importantly, primers will be designed to include genetic variation down to a user specified detection limit d, which, if necessary, will include degenerate nucleotide states at some primer positions. The user can also restrict the design by requiring primer sites to have G/C characters at 3′- and/or 5′-ends.
In addition to user specified constraints, the sequencing method also puts restrictions upon the design process. For instance, if the user specifies 454 Titanium as adaptors, this automatically sets restrictions on the barcode generation as well as possible primer sites, to avoid di-nucleotides of the same state at junctions and the potential for dimerization. Another important method-specific restriction is the total length of the fragment to be sequenced: this is set automatically based on published sequencing lengths for different sequencing methods, but the user can change this by specifying “my adaptor” and a desired de novo length.
Entropy informs about primer locations
In order to find good locations for the primers within the alignment, the algorithm first finds the nucleotide frequencies at each position of the alignment. It then excludes nucleotides whose frequency at any position falls below a desired detection limit d (given by the user), and calculates a multi-state consensus character (IUPAC code) representative of the remaining nucleotides at each position. The remaining variability is then estimated by the Shannon entropy for each site [25, 26]. Next, the locations of all forward-reverse primer pairs are found around the region of interest (ROI) with primer lengths between minimum and maximum primer length (given by the user) sorted by their entropy scores. Lower entropies are preferred. These potential primer pairs are then filtered by T m and dimerization risk.
Estimating T m for matching to experimental conditions
The primer melting temperature T m is estimated by the empirical nearest-neighbor model [27, 28], assuming a sodium concentration of 0.15 M and primer concentration at 30 μM, and dinucleotide entropies and enthalpies according to published empirical values [27, 28]. Because the consensus sequence may contain multistate characters (aka. degenerate sites), each such site is deconvoluted into possible A, C, G, and T characters and all possible primer sequences are generated. The complexity measures how many individual primers composed of only A, C, G, and T characters exist for a given primer (and across the entire alignment). For each of the deconvoluted primers the T m is calculated, and the average is presented for each potential multistate-containing primer. If the difference in T m between a forward and a reverse primer is within the maximum allowable limit (given by the user), then that primer-pair is appended to the list of potential constructs and sent to the dimerization risk estimation.
Bio-barcode tags label sequences
To avoid time-consuming generation of barcodes every time the software is run we have constructed prior lists that PrimerDesign accesses during the primer-tag-adaptor design. There are two sets of potential barcodes. The first set contains all tags of lengths between 3 and 18 nucleotides. This list was generated recursively. Thus, for tag length n, nucleotides were appended to the tags of length n-1. During the generation of the tags, no tags with repeated nucleotides at adjacent sites were allowed as this is known to cause potential misreading in pyrosequencing [29, 30]. The second set of tags was derived from the first set where tags were filtered out when the edit distance was less than a user-specified minimum. All sets with minimum edit distances of 3-10 were pre-calculated. In detail, to effectively generate the second set the algorithm checks for the longest common substring of two tags. If the difference between the tag length and the common-substring length was less than the specified edit distance, then the Levenshtein distance between the two tags must also be less than the edit distance. Because the lists are recursive, this procedure led to a great reduction of the number of calculations for longer tag lengths.
Automated dimerization risk filters design constructs
Hence, all unique primer constructs are checked against each other, and if an interaction shows more than the user-specified number or ratio of matches, then that set is discarded. The default values (window = 10; ratio = 0.9) are conservative values based on recently published estimates of dimerization risk .
PrimerDesign suggests primer pairs
In addition to the graphical output, we also provide a simple table with the primer constructs and T m , which can be used for primer synthesis and experimental setup, and a log file with additional quality control and iteration data.
The execution speed of the program depends on a number of factors, particularly the size of the input multiple alignment, the level of genetic variation, and the number of barcodes requested. The level of genetic variation affects several steps; as the variability increases, the number of degenerate sites may also increase, which adds to 1) the complexity and the number of primers to average T m over, and 2) the number of potential primer-dimers. The level of complexity to consider is adjusted by the user-specified detection limit d. It is noteworthy in this context that adjusting target T m , and the allowable matching intervals can make primers designed with less complexity/lower detection limit still be sufficiently promiscuous to amplify all genetic variants in the target population. At the web site we provide a “sample input”; this imports an alignment and several parameter values, with no adaptors, de novo length of 200 bp, and no barcodes; it finds optimal primers in about 25 seconds, depending on server load and internet speed. When calculations take long times, i.e., when web browsers may time out before the design is completed, we provide an email notification option. This sends an email to the user with a link to the results.
Primers used in successful 454 sequencing of HIV-1 populations
This PrimerDesign algorithm was used to design primers to investigate CD8 T-cell driven HIV-1 immune escape over time, starting with acute infection samples from 3 human patients . We were also interested in using 454 pyrosequencing to better estimate the number of HIV-1 strains that established the primary infection in these subjects . The env V3 region is a highly variable region of the HIV-1 genome, and was therefore, as in a previous HIV-1 deep-sequencing study of long-term infections , sequenced as a reference region in all study subjects to examine non-T-cell-selected diversification. Thus for each patient the ROIs were chosen to encompass the env V3 region, along with additional regions spanning one or two verified CD8+ T cell epitope(s) recognized early in infection. These regions were located in nef in one subject, rev and tat in the second, and env in the third . We were interested in detecting the emergence of low frequency variants in each patient, and tracking the progression of immune escape over time. In addition to the 3 acutely infected patients, we had access to one sample from a chronically infected donor who was expected to have a very diverse virus population. Based on alignments of previous Sanger sequences from these 4 patients , the previous 454 study of chronic patients , and additional sequences from the HIV database (http://www.hiv.lanl.gov), we designed 30 unique adaptor-tag-primer constructs: 2 patients × 2 regions (a T-cell epitope region and the V3 variable region) × 4 samples per patient (3 longitudinal samples + 1 control) = 16, plus 1 patient × 3 regions (2 T-cell epitope region and the V3 variable region) × 4 samples per patient (3 longitudinal samples + 1 control) = 12, plus 2 regions for the single time-point for the chronically infected donor. Each of these constructs successfully revealed accumulating genetic variation in the investigated regions over time, and extensive within-epitope diversity. The number of sequences retrieved varied in the patients, but was generally similar between regions within the same patient . Furthermore, the variation found in the HIV-1 sequencing experiments agrees very well with results from Sanger sequencing by single-genome amplification (SGA), with 95% confidence intervals for variant frequency detected by SGA overlapping frequencies from 454, and increased sensitivity to detect rare variants .
The generation of primers across the entire HIV-1 genome was done with an in-house semi-automated version of PrimerDesign, but it could be easily done manually using multiple ROIs in separate runs. We may add this as an automatic feature in the future if requested by users, which is customary for LANL HIV database bioinformatic tools. Similarly, we welcome other user requests for further improvements, and we will implement such requests if feasible.
PrimerDesign compared to other software
The biggest strength of PrimerDesign compared to the majority of previous primer design programs is PrimerDesign’s comprehensive approach. Specifically, PrimerDesign uses a multiple alignment to address deep-sequencing needs of primer localization in relatively conserved regions of highly variable DNA targets, allows the integrated design of bio-barcode tags and adaptors, and allows primer-dimer evaluation based on experimental data. Most primer design software tools base their design on a single sequence (e.g., Primer3 ), which could result in selective or biased amplification from a diverse population by inadequate coverage of variants, or inadvertent selection of primers in relatively diverse regions, when conserved regions could be used instead, thereby risking missed coverage due to biological variability in primer regions. Other programs that use multiple alignments (e.g., GeneFisher ) lack the ability to simultaneously evaluate primers appropriately, for instance, by matching T m ’s and checking for dimerization risk. In these comparisons, it is important to point out that many previous primer design programs have focused on specific needs of certain experimental protocols, and therefore comparisons are difficult. Another advantage with PrimerDesign is its automatic and flexible tag generation, which is becoming more important with the increasing usage of multiplexed and next-generation sequencing. Tag generation, together with the possibility of adding DNA adaptors, and testing the entire construct’s dimerization risks, are to our knowledge unique features of PrimerDesign. Primers designed using PrimerDesign could readily be used in conjunction with the Primer ID strategy, where a random sequence tag is introduced so that each template receives a unique primer ID . The goal of the primer ID is to minimize errors in estimating sequence frequencies resulting from initial sequence resampling and amplification biases, and template consensus sequences, and to minimize the impact of recombination during amplification and misincorporation/sequencing errors . The introduction of such tags may introduce some primer issues, as these tags are by definition random, and added at the DNA synthesis stage, and so will not be designed to avoid homopolymer strings, dimerization issues, and some bias may be introduced by the chemistry of synthesis.
We have created a novel primer design tool, PrimerDesign, which suggests primer pairs according to a comprehensive algorithm and user requirements. The overall software workflow proceeds through inter-connected steps: 1) the target locations for primers are determined, guided by sequence entropy estimates’ and complexity, 2) primer melting temperatures are optimized, 3) bio-barcodes and adaptors are added, and finally 4) dimerization risks are estimated. Each inter-connected step informs the subsequent steps; if previous steps have to be re-optimized, this occurs automatically. Thus, each step considers both user requirements and automatic parameters used within the algorithm.
PrimerDesign was originally designed for HIV studies; however it should be equally useful for designing primers for other biological systems that have high levels of genetic variation. Thus, we have presented a novel computer program for designing primers for highly variable DNA targets. The design takes into account genetic variation, and several user-specified as well automatic design features related to the goal of a particular study and the intended experimental setting. In particular, we demonstrate that our method effectively designs PCR and sequencing primers for the 454 system.
The software is available as a web application at http://www.hiv.lanl.gov/tools/primer/main. As a web tool, it is platform independent. Like other bioinformatic tools at the HIV database, the tool will be maintained in the long term, and users can suggest new features as well as report problems to firstname.lastname@example.org. Running it as a web tool therefore also guarantees that a user always will use the latest version.
Project name: HIV databases; PrimerDesign
Project home page: http://www.hiv.lanl.gov/tools/primer/main
Operating system(s): Platform independent
Programming language: Perl, C, Catalyst, Moose
Other requirements: web browser
License: Copyright 2012. Los Alamos National Security, LLC. This material was produced under U.S. Government contract DE-AC52-06NA25396 for Los Alamos National Laboratory (LANL), which is operated by Los Alamos National Security, LLC for the U.S. Department of Energy. The U.S. Government has rights to use, reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR LOS ALAMOS NATIONAL SECURITY, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is modified to produce derivative works, such modified software should be clearly marked, so as not to confuse it with the version available from LANL. Additionally, this program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; version 2.0 of the License. Accordingly, this program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
Any restrictions to use by non-academics: see license
We thank George Shaw, Beatrice Hahn, Brandon Keele, and Shuyi Wang for providing full-length genome sequences and amplicons from subject CH40. We thank Cliff S. Han and Jennifer P. Macke for valuable discussions and comments. This work was supported by a Los Alamos National Laboratory Directed Research grant, a NIH grant (AI087520), and a NIH-DOE interagency agreement (Y1-AI-8309).
- Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG: Primer3-new capabilities and interfaces. Nucleic Acids Res. 2012, 40 (15): e115-10.1093/nar/gks596.PubMed CentralView ArticlePubMedGoogle Scholar
- Rychlik W: OLIGO 7 primer analysis software. Methods Mol Biol. 2007, 402: 35-60. 10.1007/978-1-59745-528-2_2.View ArticlePubMedGoogle Scholar
- Vallone PM, Butler JM: AutoDimer: a screening tool for primer-dimer and hairpin structures. Biotechniques. 2004, 37 (2): 226-231.PubMedGoogle Scholar
- Kalendar R, Lee D, Schulman AH: Java web tools for PCR, in silico PCR, and oligonucleotide assembly and analysis. Genomics. 2011, 98 (2): 137-144. 10.1016/j.ygeno.2011.04.009.View ArticlePubMedGoogle Scholar
- Giegerich R, Meyer F, Schleiermacher C: GeneFisher-software support for the detection of postulated genes. Proc Int Conf Intell Syst Mol Biol. 1996, 4: 68-77.PubMedGoogle Scholar
- Fredslund J, Madsen LH, Hougaard BK, Nielsen AM, Bertioli D, Sandal N, Stougaard J, Schauser L: A general pipeline for the development of anchor markers for comparative genomics in plants. BMC Genomics. 2006, 7: 207-10.1186/1471-2164-7-207.PubMed CentralView ArticlePubMedGoogle Scholar
- Weckx S, De Rijk P, Van Broeckhoven C, Del-Favero J: SNPbox: a modular software package for large-scale primer design. Bioinformatics. 2005, 21 (3): 385-387. 10.1093/bioinformatics/bti006.View ArticlePubMedGoogle Scholar
- Rouillard JM, Zuker M, Gulari E: OligoArray 2.0: design of oligonucleotide probes for DNA microarrays using a thermodynamic approach. Nucleic Acids Res. 2003, 31 (12): 3057-3062. 10.1093/nar/gkg426.PubMed CentralView ArticlePubMedGoogle Scholar
- Fischer W, Ganusov VV, Giorgi EE, Hraber PT, Keele BF, Leitner T, Han CS, Gleasner CD, Green L, Lo CC, et al: Transmission of single HIV-1 genomes and dynamics of early immune escape revealed by ultra-deep sequencing. PLoS ONE. 2010, 5 (8): e12303-10.1371/journal.pone.0012303.PubMed CentralView ArticlePubMedGoogle Scholar
- Henn MR, Boutwell CL, Charlebois P, Lennon NJ, Power KA, Macalalad AR, Berlin AM, Malboeuf CM, Ryan EM, Gnerre S, et al: Whole genome deep sequencing of HIV-1 reveals the impact of early minor variants upon immune recognition during acute infection. PLoS Pathog. 2012, 8 (3): e1002529-10.1371/journal.ppat.1002529.PubMed CentralView ArticlePubMedGoogle Scholar
- Cale EM, Hraber P, Giorgi EE, Fischer W, Bhattacharya T, Leitner T, Yeh WW, Gleasner C, Green LD, Han CS, et al: Epitope-specific CD8+ T lymphocytes cross-recognize mutant simian immunodeficiency virus (SIV) sequences but fail to contain very early evolution and eventual fixation of epitope escape mutations during SIV infection. J Virol. 2011, 85 (8): 3746-3757. 10.1128/JVI.02420-10.PubMed CentralView ArticlePubMedGoogle Scholar
- Bar KJ, Tsao CY, Iyer SS, Decker JM, Yang Y, Bonsignori M, Chen X, Hwang KK, Montefiori DC, Liao HX, et al: Early low-titer neutralizing antibodies impede HIV-1 replication and select for virus escape. PLoS Pathog. 2012, 8 (5): e1002721-10.1371/journal.ppat.1002721.PubMed CentralView ArticlePubMedGoogle Scholar
- Archer J, Rambaut A, Taillon BE, Harrigan PR, Lewis M, Robertson DL: The evolutionary analysis of emerging low frequency HIV-1 CXCR4 using variants through time-an ultra-deep approach. PLoS computational biology. 2010, 6 (12): e1001022-10.1371/journal.pcbi.1001022.PubMed CentralView ArticlePubMedGoogle Scholar
- Tsibris AM, Korber B, Arnaout R, Russ C, Lo CC, Leitner T, Gaschen B, Theiler J, Paredes R, Su Z, et al: Quantitative deep sequencing reveals dynamic HIV-1 escape and large population shifts during CCR5 antagonist therapy in vivo. PLoS ONE. 2009, 4 (5): e5683-10.1371/journal.pone.0005683.PubMed CentralView ArticlePubMedGoogle Scholar
- Hedskog C, Mild M, Jernberg J, Sherwood E, Bratt G, Leitner T, Lundeberg J, Andersson B, Albert J: Dynamics of HIV-1 quasispecies during antiviral treatment dissected using ultra-deep pyrosequencing. PLoS ONE. 2010, 5 (7): e11345-10.1371/journal.pone.0011345.PubMed CentralView ArticlePubMedGoogle Scholar
- Jabara CB, Jones CD, Roach J, Anderson JA, Swanstrom R: Accurate sampling and deep sequencing of the HIV-1 protease gene using a Primer ID. Proc Natl Acad Sci U S A. 2011, 108 (50): 20166-20171. 10.1073/pnas.1110064108.PubMed CentralView ArticlePubMedGoogle Scholar
- Castellano L, Stebbing J: Deep sequencing of small RNAs identifies canonical and non-canonical miRNA and endogenous siRNAs in mammalian somatic tissues. Nucleic Acids Res. 2013, 41 (5): 3339-3351. 10.1093/nar/gks1474.PubMed CentralView ArticlePubMedGoogle Scholar
- Elsharawy A, Warner J, Olson J, Forster M, Schilhabel MB, Link DR, Rose-John S, Schreiber S, Rosenstiel P, Brayer J, et al: Accurate variant detection across non-amplified and whole genome amplified DNA using targeted next generation sequencing. BMC Genomics. 2012, 13: 500-10.1186/1471-2164-13-500.PubMed CentralView ArticlePubMedGoogle Scholar
- Osei-Poku J, Mbogo CM, Palmer WJ, Jiggins FM: Deep sequencing reveals extensive variation in the gut microbiota of wild mosquitoes from Kenya. Mol Ecol. 2012, 21 (20): 5138-5150. 10.1111/j.1365-294X.2012.05759.x.View ArticlePubMedGoogle Scholar
- Leitner T, Kumar S, Albert J: Tempo and mode of nucleotide substitutions in gag and env gene fragments in human immunodeficiency virus type 1 populations with a known transmission history. J Virol. 1997, 71: 4761-4770. see also correction 1998: 4772; 2565PubMed CentralPubMedGoogle Scholar
- Neher RA, Leitner T: Recombination rate and selection strength in HIV intra-patient evolution. PLoS Comput Biol. 2010, 6: e1000660-10.1371/journal.pcbi.1000660.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang M, Foley B, Schultz AK, Macke JP, Bulla I, Stanke M, Morgenstern B, Korber B, Leitner T: The role of recombination in the emergence of a complex and dynamic HIV epidemic. Retrovirology. 2010, 7: 25-10.1186/1742-4690-7-25.PubMed CentralView ArticlePubMedGoogle Scholar
- Korber B, Foley B, Leitner T, Mellors JW, McCutchan F, Hahn B, Myers G, Kuiken C: Human Retroviruses and AIDS 1997: a compilation and analysis of nucleic acid and amino acid sequences. 1998, Los Alamos, NM: Los Alamos National LaboratoryGoogle Scholar
- Maljkovic Berry I, Ribeiro R, Kothari M, Athreya G, Daniels M, Lee HY, Bruno W, Leitner T: Unequal evolutionary rates in the human immunodeficiency virus type 1 (HIV-1) pandemic: the evolutionary rate of HIV-1 slows down when the epidemic rate increases. J Virol. 2007, 81 (19): 10625-10635. 10.1128/JVI.00985-07.View ArticlePubMedGoogle Scholar
- Korber BT, Kunstman KJ, Patterson BK, Furtado M, McEvilly MM, Levy R, Wolinsky SM: Genetic differences between blood- and brain-derived viral sequences from human immunodeficiency virus type 1-infected patients: evidence of conserved elements in the V3 region of the envelope protein of brain-derived sequences. J Virol. 1994, 68 (11): 7467-7481.PubMed CentralPubMedGoogle Scholar
- Reza FM: An introduction to information theory. 1994, NY: Dover Publications Inc.Google Scholar
- Allawi HT, SantaLucia J: Nearest neighbor thermodynamic parameters for internal G.A mismatches in DNA. Biochemistry. 1998, 37 (8): 2170-2179. 10.1021/bi9724873.View ArticlePubMedGoogle Scholar
- SantaLucia J, Allawi HT, Seneviratne PA: Improved nearest-neighbor parameters for predicting DNA duplex stability. Biochemistry. 1996, 35 (11): 3555-3562. 10.1021/bi951907q.View ArticlePubMedGoogle Scholar
- Ronaghi M, Elahi E: Discovery of single nucleotide polymorphisms and mutations by pyrosequencing. Comp Funct Genomics. 2002, 3 (1): 51-56. 10.1002/cfg.132.PubMed CentralView ArticlePubMedGoogle Scholar
- O’Meara D, Wilbe K, Leitner T, Hejdeman B, Albert J, Lundeberg J: Monitoring resistance to human immunodeficiency virus type 1 protease inhibitors by pyrosequencing. J Clin Microbiol. 2001, 39 (2): 464-473. 10.1128/JCM.39.2.464-473.2001.PubMed CentralView ArticlePubMedGoogle Scholar
- Desmarais SM, Leitner T, Barron AE: Quantitative experimental determination of primer-dimer formation risk by free-solution conjugate electrophoresis. Electrophoresis. 2012, 33 (3): 483-491. 10.1002/elps.201100452.PubMed CentralView ArticlePubMedGoogle Scholar
- Goonetilleke N, Liu MK, Salazar-Gonzalez JF, Ferrari G, Giorgi E, Ganusov VV, Keele BF, Learn GH, Turnbull EL, Salazar MG, et al: The first T cell response to transmitted/founder virus contributes to the control of acute viremia in HIV-1 infection. J Exp Med. 2009, 206 (6): 1253-1272. 10.1084/jem.20090365.PubMed CentralView ArticlePubMedGoogle Scholar
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.