- Open Access
Whole genome sequencing of Plasmodium falciparum from dried blood spots using selective whole genome amplification
- Samuel O. Oyola†1, 5Email author,
- Cristina V. Ariani†1Email author,
- William L. Hamilton1, 9,
- Mihir Kekre1,
- Lucas N. Amenga-Etego6,
- Anita Ghansah7,
- Gavin G. Rutledge1,
- Seth Redmond8,
- Magnus Manske1,
- Dushyanth Jyothi1,
- Chris G. Jacob1,
- Thomas D. Otto1,
- Kirk Rockett2, 3,
- Chris I. Newbold4,
- Matthew Berriman1 and
- Dominic P. Kwiatkowski1, 2, 3
© The Author(s) 2016
- Received: 2 August 2016
- Accepted: 28 November 2016
- Published: 20 December 2016
Translating genomic technologies into healthcare applications for the malaria parasite Plasmodium falciparum has been limited by the technical and logistical difficulties of obtaining high quality clinical samples from the field. Sampling by dried blood spot (DBS) finger-pricks can be performed safely and efficiently with minimal resource and storage requirements compared with venous blood (VB). Here, the use of selective whole genome amplification (sWGA) to sequence the P. falciparum genome from clinical DBS samples was evaluated, and the results compared with current methods that use leucodepleted VB.
Parasite DNA with high (>95%) human DNA contamination was selectively amplified by Phi29 polymerase using short oligonucleotide probes of 8–12 mers as primers. These primers were selected on the basis of their differential frequency of binding the desired (P. falciparum DNA) and contaminating (human) genomes.
Using sWGA method, clinical samples from 156 malaria patients, including 120 paired samples for head-to-head comparison of DBS and leucodepleted VB were sequenced. Greater than 18-fold enrichment of P. falciparum DNA was achieved from DBS extracts. The parasitaemia threshold to achieve >5× coverage for 50% of the genome was 0.03% (40 parasites per 200 white blood cells). Over 99% SNP concordance between VB and DBS samples was achieved after excluding missing calls.
The sWGA methods described here provide a reliable and scalable way of generating P. falciparum genome sequence data from DBS samples. The current data indicate that it will be possible to get good quality sequence on most if not all drug resistance loci from the majority of symptomatic malaria patients. This technique overcomes a major limiting factor in P. falciparum genome sequencing from field samples, and paves the way for large-scale epidemiological applications.
- Dried blood spot
- Selective whole genome amplification
- Field samples
- Whole genome sequencing
The last decade has seen rapid advances in whole genome sequencing technologies helping to track disease outbreaks and the spread of drug resistance genes . Clinical and public health applications for Plasmodium falciparum sequencing rely on obtaining sequenceable material from samples collected in the field, often in resource-limited conditions. To date, the practical difficulties in sample collection, storage and transportation impose significant barriers to the use of genomic approaches for malaria surveillance.
The most practical and convenient method for sampling clinical malaria parasites is through small blood volumes obtained from capillary blood using finger or heel-pricks [2, 3]. These small blood samples—about 50 µl in volume—are blotted on filter papers for efficient transportation and storage without requiring refrigeration; this is especially applicable to resource-deprived regions where the disease is endemic. Despite the convenience and ease of sampling, DNA extracted from dried blood spot (DBS) filter papers often has low parasite DNA yield and an overwhelming host DNA contamination, which poses serious limitations in downstream genetic analyses . These technical bottlenecks have prevented analysis of large numbers of pathogen samples collected by DBS at whole genome resolution, including archived clinical specimens, using current high throughput sequencing technologies.
Currently, whole blood from malaria patients used for P. falciparum sequencing is obtained through venous blood (VB) draws. This requires skilled phlebotomists or clinicians with appropriate training. Once collected, VB samples are processed by filtering out leucocytes using cellulose columns  and require refrigerated storage followed by centrifugation and blood pellet freezing or DNA extraction. These requirements limit the scope for sample collection in remote regions where healthcare infrastructure is already under strain. The cellulose filtration process, although very effective in parasite enrichment, requires large volumes of blood (>2 ml) . Such volumes can be difficult to obtain, especially from young children, who may already be anaemic as a result of P. falciparum infection  and who bear the heaviest disease burden globally.
To overcome the challenges of low sample quality and quantity, and to allow timely genetic analysis of clinical samples collected directly from patients without culture adaptation, an approach was used that selectively amplifies parasite DNA from low blood volume clinical samples. The selective whole genome amplification (sWGA) strategy, originally described by Leichty and Brisson , uses computationally selected short oligonucleotide probes of 8–12 mers as primers that preferentially bind to the target genome, and this approach has been successfully applied to Laverania parasites, including P. falciparum [9, 10]. The purpose of the present study was to undertake a detailed evaluation of sWGA approaches for sequencing the P. falciparum genome from dried blood spots.
Primer design and selection
Mock samples to test the efficacy of sWGA
To test whether selected primers would successfully amplify the parasite genome, mock clinical samples were prepared by mixing culture-infected red blood cells (infected with P. falciparum strain 3D7) with uninfected human whole blood to obtain a simulated parasitaemia ranging from 0.0001 to 1%. P. falciparum strain 3D7 parasites for the mock samples were cultured in human O+ erythrocytes with heat-inactivated 10% pooled human serum, as described in . All parasitaemia calculations were based on the estimation of approximately 4 million red blood cells per microlitre of whole blood. DNA was extracted from the samples (N = 8) without leucodepletion. In addition, 6 other mock DNA samples were manually reconstituted by mixing P. falciparum genomic DNA with host (human) genomic DNA to obtain parasite/host DNA mixtures of the ratio 1: 24 (4% parasite and 96% human DNA) that were used to investigate genome coverage following sWGA.
Whole genomes from dried blood spots
To test the efficacy of sWGA in generating reliable genomic data from DBS, WGS datasets obtained from standard leucodepleted-genomic DNA (gDNA) were compared with sWGA-DBS samples. The gDNA samples were derived from two to three ml of venous blood which were then processed by CF-11 or MN filtration to remove leucocytes [5, 6] prior to DNA extraction. On the other hand, the DBS samples were collected by spotting (on filter paper) 50 μl of whole blood obtained by finger pricking. In total, samples from 156 patients that were positive for P. falciparum clinical malaria based on rapid diagnostic test (RDT) with CareStart™ Malaria kit (Access Bio Inc, USA) were analysed. Eighty-four DBS were collected from the Kassena-Nankana Districts of Upper East Ghana, of which 48 had matching VB pairs; and 72 DBS were collected from Noguchi Memorial Hospital in Accra, Ghana, all of which had matching VB samples.
DNA extraction and quantification
Two to three ml of VB samples (mock blood or field) were used to extract DNA, using QiAamp DNA blood midi kit (Qiagen) following the kit manufacturer’s instructions. For DBS, DNA was extracted using QIAamp DNA Investigator Kit (Qiagen, Valencia, California, United States). Approximately 1.5 cm (0.6 in) diameter DBS circles from each filter paper were cut out into small pieces of 3 mm diameter using a single-hole paper punch. Punched pieces from each sample were placed into 2 ml micro-centrifuge tubes from which DNA was extracted following the manufacturer’s instructions except for the reagent volumes and incubation times, which were doubled to accommodate the increased amount of DBS used per sample. An average of 116 ng (standard deviation, SD, 116.7) of DNA was obtained from the DBS extracts out of which at least 5 ng was used as template for sWGA amplification reaction.
Selective whole genome amplification (sWGA)
The sWGA reaction was performed in 0.2 ml PCR-tubes or plates. The reaction (50 µl total volume) containing at least 5 ng of template DNA, 1× BSA (New England Biolabs), 1 mM dNTPs (New England Biolabs), 2.5 µM of each amplification primer, 1× Phi29 reaction buffer (New England Biolabs), and 30 units of Phi29 polymerase (New England Biolabs), was placed in a PCR machine (MJ thermal Cycler, Bio-Rad) programmed to run a “stepdown” protocol consisting of 35 °C for 5 min, 34 °C for 10 min, 33 °C for 15 min, 32 °C for 20 min, 31 °C for 30 min, 30 °C for 16 h then heating at 65 °C for 15 min to inactivate the enzymes prior to cooling to 4 °C. Once the product was amplified, it was quantified using Qubit® dsDNA high sensitivity (Thermo Fisher Scientific) to determine whether there was enough material for sequencing—minimum required is 500 ng of product. Standard whole genome amplified (WGA) products of the test samples were also sequenced as control to determine the extent of enrichment .
Library preparation of amplified samples and short read high throughput sequencing
sWGA products (≥500 ng total DNA) were cleaned using Agencourt Ampure XP beads (Beckman Coulter) following manufacturer’s instructions. Briefly, 1.8 volumes of beads per 1 volume of sample were mixed and incubated for 5 min at room temperature. After incubation, the tube containing bead/DNA mixture was placed on a magnetic rack to capture the DNA-bound beads while the unbound solution was discarded. Beads were washed twice with 200 µl of 80% ethanol and the bound DNA eluted with 60 µl of EB buffer. Cleaned amplified DNA products (~05–1 µg DNA) were used to prepare a PCR-free Illumina library using the NEBNext DNA sample preparation kit (New England Biolabs) for high throughput sequencing. DNA libraries were sequenced at the Wellcome Trust Sanger Institute using Illumina HiSeq 2500 instruments and Illumina V.3 chemistry. Paired-end sequencing was performed with 100-base reads and an 8-base index read. 12-multiplex sample libraries were loaded to target at least 20 million reads per sample.
Sequence data obtained from each sample was subjected to standard Illumina QC procedures and 20 million reads per sample was subjected to detailed analysis for enrichment, quality, content, and coverage. Each dataset was analysed independently by mapping sequence reads to the 3D7 reference genome using BWA . SAMtools  was used to generate coverage statistics from the BWA mapping output. For enrichment analysis, the number of reads mapping to either host, or P. falciparum reference sequences was counted. For genotype and concordance analysis, variant calls were generated using SAMtools mpileup (V0.1.1.19; with the following parameters: -DSV -C50 -m2 -F0.0005 -d 10,000 -gu) and bcftools (V0.1.17; with the following parameters: -p 0.99 -vcgN). A list of 1,241,840 (1.2 million) high-quality single-nucleotide polymorphism (SNP) positions, which were not filtered by gene class or region, but on individual properties of SNPs (such as uniqueness of the surrounding region and within an exon) [15, 16] was used. In silico genotyping of both the DBS (sWGA) and VB (leucodepleted and unamplified) samples was performed using mpileup to count alleles present in at least five reads (alleles with less than five reads were discarded). Although P. falciparum is haploid, it is common to find heterozygous calls due to the presence of multiple clonal infections in the same host. In order to genotype heterozygous sites, the 5/2 rule was applied, which requires at least two reads in both reference and alternative alleles, and the sum of both has to be higher than five reads . SNP call concordance analysis between matching DBS and VB samples was performed on sequenced data targeting SNPS present in the core genome as well as key malaria drug resistance genes, such as crt (K76T involved in chloroquine resistance) , dhfr (N51I, involved in pyrimethamine resistance) , dhps (A581G, involved in sulfadoxine resistance) , mdr1 (N86Y, involved in multiple drugs including mefloquine) , and kelch13 (C580Y, involved in artemisinin resistance) .
sWGA primer selection and amplification yield
Selected 28 primers were analysed individually (Fig. 1a) to determine their expected binding sites and distribution pattern across the P. falciparum genome. Each 1 or 2 kb block had at least one primer binding (Additional file 1: Figure S1). These 28 primers were pooled into three different sets (probes): Probe_10 (consisting of the first 10 primers), Probe_20 (consisting of the first 20 primers), and Probe_28 (a pool of all the 28 primers). In separate reactions, the three probes were used to amplify 5 ng of simulated mock samples (a mix of 3D7-infected red blood cells with uninfected human whole blood; N = 8) to determine which set gives optimal genome amplification and coverage. The amplified products were cleaned and the DNA quantified using Quant-iT™ PicoGreen® dsDNA assay kit (Invitrogen) to determine the yield for each primer pool (Fig. 1b). Different yields were observed between the three primer pools: Probe_10 produced the highest average yield (2.5 ± 0.87 µg) followed by Probe_20 (1.85 ± 0.81 µg) and Probe_28 (1.2 ± 1.0 µg) (Fig. 1b). Whole genome sequencing of the amplified products were used to compared the quality of genome coverage (number of bases with at least 5x coverage) by each set (pool) and no significant difference was found (Spearman’s correlation: Probe_10 and Probe_20, R2 = 0.97, p < 0.001; Probe_10 and Probe_28, R2 = 0.96, p < 0.001; Probe_20 and Probe_28, R2 = 0.97, p < 0.001). Probe_10 (Additional file 1: Table S2) was therefore chosen for all subsequent sWGA reactions based on amplification yield and cost.
Coverage profile of sWGA samples
sWGA enriches for Plasmodium falciparum sequence reads
sWGA enrichment analysis
Reads mapping to:
P. falciparum (%)
Parasitaemia and genome coverage threshold in mock samples
sWGA allows whole genome sequencing directly from clinical dried blood spots
Having established sWGA efficacy in mock blood samples, DBS field isolates collected from two sites in Ghana, with a parasitaemia ranging from 0.001 to 8.9% (1.25–11,125 parasites per 200 WBC or 40–356,000 parasites per µl of blood) were used to test the method. DNA was extracted from 205 DBS samples (average yield 116 ng, SD 116.7), which were subsequently subjected to sWGA (average yield 1399 ng, SD 502). From those, 156 (76%) passed the threshold of 500 ng for library preparation and were, therefore, whole genome sequenced.
Taken together, our data establishes 0.03% parasitaemia (40 parasites per 200 WBC) as the minimum threshold on which sWGA technology is capable of generating quality sequence data with coverage suitable for most genetic analyses on DBS field samples (Fig. 7, vertical dotted line marks the 0.03% parasitaemia threshold). The data also show that at least 180 parasite genomes per sample is required for efficient sWGA processing.
High concordance between dried blood spot samples and venous blood samples
SNP concordance analysis of dried blood spots (DBS; sWGA) and venous blood (VB; leucodepleted and unamplified) samples (N = 113)
Using sWGA to amplify parasite DNA from dried blood spots has immediate and important implications for public health. This work has comprehensively evaluated the potential of sWGA method. Collecting clinical malaria samples as DBS on filter paper is field-friendly and has several advantages for both patient and researcher over the venous blood (VB) draw methods currently used for parasite whole genome sequencing . Finger-prick sampling requires less advanced training than VB draws, collects ~50× less blood, and is more convenient for most patient groups. Unlike VB draws, DBS samples also do not require special facilities for transportation, refrigeration, and storage, since the blotted paper is stabilized by the membrane that preserves genetic integrity [23, 24]. VB sampling is thus relatively limited in geographic range, restricted to locations with well-established and resourced clinics. Sequencing from DBS samples would break this technical bottleneck, allowing significant expansion of sample collection to include very remote regions, increasing sampling density and coverage [23, 24].
The data presented show that ~110 ng of genomic DNA can be extracted from a 20–40 µl DBS, of which over 98% is host material. Isolating sequenceable parasite DNA from a DBS sample that is highly contaminated with host DNA has hindered applications of genetic tools in malaria research and control programmes. Previous studies have identified various methods to overcome the challenges of host DNA contamination in pathogen sequencing [4, 25]. However, most of these techniques require relatively large quantities of starting DNA material that is impossible to obtain from DBS samples. The approach described here provides a timely solution to this challenge, creating opportunities for both large-scale field isolate sequencing studies and analysing archived clinical samples that would otherwise be too contaminated and low yield for whole genome sequencing.
To thoroughly evaluate the quality and accuracy of sWGA sequence data for genetic analysis of clinical malaria samples, 156 DBS collected from clinical malaria patients was sequenced and analysed using sWGA. 120 of these had their corresponding VB counterparts collected simultaneously, allowing direct comparison between DBS (sWGA) and VB (leucodepleted and unamplified) WGS data from an identical patient cohort [5, 6]. More than 75% of the P. falciparum genome was covered at ≥5× in 117 (97.5%) DBS samples for which parasitaemia was ≥0.03%. The sWGA-derived genome sequences show a less uniform coverage profile compared to data generated from unamplified genomic DNA (VB-derived, Fig. 3). This is typical of whole genome amplified data . However, the core genome was adequately covered at depths suitable for most downstream analysis including variant detection and SNP genotyping. Further optimization is required to amplify and successfully genotype regions outside the core genome, such as telomeres and mitochondria.
The high concordance of SNP calls and allele frequencies between the DBS and VB paired samples indicates that samples that were subjected to sWGA are suitable for population genetic studies. Significantly for the potential applicability of this technology to public health surveillance projects, important malaria drug resistance loci were successfully sequenced and showed very similar allele frequencies for both DBS and VB samples (Fig. 9; Additional file 1: Table S1).
In summary, this work shows that processing DBS samples using sWGA method produces reliable sequence data, provided that: the sample has ≥180 P. falciparum genomes (parasitaemia threshold ~0.03%, or ~40 parasites per 200 WBC); the threshold for library preparation is met (≥500 ng of DNA post-sWGA); and the sequence data obtained covers at least 50% of the genome at a depth of 5× or more. Samples with much a lower parasitaemia, for example those collected from asymptomatic patients or during the low transmission season, will require further optimization to improve sensitivity and coverage. Using sWGA technology, genomic data from larger sample sizes with geospatial resolution could provide useful information to public health bodies, for example through rapid detection of emerging patterns in parasite evolution in response to control initiatives such as anti-malarial drugs.
SOO, CIN, MB, TDO and DPK conceived and coordinated the study; SOO, CVA, WLH, KR and MK performed the experiments; LNAE, AG, WLH and MK participated in sample collection and field experiments; SOO, CVA, GGR, MM, SR, CGJ and DJ participated in data analysis and interpretation of the results; SOO drafted the manuscript; SOO, CVA, WLH and DPK participated in the editing and final preparation of manuscript. All authors read and approved the final manuscript.
We are grateful to Roberto Amato for useful discussions on statistical analysis. We thank the field workers who helped collect the clinical samples, working with the Navrongo Health Research Centre and Noguchi Memorial Institute for Medical Research/University of Ghana.
The authors declare that they have no competing interests.
Availability of data and materials
All data sets used in this study have been deposited in the European Nucleotide Archive (ENA) read archive. ENA accession numbers for each sequence data and associated metadata are available at www.malariagen.net/resource/21.
Consent for publication
There are no case presentations that require disclosure of respondent’s confidential data/information in this study.
The scientific merit and use of human subjects for this study was approved by the scientific review committee and ethical review boards of the Navrongo Health Research Centre of (FWA00000250) and the Noguchi Memorial Institute for Medical Research (056-12/13). Written informed consent was obtained from all adult subjects and from the parent or legal guardians of minors.
This research was supported by the Wellcome Trust through the Wellcome Trust Sanger Institute (098051), the Resource Centre for Genomic Epidemiology of Malaria (090770/Z/09/Z) and the Wellcome Trust Centre for Human Genetics (090532/Z/09/Z). The Centre for Genomics and Global Health is supported by the Medical Research Council (G0600718). GGR is supported by the Medical Research Council (MR/J004111/1).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Ariey F, Witkowski B, Amaratunga C, Beghain J, Langlois AC, Khim N, et al. A molecular marker of artemisinin-resistant Plasmodium falciparum malaria. Nature. 2014;505:50–5.View ArticlePubMedGoogle Scholar
- Bereczky S, Martensson A, Gil JP, Farnert A. Rapid DNA extraction from archive blood spots on filter paper for genotyping of Plasmodium falciparum. Am J Trop Med Hyg. 2005;72:249–51.PubMedGoogle Scholar
- Farnert A, Arez AP, Correia AT, Bjorkman A, Snounou G, do Rosario V. Sampling and storage of blood and the detection of malaria parasites by polymerase chain reaction. Trans R Soc Trop Med Hyg. 1999;93:50–3.View ArticlePubMedGoogle Scholar
- Oyola SO, Gu Y, Manske M, Otto TD, O’Brien J, Alcock D, et al. Efficient depletion of host DNA contamination in malaria clinical sequencing. J Clin Microbiol. 2013;51:745–51.View ArticlePubMedPubMed CentralGoogle Scholar
- Auburn S, Campino S, Clark TG, Djimde AA, Zongo I, Pinches R, et al. An effective method to purify Plasmodium falciparum DNA directly from clinical blood samples for whole genome high-throughput sequencing. PLoS ONE. 2011;6:e22213.View ArticlePubMedPubMed CentralGoogle Scholar
- Venkatesan M, Amaratunga C, Campino S, Auburn S, Koch O, Lim P, et al. Using CF11 cellulose columns to inexpensively and effectively remove human DNA from Plasmodium falciparum-infected whole blood samples. Malar J. 2012;11:41.View ArticlePubMedPubMed CentralGoogle Scholar
- Sowunmi A, Gbotosho GO, Happi CT, Fateye BA. Factors contributing to anaemia after uncomplicated Plasmodium falciparum malaria in children. Acta Trop. 2010;113:155–61.View ArticlePubMedGoogle Scholar
- Leichty AR, Brisson D. Selective whole genome amplification for resequencing target microbial species from complex natural samples. Genetics. 2014;198:473–81.View ArticlePubMedPubMed CentralGoogle Scholar
- Guggisberg AM, Sundararaman SA, Lanaspa M, Moraleda C, González R, Mayor A, et al. Whole genome sequencing to evaluate the resistance landscape following antimalarial treatment failure with fosmidomycin-clindamycin. J Infect Dis. 2016;214:1085–91.View ArticlePubMedGoogle Scholar
- Sundararaman SA, Plenderleith LJ, Liu W, Loy DE, Learn GH, Li Y, Shaw KS, et al. Genomes of cryptic chimpanzee Plasmodium species reveal key evolutionary events leading to human malaria. Nat Commun. 2016;7:11078.View ArticlePubMedPubMed CentralGoogle Scholar
- Trager W, Jenson JB. Cultivation of malarial parasites. Nature. 1978;273:621–2.View ArticlePubMedGoogle Scholar
- Oyola SO, Manske M, Campino S, Claessens A, Hamilton WL, Kekre M, et al. Optimized whole-genome amplification strategy for extremely AT-biased template. DNA Res. 2014;21:661–71.View ArticlePubMedPubMed CentralGoogle Scholar
- Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25:1754–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, et al. Genome project data processing S: the sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Manske M, Miotto O, Campino S, Auburn S, Almagro-Garcia J, Maslen G, et al. Analysis of Plasmodium falciparum diversity in natural infections by deep sequencing. Nature. 2012;487:375–9.View ArticlePubMedPubMed CentralGoogle Scholar
- MalariaGen. Pf Community Project. 2016.Google Scholar
- Fidock DA, Nomura T, Talley AK, Cooper RA, Dzekunov SM, Ferdig MT, et al. Mutations in the P. falciparum digestive vacuole transmembrane protein PfCRT and evidence for their role in chloroquine resistance. Mol Cell. 2000;6:861–71.View ArticlePubMedPubMed CentralGoogle Scholar
- Cowman AF, Morry MJ, Biggs BA, Cross GA, Foote SJ. Amino acid changes linked to pyrimethamine resistance in the dihydrofolate reductase-thymidylate synthase gene of Plasmodium falciparum. Proc Natl Acad Sci USA. 1988;85:9109–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Kublin JG, Dzinjalamala FK, Kamwendo DD, Malkin EM, Cortese JF, Martino LM, et al. Molecular markers for failure of sulfadoxine-pyrimethamine and chlorproguanil-dapsone treatment of Plasmodium falciparum malaria. J Infect Dis. 2002;185:380–8.View ArticlePubMedGoogle Scholar
- Duraisingh MT, Drakeley CJ, Muller O, Bailey R, Snounou G, Targett GA, et al. Evidence for selection for the tyrosine-86 allele of the pfmdr 1 gene of Plasmodium falciparum by chloroquine and amodiaquine. Parasitology. 1997;114:205–11.View ArticlePubMedGoogle Scholar
- Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19:1639–45.View ArticlePubMedPubMed CentralGoogle Scholar
- Miotto O, Amato R, Ashley EA, MacInnis B, Almagro-Garcia J, Amaratunga C, et al. Genetic architecture of artemisinin-resistant Plasmodium falciparum. Nat Genet. 2015;47:226–34.View ArticlePubMedPubMed CentralGoogle Scholar
- Wilhelm AJ, den Burger JC, Swart EL. Therapeutic drug monitoring by dried blood spot: progress to date and future directions. Clin Pharmacokinet. 2014;53:961–73.View ArticlePubMedPubMed CentralGoogle Scholar
- Benyshek DC. Use of dried blood spots: an ideal tool for medical anthropology “in the field”. J Diabetes Sci Technol. 2010;4:255–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Melnikov A, Galinsky K, Rogov P, Fennell T, Van Tyne D, Russ C, et al. Hybrid selection for sequencing pathogen genomes from clinical samples. Genome Biol. 2011;12:R73.View ArticlePubMedPubMed CentralGoogle Scholar
- Afoakwah R, Boampong JN, Egyir-Yawson A, Nwaefuna EK, Verner ON, Asare KK. High prevalence of PfCRT K76T mutation in Plasmodium falciparum isolates in Ghana. Acta Trop. 2014;136:32–6.View ArticlePubMedGoogle Scholar
- Kiarie WC, Wangai L, Agola E, Kimani FT, Hungu C. Chloroquine sensitivity: diminished prevalence of chloroquine-resistant gene marker pfcrt-76 13 years after cessation of chloroquine use in Msambweni, Kenya. Malar J. 2015;14:1–7.View ArticleGoogle Scholar
- Laufer MK, Takala-Harrison S, Dzinjalamala FK, Stine OC, Taylor TE, Plowe CV. Return of chloroquine-susceptible falciparum malaria in Malawi was a reexpansion of diverse susceptible parasites. J Infect Dis. 2010;202:801–8.View ArticlePubMedPubMed CentralGoogle Scholar